465 results about "Superpixel segmentation" patented technology

The present invention pertains to the technical field of digital image processing. Disclosed are an unsupervised super-pixel segmentation method and system assisted by collaboration between an atrous pyramid and an attention mechanism. The method comprises: combining image RGB channel information with position information of a pixel point; constructing a channel attention module by using an attention mechanism; processing the result of the attention mechanism by using atrous spatial pyramid pooling; constructing a loss function, constructing a clustering loss term, and, by using a spatial smoothing loss term, constructing a reconstruction loss term; updating model parameters by using an Adam optimizer, and using for super-pixel generation an effective depth feature obtained from the last separation; and obtaining the maximum value of a channel dimension by using an argmax function, converting a processing result of the argmax function into a two-dimensional array, and, on the basis of a defining condition, completing adaptive super-pixel segmentation in a CPU. According to the present invention, the complexity is low, the adaptive and generalization capabilities are high, and effective support is provided for improving image processing efficiency and accuracy.

The invention discloses an image lesion recognition system for assisting a doctor in the digestive system department in sampling inspection. The image lesion recognition system comprises an image acquisition module, a preprocessing module, an AI lesion recognition module, a feature analysis module, a sampling recommendation module, a data storage module, a real-time interaction module, a system control center, a disinfection monitoring module and a power consumption optimization module. The system integrates white light, narrow-band light and fluorescent endoscope images based on a multi-modal large model, accurately identifies focuses such as polyps and ulcers in real time and marks boundaries, quantitatively analyzes the forms, infiltration depths and texture features of the focuses in combination with super-pixel segmentation and three-dimensional reconstruction technologies, and significantly improves the diagnosis objectivity. According to the system, a sampling scheme matched with a clinical guide is dynamically generated by relying on a reinforcement learning strategy and biomechanical simulation, a focus three-dimensional model and a real-time endoscope picture space are subjected to registration through an augmented reality technology, an operation path is guided by means of tactile feedback, and the accuracy and efficiency of doctor operation are greatly optimized.

The invention provides a target identification tracking guiding method of a photoelectric pod, detection equipment, a program product and a storage medium. And super-pixel segmentation is performed on the scanned image information to remove the background, so that irrelevant information interference can be effectively reduced, and subsequent target identification is more accurate and efficient. And calculating a three-dimensional coordinate by integrating the position and attitude of the sensor, the position of the target image and distance information, and locking the spatial position of the target. Target features are obtained, multi-hypothesis tracking is performed in combination with three-dimensional coordinates, complex motion states can be dealt with, the stability and accuracy of target trajectory detection are ensured, and the target is not easy to lose even if the target moves variably. Characteristics and trajectory data are input into a model to identify target identities, tracking conditions are judged according to a preset list, it is ensured that only key targets are tracked, and resource waste is avoided. And the target tracking capability of the autonomous or semi-autonomous aircraft in a complex environment is integrally improved.

The invention relates to the field of image processing, in particular to a paint defect detection method and system based on machine vision, paint surface images are collected through an industrial camera, and brightness correction is carried out in combination with an illumination correction factor; fusing color fluctuation and texture features to construct a fluctuation factor, and optimizing a distance measurement formula of super-pixel segmentation to realize accurate segmentation of a complex surface area; and finally, carrying out classification detection on the super-pixel region features by utilizing a convolutional neural network, and identifying the type of the paint surface defect. According to the invention, through illumination correction, fluctuation factor construction, improved superpixel segmentation algorithm and convolutional neural network classification, automatic and accurate detection and classification of paint surface defects are realized.

