CN111476231A - Image area identification method and device and computer readable storage medium - Google Patents
Image area identification method and device and computer readable storage medium Download PDFInfo
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- A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
- A63F13/40—Processing input control signals of video game devices, e.g. signals generated by the player or derived from the environment
- A63F13/42—Processing input control signals of video game devices, e.g. signals generated by the player or derived from the environment by mapping the input signals into game commands, e.g. mapping the displacement of a stylus on a touch screen to the steering angle of a virtual vehicle
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Abstract
The invention discloses an image area identification method, equipment and a computer readable storage medium, wherein the method comprises the following steps: performing feature extraction and matching on the current picture to obtain a plurality of feature points; then, determining a target feature point with the maximum weight in the feature points; and finally, taking the minimum circumscribed area formed by the target characteristic points and the related characteristic points as a target area. The method and the device realize a humanized image area identification scheme, reduce the learning difficulty and the reaction capability requirement of the user, obtain a corresponding auxiliary control scheme by searching for a target area, and improve the actual use experience of the user for various types of touch control.
Description
Technical Field
The present invention relates to the field of mobile communications, and in particular, to a method and apparatus for identifying an image area, and a computer-readable storage medium.
Background
In the prior art, with the rapid development of intelligent terminal devices, more and more application programs or game programs require users to perform a large amount of or various types of operations to achieve required functions or achieve certain game experiences. In the process, various types of touch operation bring high requirements to the reaction capability and the learning capability of the user. Therefore, an auxiliary control scheme capable of reducing the learning difficulty and the response capability requirement of the user is needed, so as to improve the actual use experience of the user for various types of touch control.
Disclosure of Invention
In order to solve the technical defects in the prior art, the invention provides an image area identification method, which comprises the following steps:
performing feature extraction and matching on the current picture to obtain a plurality of feature points;
determining a target feature point with the largest weight from the feature points;
and taking the minimum circumscribed area formed by the target characteristic points and the related characteristic points as a target area.
Optionally, the extracting and matching the features of the current picture to obtain a plurality of feature points includes:
acquiring a first characteristic point of a current picture and a second characteristic point of a reference area;
and performing feature matching on the first feature points and the second feature points, and taking the first feature points with feature values smaller than a preset value as the feature points.
Optionally, the determining the target feature point with the largest weight from among the feature points includes:
presetting a rectangular parameter;
and selecting the feature points one by one, taking the feature points as geometric centers, and determining rectangular areas corresponding to the feature points according to the rectangular parameters.
Optionally, the determining the target feature point with the largest weight from among the feature points further includes:
identifying feature points within the rectangular region;
and determining the weight of the selected characteristic points according to the number of the characteristic points.
Optionally, the taking a minimum circumscribed area formed by the target feature points and the related feature points together as a target area includes:
determining a rectangular area corresponding to the target characteristic point, and taking other characteristic points in the rectangular area as characteristic points related to the target characteristic point;
and determining a minimum circumscribed rectangle formed by the target feature points and the related feature points, and taking the area covered by the minimum circumscribed rectangle as the target area.
The present invention also proposes an image area identification device comprising a memory, a processor and a computer program stored on said memory and executable on said processor, said computer program realizing, when executed by said processor:
performing feature extraction and matching on the current picture to obtain a plurality of feature points;
determining a target feature point with the largest weight from the feature points;
and taking the minimum circumscribed area formed by the target characteristic points and the related characteristic points as a target area.
Optionally, the computer program when executed by the processor implements:
acquiring a first characteristic point of a current picture and a second characteristic point of a reference area;
and performing feature matching on the first feature points and the second feature points, and taking the first feature points with feature values smaller than a preset value as the feature points.
Optionally, the computer program when executed by the processor implements:
presetting a rectangular parameter;
and selecting the feature points one by one, taking the feature points as geometric centers, and determining rectangular areas corresponding to the feature points according to the rectangular parameters.
Optionally, the computer program when executed by the processor implements:
identifying feature points within the rectangular region;
determining the weight of the selected feature points according to the number of the feature points;
determining a rectangular area corresponding to the target characteristic point, and taking other characteristic points in the rectangular area as characteristic points related to the target characteristic point;
and determining a minimum circumscribed rectangle formed by the target feature points and the related feature points, and taking the area covered by the minimum circumscribed rectangle as the target area.
The invention also proposes a computer-readable storage medium having stored thereon an image area identification program which, when executed by a processor, implements the steps of the image area identification method as defined in any one of the preceding claims.
