43results about How to "Guaranteed segmentation effect" patented technology

The application provides an image segmentation method and a system, and relates to the field of image processing. The method comprises the following steps: according to an original image, a first initial prospect area and an initial background area are delimited; down-sampling is carried out on the original image to obtain a first image, and pixels, which belong to the first initial prospect area, of the first image are delimited as an initial prospect area; pixels in the initial background area are utilized to establish an initial background mixed Gauss model, and the pixels in the initial prospect area are utilized to establish an initial prospect mixed Gauss model; the image is input into an image cutting process, and a new prospect area and a new background area are delimited; the pixels in the new prospect area are utilized to optimize parameters of the prospect mixed Gauss model, and pixels in the new background area are utilized to optimize parameters the background mixed Gauss model until that the image cutting process converges. According to the invention, a processing time of a cutting algorithm can be raised.

The present invention discloses a method for detecting and locating insulator defects in power transmission line image via a drone, and belongs to the technical field of image extraction and recognition. The detection and location method for the insulator defects comprises the steps of: S1. converting an aerial image from an RGB color space into an HSI (Hue-Saturation-Lightness) space, and carrying out binarization on extracted H component image and S component image separately to obtain a binary image of a preliminary contour of an insulator string; S2. using an ant colony algorithm based on a particle swarm optimization parameter to extract a contour of an insulator monomer; and S3. using a least square method to perform ellipse fitting on the contour of the insulator monomer, and locating the insulator defects by detecting the space between the contours of insulator monomers. By using the technical scheme provided by the invention, the detection precision of the insulator defects can be significantly improved, background interferences are reduced, and the operation speed is relatively high.

The invention discloses an iris image segmenting method and device. The method includes performing rough positioning on a pupil in an iris image; expanding an image in a candidate area on the outer boundary of an annular iris into a rectangular image in a polar coordinate system and acquiring an excircle boundary estimation value of a target iris according to the gray level change of the rectangular image; intercepting an upper eyelid zone and a lower eyelid zone of the iris image according to the excircle boundary, acquiring boundary points of an upper eyelid and a lower eyelid, performing curve fitting on the boundary points of the upper eyelid and the lower eyelid, and obtaining an upper eyelid boundary curve and a lower eyelid boundary curve; performing noise detection on valid zones determined according to the upper eyelid boundary and a lower eyelid boundary; performing fine positioning on the excircle boundary of the iris through a radial projection method and performing fine positioning on the boundary of the pupil by utilizing a calculus detection operator. The invention solves a problem of low accuracy of low quality iris image segmentation performed by iris image segmenting methods in the prior art.

The invention provides a method for manufacturing femtosecond laser complex components in a large batch. The surface processing of the complex component comprises: setting, according to the area and the focal depth of a laser processing region, a constraint condition and a region growing algorithm by using a complex component model reconstruction and surface processing pattern splicing algorithm;dividing the processing region, simplifying the region boundary to obtain the processing region, extracting the key features of the processing pattern types and layout features; designing a slicing algorithm according to the process of periodic and non-periodic layout in order that the processing pattern is automatically and quickly laid out on the surface of the complex component; by using the processing region division and pattern segmentation algorithm and setting the element type and combination in the processing pattern, obtaining the intersection of the element and the processing regionboundary and classifying the sub-pattern to the corresponding processing region; by using processing pattern real-time detection and online splicing manufacturing technology and by setting and installing a high-precision CCD camera, obtaining the video image of a processed part, recognizing the boundary of the processed pattern in real time based on an image processing algorithm, and then according to the boundary information of the pattern to be processed, predetermining the registration error of the two processing patterns, obtaining a required correction transformation parameter, and achieving online splicing manufacture in a laser processing process.

The invention discloses a self-adaptive image target area segmentation method based on SLIC, and relates to a superpixel segmentation technology. The objective of the invention is to solve the problemof low segmentation efficiency caused by the fact that parameters need to be manually input when super-pixel segmentation is carried out on a main body image containing a plurality of targets by a traditional super-pixel segmentation method. The method comprises the steps that firstly, super-pixel pre-segmentation processing is conducted on an image through SLIC, then super-pixel units are established with pre-segmented super-pixel points as center points, and the super-pixel measurement units comprise the gray level, the position and the Hash value; The measurement unit is used as a super-pixel parameter, and finally, through a distance-based clustering algorithm of adaptive parameters, the segmented too small regions are combined, so that the super-pixels are clustered into a determinedmain body and an obvious main body segmentation boundary. According to the method, a user does not need to carry out setting input, and the number of types of superpixels needing to be segmented is determined through a method of calculating the image complexity. The method is suitable for the fields of target recognition, mode recognition and artificial intelligence.

