90 results about "Texture recognition" patented technology

The invention discloses a garment steamer. The garment steamer comprises a texture recognition device, a parameter adjustment device and an ironing execution device, wherein the texture recognition device collects feature detection data of fabric to be ironed and recognizes the texture of the fabric according to the feature detection data; the parameter adjustment device adjusts ironing parameters according to the texture of the fabric; the ironing execution device irons the fabric according to the ironing parameters. The garment steamer can recognize the texture of the fabric, automatically adjusts the ironing parameters, guarantees the ironing effect, prevents a garment from being burned and is easy to operate. The invention further discloses a control method of the garment steamer, the texture recognition device used for the garment steamer and a modeling system and method of a texture recognition model.

The invention discloses a deep network lung texture recognition method combined with multi-scale attention, which belongs to the field of image processing and computer vision. In order to accurately recognize the typical texture of diffuse lung disease in computed tomography (CT) images of the lung, a unique attention mechanism module and multi-scale feature fusion module were designed to construct a deep convolutional neural network combing multi-scale and attention, which achieves high-precision automatic recognition of typical textures of diffuse lung diseases. In addition, the proposed network structure is clear, easy to construct, and easy to implement.

The invention relates to a garment texture recognition and classification method based on LBP and GLCM. The classification method for the texture types in fashionable garment images comprises the following steps: S1, extracting LBP texture features; S2, designing a GLCM texture image feature statistic extraction function; S3, calculating a texture feature value; and S4, training the feature vectors by using an SVM algorithm, and classifying the texture pictures. The method has the advantages that the challenge of texture recognition in fashionable clothes is well solved in the actual production process, the constraint of manual recognition is liberated, convenience is brought to later data analysis and algorithm development optimization, and the method has important practical significance.

The invention discloses an anti-counterfeiting texture recognition method. The method includes the following steps that: a) an anti-counterfeiting information carrier with a random texture structure is selected as a printing area for a code identifier or graphic & text identifier on a printed material; b) the printed material is printed, so that the code identifier or graphic & text identifier can be formed, wherein the code identifier or graphic & text identifier at least partially covers the random texture structure on the anti-counterfeiting information carrier; and c) the overlapping point of the code identifier or graphic & text identifier and the random texture structure is obtained and is adopted as anti-counterfeiting feature record information. With the anti-counterfeiting texture recognition method of the present invention adopted, requirements for the clarity of the random texture structure can be lowered, the complexity of subsequent recognition processing can be greatly simplified, and recognition accuracy can be improved; and since the texture structure is randomly generated, the overlapping point covered by codes, graphs or texts is also random, and therefore, anti-counterfeiting difficulty can be greatly increased, and stolen anti-counterfeiting data can be prevented from being applied to counterfeiting. The anti-counterfeiting texture recognition method is simple in process and easy to implement and popularize.

A dynamic texture recognition method on the basis of an image sequence is used for realizing dynamic texture recognition by comparison of three-dimension HMT (Hidden Markov Tree) model parameters and includes performing Surfacelet transformation to the image sequence to obtain a coefficient matrix, particularly realizing multiscale decomposition to the image sequence by pyramid decomposition, decomposing three-dimension signals to different directions through 3D-DFB(3D directional filter banks) which are in series connection with two 2D-DFB, saving sub-band data acquired after the Surfacelet transformation through the three-dimension matrix, generating a coefficient matrix through extracted feature vectors; building a three-dimension HMT model to the coefficient matrix by details of realizing distributed modeling of coefficient by utilizing a Gauss mixture model, realizing inter-scale continuous modeling of the coefficient by utilizing the three-dimension HMT model, and then solving parameters of the HMT model by an EM algorithm. Corresponding expansion schemes are provided for handling the situation that processed data contain different types of dynamic textures. The dynamic texture recognition method is easy, high in adaptability and good in recognition effect.