By implementing the image area identification method, the equipment and the computer readable storage medium, a plurality of feature points are obtained by carrying out feature extraction and matching on the current picture; then, determining a target feature point with the maximum weight in the feature points; and finally, taking the minimum circumscribed area formed by the target characteristic points and the related characteristic points as a target area. The method and the device realize a humanized image area identification scheme, reduce the learning difficulty and the reaction capability requirement of the user, obtain a corresponding auxiliary control scheme by searching for a target area, and improve the actual use experience of the user for various types of touch control.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
fig. 1 is a schematic diagram of a hardware structure of a mobile terminal according to the present invention;
fig. 2 is a communication network system architecture diagram provided by an embodiment of the present invention;
FIG. 3 is a flow chart of a first embodiment of the image area identification method of the present invention;
FIG. 4 is a flow chart of a second embodiment of the image region identification method of the present invention;
FIG. 5 is a flow chart of a third embodiment of the image region identification method of the present invention;
FIG. 6 is a flow chart of a fourth embodiment of the image area identification method of the present invention;
FIG. 7 is a flow chart of a fifth embodiment of the image region identification method of the present invention;
fig. 8 is a schematic diagram of feature points of the image region identification method of the present invention.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for facilitating the explanation of the present invention, and have no specific meaning in itself. Thus, "module", "component" or "unit" may be used mixedly.
The terminal may be implemented in various forms. For example, the terminal described in the present invention may include a mobile terminal such as a mobile phone, a tablet computer, a notebook computer, a palmtop computer, a Personal Digital Assistant (PDA), a Portable Media Player (PMP), a navigation device, a wearable device, a smart band, a pedometer, and the like, and a fixed terminal such as a Digital TV, a desktop computer, and the like.
The following description will be given by way of example of a mobile terminal, and it will be understood by those skilled in the art that the construction according to the embodiment of the present invention can be applied to a fixed type terminal, in addition to elements particularly used for mobile purposes.
Referring to fig. 1, which is a schematic diagram of a hardware structure of a mobile terminal for implementing various embodiments of the present invention, the mobile terminal 100 may include: RF (Radio Frequency) unit 101, WiFi module 102, audio output unit 103, a/V (audio/video) input unit 104, sensor 105, display unit 106, user input unit 107, interface unit 108, memory 109, processor 110, and power supply 111. Those skilled in the art will appreciate that the mobile terminal architecture shown in fig. 1 is not intended to be limiting of mobile terminals, which may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
The following describes each component of the mobile terminal in detail with reference to fig. 1:
the Radio Frequency unit 101 may be configured to receive and transmit signals during a message transmission or call, specifically, receive downlink information of a base station and then process the received downlink information to the processor 110, and transmit uplink data to the base station, in General, the Radio Frequency unit 101 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like, and in addition, the Radio Frequency unit 101 may further communicate with a network and other devices through wireless communication, and the wireless communication may use any communication standard or protocol, including, but not limited to, GSM (Global System of Mobile communication), GPRS (General Packet Radio Service), CDMA2000(Code Division Multiple Access 2000), WCDMA (Wideband Code Division Multiple Access ), TD-SCDMA (Synchronous Time Division Multiple Access, Code Division Multiple Access, Time Division Multiple Access, etc., TDD — Time Division Multiple Access, L Time Division Multiple Access, etc.
WiFi belongs to short-distance wireless transmission technology, and the mobile terminal can help a user to receive and send e-mails, browse webpages, access streaming media and the like through the WiFi module 102, and provides wireless broadband internet access for the user. Although fig. 1 shows the WiFi module 102, it is understood that it does not belong to the essential constitution of the mobile terminal, and may be omitted entirely as needed within the scope not changing the essence of the invention.
The audio output unit 103 may convert audio data received by the radio frequency unit 101 or the WiFi module 102 or stored in the memory 109 into an audio signal and output as sound when the mobile terminal 100 is in a call signal reception mode, a call mode, a recording mode, a voice recognition mode, a broadcast reception mode, or the like. Also, the audio output unit 103 may also provide audio output related to a specific function performed by the mobile terminal 100 (e.g., a call signal reception sound, a message reception sound, etc.). The audio output unit 103 may include a speaker, a buzzer, and the like.
The a/V input unit 104 is used to receive audio or video signals. The a/V input Unit 104 may include a Graphics Processing Unit (GPU) 1041 and a microphone 1042, the Graphics processor 1041 Processing image data of still pictures or video obtained by an image capturing device (e.g., a camera) in a video capturing mode or an image capturing mode. The processed image frames may be displayed on the display unit 106. The image frames processed by the graphic processor 1041 may be stored in the memory 109 (or other storage medium) or transmitted via the radio frequency unit 101 or the WiFi module 102. The microphone 1042 may receive sounds (audio data) via the microphone 1042 in a phone call mode, a recording mode, a voice recognition mode, or the like, and may be capable of processing such sounds into audio data. The processed audio (voice) data may be converted into a format output transmittable to a mobile communication base station via the radio frequency unit 101 in case of a phone call mode. The microphone 1042 may implement various types of noise cancellation (or suppression) algorithms to cancel (or suppress) noise or interference generated in the course of receiving and transmitting audio signals.
