WO2018028102A1 - 一种仿记忆引导的模式识别方法 - Google Patents
一种仿记忆引导的模式识别方法 Download PDFInfo
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- WO2018028102A1 WO2018028102A1 PCT/CN2016/109001 CN2016109001W WO2018028102A1 WO 2018028102 A1 WO2018028102 A1 WO 2018028102A1 CN 2016109001 W CN2016109001 W CN 2016109001W WO 2018028102 A1 WO2018028102 A1 WO 2018028102A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/20—Movements or behaviour, e.g. gesture recognition
- G06V40/23—Recognition of whole body movements, e.g. for sport training
- G06V40/25—Recognition of walking or running movements, e.g. gait recognition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
- G06T7/215—Motion-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/62—Extraction of image or video features relating to a temporal dimension, e.g. time-based feature extraction; Pattern tracking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
- G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/49—Segmenting video sequences, i.e. computational techniques such as parsing or cutting the sequence, low-level clustering or determining units such as shots or scenes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/103—Static body considered as a whole, e.g. static pedestrian or occupant recognition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10016—Video; Image sequence
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20048—Transform domain processing
- G06T2207/20056—Discrete and fast Fourier transform, [DFT, FFT]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30242—Counting objects in image
Definitions
- the invention relates to a pattern recognition method, in particular to a pattern recognition and motion detection method in a video sequence guided by a memory.
- the human visual perception system has a prominent advantage in pattern recognition. More advanced memory and selection systems enable human vision to be quickly focused on motion and continuous motion changes. This mechanism is implemented by the guidance of global timing features in short-term memory and long-term memory, called memory guidance. Based on this mechanism, pattern recognition for continuous motion states is achieved by merging spatial positions and motion trajectories of targets at different times. For example, in Figure 1, the human perception system describes the scene as “a girl running from the left to the right", “a lot of vehicles are moving in the opposite direction", and “two people come together.” It can be found that each description except “running girl”, “traveling car” and “pedestrian” emphasizes mode information such as a motion trajectory in a continuous time series up to the current time.
- a temporal filter that simulates the center of the visual periphery, a motion saliency model based on spectral differences, etc., but these models cannot describe the motion trajectory in the entire time series, and the ability to suppress motion noise is weak, and the result is weak. There are more noise interferences.
- the basic basis of the invention is that the changes produced by the motion are not only related to the samples of the adjacent fields but also to the global context.
- long-term memory segments need to be introduced into the pattern recognition process, and the state of change of motion throughout the time series can be obtained in one detection process.
- the present invention provides a time-sequence pattern recognition method in a memory-guided manner, which can obtain a target motion state in an entire time series in a single recognition process, and It can solve the problem that noise is difficult to suppress in motion detection of complex natural scenes.
- a pattern recognition method based on imitation memory guidance comprising the following steps:
- Step 1 The imitation memory calling mechanism and its process divide the historical time series and combine it with the current time frame to become a segment sequence as a primitive for pattern recognition. For a video sequence we can get multiple sequence sequences, independent of each other, without overlap, and processed in parallel.
- Step 2 Simulate the motion saliency mechanism and its process, extract the motion saliency in each segment sequence, and detect the motion information in the short-term sequence.
- the visual motion saliency model is used to detect the motion changes occurring in the sequence of the segment.
- Step 3 imitating the memory decay mechanism and its process, weighting the motion information, and weighting the motion information in all the sequence sequences, outputting the current moment motion information and the motion trajectory in the entire time series, and synthesizing it as a pattern recognition result.
- the weighted cumulative fusion method can improve the accuracy of the current time motion detection, and can obtain the motion trajectory in the entire memory time sequence interval, and comprehensively obtain the overall pattern recognition result.
- the present invention takes the fragment sequence as the basis Pattern recognition is performed by performing motion detection and fusion processing.
- This strategy can accurately detect the motion information of the current time and generate the trajectory information of the motion state change in the entire time series, and can calibrate the time information of the trajectory and motion, and has the ability to suppress the motion noise.
- Figure 1 is a schematic diagram of pattern recognition of memory guidance
- FIG. 2 is a flow chart of pattern recognition according to an embodiment of the present invention, (a) inputting a frame image of a current time in a video sequence, (b) motion detection in short-term memory, and (c) motion detection in long-term memory, ( d) motion detection in the frame image at the current time, (e) motion state in the entire time series;
- FIG. 3 is a schematic diagram of time series segmentation in an embodiment of the present invention.
- the implementation is as shown in Fig. 2, a pattern recognition method based on memory-guided.
- the present invention mainly relies on four biological discoveries: 1 short-term memory can guide the recognition of recent movement mutations; 2 long-term memory can guide the recognition of stable movement changes; 3 memory-based motion saliency mechanisms are concentrated In the short-term memory, long-term memory, the part of the movement changes, ignoring the irrelevant background and sporadic noise information; 4 through the interaction of memory and motion saliency mechanism can describe the current time information and the entire time series in memory Motion state and state change (motion trajectory).
- pattern recognition in short-term memory should be able to calibrate motion information for those mutations that have recently occurred and suppress static background information (see Figure 2(b)).
- pattern recognition in long-term memory should be able to calibrate motion information that is stable over a longer time range (see Figure 2(c)) and suppress sporadic motion noise.
- the highest intensity pattern recognition results obtained are concentrated in those parts of the short-term/long-term memory that have undergone motion changes (Fig. 2(d)).
- the pattern recognition method should be able to detect the trajectory of motion changes throughout the time series in memory to accurately describe the motion state and state changes (see Figure 2(e)).
