124 results about "Adaptive histogram equalization" patented technology

The invention relates to a hydraulic engineering concrete crack intelligent identification and quantitative analysis method and system based on machine vision. The method comprises the following steps: firstly, acquiring an original image sequence of the surface of a hydraulic engineering concrete structure, classifying according to illumination intensity, shooting angle and shooting distance, extracting crack edge features through a convolutional neural network, and fusing to obtain a crack feature set; correcting illumination through adaptive histogram equalization, correcting angles and distances through geometric transformation, and combining edge detection and scale invariant feature transformation to obtain standardized geometric parameters including crack length, maximum width and the like; if the parameter exceeds the engineering safety standard threshold value, tracking a crack track through an optical flow method to calculate increment, and inputting a neural network to output a damage trend; and finally, generating a three-color risk distribution diagram by using a finite element based on the trend, extracting high-risk data to calculate a real-time evaluation value, and dynamically adjusting the monitoring frequency to generate an optimization strategy. By adopting the method, the reliability and economy of engineering safety monitoring can be remarkably improved.

The invention discloses a mobile terminal streetscape image real-time segmentation method based on a lightweight neural network, and relates to the technical field of image segmentation. The method comprises the following steps: firstly, carrying out 320 * 320 adjustment, Z-score standardization, adaptive histogram equalization and 3 * 3 Gaussian filtering preprocessing on an input streetscape image; then, an improved MobileNetV3 backbone network is used, and a five-scale feature map is output in combination with DropBlock regularization through eight feature extraction stages including depth separable convolution and an SE attention module; multi-scale features are fused through a U-shaped structure, and a fusion feature map is generated through up-sampling, element-by-element addition of dimension reduction low-layer features and an attention gating module; and during reasoning, outputting a segmentation mask by using a convolutional layer, Softmax and a conditional random field, and finally performing knowledge distillation, weight pruning, 8-bit quantization and TensorRT optimization. According to the invention, high-precision real-time street view segmentation is realized, the robustness is high, and the method is suitable for different devices and scenes.

The invention belongs to the technical field of image processing, and particularly relates to a monitoring image defogging enhancement method in a high-dust environment of a mineral separation site, which comprises the following steps of: performing smooth denoising on an original image by using weighted guided filtering, and obtaining global atmospheric light through a quadtree subdivision method; constructing a same-color heterogeneous discrimination index combining a spectrum similarity factor and a texture confidence factor for distinguishing dust and ore with similar colors; calculating a pixel-level dynamic defogging coefficient based on the discriminant index, and obtaining an adaptive transmissivity in combination with dark channel prior; and finally, restoring the image by using an atmospheric scattering model and carrying out contrast-limited adaptive histogram equalization processing. According to the method, the problem of misjudgment caused by the fact that the colors of the ore and the dust on the ore dressing site are similar is effectively solved, powerful defogging of the dust area and detail reservation of the ore area are achieved, and the definition and the contrast ratio of the monitoring image are improved.

The invention relates to the technical field of computer vision, and discloses a jade defect intelligent detection method and system based on machine vision and deep learning, and the method comprises the following steps: S1, based on a high-resolution industrial camera and a laser three-dimensional scanner, adopting a multi-mode synchronous collection strategy, and rotating a jade sample through a precise motion control system, a jade surface high-resolution two-dimensional color image and high-precision three-dimensional point cloud data are respectively obtained, and a jade multi-mode original data set is generated. A high-resolution two-dimensional color image and high-precision three-dimensional point cloud data are integrated through a multi-modal synchronous acquisition strategy, and multi-dimensional feature expression under unified coordinates is constructed, so that the limitation of a single data source is effectively overcome; an image registration algorithm and a feature pyramid network are combined with a point cloud network to perform multi-modal feature fusion, complementarity of color texture and geometric morphology information is enhanced, and image quality is optimized based on adaptive histogram equalization and non-local mean filtering.