The invention discloses a method for segmenting and denoising the lung parenchyma through lateral scanning and four-corner rotary scanning. The method includes the steps that firstly, a minimum enclosing rectangle of an ROI lung region of the thoracic cavity is extracted; the minimum enclosing rectangle belonging to which portion of each lung is judged, lateral scanning or four-corner rotary scanning is selected according to the portion to which the minimum enclosing rectangle belongs, seed points are acquired, only lateral scanning is adopted in the upper portion of each lung, either lateral scanning or four-corner rotary scanning is adopted in the middle of each lung, and only four-corner rotary scanning is adopted in the bottom of each lung; eventually, residual trachea noise, main bronchus noise and other noise are removed according to an eight-neighborhood region growing method. The method is simple and easy to achieve, the segmentation speed is very high, for each image in a CT image sequence, a lung region of each image in a PET-CT image sequence can be completely, automatically and accurately segmented according to the method combining an iteration threshold value and morphology.

The invention discloses an automatic segmentation method for a brain glioma region. The method comprises the steps that a multi-mode three-dimensional brain body data MRI image is processed through asegmentation network of a cascade attention mechanism, the front end of the segmentation network of the cascade attention mechanism is a shared feature extractor, and the rear end of the segmentationnetwork of the cascade attention mechanism is connected with three coding and decoding device branches of the same structure; the feature extractor respectively inputs the extracted feature map into three codec branches, and the three codec branches are used for segmenting different areas; an attention mechanism module is arranged between every two adjacent encoder and decoder branches and used for capturing the spatial hierarchical relationship of different areas, and finally different areas in the brain glioma area are segmented through the three encoder and decoder branches. By means of themethod, automatic segmentation of the brain glioma area can be rapidly and accurately achieved.

The invention belongs to the field of medical image assistant analysis technologies, and particularly relates to the CSM assistant analysis system and the CSM assistant analysis method based on tensor images. The CSM assistant analysis system comprises an image pre-processing module, an expert knowledge base module, an ELM learning module, a classifier module and a result output module, wherein the image pre-processing module is used for acquisition of diffusion tensor images, dual measurement registration of the diffusion tensor images, segmentation of the diffusion tensor images, and dimensionality reduction and feature extraction of the diffusion tensor images; the ELM learning module is used for utilizing an ELM learning algorithm for analyzing and solving information in an expert knowledge base; and the classifier module is used for classifying feature information extracted by the image pre-processing module according to parameters determined by the ELM learning module. The CSM assistant analysis system and the CSM assistant analysis method based on the tensor images fully excavate original information of the images, increases pattern classification precision, ensure image segmentation effect, avoid time-consuming iteration process, significantly reduce training time, and can better adapt to efficiency requirement of mass data.

The invention provides a video object segmentation method and device and computer equipment, obtaining wherein the method comprises the steps: obtaining a historical mask image of a first video frame and a historical video frame adjacent to the first video frame; carrying out the object segmentation processing through employing the historical mask image and the first video frame according to a first segmentation mode; and obtaining a first mask image of the first video frame; according to a second segmentation mode, carrying out object segmentation processing by using the first video frame to obtain a calibration mask image of the first video frame. It is ensured that the calibration mask image is not adversely affected by the historical mask image, and under the condition that a comparison result of the calibration mask image and the first mask image meets a video segmentation calibration condition, it can be determined that the first mask image is inaccurate, and the calibration mask image is directly used as the target mask image of the first video frame to be output, so that the technical problem that the video object segmentation effect is worse and worse when the video object moves quickly and the like when the first mask image is directly output is solved.

The invention discloses a thermal power plant time sequence variable abnormity monitoring method and system. The method comprises the steps of collecting time sequence data of thermal power plant analog signal monitoring variables; taking the whole time sequence data as an initial optimal PLR segment, calculating a decision coefficient of the optimal PLR segment, and taking the whole time sequencein the historical sample data as a first-layer original data segment; segmenting the original data segment to form two new data segments, and respectively calculating decision coefficients of the twonew data segments; judging whether the segments are allowed or not, and if yes, continuing to segment the segments; and judging whether the segmentation is finished or not to obtain the optimal PLR segmentation number. According to the method, the optimal segment number of the PLR is determined through a coefficient determination method, and the problems that an L method highly depends on a fitting error and left and right points in a time sequence are solved.