The invention discloses an identity authentication method based on a finger vein, which comprises the following steps: pre-processing the received finger vein image to be identified to obtain a pre-processed image; Convolution calculation is carried out on the preprocessed image, and characteristic point map and vein pattern map are generated according to the obtained convolution value. Accordingto the eigenvalue size and coordinate information of the feature points in the feature point map, the feature points in the feature point map are selected globally according to the preset conditions,and the feature points with high eigenvalue in the global distribution are taken as the feature points to be matched. The vein pattern and template image are matched and compared according to the feature points to be matched, and the identity authentication result is generated according to the matched result. This method extracts feature points from global and local features to obtain globally distributed feature points with high eigenvalues, which can improve the accuracy of vein texture recognition and generate high-precision recognition results. The invention also discloses an identity authentication device e based on a finger vein and equipment, which have the beneficial effects mentioned above.

The invention relates to a method for converting a texture image into a tactile signal based on deep learning, and belongs to the technical field of artificial intelligence and signal processing. Themethod comprises the steps of firstly, learning to train texture image data to obtain feature information of an image, so as to classify the various types of textures; using a short-time Fourier algorithm to convert a triaxial acceleration signal of frictional vibration of a material surface into a frequency spectrum image, and then conducting training to obtain a frequency spectrum generator; combining the classification information with the frequency spectrum generator to automatically generate a frequency spectrum of a texture image, converting the frequency spectrum into tactile signals ofdifferent types of images, so as to realize conversion of different texture images to tactile signals. A result is transmitted to a palm through a tactile feedback device connected to the inside of amouse, and an area where the mouse pointer is located is a material area to be tested, so that feedback of the material property of an measured object is achieved in real time by sliding of the mouse. The conversion result has high similarity to the real touch of an image texture, the application scenes are rich, and the method has high practical value.

The invention relates to a texture recognition method based on a local binary threshold learning network. The method comprises steps: 1, a to-be-classified texture image data set D is prepared, and the data set D is divided to a training data set Dt and a test data set Dv; 2, the local binary threshold learning network is built, the training data set Dt is inputted, and the local binary threshold learning network is trained through error sensitive term reverse propagation and a random gradient algorithm, wherein the local binary threshold learning network comprises one input layer, one threshold coding layer, two convolution layers, three down sampling layers, one full connection layer and one output layer; and 3, the test data set Dv is inputted to the well-trained local binary threshold learning network to verify a training result. According to the texture image classification method based on the local binary threshold learning network, through learning the structure information of the texture features, the method is applicable to texture image recognition in a small sample condition.

The invention discloses an effective multiscale texture recognition method. The method comprises the steps that an image pyramid of an input image is calculated firstly, then an LBP operator is applied to the image pyramid with various scales, next, the image pyramid of each scale generates a feature vector, multiscale information is integrated through similarity fusion on each scale according to the D-S evidence principle, and particularly, the similarity of the tested image and a target sample is calculated by fusing the similarity between the tested image and the sample of each scale. By means of the effective multiscale texture recognition method, the identification precision of a public data set Brodatz'salbum and an MIT video texture database (VisTex) reaches 96.43% and 91.67%. Meanwhile, the method has a certain robustness to image rotation invariance and has a certain application value in the practical application.

A three-dimensional multilayer skin texture recognition system and method based on hyperspectral imaging. Three-dimensional facial model associated with an object may be acquired from a three-dimensional image capturing device. A face reconstruction approach may be implemented to reconstruct and rewarp the three-dimensional facial model to a frontal face image. A hyperspectral imager may be employed to extract a micro structure skin signature associated with the skin surface. The micro structure skin signature may be characterized utilizing a weighted subtraction of reflectance at different wavelengths that captures different layers under the skin surface via a multilayer skin texture recognition module. The volumetric skin data associated with the face skin can be classified via a volumetric pattern.

The invention relates to a texture recognition method based on a deep self-attention network and local feature coding. The method comprises the following steps: designing a deep self-attention module with four stages according to characteristics of a texture image, merging local image blocks in the first three stages to increase a receptive field, and limiting a self-attention calculation in local space with a fixed size; in the last stage, canceling local image block merging, globally calculating self-attention, and obtaining a relation between local blocks; therefore, better extracting texture features of a local area, and keeping global features not lost. According to a PET network provided by the invention, texture information in a local area in an image is fully combined, and two-dimensional features output by a backbone network are remodeled into a three-dimensional feature map; block descriptors of multiple scales are densely sampled in a feature map through a moving window, and a group of multi-scale local representations is obtained; finally, local feature coding and fusion are carried out on the multi-scale block features, and a fixed-scale texture representation is generated for final classification.