- the time series in one memory segment (memory storage is l) can be combined by x n and the sample in memory And get it. Therefore, for the point x n in the current moment, the corresponding sequence fragment can be constructed as:
- x n is the point considered at the current time
- x n-1 , x n-2 ,..., x nl ⁇ k is the sample in memory
- k is the length of the time series (the minimum value is set to 16)
- l is the amount of memory stored (the minimum value is set to 10).
- motion information in each segment sequence is detected using a time-based Fourier transform-based visual motion saliency model.
- the visual motion saliency model based on time Fourier transform considers that the fluctuation of the phase spectrum in the time series spectrum corresponds to the change of the sequence information in the time domain. Motion information can thus be detected by calculation of the phase spectrum. It mainly includes the following steps:
- Step 1 Construct a sequence of fragments consisting of the current time point and the historical time sample:
- Step two calculating the Fourier transform and the corresponding phase spectrum for the sequence of the segment:
- step three the inverse Fourier transform is calculated for the obtained phase spectrum:
- F and F -1 represent the Fourier transform and the inverse Fourier transform, respectively.
- Step 4 performing threshold filtering on the inverse transform result of the phase spectrum, if If the value is greater than the threshold, the motion information appears at the corresponding position, otherwise there is no motion change:
- T is the threshold and the typical value is set to:
- the motion detection results in the plurality of sequence segments are fused to form a memory-guided motion detection result, which not only takes into account the short-term motion mutation but also considers the stable motion change over a long period of time.
- This mechanism can be modeled as the accumulation of motion detection results in all 1 sequence segments:
- E n is the accumulation of motion detection results.
- the result obtained is shown in Fig. 4: in the obtained motion detection result map (Fig. 4), the point at which the intensity is the largest corresponds to the motion information at the current time, and the intensity is weakened with the continuation of time. Therefore, the motion detection result map E n can be divided by the method based on the gray histogram. By this calculation, it is possible to obtain the motion information S n for only the current time as the pattern recognition result of the current time.
- the intensity of motion information in the human brain should diminish over time, known as memory attenuation.
- the present invention employs a weighted approach to simulate this attenuation mechanism. For any sequence of segments, the intensity of the detected motion information is inversely proportional to the delay of the segment from the current time. This weight and attenuation can be calculated as:
- the motion information in the current moment can be combined with the motion information of the historical moment to form a motion trajectory, and the present invention formalizes the simulation of the mechanism as the accumulation of the motion information after the attenuation:
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Abstract
Description
Claims (3)
- 一种仿记忆引导的模式识别方法,其特征在于,包括如下步骤:步骤一:仿记忆调用机制及其过程,分割历史时间序列并同当前时刻帧组合成为片段序列作为模式识别的基元;步骤二:仿视觉运动显著性机制及其过程,提取每个片段序列中的运动显著性,检测得到该短时序列中的运动信息;步骤三:仿记忆衰退机制及其过程,对运动信息进行加权,并加权融合所有片段序列中的运动信息,输出当前时刻运动信息及整个时间序列中的运动轨迹,综合作为模式识别结果。
- 如权利要求1所述的仿记忆引导的模式识别方法,其特征在于,仿记忆调用机制中基于片段序列的运动检测策略,既对于所存储的历史记忆进行分段,以记忆片段作为运动检测的基元;具体,对于视频序列中当前时刻t的图像帧,将1,2,…,t时刻的帧按照长度k进行分割,分别对分割出每个片段序列模拟视觉运行显著性机制检测片段序列中所出现的运动变换,作为该片段中的模式识别结果。
- 如权利要求1所述的仿记忆引导的模式识别方法,其特征在于,仿记忆衰退机制中,对于每个片段中的运动检测结果,考虑到记忆片段同当前时刻的时延,认为时延越大的片段序列中运动检测结果同当前时刻的时间相关性越弱,所赋相应权重值越小;反之,认为时延越小的片段序列中运动检测结果同当前时刻的时间相关性越强,所赋相应权重值越大;通过加权累积融合的方式能够提高对当前时刻运动检测的准确性,并能够得到整个记忆时序列区间中的运动轨迹,综合得到整体的模式识别结果。
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AU2016418565A AU2016418565B2 (en) | 2016-08-08 | 2016-12-08 | Memory mimicry guided pattern recognition method |
NZ750970A NZ750970A (en) | 2016-08-08 | 2016-12-08 | Memory-guide simulated pattern recognition method |
GB1902335.7A GB2567595B (en) | 2016-08-08 | 2016-12-08 | Memory-guide simulated pattern recognition method |
US16/323,113 US10860891B2 (en) | 2016-08-08 | 2016-12-08 | Memory-guide simulated pattern recognition method |
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CN114770511A (zh) * | 2022-05-09 | 2022-07-22 | 上海傅利叶智能科技有限公司 | 基于物理触觉的机器人控制方法、装置及机器人 |
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CN109256184A (zh) * | 2018-07-30 | 2019-01-22 | 邓建晖 | 一种基于认知记忆识别与恢复方法及*** |
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CN116612125B (zh) * | 2023-07-21 | 2023-09-29 | 山东庆葆堂生物科技有限公司 | 基于人工智能的食药品胶囊质量检测方法 |
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GB201902335D0 (en) | 2019-04-03 |
CN106296736B (zh) | 2019-03-29 |
AU2016418565A1 (en) | 2019-03-14 |
GB2567595A (en) | 2019-04-17 |
GB2567595B (en) | 2022-03-02 |
US20190164013A1 (en) | 2019-05-30 |
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AU2016418565B2 (en) | 2020-04-16 |
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