The mobile terminal 100 also includes at least one sensor 105, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor includes an ambient light sensor that can adjust the brightness of the display panel 1061 according to the brightness of ambient light, and a proximity sensor that can turn off the display panel 1061 and/or a backlight when the mobile terminal 100 is moved to the ear. As one of the motion sensors, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally, three axes), can detect the magnitude and direction of gravity when stationary, and can be used for applications of recognizing the posture of a mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer and tapping), and the like; as for other sensors such as a fingerprint sensor, a pressure sensor, an iris sensor, a molecular sensor, a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which can be configured on the mobile phone, further description is omitted here.
The Display unit 106 may include a Display panel 1061, and the Display panel 1061 may be configured in the form of a liquid Crystal Display (L acquired Crystal Display, L CD), an Organic light-Emitting Diode (O L ED), or the like.
The user input unit 107 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the mobile terminal. Specifically, the user input unit 107 may include a touch panel 1071 and other input devices 1072. The touch panel 1071, also referred to as a touch screen, may collect a touch operation performed by a user on or near the touch panel 1071 (e.g., an operation performed by the user on or near the touch panel 1071 using a finger, a stylus, or any other suitable object or accessory), and drive a corresponding connection device according to a predetermined program. The touch panel 1071 may include two parts of a touch detection device and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 110, and can receive and execute commands sent by the processor 110. In addition, the touch panel 1071 may be implemented in various types, such as a resistive type, a capacitive type, an infrared ray, and a surface acoustic wave. In addition to the touch panel 1071, the user input unit 107 may include other input devices 1072. In particular, other input devices 1072 may include, but are not limited to, one or more of a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like, and are not limited to these specific examples.
Further, the touch panel 1071 may cover the display panel 1061, and when the touch panel 1071 detects a touch operation thereon or nearby, the touch panel 1071 transmits the touch operation to the processor 110 to determine the type of the touch event, and then the processor 110 provides a corresponding visual output on the display panel 1061 according to the type of the touch event. Although the touch panel 1071 and the display panel 1061 are shown in fig. 1 as two separate components to implement the input and output functions of the mobile terminal, in some embodiments, the touch panel 1071 and the display panel 1061 may be integrated to implement the input and output functions of the mobile terminal, and is not limited herein.
The interface unit 108 serves as an interface through which at least one external device is connected to the mobile terminal 100. For example, the external device may include a wired or wireless headset port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device having an identification module, an audio input/output (I/O) port, a video I/O port, an earphone port, and the like. The interface unit 108 may be used to receive input (e.g., data information, power, etc.) from external devices and transmit the received input to one or more elements within the mobile terminal 100 or may be used to transmit data between the mobile terminal 100 and external devices.
The memory 109 may be used to store software programs as well as various data. The memory 109 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. Further, the memory 109 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
The processor 110 is a control center of the mobile terminal, connects various parts of the entire mobile terminal using various interfaces and lines, and performs various functions of the mobile terminal and processes data by operating or executing software programs and/or modules stored in the memory 109 and calling data stored in the memory 109, thereby performing overall monitoring of the mobile terminal. Processor 110 may include one or more processing units; preferably, the processor 110 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 110.
The mobile terminal 100 may further include a power supply 111 (e.g., a battery) for supplying power to various components, and preferably, the power supply 111 may be logically connected to the processor 110 via a power management system, so as to manage charging, discharging, and power consumption management functions via the power management system.
Although not shown in fig. 1, the mobile terminal 100 may further include a bluetooth module or the like, which is not described in detail herein.
In order to facilitate understanding of the embodiments of the present invention, a communication network system on which the mobile terminal of the present invention is based is described below.
Referring to fig. 2, fig. 2 is an architecture diagram of a communication Network system according to an embodiment of the present invention, the communication Network system is L TE system of universal mobile telecommunications technology, and the L TE system includes a UE (User Equipment) 201, an E-UTRAN (Evolved UMTS Terrestrial Radio Access Network) 202, an EPC (Evolved Packet Core) 203, and an IP service 204 of an operator, which are in communication connection in sequence.
Specifically, the UE201 may be the terminal 100 described above, and is not described herein again.
The E-UTRAN202 includes eNodeB2021 and other eNodeBs 2022, among others. Among them, the eNodeB2021 may be connected with other eNodeB2022 through backhaul (e.g., X2 interface), the eNodeB2021 is connected to the EPC203, and the eNodeB2021 may provide the UE201 access to the EPC 203.
The EPC203 may include an MME (Mobility Management Entity) 2031, an HSS (Home Subscriber Server) 2032, other MMEs 2033, an SGW (Serving gateway) 2034, a PGW (PDN gateway) 2035, and a PCRF (Policy and charging functions Entity) 2036, and the like. The MME2031 is a control node that handles signaling between the UE201 and the EPC203, and provides bearer and connection management. HSS2032 is used to provide registers to manage functions such as home location register (not shown) and holds subscriber specific information about service characteristics, data rates, etc. All user data may be sent through SGW2034, PGW2035 may provide IP address assignment for UE201 and other functions, and PCRF2036 is a policy and charging control policy decision point for traffic data flow and IP bearer resources, which selects and provides available policy and charging control decisions for a policy and charging enforcement function (not shown).