The invention provides a small target identification method and system based on YOLOv5, and the method comprises the steps: collecting a multi-scale image in a target range, and forming an original data set; performing image enhancement on the original data set through superpixel segmentation and adversarial enhancement operation to obtain an enhanced data set; performing multi-scale frequency domain aliasing enhancement on the enhanced data set through frequency domain decomposition and frequency band exchange operation to obtain a to-be-detected data set; improving the YOLOv5 model to obtain a small target recognition model; and performing small target identification on the to-be-detected data set through the small target identification model to obtain an identification result. According to the method, small target recognition is realized through the dynamic feature pyramid and the double-path detection head in combination with cross-level kernel sharing and a space-frequency double-domain attention mechanism, the problem of detail loss caused by a traditional static feature pyramid is solved, the recognition precision is improved, and the false detection rate is reduced.

The invention discloses an optical performance detection system and method for a camouflage material, and relates to the technical field of camouflage material detection, and the system comprises a structure identification and illumination control module which is used for collecting a surface image of a camouflage material sample, identifying the surface structure of the sample, starting a full-spectrum light source, and simulating step-by-step illumination from weak light to strong light; carrying out RGB imaging based on the standard illumination; and the region division module is used for applying a superpixel segmentation algorithm to the image, automatically dividing sub-regions, performing hyperspectral reflectivity scanning on each region, synchronously acquiring a light intensity image in a polarization direction, calculating a Stokes vector and constructing a polarization enhanced image. According to the method, frequency domain quantitative analysis of microcosmic interference fringes and accurate mask extraction of polarization abnormal areas can be realized, optical exposure risks of camouflage materials under complex illumination and visual angle conditions are effectively quantified through multi-angle reflection sampling and scoring function modeling, and the detection precision, the area pertinence and the automation level are remarkably improved.

The invention belongs to the technical field of computer vision, and relates to a lightweight image feature point detection and matching method and related equipment thereof. The method comprises the following steps: performing feature extraction on a target image through a lightweight SuperPoint neural network model to generate feature information; performing super-pixel segmentation processing on the image to generate a super-pixel segmentation result; performing initial matching on the feature information through a lightweight SuperGlue neural network model to generate an initial matching pair; performing outlier filtering on the initial matching pair based on a superpixel segmentation result, and determining an optimized target matching pair; performing geometric consistency optimization on the optimized target matching pair by adopting an adaptive exponential moving average algorithm to generate a final matching result; the lightweight SuperPoint neural network model is generated by carrying out hierarchical progressive pruning processing on an original SuperPoint neural network model; the lightweight SuperGlue neural network model is generated by carrying out attention head pruning processing and graph network layer pruning processing on an original SuperGlue neural network model.

The invention relates to the technical field of metal material detection, in particular to a titanium alloy bar machining quality detection method and system, and the method specifically comprises the steps: carrying out the superpixel segmentation of the section of a titanium alloy bar, constructing a mirror image edge texture saliency value of a superpixel block based on the proportion of edge points in the superpixel block and the edge gradient, and carrying out the detection of the machining quality of the titanium alloy bar. Constructing the mirror image texture intensity of the titanium alloy bar by combining the gray level distribution concentration characteristics of the titanium alloy bar, and performing image segmentation on the section of the titanium alloy bar; and in each segmented region, based on the edge line fuzzy degree difference and the pixel gray level difference of each super-pixel block and the rest of super-pixel blocks, combining a mirror image edge texture saliency value to construct a segregation evaluation value of each super-pixel block, and based on the segregation evaluation value, carrying out titanium alloy bar segregation quality defect detection. The titanium alloy bar segregation defect identification capability is enhanced, the titanium alloy bar segregation defect detection accuracy under the titanium alloy bar mirror image scene is improved, potential problems can be found earlier, missing detection is avoided, and the defective rate is reduced.

The invention relates to a remote sensing image target detection method fused with a multi-scene random field, and the method comprises the steps: building a model through a method combining multi-task learning and a conditional random field, extracting the context information near a suspected small target, assisting the model to judge a real target, integrating the effective results searched by the model, and outputting an image. Aiming at the problems of wide distribution range of remote sensing images and low model test analysis efficiency, a super-pixel segmentation method of the remote sensing images is designed according to the particularity of similar sizes and shapes of similar targets in the remote sensing images, gridding of a remote sensing large-range region is realized in a model reasoning test process, a retrieval region is divided into a plurality of small regions, and a remote sensing image is obtained. The network target searching efficiency is effectively improved; a target image data set containing different backgrounds is constructed, and the obtained effective feature information is input into a target detection network for training, so that the target detection accuracy of the electric power facility in the remote sensing image is realized, and the inspection efficiency is improved.