The invention provides a real-time processing method for endoscopic image blood vessel enhancement and microcirculation evaluation, which comprises the following steps of: processing an original endoscopic image, and locally enhancing a brightness channel through a contrast-limited adaptive histogram equalization technology; self-adaptive frequency domain-space domain decomposition is realized based on local texture complexity analysis; performing multi-scale Hessian matrix blood vessel detection on the low-frequency component; applying a directional Gabor filter bank to the high-frequency component; spatial-temporal feature fusion is realized through multi-resolution pyramid optical flow calculation; synchronously completing blood vessel probability prediction, blood vessel diameter estimation and blood flow direction prediction by using a lightweight multi-task deep learning network; the blood flow velocity is analyzed and calculated based on a speckle mode, the perfusion density is subjected to accelerated statistics through an integrogram, and vascular morphological parameters are extracted by adopting an improved skeleton algorithm. According to the invention, an enhanced blood vessel visualization effect and a real-time microcirculation quantitative evaluation function can be provided, and the overall improvement of the endoscope image processing quality and efficiency is realized.

The invention relates to a balanced color perception enhancement method for rail transit target detection, and belongs to the technical field of rail transit and computer vision. According to the method, brightness channel adaptive histogram equalization enhancement CLAHE-LC, a multi-segment tone channel mask mechanism MSHCM-M and a three-stage hybrid mechanism non-maximum suppression TSLSH-NMS technology are combined, so that the problems of complex illumination, multi-target shielding, color interference and the like in a rail transit scene are solved, and the accuracy and recall rate of target detection are improved. The method can be seamlessly integrated into an existing deep learning target detection framework, is compatible with multi-label, multi-category and multi-scale features, and remarkably improves the precision and recall rate of target detection in a rail transit scene. Experimental verification shows that the real-time performance is guaranteed, meanwhile, the detection performance is obviously improved compared with a traditional scheme, and the method is suitable for being applied to an actual rail transit safety monitoring and intelligent maintenance system.

The invention discloses an SLAM front-end optimization method based on brightness grading and gradient constraint, and relates to the technical field of computer vision and vision SLAM. In order to solve the defects that adaptive grading enhancement and parameterization control based on illumination types are lacked in the prior art, and feature point quality, spatial distribution uniformity and front-end real-time performance are difficult to consider in a high-frequency input scene, the invention provides a comprehensive brightness grading and feature optimization scheme. The method comprises the following steps: firstly, calculating the average brightness of an input image, dividing the image into a dark light type, a normal type and an overexposure type, and respectively adopting gamma correction, contrast limited adaptive histogram equalization and inversion enhancement strategies for different types to realize illumination adaptive enhancement; then screening high-quality feature points with significant local curvature changes; and updating the detection area. According to the method, the feature stability, the matching precision and the real-time performance of the SLAM front end in a complex indoor environment are remarkably improved, and the method is suitable for a self-localization and mapping system.

The invention discloses an unmanned aerial vehicle small target detection method based on Mama feature fusion, and relates to the technical field of computer vision, and the method comprises the steps: multi-modal data collection, synchronous collection of images and point cloud data through three types of sensors, and coverage of multi-scene and environment conditions; image preprocessing adopts improved bilateral filtering, adaptive histogram equalization and point cloud downsampling to unify a coordinate system; in the multi-scale feature extraction, five-level scale features are output through an improved CSPDarknet network, and the five-level scale features are enhanced through an SENet attention module; in the Mama feature fusion, multi-modal and multi-scale features are processed through a three-stage unit; generating a candidate frame by a dynamic anchor frame, screening according to IOU, and improving YOLOHead to realize classification and positioning; the dynamic optimization of the detection result finely adjusts parameters through on-line distillation. According to the method, the precision, the real-time performance and the anti-interference capability of small target detection of the unmanned aerial vehicle are improved, and reliable technical support is provided for low-altitude security, exploration and other scenes.