The invention relates to an environment-friendly recycling robot applied to cutting of plastic bottles. The environment-friendly recycling robot comprises a base plate, a worktable, a supporting device, a cutting device, a pushing device, a No.1 storage box and a No.2 storage box; the worktable is installed on the base plate; the supporting device and the cutting device are sequentially installedfrom front to back on the worktable; the pushing device is arranged at the rear end of the worktable; the pushing device is installed on the base plate; the worktable is provided with a square groove;the No.1 storage box is arranged at the lower end of the square groove; the No.2 storage box is arranged at the front end of the No.1 storage box; and the lower ends of the No.1 storage box and the No.2 storage box both cling to the base plate. The environment-friendly recycling robot provided by the invention can solve the problems that the labor cost is high, the work efficiency is low, potential safety hazards exist, manpower is needed for collection and the like in an existing bottom cutting process of plastic beverage bottles, and can realize the functions of automatically cutting and collecting the bottoms of the plastic beverage bottles.

The invention discloses an elliptical object segmentation method based on ellipse fitting, and the method comprises the steps: obtaining an object of a target region in an image through extracting a maximum connected region of a G component value in an image RGB gradient value, removing the interference of background factors in the image through employing an RGB ultra-green segmentation algorithm, and removing a non-target region through employing a top-hat transformation method; extracting a central point of the elliptical object by combining a rapid radial symmetry method based on distance transformation and a global threshold value; segmenting the edge part of the elliptical object by using a concave point detection and contour estimation method; and finally, complementing the shielded parts of the overlapped objects by using an ellipse fitting algorithm based on least squares. According to the method, accurate extraction of overlapped target edge points is ensured; the problems of incomplete object edge extraction and non-ideal segmentation effect caused by similar RGB color gradient values in the image are avoided; the problem that the complete contour of the partially overlapped elliptical target cannot be obtained in the two-dimensional image under the view angle of the camera is solved.

The invention discloses a road scene semantic segmentation method based on category grouping in abnormal weather. The method comprises the following steps: (1) preparing data; (2) road scene categories are grouped according to the importance of automatic driving safety in abnormal weather, it is guaranteed that important categories are separated from non-important categories, and a road scene semantic segmentation model is constructed according to the grouping result; (21) grouping the categories according to the importance of the automatic driving safety in the abnormal weather; (22) constructing a model according to a category grouping result; (3) inputting data into the model to obtain a segmentation result; (31) inputting data, and extracting data features; (32) acquiring full-category features; (33) encoding the category relationship by using the full-category features to obtain category relationship features; (34) completing category relationship feature decoding to obtain a segmentation result; (4) carrying out model iteration training; and (5) model testing. According to the method, the segmentation effect of important groups in abnormal weather can be ensured, and safe proceeding of an automatic driving task is ensured to the greatest extent. And meanwhile, an overall segmentation effect equivalent to that of a normal model can be obtained under the condition that the abnormal degree is relatively small.

The invention discloses a medical image segmentation model compression method, and belongs to the field of medical image processing. The method comprises the following steps: aiming at a medical image segmentation basic model, constructing a search space according to the number of convolution kernels used at each position in the model, aiming at a coding-decoding structure of a segmentation network, searching a sub-network with small calculation amount and high segmentation precision in the search space by using a symmetric neural network, and carrying out segmentation on the sub-network, wherein the coding and decoding structures are symmetrical, and weight sharing policies are used to mitigate computational costs and training resources when traversing the entire search space; finally, by using a knowledge distillation method in the network training process, the basic model serving as a teacher mode, the compressed sub-network serving as a student model, and completing the knowledge transfer between the basic model and the student model. Through neural network search and knowledge distillation, the calculation cost of network construction is greatly reduced on the premise of ensuring the segmentation effect of the medical image segmentation model, and the method can be applied to various medical image segmentation models.

The invention discloses a fruit segmentation method based on a sparse convolution kernel. According to the invention, the method comprises the steps: analyzing the distinction degree of a main objectin a fruit image under different color factors; selecting an appropriate color channel to reconstruct an image, and then proposing a sparse convolution kernel construction mode that elements are spaced from one another and are not adjacent to one another, and determining elements in a sparse convolution kernel by adopting a linear classifier; and finally performing convolution operation on the reconstructed image by adopting the sparse convolution kernel, so fruit segmentation is realized.