The IP services 204 may include the internet, intranets, IMS (IP Multimedia Subsystem), or other IP services, among others.
Although L TE system is described as an example, it should be understood by those skilled in the art that the present invention is not limited to L TE system, but also applicable to other wireless communication systems, such as GSM, CDMA2000, WCDMA, TD-SCDMA, and future new network systems.
Based on the above mobile terminal hardware structure and communication network system, the present invention provides various embodiments of the method.
Example one
Fig. 3 is a flowchart of a first embodiment of the image area identification method of the present invention. An image region identification method, the method comprising:
s1, extracting and matching the features of the current picture to obtain a plurality of feature points;
s2, determining the target feature point with the maximum weight in the feature points;
and S3, taking the minimum circumscribed area formed by the target characteristic points and the related characteristic points as a target area.
In this embodiment, first, feature extraction and matching are performed on a current picture to obtain a plurality of feature points, where feature extraction is performed on the current picture with preset parameter configuration, then, the extracted features are matched with preset feature parameters, and a point with a certain correlation is determined as a feature point of this embodiment. Optionally, the corresponding parameter configuration, the feature parameter, and the correlation threshold are determined according to the content of the current picture, optionally, the corresponding parameter configuration, the feature parameter, and the correlation threshold are determined according to the application program to which the current picture belongs, and/or the functional component of the application program, and/or the current picture feature of the functional component of the application program.
In this embodiment, after determining each feature point that meets the requirement in the current screen, the target feature point with the largest weight is determined among all the feature points. The weight is used to measure the aggregation degree of each feature point, that is, the feature point with the largest weight is found by determining the weight value of each feature point, and the target feature point is regarded as a target feature point in the target area to be determined. Optionally, a calculation parameter for calculating the weight value is determined according to the predicted characteristic parameter of the target region, optionally, a calculation parameter for calculating the weight value is determined according to the region range of the predicted target region, and optionally, a calculation parameter for calculating the weight value is determined according to the predicted geometric characteristic of the target region. In the process of implementing the embodiment, certain area prediction needs to be performed on the target area in advance, so that a real target area to be found in any current picture is determined according to the predicted target area.
In this embodiment, after the target feature point with the largest weight is determined among the feature points, the minimum circumscribed area formed by the target feature point and the related feature points is used as the target area. The target feature point is considered to be one point in the target area to be determined, and the plurality of feature points related to the target feature point are also considered to be respective points in the target area to be determined, that is, the minimum circumscribed area formed by the target feature point and the related feature points is taken as the target area in the present embodiment. Optionally, the minimum circumscribed area may be a graphic area with certain geometric features, such as a rectangular area, an elliptical area, a circular area, or a triangular area.
Specifically, fig. 8 is a schematic diagram of feature points of the image region identification method according to the present invention. The left side of fig. 8 shows the target region that needs to be determined in the present embodiment, that is, in the implementation process of the present embodiment, the graphical overview shown on the left side is obtained through prediction. Specifically, the graphic overview is predicted to be a graphic area with four feature points as angular stars, and the predicted graphic area is used as a template to search for a corresponding target area in the right current picture. In the on-screen image on the right side of fig. 8, first, feature point 1, feature point 2, and feature point …, feature point 12, a total of twelve feature points, then, the feature point having the largest weight value, that is, the feature point having the largest aggregation degree is found, that is, the feature point with the maximum aggregation degree may be any one of the feature point 8, the feature point 9, the feature point 10, and the feature point 11, taking the feature point 8 as the feature point with the maximum weight as an example, after the feature point 8 is determined as the feature point with the largest weight, the feature points related to the feature point 8, that is, the feature point 9, the feature point 10, and the feature point 11 are determined, and finally, the minimum circumscribed area (for example, the star area in this example) formed by the feature point 8, the feature point 9, the feature point 10, and the feature point 11 together obtains the target area of the current screen.
Optionally, in this embodiment, after the target area is determined, the functional features of the target area are extracted, and are associated and mapped to a location area adapted to the usage habit and the response capability of the user.
The method has the advantages that a plurality of feature points are obtained by extracting and matching features of the current picture; then, determining a target feature point with the maximum weight in the feature points; and finally, taking the minimum circumscribed area formed by the target characteristic points and the related characteristic points as a target area. The method and the device realize a humanized image area identification scheme, reduce the learning difficulty and the reaction capability requirement of the user, obtain a corresponding auxiliary control scheme by searching for a target area, and improve the actual use experience of the user for various types of touch control.