The invention provides a chromosome image enhancement method and system based on semantic guidance. The method comprises the following steps: S1, preprocessing; s2, outputting a deep-band probability graph, a grey-band probability graph and a shallow-band probability graph through a semantic segmentation network composed of a lightweight encoder and a multi-scale decoder; s3, implementing differential layered enhancement according to a band type: adopting local adaptive histogram equalization for a deep band, adopting central axis constraint bilateral filtering for a gray band, adopting dynamic threshold truncation and gamma correction for a shallow band, and performing weighted fusion for a transition region according to probability; s4, carrying out structure strengthening, wherein the structure strengthening comprises centromere local sharpening, stripe phase alignment, edge sensing super-resolution and overlapping region separation; and S5, performing quality evaluation based on the deep band integrity, the band stripe contrast uniformity, the SSIM and the noise density, and triggering adaptive re-enhancement if necessary. According to the scheme, the contrast ratio and details are remarkably improved while the stripe structure and the position relation are kept, and the method has the advantages of light weight, interpretability and cross-sample robustness and is suitable for being integrated into an automatic karyotype analysis process.

The invention discloses an oil leakage detection and traceability method and system for an oil-immersed power transformer, and belongs to the technical field of transformer detection. Irradiating the surface of the transformer by adopting an ultraviolet light source with controllable power to obtain an oil leakage characteristic image of the surface of the transformer; carrying out oil leakage feature image preprocessing through graying, 3 * 3 convolution kernel Gaussian filtering, 3 * 3 weighted median filtering and a contrast-limited adaptive histogram equalization algorithm in sequence; an improved double-threshold maximum between-class variance algorithm is adopted to determine a background and a fluorescence region, and oil stains are accurately extracted through connected region analysis and texture feature screening; a diffusion main direction is determined through a gradient vector weighted voting algorithm, candidate sources are screened from an easy leakage part database in combination with a K nearest neighbor algorithm, and a final leakage source is determined through gray profile analysis and verification. Through multi-algorithm cooperation, the oil leakage detection precision and traceability accuracy are effectively improved, the method is suitable for daily operation and maintenance of the transformer, and the equipment fault risk is reduced.

The invention relates to the technical field of remote sensing image processing, in particular to a homomorphic filtering-CLAHE remote sensing image enhancement method based on integration strategy multi-target particle swarm optimization, which comprises the following steps: acquiring remote sensing image data, and preprocessing the remote sensing image data; converting the preprocessed remote sensing image into an HSV color space, and extracting a V component in the HSV color space; constructing a frequency domain-spatial domain hybrid enhancement framework of homomorphic filtering and contrast-limited adaptive histogram equalization, and performing enhancement processing on a V-channel component by using the framework; constructing an integrated strategy multi-target particle swarm optimization algorithm; constructing a four-target fitness function including structural similarity, average gradient, information entropy and gray variance, and guiding particles to search in the direction of optimizing a plurality of key image quality indexes at the same time by using the function so as to realize optimal selection of remote sensing image enhancement parameters; the method can effectively enhance the definition and structural integrity of the terrain texture in the remote sensing image of the complex mountainous area.

The invention relates to an underwater image enhancement method based on a regional differentiation fusion network, and belongs to the technical field of image information processing. The method comprises the following steps: inputting an original underwater image, and executing self-adaptive histogram equalization processing for limiting contrast to generate a corresponding self-adaptive enhanced image; respectively inputting the original underwater image and the self-adaptive enhanced image into an original encoder and a self-adaptive encoder, and generating initial original features and self-adaptive enhanced features through a shallow feature extraction module; the original features and the self-adaptive enhancement features are input into a cross fusion Swin Transform module, and regional differentiation feature fusion is carried out; inputting the fused regional differentiation features into a reconstruction decoder, carrying out gradual decoding and reconstruction through interpolation up-sampling and convolution activation operation, and outputting an enhanced underwater image; in the training stage, a joint loss function is adopted to supervise and optimize an enhancement result, and the joint loss function comprises Charbonier loss and VGG loss.

