CN111208138A - Intelligent wood recognition device - Google Patents
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- CN111208138A CN111208138A CN202010131315.5A CN202010131315A CN111208138A CN 111208138 A CN111208138 A CN 111208138A CN 202010131315 A CN202010131315 A CN 202010131315A CN 111208138 A CN111208138 A CN 111208138A
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
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- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
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Abstract
The invention provides a timber identification device and a method, comprising an image acquisition device and a rotating device, wherein the image acquisition device and a timber section perform relative motion under the driving of the rotating device; the image acquisition device acquires a plurality of images of the timber at different angles; the images can be used for constructing a three-dimensional model of the section of the timber. The method for identifying the wood by constructing the three-dimensional model of the wood texture and comparing the three-dimensional appearance with the texture is firstly provided. The method makes full use of the difference of three-dimensional shapes of the cross sections of the wood, and has higher identification accuracy.
Description
Technical Field
The invention relates to the technical field of wood morphology measurement, in particular to the technical field of 3D wood morphology measurement.
Background
The price difference of different woods is large, and the situation that the wood with better wood charge is traded frequently at present. Especially in collectibles, the price difference between different woods is huge and is difficult to distinguish by common consumers.
There are also some techniques for identifying wood, which usually take many photographs as samples, and train the neural network to obtain a neural network capable of identifying a certain wood. When the wood needs to be detected, the wood to be identified is photographed and sent to a neural network for identification. The method utilizes big data and the learning function of a neural network, and has certain discriminability. However, since the counterfeiting technology is also promoted at present, the wood texture very similar to the target wood can be made through manual intervention. Thereby leading to a great reduction in the accuracy of the way of neural network recognition.
The reason for this is that the pictures sent into the neural network are two-dimensional, which is equivalent to only providing the surface color and pattern information of the wood, and abandoning other information.
Therefore, ① can make full use of wood information, so that the recognition accuracy is improved, ② can simultaneously and greatly improve the recognition speed and the recognition accuracy, ③ is convenient to operate, special equipment is not needed, complicated and excessive measurement is not needed, and the device is simple in structure and easy to implement.
Disclosure of Invention
In view of the above, the present invention has been developed to provide a timber identification apparatus and method that overcomes, or at least partially solves, the above-identified problems.
The invention provides a timber identification device and a method, comprising an image acquisition device and a rotating device, wherein the image acquisition device and a timber section perform relative motion under the driving of the rotating device;
the image acquisition device acquires a plurality of images of the timber at different angles;
the images can be used for constructing a three-dimensional model of the section of the timber.
Alternatively, the wood is surface removed and has a cross-section that exposes the original wood topography.
Optionally, the image capture device rotates around the section of the timber.
Optionally, the rotating device is an L-shaped rotating arm.
Optionally, a database with three-dimensional model data of wood standards is also included.
Optionally, the collected and synthesized three-dimensional model data of the timber to be detected is compared with the standard three-dimensional model data of the timber, and whether the timber to be detected is similar to the timber is judged.
Optionally, when the image acquisition device acquires the target object, the two adjacent acquisition positions meet the following conditions:
l is the straight line distance of the optical center of the image acquisition device at two adjacent acquisition positions; f is the focal length of the image acquisition device; d is the rectangular length or width of the photosensitive element of the image acquisition device; t is the distance from the photosensitive element of the image acquisition device to the surface of the target along the optical axis; δ is the adjustment coefficient.
Optionally, and δ < 0.576; preferably δ < 0.402; or δ < 0.329.
Optionally, the system further comprises a processor, configured to synthesize a three-dimensional model according to the plurality of images acquired by the image acquisition device, so as to obtain three-dimensional information.
Optionally, the wood to be identified is placed on a solid background plate.
Invention and technical effects
1. The method for identifying the wood by constructing the three-dimensional model of the wood texture and comparing the three-dimensional appearance with the texture is firstly provided. The method makes full use of the difference of three-dimensional shapes of the cross sections of the wood, and has higher identification accuracy.