The invention discloses an activity signal separation method based on commercial Wi-Fi, and the method comprises the following steps: (10) CSI data collection: obtaining a group of CSI amplitude information Ak*t containing k subcarriers from a data packet in sampling time t; (20) active signal segmentation: calculating an average cross correlation coefficient of subcarriers in each second, and judging whether a person moves in the current environment or not, and if someone moves, continuing to calculate the amplitude difference of the interval time window within one second, and further refining the accurate moment of the user activity; (30) effective signal separation: iteratively calculating the minimum error of A-L-S by using an alternating direction multiplier method in an augmented Lagrange form, constraining the rank r of a background matrix L and the sparsity c of an activity matrix S, introducing bilateral random projection to reduce the calculated amount, performing wavelet transformation on the main components of the activity signals, and selecting specific frequency band activity signals generated by specific limb actions. The activity signal separation method is high inrobustness and high in operation efficiency.

The invention discloses a truck brake cylinder inclination detection method based on variance region segmentation, and belongs to the technical field of brake cylinder inclination detection. The problem that an existing brake cylinder inclination detection method is prone to missing detection and wrong detection is solved. Aiming at the problem of uneven illumination of the shot image, the threshold values are independently selected for different local area images, the influence caused by uneven illumination is avoided, the segmentation effect can be ensured, the brake cylinder inclination detection is carried out according to the segmentation result, and the detection accuracy is improved. By the adoption of the automatic detection mode, missing detection and wrong detection can be avoided to the maximum extent. The method can be applied to brake cylinder inclination detection.

The invention provides a cotton detection, segmentation and counting method and system, and belongs to the technical field of computer vision, and the method comprises the steps: obtaining a mask pattern matrix of an image background and a foreground, initializing a grabCut algorithm, and carrying out the segmentation; performing morphological processing, and extracting related attributes of each connected domain; combining the two connected domains meeting the condition into one region; for the connected domain whose area is larger than a preset first area threshold value and the ratio of the long axis to the short axis is larger than a preset threshold value of the ratio of the long axis to the short axis, dividing the connected domain into two independent areas; and performing cotton counting on the split single connected domain. According to the method, the accuracy is ensured while the model is kept light in volume, rapid and convenient to deploy; the segmentation effect of the algorithm is improved, and the efficiency of the subsequent merging and splitting processing process is improved; repeated operation is avoided, and the merging speed is increased; length detection does not need to be carried out on positions such as a bottleneck in the splitting process, it is guaranteed that the detected position with the highest brightness belongs to the two areas, and robustness is good.

The present invention discloses a method for segmenting and denoising lung parenchyma using left and right scanning and four-corner rotation scanning. First, extract the minimum circumscribed rectangle of the lung area of ​​the chest ROI; then determine which part of the lung it is, and determine which part of the lung it belongs to. Select the left and right scan or the four-corner rotation scan to obtain the seed point. The upper part of the lung can only use the left and right scan, the middle part of the lung can use both the left and right scan or the four-corner rotation scan, and the bottom of the lung can only use the four-corner rotation scan. Finally, the eight-neighbor region growing method was used to remove the residual tracheal and main bronchi noise. The invention is simple and easy to implement, and the segmentation speed is very fast. It can use a combination of iterative threshold and morphology-based methods for each image in a CT image sequence to completely automatically and accurately segment the PET-CT image sequence. The lung area of ​​each frame of image.

The invention discloses a duck slaughtering dividing device and a working method thereof. The device comprises a support frame, a moving plate is movably installed at the bottom of the support frame, a lifting cylinder is fixedly installed at the bottom of the moving plate, and the bottom end of an output rod of the lifting cylinder is fixed A connecting seat is installed, two connecting arms are movably installed at the bottom of the connecting seat, a cutting motor is fixedly installed at the bottom of the two connecting arms, a cutting blade is fixedly installed at the end of the output shaft of the cutting motor, and a belt is arranged under the support frame In the conveyor, the end of the output shaft of the rotating motor is fixedly connected with the top of the rotating rod, and the bottom of the rotating rod is fixedly installed with a pull ring; the invention can automatically unfold the legs and wings of the duck, which is convenient for the division of the duck, thereby improving the In order to improve the work efficiency and ensure the cutting effect of the ducks, the distance between the two cutting blades of the present invention is easy to adjust, the legs and wings of the ducks can be divided respectively, and the ducks of different sizes can also be divided, and the applicability is strong.