The present application relates to a kind of solar filament fine structure enhancement method based on line Gauss filter, belong to image enhancement field.To overcome the current H-alpha image in solar filament fiber because of low contrast, leading to artificial marking difficult, the problem that the fiber attribute information is not accurate enough is extracted, the present application proposes a kind of image enhancement method with line Gauss convolution as core.Firstly, H-alpha image is normalized to preliminary stretch image contrast, while suppressing noise using Laplace-Gauss operator to enhance edge, then using line Gauss filter to enhance the contrast of solar filament fiber.In post-processing stage, using the adaptive histogram equalization of restriction contrast and top hat, bottom hat transformation method to improve the uneven problem of line Gauss filter enhancement effect.Finally, line Gauss filter is used again to further improve the contrast of solar filament fiber.After the enhancement of the method, the filamentary structure of solar filament fiber is clear, which makes it possible to objectively and accurately measure the filamentary structure attribute information of solar filament.

This invention discloses a small target tracking method based on image enhancement and attention mechanisms, comprising: performing adaptive histogram equalization on shallow feature maps to enhance local contrast, and fusing with deep semantic features to construct a multi-scale feature pyramid; generating attention weight maps in the horizontal and vertical directions through a coordinate-decoupled attention module to highlight the salient regions of small targets; introducing a dynamic anchor box generation algorithm based on Gaussian distribution, dynamically sampling and generating anchor boxes based on a Gaussian distribution model constructed from the target position in the previous frame; and using the deep cross-correlation layer of a Siamese network to calculate the similarity response map between the template and the search region to determine the center coordinates and bounding box of the small target. This invention effectively solves the problems of insufficient feature extraction, low spatial positioning accuracy, and inflexible anchor box generation in existing methods, significantly improving the accuracy and robustness of small target tracking.

The present application belongs to the field of medical image segmentation, and particularly relates to a retinal blood vessel image segmentation method. In view of the problems of low segmentation accuracy, insufficient segmentation ability of small blood vessels at the edge of eyeball, fracture at the blood vessel branch, and excessive interference of image noise in the existing retinal blood vessel image segmentation, the method comprises the steps of retinal image preprocessing and establishment of a retinal blood vessel segmentation model, wherein the preprocessing comprises converting a color retinal image into a gray image by giving different weights to the RGB three channels of the color retinal image; using a normalized and contrast-limited adaptive histogram equalization method to improve the image; using a local adaptive gamma change algorithm to adjust the retinal image; using translation, rotation, and noise increase to expand the data set; and the model establishment comprises feature extraction, feature fusion, and retinal blood vessel image segmentation.

The invention relates to the technical field of digital X-ray image processing, in particular to a defect extraction and three-dimensional reconstruction method for a pipeline digital X-ray image. The method comprises the following steps: acquiring an X-ray original image of a pipeline welding seam and carrying out graying processing to obtain a grayscale image; performing noise reduction processing on the grayscale image; performing contrast enhancement processing on the grayscale image after noise reduction processing by using a contrast-limited adaptive histogram equalization algorithm; carrying out edge feature extraction, and segmenting the defect region based on a region growing algorithm to obtain sub-segmented images; performing defect three-dimensional reconstruction based on pipeline geometric parameters, and mapping two-dimensional defect points on the sub-segmented images to a three-dimensional pipeline wall; and identifying and calculating the defect after three-dimensional reconstruction, and determining the type and size of the defect. According to the defect extraction and three-dimensional reconstruction method, noise suppression, edge reservation and geometric distortion correction can be realized at the same time, and the accuracy of pipeline weld defect detection can be remarkably improved.