Example two
Fig. 4 is a flowchart of a second embodiment of the image region identification method of the present invention, and based on the above embodiment, optionally, the performing feature extraction and matching on the current picture to obtain a plurality of feature points includes:
s11, acquiring a first feature point of the current picture and a second feature point of the reference area;
and S12, performing feature matching on the first feature point and the second feature point, and taking the first feature point with the feature value smaller than a preset value as the feature point.
Optionally, the reference region includes the predicted region or the region where the template is located;
optionally, the reference region includes basic graphic features or template parameters of the predicted region;
optionally, the first feature point and the second feature point are subjected to feature matching, and the first feature point with a feature value smaller than a preset value is used as the feature point, wherein a corresponding preset value is determined according to the identification complexity of the current picture, the higher the complexity is, the higher the preset value is, otherwise, the lower the complexity is, the lower the preset value is, thereby enabling the feature point to be extracted more accurately and efficiently.
Optionally, a first feature point is found in an effective content area of a current picture;
optionally, a first feature point is found in a functional area of the current picture;
optionally, the first feature point is found in the dynamic content area of the current frame.
The method has the advantages that the first characteristic point of the current picture and the second characteristic point of the reference area are obtained; and then, performing feature matching on the first feature points and the second feature points, and taking the first feature points with feature values smaller than a preset value as the feature points. The method and the device realize a humanized image area identification scheme, reduce the learning difficulty and the reaction capability requirement of the user, obtain a corresponding auxiliary control scheme by searching for a target area, and improve the actual use experience of the user for various types of touch control.
EXAMPLE III
Fig. 5 is a flowchart of a third embodiment of the image region identification method of the present invention, and based on the above embodiment, optionally, the determining a target feature point with the largest weight from among the feature points includes:
s21, presetting a rectangular parameter;
and S22, selecting the feature points one by one, taking the feature points as geometric centers, and determining rectangular areas corresponding to the feature points according to the rectangular parameters.
Optionally, determining a corresponding rectangular parameter according to the identification complexity of the current picture;
optionally, the higher the complexity is, the smaller the rectangular region is, otherwise, the lower the complexity is, the larger the rectangular region is;
optionally, determining different rectangular parameters according to different feature points;
optionally, in the process of determining the rectangular area, the rectangular parameter is dynamically adjusted.
The method has the advantages that a rectangle parameter is preset; and then, selecting the feature points one by one, taking the feature points as geometric centers, and determining rectangular areas corresponding to the feature points according to the rectangular parameters. The method and the device realize a humanized image area identification scheme, reduce the learning difficulty and the reaction capability requirement of the user, obtain a corresponding auxiliary control scheme by searching for a target area, and improve the actual use experience of the user for various types of touch control.
Example four
Fig. 6 is a flowchart of a fourth embodiment of the image region identification method of the present invention, based on the above embodiment, optionally, the determining a target feature point with the largest weight from among the feature points further includes:
s23, identifying the feature points in the rectangular area;
and S24, determining the weight of the selected feature points according to the number of the feature points.
Optionally, identifying other feature points in the rectangular area except the feature point at the center of the rectangle;
optionally, determining the weight of the selected feature point according to the number of other feature points in the rectangular region except the feature point in the center of the rectangle;
optionally, determining the weight of the selected feature point according to a geometric figure feature formed by other feature points except the feature point at the center of the rectangle in the rectangular region and the feature point at the center of the rectangle;
optionally, determining the weight of the selected feature point according to the distance between the feature point located in the rectangular region except the feature point located in the center of the rectangle and the feature point located in the center of the rectangle;
optionally, the weight of the selected feature point is determined according to the number of other feature points in the rectangular region except the feature point in the center of the rectangle, or the geometric feature formed by the feature point in the center of the rectangle.
The method has the advantages that the characteristic points in the rectangular area are identified; then, the weight of the selected feature points is determined according to the number of the feature points. The method and the device realize a humanized image area identification scheme, reduce the learning difficulty and the reaction capability requirement of the user, obtain a corresponding auxiliary control scheme by searching for a target area, and improve the actual use experience of the user for various types of touch control.
EXAMPLE five
Fig. 7 is a flowchart of a fifth embodiment of the image region identification method of the present invention, and based on the above embodiments, optionally, the step of using a minimum circumscribed region formed by the target feature point and the related feature points together as the target region includes:
s31, determining a rectangular area corresponding to the target characteristic point, and taking other characteristic points in the rectangular area as characteristic points related to the target characteristic point;
and S32, determining a minimum circumscribed rectangle formed by the target feature points and the related feature points, and taking the area covered by the minimum circumscribed rectangle as the target area.