The invention discloses an iris image segmenting method and device. The method includes performing rough positioning on a pupil in an iris image; expanding an image in a candidate area on the outer boundary of an annular iris into a rectangular image in a polar coordinate system and acquiring an excircle boundary estimation value of a target iris according to the gray level change of the rectangular image; intercepting an upper eyelid zone and a lower eyelid zone of the iris image according to the excircle boundary, acquiring boundary points of an upper eyelid and a lower eyelid, performing curve fitting on the boundary points of the upper eyelid and the lower eyelid, and obtaining an upper eyelid boundary curve and a lower eyelid boundary curve; performing noise detection on valid zones determined according to the upper eyelid boundary and a lower eyelid boundary; performing fine positioning on the excircle boundary of the iris through a radial projection method and performing fine positioning on the boundary of the pupil by utilizing a calculus detection operator. The invention solves a problem of low accuracy of low quality iris image segmentation performed by iris image segmenting methods in the prior art.

The invention discloses a method for automatic segmentation of brain glioma regions, comprising: using a segmentation network of a cascaded attention mechanism to process multimodal three-dimensional brain volume data MRI images, and using a segmentation network of a cascaded attention mechanism The front end is a shared feature extractor, and its back end is connected with three codec branches with the same structure; the feature extractor inputs the extracted feature maps to the three codec branches respectively, and the three codec branches are used for segmentation Different regions; there are attention mechanism modules between adjacent codec branches to capture the spatial hierarchical relationship of different regions, and finally segment different regions in the glioma region through three codec branches. By this method, automatic segmentation of glioma regions can be realized quickly and accurately.

The invention relates to an environment-friendly recycling robot applied to cutting of plastic bottles. The environment-friendly recycling robot comprises a base plate, a worktable, a supporting device, a cutting device, a pushing device, a No.1 storage box and a No.2 storage box; the worktable is installed on the base plate; the supporting device and the cutting device are sequentially installedfrom front to back on the worktable; the pushing device is arranged at the rear end of the worktable; the pushing device is installed on the base plate; the worktable is provided with a square groove;the No.1 storage box is arranged at the lower end of the square groove; the No.2 storage box is arranged at the front end of the No.1 storage box; and the lower ends of the No.1 storage box and the No.2 storage box both cling to the base plate. The environment-friendly recycling robot provided by the invention can solve the problems that the labor cost is high, the work efficiency is low, potential safety hazards exist, manpower is needed for collection and the like in an existing bottom cutting process of plastic beverage bottles, and can realize the functions of automatically cutting and collecting the bottoms of the plastic beverage bottles.

The invention relates to a plastic bottle dividing environment-friendly recovery equipment. The equipment comprises a bottom plate, a workbench, a support apparatus, a dividing device, an auxiliary device and a first material receiving box. The workbench is mounted on the bottom plate, the supporting device, the dividing device and the auxiliary device are sequentially mounted on the workbench from front to back, a square groove is formed in the workbench, the first material receiving box is disposed at a lower end of the square groove, the lower end of the first material receiving box is tightly attached to the bottom plate, and the supporting device comprises a steering mechanism and a supporting mechanism, and the auxiliary device comprises a mounting frame, an auxiliary plate, an air pump and a sucker. The plastic bottle dividing environment-friendly recovery equipment can solve the problems of high labor cost, low working efficiency, potential safety hazards and poor stability inthe existing plastic beverage bottle dividing process, can realize the function of automatically dividing and collecting the plastic beverage bottle, and has the advantages of low labor cost, high work efficiency, good safety and good stability.

The invention discloses a cutting knife installation shaft of an environment-friendly, flame-retardant and waterproof luggage fabric cutting machine, which includes a rotating shaft, a convex ring, a fixing plate, a mounting cylinder, an adjusting bolt, an adjusting cylinder, a limiting cylinder, a blade, and a limiting nut. There are several protruding rings on the outside of the rotating shaft. The protruding ring is composed of a threaded section with external threads and a polished rod section without external threads. A fixed plate is arranged at the end of the polished rod section, and a fixed plate is fixed on the side of the fixed plate close to the threaded section. An installation cylinder, the installation cylinder is set outside the polished rod section, and several radially arranged adjusting bolts are screwed on the installation cylinder, and a tapered body is fixed at the end of the adjusting bolt. The truncated conical shape is set, and the conical surface of the adjustment cylinder is adapted to the outer wall of the conical body. The present invention cooperates the adjusting bolt, the adjusting cylinder, the limiting cylinder and the limiting nut so that the position of the blade is convenient to adjust and the cutting effect is ensured.