The application discloses a consumer complaint response method and system based on AI assistance and language understanding, which splits the complaint response process into two core sub-problems of multi-modal consumer complaint data processing and feature fusion and demand attribution and response generation. In multi-modal consumer complaint data processing and feature fusion, first, regular expressions are used to denoise text, spectral subtraction is used to denoise voice, and Gaussian filtering and adaptive histogram equalization are used to denoise images; then, modal features are extracted, text is used as the core of cross-modal fusion, and entity and relationship are extracted to construct a multi-modal semantic knowledge graph. In demand attribution and response generation, Graph Transformer is used in combination with the graph and domain prior knowledge to output primary and secondary demands; a static complaint graph is constructed, an attribution path is mined through BFS and is verified through multi-modal verification; and an "emotion-demand-attribution-prevention" structure is used to optimize text and adjust the format by using LLM, and an individualized complaint response is output.

The invention relates to the field of computer vision and intelligent manufacturing, in particular to a vehicle charging port image marking method and device, and the method comprises the steps: obtaining a vehicle charging port image, and carrying out the local frame selection processing; sequentially carrying out graying processing, contrast-limited adaptive histogram equalization processing and median filtering processing; carrying out edge detection by using a Canny edge detection algorithm; adopting a Hough circle detection algorithm to generate an initial position parameter of the circular hole; or, adopting a pre-trained convolutional neural network semantic segmentation algorithm to carry out pixel-level prediction, and extracting corresponding edge points as initial position parameters of the circular holes; if it is judged that the precision of the initial position parameter does not meet the preset labeling requirement, click operation is executed, and a correction point set is formed; and based on the correction point set and the edge points corresponding to the initial position parameters, fitting an ellipse to obtain a marking result of the charging port circular hole. The method can improve the labeling efficiency, guarantees the labeling precision and stability, and is adaptive to subsequent deep learning segmentation and pose estimation algorithms.

This invention relates to the field of remote sensing technology, specifically to remote sensing image classification methods, systems, computer equipment, and storage media. The remote sensing image classification method includes the following steps: based on raw remote sensing data, using satellite cloud image analysis and surface temperature mapping techniques to perform comprehensive analysis of microclimate characteristics; using K-means clustering analysis algorithm to classify the data; and generating a comprehensive climate characteristic dataset. The beneficial effects of this invention are that by integrating K-means clustering, adaptive histogram equalization, convolutional neural networks, long short-term memory networks, and fully convolutional network algorithms, it can effectively identify geographic patterns and monitor abnormal climate events. Simultaneously, it optimizes surface classification, combines Gaussian filtering, MODIS cloud detection algorithms, and data assimilation techniques to deeply preprocess raw data, improve data quality, enrich information content, and effectively predict and respond to climate change using long short-term memory networks and time series analysis techniques.

A tail water pipe air supply system sealing state recognition method based on computer vision, the method comprises collecting unit large shaft images under multiple working conditions, and constructing an image data set; then image preprocessing is completed through adaptive histogram equalization, median filtering, bilateral filtering and RGB- HSV space conversion; then label three types of areas of normal state, trace leakage and obvious water leakage, and divide training set, validation set and test set; then a YOLOv5 model embedded with an improved channel attention mechanism is constructed, the backbone network adopts CSPDarknet structure to extract multi-scale features, the neck network combines FPN and PAN to realize bidirectional fusion of features, and the detection head adopts a decoupling structure to improve positioning and classification accuracy; finally, the pre-training weight fine-tuning model is loaded, the training and verification are completed by using a multi-objective loss function, the real-time and accurate recognition of the sealing state of the tail water pipe air supply system is realized, the equipment fault risk of the unit is effectively reduced, and the method is suitable for the safety monitoring scene of the hydroelectric generating set.

The invention discloses a cross-modal lightweight colorectal tumor segmentation method and device, and aims to solve the problems that an existing medical image segmentation model depends on large-scale annotation data, small lesions and fuzzy boundaries are inaccurately recognized, the calculation complexity is high, clinical deployment is difficult, and the like. According to the method, on the basis of an improved U-shaped network architecture, adaptive histogram equalization and Gamma correction are introduced in a data preprocessing stage to enhance boundary information of a low-contrast CT image; the encoder captures a multi-level context by adopting a multi-scale convolution and cavity convolution fusion module, and strengthens tumor edge response in combination with a boundary attention module; and meanwhile, the calculation amount is reduced by introducing rapid spatial pyramid pooling. The decoder effectively recovers details and avoids artifacts through lightweight jump connection and bilinear interpolation up-sampling. The parameter quantity of the whole model is only about 4.25 M, the reasoning efficiency and the cross-equipment generalization ability are remarkably improved while the high segmentation precision is guaranteed, and the method is suitable for a resource-limited clinical environment.