2. By optimizing the position of the camera for collecting the picture, the synthesis speed and the synthesis precision can be ensured to be improved simultaneously, so that the identification speed and the identification precision are improved. And when the position is optimized, the angle and the target size do not need to be measured, and the applicability is stronger.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
the correspondence of reference numerals to the respective components is as follows:
FIG. 1 is a schematic view of a timber identification device in an embodiment of the invention;
FIG. 2 is a schematic representation of wood grain in an embodiment of the present invention;
wherein, the corresponding relation between each part and the reference numeral is as follows:
the method comprises the following steps of 1 image acquisition device, 2 rotating arms, 3 rotation driving devices, 4 rotating shafts, 5 target objects and 6 background plates.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Timber recognition device structure
In order to solve the above technical problem, an embodiment of the present invention provides a timber identifying device, as shown in fig. 1, including: the device comprises an image acquisition device 1, a rotating arm 2 and a rotation driving device 3. Wherein the rotating arm 2 is L-shaped, the image acquisition device 1 is arranged at the horizontal part of the rotating arm, and the optical axis is vertically downward. The vertical part of the rotating arm 2 is connected with a rotation driving device 3 through a rotating shaft 4, and is driven by the rotation driving device 3 to rotate, so that the rotating arm 2 is driven to rotate within the range of 180 degrees. So that the optical axis of the image pickup device 1 is rotated by ± 90 ° left and right on a vertically downward basis. That is, when the object 5 is placed directly below the image pickup device 1 so that it is preferably located at the center of the rotation locus of the image pickup device 1, the image pickup device 1 is rotated by 180 ° around the upper surface of the object.
An object (wood piece to be measured) 5 is placed on the stage. In order to acquire image simplicity and improve synthesis speed and accuracy, a background plate 6 may be placed between the stage and the object 5, or the stage may be directly set as the background plate 6. The background plate 6 is entirely of a solid color, or mostly (body) of a solid color. In particular, the color plate can be a white plate or a black plate, and the specific color can be selected according to the color of the object body. The background plate 6 is preferably a flat plate, but may also be a curved plate, such as a concave plate, a convex plate, a spherical plate, or even a background plate with a wavy surface in some application scenarios; the plate can also be made into various shapes, for example, three sections of planes can be spliced to form a concave shape as a whole, or a plane and a curved surface can be spliced. In addition to the surface shape of the background plate 6 being variable, the edge shape thereof may be selected as desired. Typically rectilinear, to form a rectangular plate. But in some applications the edges may be curved.
The image capturing device 1 is used for capturing an image of an object, and may be a fixed focus camera or a zoom camera. In particular, the camera may be a visible light camera or an infrared camera. Of course, it is understood that any device with image capturing function can be used, and does not limit the present invention, and for example, the device can be a CCD, a CMOS, a camera, a video camera, an industrial camera, a monitor, a camera, a mobile phone, a tablet, a notebook, a mobile terminal, a wearable device, a smart glasses, a smart watch, a smart bracelet, and all devices with image capturing function.
Usually, the light sources are distributed around the lens of the image capturing device, for example, the light sources are ring-shaped LED lamps around the lens. The light source can also be positioned on the rotating arm or an external light source. In particular, a light softening means, for example a light softening envelope, may be arranged in the light path of the light source. Or the LED surface light source is directly adopted, so that the light is soft, and the light is more uniform. Preferably, an OLED light source can be adopted, the size is smaller, the light is softer, and the flexible OLED light source has the flexible characteristic and can be attached to a curved surface. The light source can be visible light or infrared light, and particularly, the light source is a narrow-band light source, so that the model building device is suitable for different kinds of wood, and the model building accuracy is improved.
The device further comprises a processor, also called processing unit, for synthesizing a 3D model of the object according to the plurality of images acquired by the image acquisition means and according to a 3D synthesis algorithm, to obtain 3D information of the object.
Although the above-mentioned image acquisition of the wood section is realized by rotating the image acquisition device, it can be understood that the wood may be rotated, and the two may have relative movement.
3D information acquisition method flow
The wood (target object) to be identified is subjected to preliminary treatment, particularly, the surface is cut off to expose the wood texture, as shown in fig. 2, so that the three-dimensional appearance of the wood section can be accurately acquired. In particular, the natural three-dimensional appearance of the wood can be acquired. The three-dimensional shape is determined by the wood density and the fiber structure of the wood, and different types of wood have certain difference and are difficult to counterfeit. Therefore, the three-dimensional shape of the cross section of the wood is a specific means for identifying the wood. The invention firstly provides the idea and designs a device for collecting the three-dimensional shape of the wood in a targeted manner. Therefore, the invention firstly provides the method for identifying the wood species through the three-dimensional shape of the wood, and belongs to one of the invention points.