Optionally, a rectangular region corresponding to the target feature point is determined, and other feature points in the rectangular region are used as feature points related to the target feature point;
optionally, determining the weight of the selected feature point according to the number of other feature points in the rectangular region except the feature point in the center of the rectangle;
optionally, the feature point located in the rectangular region except the feature point located in the center of the rectangle is taken as a related feature point;
optionally, determining related feature points according to distances between feature points located in the rectangular region except for feature points located in the center of the rectangle and the feature points located in the center of the rectangle;
optionally, determining related feature points according to the number of other feature points in the rectangular region except the feature point in the center of the rectangle, and/or the geometric feature composed of the feature points in the center of the rectangle;
optionally, after the target area is determined, extracting functional features of the target area, and associating and mapping the functional features to a position area adapted to the use habits and reaction capabilities of the user;
optionally, after the position area adapted to the use habit and the reaction capability of the user is determined, the touch signal of the position area is correlated to the target area, and a touch event of the target area is generated.
The method has the advantages that the rectangular area corresponding to the target characteristic point is determined, and other characteristic points in the rectangular area are used as characteristic points related to the target characteristic point; then, a minimum circumscribed rectangle formed by the target feature points and the related feature points is determined, and an area covered by the minimum circumscribed rectangle is used as the target area. The method and the device realize a humanized image area identification scheme, reduce the learning difficulty and the reaction capability requirement of the user, obtain a corresponding auxiliary control scheme by searching for a target area, and improve the actual use experience of the user for various types of touch control.
EXAMPLE six
Based on the above embodiments, the present invention further provides an image area identification device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when executed by the processor, the computer program implements:
performing feature extraction and matching on the current picture to obtain a plurality of feature points;
determining a target feature point with the largest weight from the feature points;
and taking the minimum circumscribed area formed by the target characteristic points and the related characteristic points as a target area.
In this embodiment, first, feature extraction and matching are performed on a current picture to obtain a plurality of feature points, where feature extraction is performed on the current picture with preset parameter configuration, then, the extracted features are matched with preset feature parameters, and a point with a certain correlation is determined as a feature point of this embodiment. Optionally, the corresponding parameter configuration, the feature parameter, and the correlation threshold are determined according to the content of the current picture, optionally, the corresponding parameter configuration, the feature parameter, and the correlation threshold are determined according to the application program to which the current picture belongs, and/or the functional component of the application program, and/or the current picture feature of the functional component of the application program.
In this embodiment, after determining each feature point that meets the requirement in the current screen, the target feature point with the largest weight is determined among all the feature points. The weight is used to measure the aggregation degree of each feature point, that is, the feature point with the largest weight is found by determining the weight value of each feature point, and the target feature point is regarded as a target feature point in the target area to be determined. Optionally, a calculation parameter for calculating the weight value is determined according to the predicted characteristic parameter of the target region, optionally, a calculation parameter for calculating the weight value is determined according to the region range of the predicted target region, and optionally, a calculation parameter for calculating the weight value is determined according to the predicted geometric characteristic of the target region. In the process of implementing the embodiment, certain area prediction needs to be performed on the target area in advance, so that a real target area to be found in any current picture is determined according to the predicted target area.
In this embodiment, after the target feature point with the largest weight is determined among the feature points, the minimum circumscribed area formed by the target feature point and the related feature points is used as the target area. The target feature point is considered to be one point in the target area to be determined, and the plurality of feature points related to the target feature point are also considered to be respective points in the target area to be determined, that is, the minimum circumscribed area formed by the target feature point and the related feature points is taken as the target area in the present embodiment. Optionally, the minimum circumscribed area may be a graphic area with certain geometric features, such as a rectangular area, an elliptical area, a circular area, or a triangular area.
Specifically, fig. 8 is a schematic diagram of feature points of the image region identification method according to the present invention. The left side of fig. 8 shows the target region that needs to be determined in the present embodiment, that is, in the implementation process of the present embodiment, the graphical overview shown on the left side is obtained through prediction. Specifically, the graphic overview is predicted to be a graphic area with four feature points as angular stars, and the predicted graphic area is used as a template to search for a corresponding target area in the right current picture. In the on-screen image on the right side of fig. 8, first, feature point 1, feature point 2, and feature point …, feature point 12, a total of twelve feature points, then, the feature point having the largest weight value, that is, the feature point having the largest aggregation degree is found, that is, the feature point with the maximum aggregation degree may be any one of the feature point 8, the feature point 9, the feature point 10, and the feature point 11, taking the feature point 8 as the feature point with the maximum weight as an example, after the feature point 8 is determined as the feature point with the largest weight, the feature points related to the feature point 8, that is, the feature point 9, the feature point 10, and the feature point 11 are determined, and finally, the minimum circumscribed area (for example, the star area in this example) formed by the feature point 8, the feature point 9, the feature point 10, and the feature point 11 together obtains the target area of the current screen.
Optionally, in this embodiment, after the target area is determined, the functional features of the target area are extracted, and are associated and mapped to a location area adapted to the usage habit and the response capability of the user.