A trunk texture recognition method based on a four-channel convolutional neural network comprises the following steps: S1, collecting a trunk image data set to ensure the integrity of an image; s2, preprocessing the image; and S3, training a multi-classifier by using the four-channel convolutional neural network. According to the invention, richer image feature information can be obtained, features with higher discrimination can be learned automatically, and the classification precision and robustness of the classifier obtained by training are higher.

The invention provides a geological information intelligent acquisition system and method based on an image identification technology. The method includes acquiring an environment panoramic picture through an image acquisition module; extracting and identifying coordinate and contour information in the panoramic picture through a feature point positioning module, linking two-dimensional code and database information through a marking conversion module, identifying geological information in the panoramic picture through a texture identification module, after information obtained through the abovementioned modules is integrated and compared in the database, construction engineering data are extracted, and the construction material use condition is monitored. The geological information intelligent acquisition system and method based on the image identification technology has the advantages that through big data of geological and construction conditions extracted in environment, geological data are obtained, engineering construction quality is monitored, the risk level is assessed, expenditure of enterprise personnel is greatly saved, and authenticity and objectivity of the acquired data are guaranteed.

The invention provides a space-time image flow measurement texture identification method based on a frequency domain filtering technology, and the method comprises the following steps: 1, reading a space-time image, and carrying out the window function processing of the space-time image; step 2, frequency spectrum construction: obtaining a spectrogram of the time-space image processed by the window function through fast two-dimensional discrete Fourier transform, center translation and amplitude spectrum calculation; 3, solving a frequency spectrum main direction through radial integration; 4,setting a threshold value and a shape of a filter according to the main direction of the frequency spectrum to perform filtering processing; and step 5, carrying out anti-center translation and two-dimensional discrete Fourier inverse transformation on the filtered space-time image frequency spectrum to obtain a noiseless space-time image texture. According to the method, noise in the space-timeimage can be effectively removed, the clear space-time image texture can be identified, the robustness, applicability and accuracy of the space-time image flow velocity measurement method are improved, and the method can adapt to monitoring of the surface flow velocity under various severe and complex water surface imaging conditions.

An embodiment of the invention discloses a texture identification method based on complete local characteristics, which belongs to the technical field of mode identification. The method comprises the following steps of calculating an amplitude histogram hm, a symbol histogram hs and a center coded histogram hc of a trained grayscale texture image; based on texture identification characteristic vectors of the histograms, using a support vector machine for carrying out training to obtain a texture identification classification model; and obtaining a texture identification characteristic vector for testing the grayscale texture image, and inputting the texture identification characteristic vector into the texture identification classification model to obtain a texture identification result. A transformation matrix is used for processing the grayscale texture image to achieve the object of adapting to the environment, so that the robustness of texture identification is achieved.

The invention discloses a vehicle face recognition method and device. The method comprises the steps that a vehicle image is acquired; the acquired video face image is preprocessed, wherein preprocessing includes the steps that the acquired color image is converted into a grayscale image, and gray stretching, image noising and enhancement, image edge detection and image binarization are performed on the grayscale image; vehicle face image positioning is performed on the vehicle face image after preprocessing, wherein vehicle face image positioning includes the steps of color recognition, shape recognition and texture recognition; vehicle face tilt correction is performed on the vehicle face image after vehicle face image positioning, and then vehicle face image positioning is performed again; feature extraction is performed according to the vehicle face positioning coordinates after secondary vehicle face image positioning, and binarization processing is performed on the extracted features; and the feature part of the video face image after binarization is recognized by using the one-to-many classifier of a SVM. The real-time performance and the accuracy of a vehicle face recognition system can be mainly enhanced so that the recognition system is enabled to be more intelligent, effective and accurate.