And placing the wood to be detected between the image acquisition device and the background plate and on the optical axis scanning track of the image acquisition device. That is to say, when image acquisition device optical axis was located vertical direction, timber that awaits measuring just in time was located under the optical axis, guarantees like this that image acquisition device rotates the in-process, can follow different angles and gather a plurality of images of timber that awaits measuring.
The image acquisition device is rotated 180 deg., or at least 120 deg. around the wood to be measured (although other angles are possible). The initial position of the image acquisition device can be positioned right above the wood to be measured or beside the wood to be measured, and the movement track only covers the range of 120 degrees above the wood to be measured.
Acquisition position optimization of image acquisition device
According to a number of experiments, the separation distance of the acquisitions preferably satisfies the following empirical formula:
when 3D acquisition is performed, the two adjacent acquisition positions of the image acquisition device 1 satisfy the following conditions:
wherein L is the linear distance of the optical center of the image acquisition device 1 at two adjacent acquisition positions; f is the focal length of the image acquisition device 1; d is the rectangular length or width of the photosensitive element (CCD) of the image acquisition device 1; t is the distance from the photosensitive element of the image acquisition device 1 to the surface of the target along the optical axis; δ is the adjustment coefficient.
When the two positions are along the length direction of the photosensitive element of the image acquisition device 1, d is a rectangular length; when the two positions are along the width direction of the photosensitive element of the image pickup device 1, d takes a rectangular width.
When the image pickup device 1 is in any one of the two positions, the distance from the photosensitive element to the surface of the object along the optical axis is taken as T. In addition to this method, in another case, L is An、An+1Linear distance between optical centers of two image capturing devices 1 and An、An+1Two image capturing devices 1 adjacent to each other An-1、An+2Two image capturing devices 1 and An、An+1The distances from the respective photosensitive elements of the two image acquisition devices 1 to the surface of the target along the optical axis are respectively Tn-1、Tn、Tn+1、Tn+2，T＝(Tn-1+Tn+Tn+1+Tn+2)/4. Of course, the average value may be calculated by using more positions than the adjacent 4 positions.
L should be a straight-line distance between the optical centers of the two image capturing devices 1, but since the optical center positions of the image capturing devices are not easily determined in some cases, the centers of the photosensitive elements of the image capturing devices 1, the geometric centers of the image capturing devices 1, the axial centers of the image capturing devices 1 connected to the pan/tilt head (or platform, support), and the centers of the lens proximal and distal surfaces may be used instead in some cases, and the errors caused by the replacement are found to be within an acceptable range through experiments.
In general, parameters such as object size and angle of view are used as means for estimating the position of a camera in the prior art, and the positional relationship between two cameras is also expressed in terms of angle. Because the angle is not well measured in the actual use process, it is inconvenient in the actual use. Also, the size of the object may vary with the variation of the measurement object. The inconvenient measurement and the repeated measurement bring errors in measurement, thereby causing errors in camera position estimation. According to the scheme, the experience conditions required to be met by the position of the camera are given according to a large amount of experimental data, so that the problem that the measurement is difficult to accurately measure the angle is solved, and the size of an object does not need to be directly measured. In the empirical condition, d and f are both fixed parameters of the camera, and corresponding parameters can be given by a manufacturer when the camera and the lens are purchased without measurement. And T is only a straight line distance, and can be conveniently measured by using a traditional measuring method, such as a ruler and a laser range finder. Therefore, the empirical formula of the invention enables the preparation process to be convenient and fast, and simultaneously improves the arrangement accuracy of the camera position, so that the camera can be arranged in an optimized position, thereby simultaneously considering the 3D synthesis precision and speed, and the specific experimental data is shown in the following.