The invention discloses a thermal power plant time sequence variable abnormity monitoring method and system. The method comprises the steps of collecting time sequence data of thermal power plant analog signal monitoring variables; taking the whole time sequence data as an initial optimal PLR segment, calculating a decision coefficient of the optimal PLR segment, and taking the whole time sequencein the historical sample data as a first-layer original data segment; segmenting the original data segment to form two new data segments, and respectively calculating decision coefficients of the twonew data segments; judging whether the segments are allowed or not, and if yes, continuing to segment the segments; and judging whether the segmentation is finished or not to obtain the optimal PLR segmentation number. According to the method, the optimal segment number of the PLR is determined through a coefficient determination method, and the problems that an L method highly depends on a fitting error and left and right points in a time sequence are solved.

The invention belongs to the technical field of bread production, and discloses a dividing structure which comprises a shelf, a powder scraping assembly, a driving assembly and a limiting assembly, a frame is arranged on the upper surface wall of the shelf, through holes are formed in the lower ends of the two side walls of the frame, an air cylinder is installed at the top end of the frame, and a telescopic rod is installed at the bottom end of the air cylinder; according to the bread cutting device, the flour scraping assemblies are additionally arranged, when the air cylinder drives the cutter to move up and down to cut bread, a scraping plate fixedly provided with bristles can scrape off flour accumulated on the outer wall of the cutter, and the flour scraping assemblies are arranged on the two sides of the cutter, so that the bread cutting device has the advantages that the flour scraping assemblies are additionally arranged, and the bread cutting efficiency is improved; flour is prevented from being adhered to the outer wall of the cutter, the resistance of the outer wall of the cutter is reduced, the cutting effect of the cutter can be continuously kept, in addition, when the flour scraping assembly is damaged and needs to be replaced, the mounting plate can be directly pulled upwards, the dovetail insertion block is pulled out from the interior of the dovetail insertion groove, disassembly and replacement are rapid and convenient, and time and labor are saved.

The invention relates to the field of sludge treatment, in particular to a sludge treatment device which comprises a treatment chamber, and a pre-pressing box is fixedly connected into the treatment chamber. A pre-pressing device is arranged in the pre-pressing box body, and the pre-pressing device comprises two grinding rollers rotationally connected with the pre-pressing box body. A first conveying belt is arranged on a machine frame, a cutting device is arranged above the first conveying belt, and a gap is reserved between the cutting device and the first conveying belt. When sludge is recycled, the sludge can enter the pre-pressing box body from a feeding opening, and the pre-pressing device in the pre-pressing box body enables large sludge to be rolled to be in a thin state. The sludge falls onto the first conveying belt, and the first conveying belt conveys the sludge. The cutting device can play a role in cutting the sludge, and as the gap is reserved between the cutting device and the first conveying belt, the sludge cannot adhere to the cutting device when the sludge is cut, so that the cutting effect and the cutting efficiency are ensured.

The invention belongs to the field of medical image assistant analysis technologies, and particularly relates to the CSM assistant analysis system and the CSM assistant analysis method based on tensor images. The CSM assistant analysis system comprises an image pre-processing module, an expert knowledge base module, an ELM learning module, a classifier module and a result output module, wherein the image pre-processing module is used for acquisition of diffusion tensor images, dual measurement registration of the diffusion tensor images, segmentation of the diffusion tensor images, and dimensionality reduction and feature extraction of the diffusion tensor images; the ELM learning module is used for utilizing an ELM learning algorithm for analyzing and solving information in an expert knowledge base; and the classifier module is used for classifying feature information extracted by the image pre-processing module according to parameters determined by the ELM learning module. The CSM assistant analysis system and the CSM assistant analysis method based on the tensor images fully excavate original information of the images, increases pattern classification precision, ensure image segmentation effect, avoid time-consuming iteration process, significantly reduce training time, and can better adapt to efficiency requirement of mass data.