The invention discloses a hyperspectral image segmentation method based on semi-supervision and super-pixel progressive growth. The method comprises the following steps: carrying out normalization processing on a hyperspectral image to be segmented; taking the normalized hyperspectral image as input, constructing a full convolutional network comprising an input layer, a convolutional layer, a batch normalization layer and an output layer, and taking the network output as a semantic segmentation result corresponding to the hyperspectral image; performing superpixel segmentation on the normalized hyperspectral image, and endowing each initial superpixel area with an initial pseudo label; combining the similar initial superpixel areas, and updating the pseudo labels corresponding to the combined superpixel areas; the method comprises the following steps: minimizing a loss function, reversely optimizing feature extraction of a feature extractor, performing fine adjustment on a full convolutional network by using a small number of labeled samples, and continuously optimizing a superpixel region to converge a model to obtain a final segmentation result.

The invention discloses a hyperspectral image classification method based on super-pixel multi-scale image fusion Transform, and belongs to the technical field of image processing and computer vision. The invention aims to provide a hyperspectral image classification method based on super-pixel multi-scale image fusion Transform, which realizes accurate classification of hyperspectral images through multi-scale image network feature extraction and super-pixel feature global modeling. According to the method, features of a hyperspectral image are divided into a plurality of superpixel areas through superpixel segmentation, a superpixel-based image structure is constructed, and global information fusion is carried out on extracted multi-scale superpixel features through a Transform network self-attention mechanism by applying multi-scale image convolution operation, so that a final classification result is generated. According to the invention, a multi-scale image convolution feature extraction network is developed, context feature extraction is carried out on a superpixel feature pyramid, the spectral feature extraction capability is improved, and the robustness of the model to different scale feature changes is enhanced.

The invention discloses an unmanned aerial vehicle power distribution network equipment identification method based on mutual neighbor density peak value clustering, and relates to the technical field of power distribution network equipment inspection, and the method comprises the steps: obtaining an original RGB image of a power distribution line; outputting the contrast characteristic coefficient of each channel; constructing a saturation retention item; calculating a contrast feature retention item, constructing a total energy function, solving an optimal channel weight by adopting a discrete search strategy, and outputting an initial grayscale image; sequentially carrying out weighted guide filtering, morphological reconstruction and super-pixel segmentation operation; performing two-dimensional wavelet decomposition on the super-pixel segmented image, and extracting a feature vector; and calculating the local density and the relative distance, selecting a clustering center, and completing sample distribution based on the shared mutual neighbor similarity to realize a power distribution network equipment identification effect. According to the method, the problems of detail loss, noise interference, edge breakage, disordered classification of multi-scale equipment and the like under complex illumination are effectively solved, and the identification precision and efficiency of unmanned aerial vehicle inspection are effectively improved.

The invention provides an annotation method and system based on forest land remote sensing data. The method comprises the following steps: acquiring multi-source remote sensing data of a forest land; processing the multi-source remote sensing data; an improved Mask R-CNN network is adopted to extract optical image features, the weight of a near-infrared band is enhanced through a compression and excitation SE module, and feature fusion is carried out to obtain a multi-modal enhanced feature map; carrying out feature extraction by adopting a 3D convolutional network to obtain a multi-level three-dimensional space feature map; inputting the processed data into a Transform model to obtain a forest growth curve graph; performing initial labeling based on the feature map and the curve map to obtain an initial labeling result; performing morphological filtering on the initial labeling result to remove small-area noise, and optimizing a labeling boundary in combination with a superpixel segmentation algorithm to obtain an optimized labeling result; and generating a vector annotation file according to the optimized annotation result. Multi-dimensional information complementation of spectral features, three-dimensional structure features and time sequence dynamic features is achieved, and data availability and reliability are remarkably improved.