From the above experimental results and a lot of experimental experiences, it can be found that when the detection of the wood is performed, the value of δ should satisfy δ <0.576, and at this time, the partial 3D model can be synthesized, although some parts cannot be automatically synthesized, it is acceptable in the case of low requirements, and the parts which cannot be synthesized can be compensated manually or by replacing the algorithm. Particularly, when the value of δ satisfies δ <0.402, the balance between the synthesis effect and the synthesis time can be optimally taken into consideration; delta <0.329 can be chosen for better synthesis, where the synthesis time increases but the synthesis quality is better. Of course to further enhance the effect of the synthesis δ <0.217 may be chosen. When δ is 0.641, synthesis is not possible. It should be noted that the above ranges are only preferred embodiments and should not be construed as limiting the scope of protection.
Moreover, as can be seen from the above experiment, for the determination of the photographing position of the camera, only the camera parameters (focal length f, CCD size) and the distance T between the camera CCD and the object surface need to be obtained according to the above formula, which makes it easy to design and debug the device. Since the camera parameters (focal length f, CCD size) are determined at the time of purchase of the camera and are indicated in the product description, they are readily available. Therefore, the camera position can be easily calculated according to the formula without carrying out complicated view angle measurement and object size measurement. Particularly, in some occasions, the lens of the camera needs to be replaced, and then the position of the camera can be obtained by directly replacing the conventional parameter f of the lens and calculating; similarly, when different objects are collected, the measurement of the size of the object is complicated due to the different sizes of the objects. By using the method of the invention, the position of the camera can be determined more conveniently without measuring the size of the object. And the camera position determined by the invention can give consideration to both the synthesis time and the synthesis effect. Therefore, the above-described empirical condition is one of the points of the present invention.
The above data are obtained by experiments for verifying the conditions of the formula, and do not limit the invention. Without these data, the objectivity of the formula is not affected. Those skilled in the art can adjust the equipment parameters and the step details as required to perform experiments, and obtain other data which also meet the formula conditions.
In the prior art, it has also been proposed to use empirical formulas including rotation angle, object size, and object distance to define the camera position, thereby taking into account the speed and effect of the synthesis. However, in practical applications it is found that: unless a precise angle measuring device is provided, the user is insensitive to the angle and is difficult to accurately determine the angle; the size of the target is difficult to accurately determine, and particularly, the target needs to be frequently replaced in certain application occasions, each measurement brings a large amount of extra workload, and professional equipment is needed to accurately measure irregular targets. The measured error causes the camera position setting error, thereby influencing the acquisition and synthesis speed and effect; accuracy and speed need to be further improved. With the method of the present invention, the above problems are overcome. Therefore, the invention also belongs to the invention.
3D Synthesis Process
According to the above-mentioned acquisition method, the image acquisition device acquires a set of images of the object by moving relative to the object;
the processing unit obtains the 3D information of the target object according to a plurality of images in the group of images. Of course, the processing unit may be directly disposed in the housing where the image capturing device is located, or may be connected to the image capturing device through a data line or in a wireless manner. For example, an independent computer, a server, a cluster server, or the like may be used as a processing unit, and image data acquired by the image acquisition device may be transmitted thereto to perform 3D synthesis. Meanwhile, the data of the image acquisition device can be transmitted to the cloud platform, and 3D synthesis is performed by utilizing the strong computing power of the cloud platform.
When the collected pictures are used for 3D synthesis, the existing algorithm can be adopted, and the optimized algorithm provided by the invention can also be adopted, and the method mainly comprises the following steps:
step 1: and performing image enhancement processing on all input photos. The contrast of the original picture is enhanced and simultaneously the noise suppressed using the following filters.
In the formula: g (x, y) is the gray value of the original image at (x, y), f (x, y) is the gray value of the original image at the position after being enhanced by the Wallis filter, and mgIs the local gray average value, s, of the original imagegIs the local standard deviation of gray scale of the original image, mfFor the transformed image local gray scale target value, sfThe target value of the standard deviation of the local gray scale of the image after transformation. c belongs to (0, 1) as the expansion constant of the image variance, and b belongs to (0, 1) as the image brightness coefficient constant.
The filter can greatly enhance image texture modes of different scales in an image, so that the quantity and the precision of feature points can be improved when the point features of the image are extracted, and the reliability and the precision of a matching result are improved in photo feature matching.
Step 2: and extracting feature points of all input photos, and matching the feature points to obtain sparse feature points. And extracting and matching feature points of the photos by adopting a SURF operator. The SURF feature matching method mainly comprises three processes of feature point detection, feature point description and feature point matching. The method uses a Hessian matrix to detect characteristic points, a Box filter (Box Filters) is used for replacing second-order Gaussian filtering, an integral image is used for accelerating convolution to improve the calculation speed, and the dimension of a local image characteristic descriptor is reduced to accelerate the matching speed.
