486 results about "Gabor filter" patented technology

A robot includes a face extracting section for extracting features of a face included in an image captured by a CCD camera, and a face recognition section for recognizing the face based on a result of face extraction by the face extracting section. The face extracting section is implemented by Gabor filters that filter images using a plurality of filters that have orientation selectivity and that are associated with different frequency components. The face recognition section is implemented by a support vector machine that maps the result of face recognition to a non-linear space and that obtains a hyperplane that separates in that space to discriminate a face from a non-face. The robot is allowed to recognize a face of a user within a predetermined time under a dynamically changing environment.

Fingerprint recognition and matching systems and methods are described that utilize features at all three fingerprint friction ridge detail levels, i.e., Level 1, Level 2 and Level 3, extracted from 1000 ppi fingerprint scans. Level 3 features, including but not limited to pore and ridge contour characteristics, were automatically extracted using various filters (e.g., Gabor filters, edge detector filters, and/or the like) and transforms (e.g., wavelet transforms) and were locally matched using various algorithms (e.g., the iterative closest point (ICP) algorithm). Because Level 3 features carry significant discriminatory and complementary information, there was a relative reduction of 20% in the equal error rate (EER) of the matching system when Level 3 features were employed in combination with Level 1 and Level 2 features, which were also automatically extracted. This significant performance gain was consistently observed across various quality fingerprint images.

The invention relates to a texture-based insulator fault diagnostic method. According to the invention, a visible light image collected in the inspection process of a high voltage transmission line by a helicopter is used as an object to be processed, and the diagnosis can be carried out based on an insulator fault of the visible light image. The method comprises the following steps of: inputting an insulator image, carrying out gray processing, obtaining a bounding rectangle and rotating, carrying out a GLCM (gray level co occurrence matrix) method, blocking, obtaining textural features, carrying out Gabor filtering, blocking, calculating block-mean value and variance, performing feature fusion, and determining whether to have a string-drop phenomenon based on a threshold value. The method provided by the invention diagnoses the insulator string-drop characteristic by texture, integrates the thoughts of the most classical GLCM texture diagnostic method in the texture diagnosis and the recent research focus Gabor filter texture diagnosis, adjusts the parameter settings of the GLCM and the Gabor filter and efficiently and accurately finds out the string-drop insulators. The method can effectively improve the efficiency of the thermal defect detection of the power transmission line and can be effectively applied to the inspection business of the vehicle-mounted or helicopter power transmission line.

An object detection apparatus is provided for detecting both stationary objects and moving objects accurately from an image captured from a moving mobile unit.

The object detection apparatus of the present invention applies Gabor filter to two or more input images captured by an imaging device such as CCD camera mounted on a mobile unit, and calculates optical flow of local areas in the input images. Then the object detection apparatus closely removes optical flow produced by motion of the mobile unit by estimating optical flow produced from background of the input images. In other words, the object detection apparatus clarifies the area where object is not present (“ground”) in the input images. By removing such “ground” part, the area where objects seems to be present (“feature”) is extracted from the input images. Finally, the object detection apparatus determines whether objects are present or not using flow information of the extracted “feature” part.

The invention provides a synthetic aperture radar (SAR) image bionic recognition method based on sample generation and nuclear local feature fusion and belongs to the field of image processing technologies and SAR target recognition. According to the method, a super complete training sample set is firstly constructed for training to obtain geometry manifold, then a sample to be recognized is recognized, specifically, each sample is firstly subjected to image denoising by a K-SVD dictionary learning method, and object region extraction is achieved by means of an object centroid method; and feature extraction is performed respectively by combining local phase quantization (LPQ) and a Gabor filtering method, feature fusion is performed, finally, classification is performed by covering of high-dimensional geometry manifold, and recognition is performed by a bionic mode. According to the SAR image bionic recognition method based on sample generation and nuclear local feature fusion, inhibiting effects of image coherent noises are obvious, SAR image features can be effectively extracted, the problem of the unstable extracted features, which is caused by changes of attitude angles of SAR images, is solved, the recognition accuracy is high, and the method has good robustness.

A Gabor filter is approximated as a block-Gabor filter. The Gabor filter is represented by a matrix of numbers in which each number is a sample derived from a continuous Gabor function. The block-Gabor filter is partitioned into a set of blocks. Identical filter values are assigned to all the pixels in any particular block based on the Gabor filter. Then, a feature can be extracted from an image by filtering the image with a set of the block-Gabor filters to obtain a corresponding set of filtered images. Each filtered image is partitioned into regions of pixels. For each pixel, an N-bit signature is determined. Histograms of the N-bit signatures of the pixels in each region are combined to form the feature. The features of multiple images can be used for face recognition.