The invention relates to a multi-modal remote sensing image change detection method and system and a readable storage medium, and the method comprises the steps: carrying out the normalization of a multi-modal remote sensing image, carrying out the superpixel segmentation through employing simple linear iteration clustering, and employing the pixels of superpixels as nodes to construct an original image structure; inputting the original graph structure into a graph attention network to extract graph structure features of superpixels; performing style regularization on the graph structure features, and performing style randomization on the graph structure features and adjacent superpixels to obtain new style features of the superpixels; inputting the new style features into a graph structure decoder to obtain a super-pixel reconstructed graph structure; training a graph attention network by using the content consistency of the reconstructed graph structure and the original graph structure to obtain a target graph attention network; and inputting an original image structure obtained by processing a to-be-detected multi-modal remote sensing image before and after change into the target image attention network to obtain image structure features before and after change, and obtaining a change binary image by using an Otsu method. The change detection precision is effectively improved.

The invention relates to the field of image processing, in particular to an oilstone static pressure forming quality detection method based on image processing, and the method comprises the steps: obtaining an infrared image set of an oilstone sintering and cooling stage, carrying out the superpixel segmentation to obtain a superpixel region of each frame of image, matching the superpixel regions of adjacent frames, and building a corresponding relation, and calculating a temperature anomaly characteristic value of each region. And analyzing the temperature change trend of each region, calculating a correlation coefficient, and introducing distance and temperature anomaly weight to carry out weighted fusion to obtain a comprehensive correlation index. And in combination with the temperature anomaly characteristic value and the comprehensive correlation index, evaluating the possibility value of occurrence of pore anomaly in each superpixel region, and judging whether pore distribution anomaly exists in the oilstone or not, thereby completing quality detection. According to the method, through superpixel segmentation and matching, in combination with temperature anomaly characteristics and comprehensive correlation analysis, precise detection of oilstone internal pore anomaly is realized, and the reliability of static pressure forming quality detection is improved.

The invention discloses a multisource remote sensing image surface water body extraction method based on a graph neural network. The SAR image is used as a main part, and the optical image, the digital elevation model and the local incident angle information are fused. Firstly, radiation correction, geometric correction and noise suppression are carried out on SAR data, and multi-source data spatial resolution matching is carried out. And then converting the image into a graph structure by adopting a self-adaptive Bayesian superpixel segmentation method. Then, geometric, texture and physical features are constructed for the superpixel nodes, and feature dimensions are optimized through a feature selection algorithm; and finally, constructing a classification aggregation graph convolutional neural network, wherein the core lies in a self-adaptive microaggregation mechanism of the classification aggregation graph convolutional neural network. According to the mechanism, neighborhood superpixels can be dynamically divided according to categories and feature fusion is carried out, and the recognition capability of the model on water body boundaries and regional heterogeneity is remarkably improved. According to the method, multi-source remote sensing data, superpixel graph structure conversion and an optimized graph convolutional network are comprehensively utilized, and the precision and robustness of water body extraction in the complex surface environment are effectively improved.

The invention relates to a hyperspectral remote sensing image radiation normalization method, in particular to a hyperspectral remote sensing image relative radiation normalization method based on pseudo-invariant region pairs, and solves the technical problems that an existing pixel-based relative radiation normalization method is low in space matching precision and has spectral feature deviation. According to the method, a pseudo-invariant region is screened by adopting super-pixel segmentation, morphological processing and super-pixel guiding methods, then relative radiation normalization is performed on a to-be-corrected highlight remote sensing spectrum image through adaptive learning by adopting a self-correction model based on spatial spectrum constraint, and a quantitative method is adopted to correct the relative radiation of the to-be-corrected highlight remote sensing spectrum image. Not only can the spatial spectrum characteristics of the hyperspectral remote sensing image be fully utilized, but also the radiation intensity of the hyperspectral remote sensing image under different time phases can be adaptively corrected, and the consistency between time sequences and the quantitative inversion precision are improved, so that the accuracy and consistency of relative radiation normalization are improved, and the accuracy and the reliability of the relative radiation normalization are improved. The method is suitable for radiation normalization of hyperspectral remote sensing images with high spatial resolution and complex ground object scenes.

