11 results about "Texture Descriptor" patented technology

The invention belongs to the technical field of lattice tower structure health detection, and relates to a lattice tower rod piece missing intelligent detection method based on multi-view three-dimensional reconstruction. According to the method, an adaptive feature matching algorithm fusing geometric features and texture descriptors is provided, camera pose estimation is optimized by constructing a global depth consistency scoring mechanism, and high-precision sparse point cloud reconstruction is realized; designing a depth map optimization strategy based on energy minimization, and effectively overcoming the interference of complex illumination and a low-texture environment on three-dimensional reconstruction in combination with density threshold filtering and geometric structure feature analysis; an ordered / disordered point cloud adaptive noise reduction method is innovatively provided, and noise robustness processing is realized through gradient constraint and covariance matrix eigenvalue analysis; and finally, an improved neighborhood radius adaptive registration algorithm is adopted, and a rod piece missing region is accurately identified through a significance scoring mechanism. The method remarkably improves the automation level and reliability of rod piece missing detection, and can be widely applied to structural health detection of the lattice tower.

The invention provides an image texture feature extraction method and system based on a Hamming-Pedeson graph, and the method comprises the steps: directionally analyzing the relation between pixels of an image according to the Pedeson graph, describing the local fluctuation trend of the relation through employing a threshold function and a structural mode, constructing a local three-valued pattern PLTP feature vector of the Pedeson graph, and carrying out the feature extraction of the local three-valued pattern PLTP feature vector of the Pedeson graph. The robustness, the discrimination capability and the applicability of texture feature extraction operators are improved; the Hamming distance between the central point of the PLTP code and the adjacent point is calculated to extract the spatial structure of the PLTP code, so that the HDPLBP feature vector based on the Hamming distance is generated, and the accuracy of image texture description is improved; the statistical histograms of the PLTP descriptor and the HDPLBP descriptor are combined into the PLTPHHDPLTP texture feature histogram, so that the texture feature quantity can more intuitively and effectively represent the image texture condition, and the texture description capability is ensured to be more robust and stable. Compared with a traditional texture descriptor, image texture analysis is more effective, local information and spatial distribution information are considered, and the function of completely extracting image texture features is achieved.

A processor receives a request to access one or more levels of a portion of a persistent texture (PRT) resource. The levels represent textures at different levels of detail (LODs) and the request includes normalized coordinates indicative of a location in the texture. The processor accesses a texture descriptor that includes dimensions of a first level of the levels and one or more offsets between a reference level and one or more second levels that are associated with one or more persistent maps indicative of texels that are resident in the PRT resource. The processor converts the normalized coordinates to texel coordinates in the one or more persistent maps based on the offsets and accesses the one or more persistent maps based on the texel coordinates in response to the request to determine whether texture data indicated by the normalized coordinates is resident in the PRT resource.

Disclosed in the present invention are an Alzheimer's disease medical image processing method and system. The method comprises the following steps: calculating statistical features of noise in an Alzheimer's disease medical image to obtain the noise type and distribution; performing denoising processing on the Alzheimer's disease medical image on the basis of the noise type and distribution to obtain a denoised image; performing morphological opening and closing operations on the denoised image to obtain a feature vector of a lesion region; acquiring the lesion region by means of an adaptive threshold segmentation method on the basis of the feature vector of the lesion region; and extracting texture features of the lesion region at different frequencies and directions by means of a multi-scale texture analysis method to obtain texture descriptors. In the present invention, the lesion region and features thereof in the Alzheimer's disease medical image are effectively extracted, providing an important basis for disease diagnosis and analysis.

Described herein is a novel approach to classify point cloud data by extending gray level co-occurrence matrix (GLCM) technique from 2D to sparse 3D domain. The method can be applied to a point cloud derived from a mesh set / mesh, such as a real world texturing thing (RWTT) mesh set. Implementations designed for a variety of purposes are described herein: sampling and quantization of RWTT grids, generation of GLCM and corresponding texture descriptors, and selection of potential candidate point clouds based on these extracted descriptors.

The invention provides a hybrid interpolation image magnification algorithm based on adaptive region selection, and aims to improve the quality and efficiency of image magnification. The method comprises the following steps of: identifying edges and smooth areas in an image through an edge detection operator, and further distinguishing the areas by using a texture descriptor; on the basis, gradient information and texture features of each pixel point are comprehensively evaluated, and a score graph reflecting image complexity is generated. And setting a threshold value according to the complexity score, dividing the image into an edge and a smooth region, and creating a corresponding mask graph to guide interpolation operation. And applying bicubic interpolation in an edge region to keep detail sharpening, and adopting bilinear interpolation in a smooth region to reduce calculation overhead. The method combines adaptive region division and intelligent interpolation, realizes efficient and high-quality image amplification, particularly pays attention to detail reservation and effective utilization of computing resources, and is suitable for high-precision image processing scenes.