A statistical system and method for generating patterns and performing online handwriting recognition based on those patterns. A plurality of predetermined patterns may be generated by performing feature extraction operations on one or more character samples utilizing a Gabor filter. An online handwritten character may be acquired. The online handwritten character may be pre-processed. One or more feature extraction operations, utilizing a Gabor filter, may be performed on the online handwritten character to produce a feature vector. One or more patterns may be generated, using a statistical algorithm, for the online handwritten character, based on the feature vector. The online handwritten character may be statistically classified based on a comparison between the one or more patterns generated for the online handwritten character and the plurality of predetermined patterns.

The invention discloses a face posture identification method based on rarefaction Bayesian regression in the image processing technique field, which comprises the following steps: extracting Gabor character for face posture image with Gabor filter; stacking the sampled character to one-dimensional vector after down sampling for Gabor character; acquiring the essential lower dimensional subspace of the face posture image and the corresponding projection matrix with the linear tangent space arrangement for the training sample; training the identification parameter with rarefaction Bayesian regression method in the lower dimensional subspace; mapping the projection matrix of every detecting sample to the lower dement ional subspace by training; proceeding the face posture identification with trained identification parameter. The invention can acquire the uncertain solution of the face posture, which reduces the wrong radio.

The invention discloses a design patent image retrieval method with multi-characteristic fusion. The method comprises the steps: according to the characteristics of the design patent image, designing a characteristic extracting method and the process of image retrieval; in the aspect of characteristic extraction, adopting a method for fusing the whole characteristics and local characteristics, and adopting a boundary direction histogram algorithm and structural characteristics to describe the shape characteristics; adopting a Gabor filter algorithm and a blocking characteristic statistical method to extract textural characteristics, and in the process of image retrieval, firstly adopting geometrical characteristics for comparison, and screening the images according to threshold values; and secondly using the shape characteristics and the textural characteristics to carry out similarity matching, and sorting and returning the images according to the size of similarity. The invention provides the design patent image retrieval method with high efficiency and high accuracy by extraction of wholly and locally more comprehensive characteristics of the design patent images and step screening in the retrieval process.

The invention discloses a fingerprint identification method. The fingerprint identification method comprises steps that a fingerprint image is segmented and filtered, the pre-processing steps comprise a segmentation sub step and a filtering sub step, for the segmentation sub step, the Harris corner energy of each pixel of the fingerprint image is calculated, the Harris corner energy smaller than a preset threshold is filtered to acquire a binary graph, and expansion and corrosion processing on the binary graph is carried out; for the filtering sub step, the direction field, the frequency field and the curvature field of the binary graph are respectively calculated, and a Log-Gabor filter is utilized to filter the processed binary graph. Through the method, computational complexity is reduced through the fingerprint method. The invention further provides a fingerprint identification system.

The invention discloses a facial expression recognition method based on an ELM. The method comprises the steps that a facial expression image is processed to obtain a facial region, image segmentation is conducted on an expression region containing expression features to obtain an eye expression region body, a nose expression region body and a lip expression region body, then Gabor filters are used for conducting expression feature extraction on the expression region bodies to obtain a total feature vector of each expression, the obtained expression feature vectors are used for training ELM models, the ELM models for all the expressions are combined to form an ELM expression classifier, and at last, the ELM classifier is tested to achieve the purpose of expression classified recognition. The facial expression recognition method based on the ELM increases the expression recognition rate on the condition that illumination influences are avoided.

The invention provides a user registration authentication method and a system based on facial features. The user registration authentication method is characterized by comprising the following steps: (1) obtaining a facial picture through a camera, conducting AdaBoost detection, gray level conversion and Gabor filtering to the facial picture, and obtaining facial information, (2) generating a login name and a secret key through combination of the new facial information input by a user and a realm name, and filling the login name and the secret key into a login form, and (3) when the user logs in, requiring content of the login form according the facial information, detecting whether the user is a registered user, if the user is the registered user, logging in successfully, modifying the secret key, and if the user is a unregistered user, conducting the step (1). According to the user registration authentication method and the system based on the facial features, the facial information and a secret system are combined, the process of authentication identification is enabled to be guaranteed, and meanwhile convenience is brought to use.