The invention discloses a semiconductor photoelectric chip anomaly detection method and device and computing equipment, and the method comprises the steps: loading a first target chip image, based on a first color model, of a target semiconductor photoelectric chip, and converting the first target chip image into a second target chip image based on a second color model, the second color model comprises a red channel, a saturation channel and a brightness channel; performing superpixel segmentation on the second target chip image to obtain a target chip superpixel image, and collecting a feature vector of each target superpixel in the target chip superpixel image; predicting a plurality of target key areas in the target chip superpixel image by using the target visual large model; based on the feature vector of each target superpixel, generating a corresponding OCSVM model for each target key area; and performing anomaly detection on any target key area based on the OCSVM model corresponding to any target key area. According to the invention, accurate anomaly detection can be carried out on the single key area of the semiconductor photoelectric chip.

The invention belongs to the technical field of target detection, and discloses a passive millimeter wave target detection method and system based on regional clustering, and the method comprises the steps: dividing a brightness temperature image into a plurality of superpixel regions through a superpixel segmentation algorithm according to the similarity between target regions and the similarity between background regions, calculating a saliency image based on the regional statistical characteristics; according to the spatial distribution difference of boundary superpixels and internal superpixels, regional local direction centrality measurement is introduced to carry out clustering analysis, so that a candidate target region is highlighted; edge compensation is carried out on the preliminarily screened candidate target area; building a total variation optimization model by using regularization construction constraints; the local features of the compensated candidate target region are enhanced by solving the total variation optimization model; and finally, realizing accurate target detection and extraction through threshold segmentation operation. According to the invention, the accuracy of target detection and the integrity of target contour extraction can be improved.

The invention provides a feature weight adaptive super-pixel segmentation method based on a sonar image. The method comprises the following steps: Step 1, converting an original sonar image into a grayscale image; step 2, denoising processing is carried out on the image by using guide filtering; step 3, extracting specific features of the sonar image; step 4, carrying out superpixel segmentation preprocessing of automatic adjustment of feature weights; step 5, carrying out maximum flow-minimum cut superpixel aggregation; step 6, carrying out aggregation on the super pixels through a preset threshold value; and Step 7, separating the target from the background to obtain a segmentation result. According to the method, a segmentation technology suitable for the target object area of the acoustic image can be realized, and a better image foundation is laid for subsequent target object disease type judgment and volume and area measurement through analysis of the segmented area, so that segmentation is more accurate, the type of the target object is easier to judge, and the size of the disease is more accurately measured.

The invention relates to the technical field of image recognition, and discloses a crop lodging image data detection method and system, and the method comprises the steps: carrying out the self-adaptive illumination correction processing of the original image data of crop lodging, and obtaining an illumination compensation image of the original image data; performing multi-scale super-pixel segmentation on the illumination compensation image to obtain a crop area image of the illumination compensation image; extracting texture features and shape features in the crop region image, and performing feature fusion on the texture features and the shape features to obtain a multi-modal fusion feature set of the crop region image; performing multi-dimensional feature collaborative analysis on the multi-modal fusion feature set to obtain an initial discrimination result of the multi-modal fusion feature set; performing confidence coefficient optimization on the initial judgment result in combination with a spatial context relationship to obtain a target lodging detection result of the crop region image; according to the invention, the efficiency of crop lodging image data detection can be improved.