The invention relates to the technical field of machine vision image processing, and discloses a part surface defect detection method based on image processing. The method comprises the following steps: acquiring a part surface image through image acquisition, extracting defect sensitive characteristics by using a multi-scale Gaussian difference operator, and filtering through a threshold value to obtain a candidate defect point set; and inputting the candidate point set into a serialized network containing an attention mechanism and a memory unit to generate an enhanced feature sequence. And carrying out space alignment on the sequence and an original topological association graph, and generating an enhanced feature graph containing node attributes and edge weights through graph convolution. And on the basis, performing region growing and merging operation to generate a connected candidate defect region. And extracting geometric and texture descriptors of each region to form a feature vector, matching the feature vector with a standard vector in a defect feature library so as to identify the defect category and position, and finally outputting a defect detection map. According to the method, the detection rate and the segmentation precision of the complex distribution defects are improved.

This invention relates to the technical field of testicular ultrasound image denoising, specifically to an automatic optimization method and system for testicular ultrasound images. The method involves acquiring testicular ultrasound images and dividing them into regions. A reference region is selected for Canny edge detection, and multiple sets of edge pixels are obtained by iteratively adjusting a high threshold. An edge variation factor is calculated based on the total number of pixels in the region, the number of reference edge points, and the number of newly added points in the neighborhood during iteration. By analyzing the gradient direction differences and positional distribution of each edge point, pixels with approximate gradient directions are determined. A texture descriptor is obtained by combining the direction differences, the number of approximate points, and the neighborhood gradient differences. The diffusion coefficient of each edge point is calculated by integrating gradient features, texture descriptors, and the edge variation factor, ultimately achieving image denoising. This invention can select an appropriate diffusion coefficient to remove noise from testicular ultrasound images while improving image quality and detail.

Method of inspecting cargo using penetrating (X-ray) radiation, comprising: obtaining an image of the cargo (Fig.4); detecting objects in the image 11,13; using a trained deep learning algorithm (1,Fig.1) to calculate a quality or dimming vector for each detected object, the dimming quality vector comprising coefficients indicating quality of the object image wherein each coefficient is calculated as a dimmed value obtained from the cargo image as compared to an ideal reference value learned by the deep learning algorithm; and determining whether the objects is an object of interest based on the quality dimming vector. Coefficients may include: statistical measures; descriptors of grey levels; entropy values; texture descriptors; geometrical characteristics; Hu moments values; and / or Fast Fourier Transforms (FFT) features. Objects of interest may be identified by comparing the quality dimming vector to a reference vector. A dimming (quality) score may be generated from the quality vector and displayed to a user. The object of interest may comprise weapons and / or contraband product, such as a drugs, cigarettes or alcohol. The deep learning algorithm may be trained on images with known quality coefficients, the difference being minimised by a mean square error loss function.

The application provides a kind of image texture feature extraction method and system based on hamming-peterson diagram, by the relationship between the pixels of image is analyzed according to peterson diagram orientation, utilize threshold function and the local fluctuation trend of structure mode description relationship, and construct the local three-value mode PLTP feature vector of peterson diagram, improve the robustness, discriminability and applicability of texture feature extraction operator;The hamming distance of the center point and its adjacent point of PLTP code is calculated to extract the spatial structure of PLTP code, to generate HDPLBP feature vector based on hamming distance, improve the accuracy of image texture description;The statistical histogram of PLTP and HDPLBP descriptor is merged into PLTP_HDPLTP texture feature histogram, so that the texture feature quantity can more intuitively and effectively represent the image texture condition, and the texture description ability is more robust and stable. For image texture analysis, it is more effective than traditional texture descriptor, and local information and spatial distribution information are considered, and the function of complete extraction of image texture feature is realized.

The application relates to a ceramic tile surface color difference detection method and device, electronic equipment and a storage medium. The ceramic tile surface color difference detection method comprises the following steps: acquiring a ceramic tile image to be detected; inputting the ceramic tile image into a multi-task model combined with a trained texture recognition network and a color classification network to obtain a texture descriptor with color information; extracting color features of the ceramic tile image and label features of a template ceramic tile image; fusing the texture descriptor with color information, the color features and the label features to obtain a feature vector corresponding to the ceramic tile image; and inputting the feature vector into a trained classification model to obtain a judgment result of whether the ceramic tile image to be detected has color difference. The ceramic tile surface color difference detection method has powerful feature extraction capacity of a deep convolutional neural network, can extract texture features of a ceramic tile surface, and can more efficiently and accurately describe the texture of an image.