The invention discloses a finger multimodal preprocessessing method based on maximum circumscribing matrix region-of-interest extract and bilateral filtering. The method comprises the following steps: firstly, gathering finger vein and finger phalangeal print images; secondly, performing finger multimodal preprocessessing on the gathered images, based on maximum circumscribing matrix region-of-interest extract and bilateral filtering, and removing background areas in the finger images through binaryzation, obtaining a final finger vein ROI area by solving the maximum circumscribing matrix among key areas, and then cutting an finger phalangeal print ROI area in the finger phalangeal print image according to the finger vein ROI area; finally normalizing the images, and performing filtering on the normalized images through a bilateral filter; thirdly, performing image layer fusion on the processed images, based on wavelet transform and Gabor filtering. The method is rich in information and high in security.

The invention provides a face authentication method and device, and belongs to the field of image processing and pattern recognition. The method comprises the following steps: extracting Gabor texture features of an acquired face image sample pair through a Gabor filter, and separating real parts from virtual parts of the extracted Gabor texture features to obtain a plurality of feature graphs; summarizing the plurality of obtained feature graphs through an LBP (Local Binary Pattern) statistical histogram to serve as face feature expression of the face image pair; performing feature selection on the face feature expression of the face image sample pair through an AdaBoost (Adaptive Boosting) algorithm; and performing feature dimension reduction, feature judgment processing and the like on the face feature expression of the face image sample pair subjected to the feature selection through a PCA (Principal Components Analysis) algorithm and an LDA (Linear Discriminant Analysis) algorithm in sequence. Compared with the prior art, the face authentication method provided by the invention has the advantages of full extraction of sample texture information, low sample quantity demand, short algorithm time and low space complexity.

The invention relates to a facial expression recognition and interaction method based on GPU acceleration, and belongs to the field of mode recognition. The method establishes a method framework of rapidly recognizing micro-expression based on GPU acceleration. The method comprises: firstly, acquiring user facial images through an ordinary camera or a video, performing facial detection and facial feature point recognition by using a recognition method based on Haar characteristics and AdaBoost, to recognize position coordinates of user pupils, apex nasi, etc., dividing a face into a plurality of critical areas, using a Gabor filtering method based on GPU acceleration, performing Gabor filtering on the whole face in five dimensions and eight directions, and performing feature extraction on the critical areas near the facial feature points. According to the method, features of CK, CK+, MMI extracted from a database are marked in labels according to whether micro-expression occurs or not, and through putting the features with the labels to a SVM, a recognition model is obtained. A user puts the extracted features into the recognition model to obtain specific information of the micro-expression.

In a face recognition apparatus adapted to recognize an input face image on the basis of one or more registered face images, similarity between an input face image and a registered face image is determined based on a Gabor jet calculated by performing Gabor filtering using Gabor filters defined by a Gaussian function representing a window and sine and cosine functions representing a frequency response for each of predetermined frequency values and for each of predetermined angles of the response function. Values of the filter window are calculated in advance based on the Gaussian function and stored in a filter window data ROM. Values of the response function are calculated in advance for respective angles based on the sine and cosine functions and stored in sine data ROMs and cosine data ROMs. In the Gabor filtering process, coefficients of the Gabor filters are determined from values read from these ROMs.

The invention discloses a method for re-identifying vehicles in sequence images of a mentoring video. The method comprises the following steps of performing image feature extraction: for all shot video data, firstly detecting out vehicle images appearing under a camera, dividing the vehicle images of the monitoring video into a plurality of equilong vehicle image sequences according to the vehicles and the camera, calculating grayscale histograms of two channels Cr and Cb in a YCrCb space, and finally obtaining vehicle contour images by using a Gabor filter and original image convolution; performing a set-to-set-based metric learning method: training a step of learning a metric function of relative distances between vehicle target image sequences, namely, performing metric learning based on a thought of maximization of a probability that inner-class distances are shorter than between-class distances; and performing between-set metric learning-based vehicle re-identification. According to the method, the complexity of an algorithm is lowered while feature dimensions are reduced; and after between-set distance measure is added based on a re-identification effect, the identification rate of the algorithm is increased and the identification effect is improved.

The invention discloses an image steganalysis method and system based on the convolutional neural network. The system comprises an image preprocessing part, a feature extraction part and a feature classification module. In the image preprocessing part, a group of Gabor filters with multiple directions and multi-dimension parameters is selected by experiments, and an image residual error with highsignal to noise ratio is obtained by input image convolution; and in the feature extraction part, a rapid connection structure is used to connect output of a shallow layer with a behind layer directlyto reduce a fitting phenomenon. The steganalysis method based on the convolutional neural network does not need a lot of knowledge in the fields of steganography and steganalysis, feature extractionand feature classification form a combined optimization process, design is simple, and enforcement is easy; the scale and direction properties of the Gabor filters can be used to help extract the image residual error from the network more effectively; and the method and system have better detection effects for adaptive steganography algorithms of J-UNIWARD and UED contents.