The invention relates to a hyperspectral image classification method based on a meta-pseudo-label multi-scale image convolution network, and the method comprises the steps: building a meta-pseudo-label multi-scale feature search image convolution network (MPL-MFSGCN), dividing the HSI into superpixel images of different scales through employing an SLIC superpixel segmentation technology, and converting the superpixel images into image structures; secondly, constructing a multi-scale feature search graph convolutional network MFSGCN architecture, optimizing a superpixel feature weight combination by using a neural network search algorithm, and enhancing the discrimination of feature expression; a semi-supervised teacher-student model sharing the same graph convolutional network is adopted to generate meta-pseudo tags, and a student network is finely adjusted to improve the robustness of the network; the key features can be automatically captured under the small sample condition, the classification precision higher than that of an advanced method is obtained compared with a common HSI data set, and the classification accuracy is effectively improved.

The invention discloses an SAR polarization calibration reference distributed target extraction method, and the method comprises the steps: carrying out the threshold method pixel classification through employing a polarization distortion parameter and a polarization orientation angle extracted from an uncalibrated full-polarization SAR image, and obtaining a pixel-level high-confidence volume scattering dominant target and a non-volume scattering dominant target; performing super-pixel segmentation on the uncalibrated full-polarization SAR image to obtain super-pixel units; extracting two polarization characteristics of the superpixel unit; processing the pixel-level high-confidence volume scattering dominant and non-volume scattering dominant targets by using a pixel proportion threshold method to obtain super-pixel unit high-confidence volume scattering dominant and non-volume scattering dominant targets, and combining the super-pixel unit high-confidence volume scattering dominant and non-volume scattering dominant targets with the two polarization features to obtain super-pixel unit positive and negative samples; and training the positive and negative samples of the super-pixel unit by using a random forest classifier to obtain a total image classification result, and the scattering dominant target of the super-pixel unit in the total image classification result being the reference distributed target meeting the polarization calibration requirement.

The invention discloses an urban landscaping tree species classification method based on a Pleiades Neo ultrahigh resolution multispectral satellite image. According to the invention, based on the Pleiades Neo image, an NDVI threshold method is adopted to rapidly identify the urban vegetation area; using a Unet < 3 + > model to separate vegetation in the shadow area and the illumination area; a shadow region tree species classification integration model is constructed by combining a superpixel segmentation algorithm and introducing a weighted dictionary and an attention mechanism, so that the classification precision is effectively improved; and carrying out tree species classification on the illumination area by using a deep learning algorithm and making an overall tree species distribution diagram. Aiming at the shadow interference problem in a high-resolution image, a set of tree species classification scheme comprehensively considering vegetation spectrum characteristics of an illumination region and a shadow region is developed by combining a deep learning algorithm to deeply mine potential information, a more accurate and feasible tree species distribution acquisition approach is provided for fine management of urban landscaping, and the urban landscaping quality is improved. And important data support is provided for health assessment and planning of the urban ecosystem.

The invention discloses a digital printing image color detection method, and belongs to the field of image data processing and the technical field of image detection. The invention provides a printed image color detection method of an improved superpixel segmentation algorithm based on saliency and color information entropy, and the method comprises the steps: delimiting an important region of a printed image through employing an image saliency algorithm, and adaptively determining the size of a superpixel of the region according to the color information entropy of the printed image in a saliency region. Performing different superpixel division on the salient region and the non-salient region of the printed image; in the printed image color detection, the color mean values of the printed image under the same superpixel label are compared, and the label with chromatic aberration is output, so that a chromatic aberration area is marked in the image, and the color offset of the chromatic aberration area is displayed. The method is used for printing image color detection of superpixel color mean value comparison, and a more accurate color detection result conforming to human vision can be obtained; the method has a fast detection speed, and can output a color difference area position and a color offset.