The method has the advantages that a plurality of feature points are obtained by extracting and matching features of the current picture; then, determining a target feature point with the maximum weight in the feature points; and finally, taking the minimum circumscribed area formed by the target characteristic points and the related characteristic points as a target area. The method and the device realize a humanized image area identification scheme, reduce the learning difficulty and the reaction capability requirement of the user, obtain a corresponding auxiliary control scheme by searching for a target area, and improve the actual use experience of the user for various types of touch control.
EXAMPLE seven
Based on the above embodiments, optionally, the computer program when executed by the processor implements:
presetting a rectangular parameter;
and selecting the feature points one by one, taking the feature points as geometric centers, and determining rectangular areas corresponding to the feature points according to the rectangular parameters.
Optionally, a first feature point of the current picture and a second feature point of the reference area are obtained;
optionally, the first feature point and the second feature point are subjected to feature matching, and the first feature point with a feature value smaller than a preset value is taken as the feature point.
Optionally, the reference region includes the predicted region or the region where the template is located;
optionally, the reference region includes basic graphic features or template parameters of the predicted region;
optionally, the first feature point and the second feature point are subjected to feature matching, and the first feature point with a feature value smaller than a preset value is used as the feature point, wherein a corresponding preset value is determined according to the identification complexity of the current picture, the higher the complexity is, the higher the preset value is, otherwise, the lower the complexity is, the lower the preset value is, thereby enabling the feature point to be extracted more accurately and efficiently.
Optionally, a first feature point is found in an effective content area of a current picture;
optionally, a first feature point is found in a functional area of the current picture;
optionally, the first feature point is found in the dynamic content area of the current frame.
The method has the advantages that the first characteristic point of the current picture and the second characteristic point of the reference area are obtained; and then, performing feature matching on the first feature points and the second feature points, and taking the first feature points with feature values smaller than a preset value as the feature points. The method and the device realize a humanized image area identification scheme, reduce the learning difficulty and the reaction capability requirement of the user, obtain a corresponding auxiliary control scheme by searching for a target area, and improve the actual use experience of the user for various types of touch control.
Example eight
Based on the above embodiments, optionally, the computer program when executed by the processor implements:
identifying feature points within the rectangular region;
determining the weight of the selected feature points according to the number of the feature points;
optionally, presetting a rectangular parameter;
optionally, the feature points are selected one by one, the feature points are used as geometric centers, and rectangular areas corresponding to the feature points are determined according to the rectangular parameters.
Optionally, determining a corresponding rectangular parameter according to the identification complexity of the current picture;
optionally, the higher the complexity is, the smaller the rectangular region is, otherwise, the lower the complexity is, the larger the rectangular region is;
optionally, determining different rectangular parameters according to different feature points;
optionally, in the process of determining the rectangular area, the rectangular parameter is dynamically adjusted.
The method has the advantages that a rectangle parameter is preset; and then, selecting the feature points one by one, taking the feature points as geometric centers, and determining rectangular areas corresponding to the feature points according to the rectangular parameters. The method and the device realize a humanized image area identification scheme, reduce the learning difficulty and the reaction capability requirement of the user, obtain a corresponding auxiliary control scheme by searching for a target area, and improve the actual use experience of the user for various types of touch control.
Example nine
Based on the above embodiment, determining a rectangular region corresponding to the target feature point, and taking other feature points in the rectangular region as feature points related to the target feature point;
and determining a minimum circumscribed rectangle formed by the target feature points and the related feature points, and taking the area covered by the minimum circumscribed rectangle as the target area.
Optionally, identifying other feature points in the rectangular area except the feature point at the center of the rectangle;
optionally, determining the weight of the selected feature point according to the number of other feature points in the rectangular region except the feature point in the center of the rectangle;
optionally, determining the weight of the selected feature point according to a geometric figure feature formed by other feature points except the feature point at the center of the rectangle in the rectangular region and the feature point at the center of the rectangle;
optionally, determining the weight of the selected feature point according to the distance between the feature point located in the rectangular region except the feature point located in the center of the rectangle and the feature point located in the center of the rectangle;
optionally, the weight of the selected feature point is determined according to the number of other feature points in the rectangular region except the feature point in the center of the rectangle, or the geometric feature formed by the feature point in the center of the rectangle.
Optionally, a rectangular region corresponding to the target feature point is determined, and other feature points in the rectangular region are used as feature points related to the target feature point;
optionally, a minimum circumscribed rectangle formed by the target feature point and the related feature points together is determined, and an area covered by the minimum circumscribed rectangle is used as the target area.