The invention discloses a video smoke detection method based on a convolutional neural network, and belongs to the field of picture recognition. Existing fire smoke identification algorithms have strong scene pertinence and are particularly susceptible to environmental interference. The invention discloses a video smoke detection method based on a convolutional neural network. The method comprisesthe following steps: preprocessing an acquired video image; performing suspected smoke area extraction on the preprocessed video image; carrying out smoke feature description on the obtained suspected smoke area; and based on a convolutional neural network smoke texture recognition framework, performing smoke recognition on the to-be-detected area obtained in the previous step by utilizing a convolutional neural network method. The moving target in the foreground image is input into the CNN model for smoke identification, so that the smoke detection efficiency is improved while the static object interference is reduced.

The invention specifically relates to a dynamic texture recognition method based on multi-task learning and is designed so as to improve dynamic texture recognition accuracy. The dynamic texture recognition method based on multi-task learning is characterized by, to begin, carrying out chaotic feature vector extraction on each pixel time sequence in a dynamic texture video, so that the video is changed into a chaotic feature vector matrix; then, carrying out video modeling through a bag-of-word model to obtain histogram features; and converting the identification problem into group sparse representation, and calculating through an ADMM method. The method can carry out dynamic texture recognition through a multi-task learning method, can be widely applied to various civilian and military systems, such as a video monitoring system, a video conference system, an industrial product detection system, a robot vision navigation system and a military target detection and classification system,and has a wide market prospect and application value.

The invention relates to a garment pattern texture recognition method and system based on a neural network. The system comprises an image feature extraction network, a garment position feature extraction network, a texture feature extraction module, a garment position regression module and a pattern texture prediction module. The method has the advantages that the specific position of the garmentin the image is judged, the specific position content of the garment serves as the ROI to be input into the neural network, background interference can be greatly eliminated, and the recognition accuracy is improved; by utilizing the system to analyze popular elements of the current fashion garment industry, the current fashion trend is analyzed, meanwhile, the system can also provide design inspiration for fashion designers, help the designers to design garment products meeting the psychology of consumers, and improve the cognition degree and satisfaction degree of the consumers; data labeling on 700-800 image frames is completed per minute, so that the efficiency is greatly improved; by using the system, the identification accuracy is improved by about 20%.

The invention provides a texture recognition model training method, a texture migration method and a related device. The method comprises the following steps: acquiring first image data; calling a map detection network to extract a plurality of frames of texture maps from the first image data; calling a parameter detection network to extract texture parameters from the first image data; differentially rendering the texture map and the texture parameter to a scene in the first image data to obtain second image data; the second image data is used as a supervision signal to train a map detection network and a parameter detection network, and the map detection network and the parameter detection network not only belong to automatic operation, are not perceived by a user and are low in learning threshold, but also have the capability of rendering textures to a scene, so that designers can reduce operations in the aspect of texture processing, and the user experience is improved. The operation convenience is improved, the time and energy consumed by modeling are reduced, the efficiency is improved, and the cost is reduced.

The invention discloses a car brake pad surface defect detecting and automatic sorting system based on machine vision. The system comprises a machine vision system. The machine vision system comprisesa camera, a camera lens and a light source. The machine vision system is connected to a CPU. The CPU is connected with an upper computer and a sorting arm. The upper computer involves image preprocessing, image segmentation, feature extraction and mode recognition, mode recognition involves color recognition, shape recognition, texture recognition and multi-feature fusion recognition, the machinevision system detects a to-be-sorted target image and transfers information to the CPU, and the CPU processes the received information. The car brake pad surface defect detecting and automatic sorting system based on machine vision is reasonable in design, brake pads are conveniently detected through image acquisition, processing and recognition, the sorting arm is controlled to act by means of detecting results, brake pads with detects are sorted to an unqualified area, qualified products leave a factory, the purpose of automatic sorting is achieved, and using is facilitated.

The invention provides a color film substrate and a display device. The color film substrate comprises an underlayer substrate, a color film layer, a plurality of texture recognition units; the colorfilm layer is arranged on one side of the underlayer substrate and comprises a black matrix and a plurality of color filters; the plurality of texture recognition units are arranged on the underlayersubstrate, each texture recognition unit comprises a thin film transistor and an optical sensor, and the orthographic projections of the texture recognition units on the substrate are located in the orthographic projection of the black matrix on the substrate. The texture recognition units are designed on the color film substrate, so that the optical path between an optical sensor and the surfaceof a finger can be effectively shortened, an acquired optical signal is enhanced, and meanwhile, the noise of the optical signal transmitted between LCD layers is greatly reduced; and meanwhile, LCD light transmittance is not influenced.