The invention provides an acne recognition method based on traditional Chinese medicine face partitioning. The method comprises the following steps: acquiring a to-be-detected face image; performing face region interception and alignment processing on the face image, and zooming the face image to a fixed size; face partitioning is carried out on the face image through a deep learning segmentation model, and segmentation masks are generated; extracting each face region image based on the segmentation mask, enhancing acne features by using contrast adaptive histogram equalization, then extracting acne regions by using threshold segmentation, morphological closed operation and contour detection methods, and screening acne feature regions by calculating a roundness value of a contour; and counting the number of acnes in each face region, and generating a structured recognition result containing the number and position of the acnes in each region. According to the scheme, quantitative statistics of acnes in different areas of the face is realized, and effective data support is provided for health prediction and skin health assessment.

The invention belongs to the technical field of skin analysis, and particularly relates to a skin classification method fusing smoothness and spot recognition, which comprises the following steps: acquiring a user skin image through a mobile terminal, and outputting a standardized image after self-adaptive histogram equalization and non-local mean filtering preprocessing; a smoothness feature vector and a spot feature vector are extracted in parallel based on a standardized image, a fusion feature vector is obtained through calculation based on the smoothness feature vector and the spot density feature vector, and after the fusion feature vector is input into a skin classification model, key parameters can be extracted and decoded based on the obtained fusion adjustment vector. Depending on a spot density enhancement value, smoothness principal component strength and comparison between a covariant weight parameter and a threshold value, the skin type of the user is determined hierarchically, and decoding based on the smoothness principal component strength can filter noise and is more sensitive than a smoothness mean value.

The invention relates to a cable tunnel fire thin smoke image recognition and risk assessment method and system, and belongs to the technical field of power equipment on-line monitoring and fault diagnosis, and the method comprises the following steps: carrying out the real-time image collection of a cable tunnel, and employing a contrast limited adaptive histogram equalization algorithm to enhance the local contrast of an image; a pre-trained YOLOv11n-seg instance segmentation model is used to identify the enhanced image, a thin smoke area is accurately segmented, and a mask file is generated; and calculating the smoke coverage area based on the mask file, calculating the smoke rising dip angle by adopting a minimum circumscribed triangle algorithm, and calculating the smoke transmissivity according to the Lambert-Beer law. And inputting a feature vector formed by the three feature parameters into a support vector machine classification model trained by historical data, and automatically outputting a fire hazard level judgment result, thereby realizing early accurate early warning of the tunnel fire.

The present application relates to the technical field of cell image analysis and medical artificial intelligence, and discloses a cell segmentation and adaptive cascade reasoning method and system based on prior frame guidance. The method comprises: acquiring a multi-modal cell image with a detection frame; performing adaptive histogram equalization preprocessing on the image and executing global preliminary screening segmentation; calculating a multi-dimensional weighted score of the mask and the detection frame, if the score is lower than a first matching threshold, extracting a local image block to dynamically adjust a flow field threshold and an estimated diameter for cascade retry; if the score after retrying is lower than a second threshold, triggering a label consistency protection mechanism to discard or back up poor samples; finally, performing connected domain purification on the retained mask, and calculating a topological solidity, and performing convex hull reconstruction on the mask with low solidity. Through two-stage reasoning, double-track quality filtering and topological constraint, the present application effectively improves the segmentation effect of dense and irregular cells and improves the generalization performance of downstream analysis.