The invention relates to the technical field of geological survey, in particular to an unmanned aerial vehicle multispectral geological survey method and system, and the method comprises the steps: fusing a multispectral image, a digital elevation model, geophysics and historical geological data, systematically constructing a geological feature priori knowledge model, including lithology, construction and alteration feature libraries, and mapping with multispectral data; multi-scale geologic features are extracted through adaptive wavelet transform and morphological analysis, and feature weight adaptive adjustment is achieved; geological units are accurately divided by adopting geological scene perception superpixel segmentation and combining geological boundary constraint and similarity recursion combination; identifying an interference mode, generating an adaptive filtering matrix, and enhancing image quality; cooperatively interpreting multi-source information by using a deep auto-encoder network to generate a high-precision geological interpretation map and a confidence map; geological professional knowledge is introduced, so that the geologic body recognition accuracy is remarkably improved; the adaptive flight control strategy ensures the consistency of complex terrain data, and improves the precision and efficiency of geological survey.

The invention provides a target tree species remote sensing recognition method based on space-air-ground cooperation, which comprises the following steps: performing adjacent superpixel combination on segmented pattern spots after superpixel segmentation, and performing feature factor-based model optimization on a current recognition model of a target tree species based on combined pattern spot distribution and field distribution data, so as to obtain a target tree species remote sensing recognition model. Target tree species recognition is executed based on the updated new recognition model, and correct recognition pattern spots are obtained; executing edge matching detection on the correctly recognized pattern spots, adjusting the current merging scale based on an edge matching detection result to obtain an optimal recognition model, and further obtaining a recognition result of the target tree species based on the optimal recognition model; according to the method, the accuracy of the pattern spot boundary is effectively improved while the recognition precision of the target tree species is ensured, so that the matching degree of the boundary of the segmented pattern spot and the actual boundary of the target tree species is effectively improved.

The invention discloses a food waste detection method and system based on image processing, belongs to the field of image recognition, and aims to realize efficient and automatic recognition and quantification of kitchen waste. According to the method, residual food images are collected at multiple periods and multiple angles in a kitchen garbage can or a dinner plate recovery area through high-resolution and multi-spectral imaging equipment, preprocessing is carried out in combination with an improved Retinex algorithm and a space self-adaptive denoising technology, and the image quality is improved. Afterwards, fine segmentation of a food area is achieved through a multi-scale super-pixel segmentation and graph segmentation algorithm, and multi-category intelligent recognition is conducted on remaining food through a recognition network fused with multi-modal features. The system further combines stereoscopic vision and Monte Carlo sampling to dynamically and accurately count the volume or weight of various residual foods. The method has the advantages of high adaptability and accurate statistical result, and can provide data support for catering management, resource recovery, nutrition evaluation and the like.

The invention discloses a hyperspectral image classification method and system. The method comprises the following steps: acquiring a hyperspectral image; preprocessing the hyperspectral image to obtain a preprocessed image; extracting spectrum, space and frequency domain features from the preprocessed image; superpixel segmentation is carried out on the preprocessed image, each superpixel of the image is regarded as a node, a graph structure is constructed, and global features of the graph structure are extracted; obtaining fusion features of the hyperspectral image according to the global features and the local features of the graph structure; and determining a classification result of the hyperspectral image according to the fusion features of the hyperspectral image. And the classification identification effect of the hyperspectral image is improved.

The invention relates to the technical field of remote sensing image processing, in particular to a land consolidation boundary line division method and system based on machine vision. The method comprises the following steps: obtaining a plurality of superpixel blocks of a satellite remote sensing image; according to the elevation information distribution of different pixel points in each super-pixel block in the digital elevation model, obtaining the terrain height change degree of each super-pixel block; according to the elevation information difference between each pixel point in each super-pixel block and other pixel points in the corresponding neighborhood range in the digital elevation model and the gradient distribution of the corresponding pixel points in different directions in the digital elevation model, obtaining the overall water flow direction value of each super-pixel block; further obtaining the merging possibility between different super-pixel blocks; and obtaining a superpixel optimal block, and dividing the arrangement boundary line of the to-be-divided land region. According to the method, the superpixel segmentation process of the land region to be divided is accurately obtained, so that the accuracy of boundary line division arrangement is improved.