The invention discloses an unsupervised content-preserved domain adaptation method for multiple CT lung texture recognition, which belongs to the field of image processing and computer vision. This method enables the deep network model of lung texture recognition trained in advance on one type of CT data (on the source domain), when applied to another CT image (on the target domain), under the premise of only obtaining target domain CT image and not requiring manually label the typical lung texture, the adversarial learning mechanism and the specially designed content consistency network module can be used to fine-tune the deep network model to maintain high performance in lung texture recognition on the target domain. This method not only saves development labor and time costs, but also is easy to implement and has high practicability.

The invention discloses a high-precision automatic processing technology with a stone texture recognition function. The high-precision automatic processing technology comprises a preprocessing process, a stone texture recognition process and a generating processing process. According to the high-precision automatic processing technology with the stone texture recognition function, texture images of the to-be-processed surface of a stone are captured through an image recognition system, the texture distribution conditions in the images are analyzed, the processing path of the stone is planned reasonably according to the actual texture distribution conditions of the stone, the textures on the stone are reserved as much as possible, the texture-following phenomenon full of changes of the stone is well utilized, the front texture, the rear texture, the left texture and the right texture or the upper texture, the lower texture, the left texture and the right texture are connected end to end, and a natural pattern which is extremely rich in aesthetic feelings can be created.

The invention discloses a structural surface crack detection method based on fusion of image features and Bayesian data, which comprises the following specific steps: A, a metal member surface video image is collected and a detection image library is built; B, image texture features are calculated by localized binarization; C, two-step support vector machine image crack scanning and collection arecarried out; and D, Bayesian data fusion and decision making are carried out. By adopting video image detection, many areas that are difficult to reach by human beings can be acquired; a computer isadopted to recognize the surface crack of a structural member, thereby greatly reducing the heavy degree of interpretation and improving the crack detection rate; the correlation with the surface light intensity of the member is good, and in comparison with the previous grayscale map or other methods, the texture recognition effects can be improved; timely early warning can be carried out on a crack disease, and a structural disease can be detected early; on the premise of keeping a high scanning speed, a radial basis function support vector machine nerve network is used to maintain a high accuracy rate; and the crack recognition accuracy is improved.

The invention provides a defective pixel detection and correction device based on texture recognition. The device comprises an image sensor data input unit for inputting image data of an image sensor;a chromatic aberration compensation unit connected to the image sensor data input unit, and used for counting the chromatic aberration of each pixel of the image data in an m*n data window, selectinga median value of the chromatic aberration to obtain a chromatic aberration value of the current pixel, subtracting the chromatic aberration value, and performing chromatic aberration compensation; adefective pixel detection unit for calculating a total variation of N pixels around the current pixel, including pixels of the same channel or different channels, determining the texture intensity ofthe current pixel according to the calculation result, and detecting defective pixels; and a defective pixel correction unit for collecting the defective pixels according to the detection result of the defective pixel detection unit. The invention further provides a defective pixel detection and correction method based on texture recognition.

The invention provides a terminal control method and system based on facial texture recognition. The method includes: acquiring current first facial texture information of a user; acquiring a target operation, which corresponds to the first facial texture information, in a facial texture database according to the first facial texture information; and executing the target operation corresponding tothe first facial texture information. Through the method, quick control which is on a terminal and is based on the facial texture information can be realized, and thus use efficiency and security ofthe terminal are improved.

The invention provides an image texture recognition method and system. The method specifically comprises the following steps: step 1, acquiring image data in an operation process; step 2, encoding the image data; step 3, carrying out point-by-point calculation on gray values of local areas of the images according to the relation between the image space position information and the image gray values, respectively counting the occurrence times of different LBP values so as to describe texture features of the images in the areas, and carrying out texture feature extraction on the encoded data; step 4, inputting the extracted texture features into an image recognition model for recognition and classification; and step 5, outputting an identification and classification result for assisting the industrial operation process. The texture features of the image data are analyzed, and the image features are better extracted, so that the image analysis result better meets the requirement of real-time operation.