The invention relates to the technical field of metal product processing quality detection, in particular to a surface defect visual detection method and system for metal product processing, and the method comprises the following steps: step 1, arranging a plurality of linear light sources above the surface of a metal product; 2, acquiring a multispectral image from a position directly facing the surface of the metal product by using a color industrial camera; 3, performing weighted average fusion to generate a fused image; step 4, carrying out adaptive histogram equalization enhancement on the fusion image after reflection suppression and carrying out noise filtering by applying Gaussian filtering; 5, extracting texture features from the enhanced image; step 6, dividing the image into a defect area and a normal area by using an adaptive threshold segmentation method; and step 7, classifying the segmented defect areas. And the support vector machine classifier is adopted to accurately classify the defects, so that reliable output of defect types and position information is ensured, and metal surface defect detection is more accurate and real-time.

This application relates to the field of computer vision technology, and in particular to a method for wildlife identification and tracing using infrared image enhancement and multi-scale fusion. The method includes: acquiring raw images from an infrared camera and enhancing the raw images using Retinex and contrast-limited adaptive histogram equalization; performing animal target detection, feature extraction, and feature fusion by optimizing the YOLOv8 model and using a multi-modal fusion Transformer to obtain multi-scale fused features; identifying species categories using a pre-defined primary species classification model and extracting and matching individual biometric features of key species using a pre-defined secondary individual identification model to generate individual identifiers for corresponding animals; and automatically associating the species identification results, individual identifiers, and multi-source forestry data to generate an intelligent decision-making report for wildlife population analysis and conservation management. This application contributes to achieving high-precision, automated wildlife identification and tracing.

The application discloses a kind of visual feedback drive's water tank cleaning robot intelligent control method and system, belong to robot control technical field.It includes: obtaining water tank inner wall original image, generates feature enhancement image by median filter and contrast limited adaptive histogram equalization;Input semantic segmentation model identifies stain area, calculates stain abundance index;According to stain abundance index, determine pollution grade, dynamically adjust cleaning motor speed and electric pressure push rod displacement, to change brush head positive pressure;In the process of advancing, collect cleaned area image and calculate residual stain abundance index, if greater than threshold value, then record non-compliance area three-dimensional coordinates, complete global scanning and plan compensation path to compensate cleaning.The application effectively improves cleaning efficiency and compliance rate, reduces energy consumption and mechanical loss.

The invention provides a visual SLAM method in a low-light environment based on image contrast optimization, and belongs to the technical field of image feature extraction. The invention aims to solve the problems of fuzzy details and difficult feature extraction of the dark part of the low-illumination image. Comprising the steps of S1, collecting a low-illumination image based on a visual sensor; s2, performing Gamma correction on the low-illumination image; s3, carrying out adaptive histogram equalization on the image after brightness distribution adjustment; and S4, Gaussian filtering is performed on the image after the overall gray histogram optimization, noise points in the image are suppressed, and a final enhanced image is obtained. According to the method, the extraction number of ORB feature points can be increased by 72.2% at most compared with that of an original image while relatively low operation time is guaranteed, the number of feature points extracted from noisy points by mistake can be remarkably reduced, and the robustness of positioning mapping can be effectively improved.

The application belongs to the field of medical image pattern recognition, and provides a knee osteoarthritis injury prediction method based on CT images, which comprises the following steps: step one, acquiring a group of healthy controls and a group of CT images of subjects with different degrees of knee joint damage; step two, pre-processing the X-ray images, cropping out the knee joint region, and converting the gray-scale images to L, a and b channels by using the LAB color model; step three, performing image enhancement on the images in the L channel by using adaptive histogram equalization; step four, integrating the three channels of L, a and b after enhancement; step five, performing denoising on the enhanced images; step six, marking the images of the healthy controls as 0, and marking the images of the patients with knee osteoarthritis as 1, 2, 3 and 4 according to the severity; and step seven, performing feature extraction by using a discriminative autoencoder; the method can reduce artificial errors, is an effective supplement for clinicians to judge knee osteoarthritis, and is expected to realize accurate prediction of knee osteoarthritis in areas where professional medical personnel are lacking.