And step 3: inputting matched feature point coordinates, resolving sparse three-dimensional point cloud of the wood and position and posture data of a photographing camera by using a light beam method adjustment, namely obtaining model coordinate values of the sparse three-dimensional point cloud of the wood model and the position; and performing multi-view photo dense matching by taking the sparse feature points as initial values to obtain dense point cloud data. The process mainly comprises four steps: stereo pair selection, depth map calculation, depth map optimization and depth map fusion. For each image in the input data set, we select a reference image to form a stereo pair for use in computing the depth map. Therefore, we can get rough depth maps of all images, which may contain noise and errors, and we use its neighborhood depth map to perform consistency check to optimize the depth map of each image. And finally, carrying out depth map fusion to obtain the three-dimensional point cloud of the whole scene.
And 4, step 4: and (5) reconstructing the curved surface of the wood by using the dense point cloud. The method comprises the steps of defining an octree, setting a function space, creating a vector field, solving a Poisson equation and extracting an isosurface. And obtaining an integral relation between the sampling point and the indicating function according to the gradient relation, obtaining a vector field of the point cloud according to the integral relation, and calculating the approximation of the gradient field of the indicating function to form a Poisson equation. And (3) solving an approximate solution by using matrix iteration according to a Poisson equation, extracting an isosurface by adopting a moving cube algorithm, and reconstructing a model of the measured point cloud.
Three-dimensional model data of the wood, such as point cloud data or grid data, is usually constructed for comparison. However, in practice, texture mapping can be performed on the three-dimensional model, and richer texture information such as colors can be provided.
The method comprises the following steps of 5, carrying out full-automatic texture mapping on a model, carrying out texture mapping after the surface model is built, wherein the main process comprises ① obtaining texture data to obtain a surface triangular surface grid of a target reconstructed through an image, ② reconstructing visibility analysis of a triangular surface of the model, calculating a visible image set and an optimal reference image of each triangular surface by using calibration information of the image, ③ clustering the triangular surfaces to generate texture patches, clustering the triangular surfaces to generate a plurality of reference image texture patches according to the visible image set, the optimal reference image and neighborhood topological relations of the triangular surfaces, automatically sequencing ④ texture patches to generate texture images, sequencing the generated texture patches according to the size relations of the texture patches, generating a texture image with the minimum surrounding area, and obtaining texture mapping coordinates of each triangular surface.
It should be noted that the above algorithm is an optimization algorithm of the present invention, the algorithm is matched with the image acquisition condition, and the use of the algorithm takes account of the time and quality of the synthesis, which is one of the inventions of the present invention. Of course, it can be implemented using conventional 3D synthesis algorithms in the prior art, except that the synthesis effect and speed are somewhat affected.
Wood identification
Through the above apparatus and method, a three-dimensional model of the target wood can be constructed. A plurality of similar wood samples can be collected through multiple times of collection to obtain a plurality of three-dimensional models, so that the wood three-dimensional model database is constructed.
The data in the database can be used as standard data for comparison with other data to be identified. Meanwhile, the neural network can be trained as sample data. But the sample is a sample of three-dimensional information, and is richer and more unique than the traditional plane image information. The recognition rate is very high.
After the standard database is provided, when the wood of the type is identified, the three-dimensional model of the wood to be identified is constructed by the method and is placed in a trained neural network or is directly compared with the standard data in the database, so that whether the wood to be identified is the target wood or not is identified.
The rotation movement of the invention is that the front position collection plane and the back position collection plane are crossed but not parallel in the collection process, or the optical axis of the front position image collection device and the optical axis of the back position image collection device are crossed but not parallel. That is, the capture area of the image capture device moves around or partially around the target, both of which can be considered as relative rotation. Although the embodiment of the present invention exemplifies more orbital rotation, it should be understood that the limitation of the present invention can be used as long as the non-parallel motion between the acquisition region of the image acquisition device and the target object is rotation. The scope of the invention is not limited to the embodiment with track rotation.