Optionally, a rectangular region corresponding to the target feature point is determined, and other feature points in the rectangular region are used as feature points related to the target feature point;
optionally, determining the weight of the selected feature point according to the number of other feature points in the rectangular region except the feature point in the center of the rectangle;
optionally, the feature point located in the rectangular region except the feature point located in the center of the rectangle is taken as a related feature point;
optionally, determining related feature points according to distances between feature points located in the rectangular region except for feature points located in the center of the rectangle and the feature points located in the center of the rectangle;
optionally, determining related feature points according to the number of other feature points in the rectangular region except the feature point in the center of the rectangle, and/or the geometric feature composed of the feature points in the center of the rectangle;
optionally, after the target area is determined, extracting functional features of the target area, and associating and mapping the functional features to a position area adapted to the use habits and reaction capabilities of the user;
optionally, after the position area adapted to the use habit and the reaction capability of the user is determined, the touch signal of the position area is correlated to the target area, and a touch event of the target area is generated.
The method has the advantages that the rectangular area corresponding to the target characteristic point is determined, and other characteristic points in the rectangular area are used as characteristic points related to the target characteristic point; then, a minimum circumscribed rectangle formed by the target feature points and the related feature points is determined, and an area covered by the minimum circumscribed rectangle is used as the target area. The method and the device realize a humanized image area identification scheme, reduce the learning difficulty and the reaction capability requirement of the user, obtain a corresponding auxiliary control scheme by searching for a target area, and improve the actual use experience of the user for various types of touch control.
Example ten
Based on the above embodiments, the present invention also provides a computer readable storage medium, having an image area identification program stored thereon, where the image area identification program, when executed by a processor, implements the steps of the image area identification method as described in any one of the above.
By implementing the image area identification method, the equipment and the computer readable storage medium, a plurality of feature points are obtained by carrying out feature extraction and matching on the current picture; then, determining a target feature point with the maximum weight in the feature points; and finally, taking the minimum circumscribed area formed by the target characteristic points and the related characteristic points as a target area. The method and the device realize a humanized image area identification scheme, reduce the learning difficulty and the reaction capability requirement of the user, obtain a corresponding auxiliary control scheme by searching for a target area, and improve the actual use experience of the user for various types of touch control.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. An image region identification method, characterized in that the method comprises:
performing feature extraction and matching on the current picture to obtain a plurality of feature points;
determining a target feature point with the largest weight from the feature points;
and taking the minimum circumscribed area formed by the target characteristic points and the related characteristic points as a target area.
2. The image region identification method according to claim 1, wherein said extracting and matching features of the current picture to obtain a plurality of feature points comprises:
acquiring a first characteristic point of a current picture and a second characteristic point of a reference area;
and performing feature matching on the first feature points and the second feature points, and taking the first feature points with feature values smaller than a preset value as the feature points.
3. The image region identification method according to claim 2, wherein the determining of the most weighted target feature point among the feature points includes:
presetting a rectangular parameter;
and selecting the feature points one by one, taking the feature points as geometric centers, and determining rectangular areas corresponding to the feature points according to the rectangular parameters.
4. The image region identification method according to claim 3, wherein the determining of the most weighted target feature point among the feature points further comprises:
identifying feature points within the rectangular region;
and determining the weight of the selected characteristic points according to the number of the characteristic points.
5. The image region identification method according to claim 4, wherein the step of using a minimum circumscribed region formed by the target feature point and the related feature points together as a target region comprises:
determining a rectangular area corresponding to the target characteristic point, and taking other characteristic points in the rectangular area as characteristic points related to the target characteristic point;
and determining a minimum circumscribed rectangle formed by the target feature points and the related feature points, and taking the area covered by the minimum circumscribed rectangle as the target area.
6. An image area identification device, characterized in that the device comprises a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program realizing, when executed by the processor:
performing feature extraction and matching on the current picture to obtain a plurality of feature points;
determining a target feature point with the largest weight from the feature points;
and taking the minimum circumscribed area formed by the target characteristic points and the related characteristic points as a target area.
7. The image area recognition device of claim 6, wherein the computer program when executed by the processor implements:
acquiring a first characteristic point of a current picture and a second characteristic point of a reference area;
and performing feature matching on the first feature points and the second feature points, and taking the first feature points with feature values smaller than a preset value as the feature points.
8. The image area recognition device of claim 7, wherein the computer program when executed by the processor implements:
presetting a rectangular parameter;
and selecting the feature points one by one, taking the feature points as geometric centers, and determining rectangular areas corresponding to the feature points according to the rectangular parameters.
9. The image area recognition device of claim 8, wherein the computer program when executed by the processor implements:
identifying feature points within the rectangular region;
determining the weight of the selected feature points according to the number of the feature points;
determining a rectangular area corresponding to the target characteristic point, and taking other characteristic points in the rectangular area as characteristic points related to the target characteristic point;
and determining a minimum circumscribed rectangle formed by the target feature points and the related feature points, and taking the area covered by the minimum circumscribed rectangle as the target area.
10. A computer-readable storage medium, characterized in that an image area identification program is stored on the computer-readable storage medium, which image area identification program, when executed by a processor, implements the steps of the image area identification method according to any one of claims 1 to 5.
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