The adjacent acquisition positions refer to two adjacent positions on a movement track where acquisition actions occur when the image acquisition device moves relative to a target object. This is generally easily understood for the image acquisition device movements. However, when the target object moves to cause relative movement between the two, the movement of the target object should be converted into the movement of the target object, which is still, and the image capturing device moves according to the relativity of the movement. And then measuring two adjacent positions of the image acquisition device in the converted movement track.
The target object, and the object all represent objects for which three-dimensional information is to be acquired. The object may be a solid object or a plurality of object components. The three-dimensional information of the target object comprises a three-dimensional image, a three-dimensional point cloud, a three-dimensional grid, a local three-dimensional feature, a three-dimensional size and all parameters with the three-dimensional feature of the target object. Three-dimensional in the present invention means having XYZ three-direction information, particularly depth information, and is essentially different from only two-dimensional plane information. It is also fundamentally different from some definitions, which are called three-dimensional, panoramic, holographic, three-dimensional, but actually comprise only two-dimensional information, in particular not depth information.
The capture area in the present invention refers to a range in which the image capture device 1 (e.g., a camera) can capture an image. The image acquisition device can be a CCD, a CMOS, a camera, a video camera, an industrial camera, a monitor, a camera, a mobile phone, a tablet, a notebook, a mobile terminal, a wearable device, intelligent glasses, an intelligent watch, an intelligent bracelet and all devices with image acquisition functions.
The 3D information of multiple regions of the target obtained in the above embodiments can be used for comparison, for example, for identification of identity. Firstly, the scheme of the invention is utilized to acquire the 3D information of the face and the iris of the human body, and the information is stored in a server as standard data. When the system is used, for example, when the system needs to perform identity authentication to perform operations such as payment and door opening, the 3D acquisition device can be used for acquiring and acquiring the 3D information of the face and the iris of the human body again, the acquired information is compared with standard data, and if the comparison is successful, the next action is allowed. It can be understood that the comparison can also be used for identifying fixed assets such as antiques and artworks, namely, the 3D information of a plurality of areas of the antiques and the artworks is firstly acquired as standard data, when the identification is needed, the 3D information of the plurality of areas is acquired again and compared with the standard data, and the authenticity is identified.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It will be appreciated by those skilled in the art that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some or all of the components in an apparatus in accordance with embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Thus, it should be appreciated by those skilled in the art that while a number of exemplary embodiments of the invention have been illustrated and described in detail herein, many other variations or modifications consistent with the principles of the invention may be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the invention. Accordingly, the scope of the invention should be understood and interpreted to cover all such other variations or modifications.
Claims (10)
1. A timber identification device and method are characterized in that: comprises an image acquisition device and a rotating device,
the image acquisition device and the section of the wood perform relative motion by being driven by the rotating device;
the image acquisition device acquires a plurality of images of the timber at different angles;
the images can be used for constructing a three-dimensional model of the section of the timber.
2. The apparatus and method of claim 1, wherein: the wood has a surface removed and has a cross-section that exposes the original wood topography.
3. The apparatus and method of claim 1, wherein: the image acquisition device rotates around the section of the wood.
4. The apparatus and method of claim 1, wherein: the rotating device is an L-shaped rotating arm.
5. The apparatus and method of claim 1, wherein: a database with standard three-dimensional model data of wood is also included.
6. The apparatus and method of claim 1, wherein: comparing the collected and synthesized three-dimensional model data of the timber to be detected with the standard three-dimensional model data of the timber, and judging whether the timber to be detected is similar to the timber.
7. The apparatus and method of claim 1, wherein: when the image acquisition device acquires a target object, the two adjacent acquisition positions meet the following conditions:
l is the straight line distance of the optical center of the image acquisition device at two adjacent acquisition positions; f is the focal length of the image acquisition device; d is the rectangular length or width of the photosensitive element of the image acquisition device; t is the distance from the photosensitive element of the image acquisition device to the surface of the target along the optical axis; δ is the adjustment coefficient.
8. The apparatus and method of claim 1, wherein: and δ < 0.576; preferably δ < 0.402; or δ < 0.329.
9. The apparatus and method of claim 1, wherein: the system also comprises a processor which is used for synthesizing a three-dimensional model according to a plurality of images acquired by the image acquisition device to obtain three-dimensional information.
10. The apparatus and method of claim 1, wherein: the wood to be identified is placed on a solid background plate.
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