252 results about "Superpixel segmentation" patented technology

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 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 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 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 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 relates to the technical field of three-dimensional map recognition, and discloses a semantic segmentation-based low-altitude three-dimensional map element autonomous recognition method and system. The method comprises the following steps: acquiring a low-altitude remote sensing image and preprocessing to generate a multi-channel image matrix containing geographic coordinates and spectral characteristics; extracting multi-scale features through a pyramid feature extraction network in combination with cavity convolution, and obtaining an adaptive weighted feature tensor through a cascade attention mechanism fusion channel and a spatial weight; adopting a bidirectional feature fusion strategy to generate fusion features, and outputting an initial category probability distribution diagram by a semantic segmentation header network; obtaining a refined mask through edge perception optimization and superpixel segmentation correction, and mapping the refined mask to a three-dimensional coordinate system to generate a vector layer with a semantic tag; a constraint rule is deduced through a topological relation inference engine, logic conflicts are eliminated through rule-driven post-processing, finally, a standardized three-dimensional map element database meeting the geographic information standard is generated, and efficient, accurate and autonomous recognition of low-altitude three-dimensional map elements is achieved.

The invention discloses a non-reference image quality evaluation method and device based on multi-perception feature fusion and dynamic enhancement, and the method comprises the steps: obtaining the quality information of a multi-domain distorted image based on superpixel segmentation and Gaussian kernel filtering texture generation; quality related feature extraction is performed on multi-domain information through a semantic perception module and a distortion perception module, bidirectional modulation is performed on global visual features of an original distorted image through a cross attention mechanism, and dynamic fusion of multiple perception features is realized; through a parallel feature enhancement unit formed by local adaptive filtering of a visual self-attention block and a dynamic residual block, dynamic allocation of perception modes to different content areas is realized; generating a weighted quality score consistent with human visual perception through a weighted dual-path regression device; and outputting a predicted score consistent with the human score from the distorted image through the three sub-networks. According to the method, the problems of insufficient adaptability to complex content of a distorted image and low local distortion sensitivity are effectively solved.

The invention discloses a welding seam defect X-ray image intelligent diagnosis and credible traceability method based on multi-modal heterogeneous information collaboration, which is characterized in that a defect analysis network fusing multi-domain feature modeling and graph structure expression is constructed on the basis of bimodal data formed by a welding seam X-ray image and an industry detection standard text. In the image mode, dividing the weld seam image into a plurality of local area units through superpixel segmentation, taking the areas as image nodes, respectively extracting spatial domain, frequency domain, wavelet domain and edge domain features, and constructing a weighted graph structure by combining the spatial adjacency relation and the feature similarity relation between the areas; realizing overall modeling and correlation analysis of weld defect structure information by using a graph convolutional network; in a text mode, feature coding is carried out on an industry detection standard text, and the feature coding is used as an important prior constraint for defect judgment. Collaborative modeling of an image detection result and standard semantic information is achieved through a gating fusion mechanism, a mapping relation between a detection conclusion and a standard term is established, and interpretable expression and result credible traceability of the weld defect diagnosis process are achieved. And a welding seam X-ray film automatic digital acquisition and observation device is adopted in a matched manner, so that stable transmission, positioning observation and high-resolution digital imaging of the industrial ray film are realized, and reliable and consistent image data input is provided for the intelligent diagnosis method. The method is suitable for intelligent defect detection under complex welding seam structures and multi-working-condition imaging conditions, and has high engineering application value and popularization prospect.

The invention provides a milling cutter wear volume in-situ calculation method and system based on three-dimensional reconstruction, and relates to the technical field of machine tool cutter state detection, and the method comprises the steps: obtaining a high-quality multi-view image, employing an improved SLIC superpixel segmentation algorithm to extract region characterization parameters of a wear region superpixel block based on the high-quality multi-view image, and calculating the wear volume of a milling cutter according to the region characterization parameters. Inputting the region characterization parameters into a multi-scale fusion feature extraction network, and extracting a two-dimensional binarization matrix of the wear region; wear region feature points of the two-dimensional binarization matrix are extracted through a fusion feature detection algorithm, and a high-precision sparse point cloud of a wear region is generated through optimization by adopting a dual-stage feature matching algorithm; reconstructing the high-precision sparse point cloud by adopting an improved PMVS dense optimization algorithm to obtain a high-precision wear area point cloud; and based on the high-precision wear area point cloud, carrying out precise quantitative calculation on the wear area by utilizing a double-model fusion algorithm, and outputting to obtain a wear volume value. According to the invention, in-place accurate measurement and calculation of the wear volume of the milling cutter are realized.

The invention relates to the technical field of image data processing, in particular to a high-voltage switch shell surface coating uniformity evaluation method and system, and the method comprises the steps: collecting a shell surface coating image; dividing the image into a plurality of super-pixel areas by using a super-pixel segmentation algorithm introducing an adaptive distance measurement mechanism; extracting geometric structure features, texture features and adjacent color difference features of the target area; and carrying out nonlinear fusion on the adjacent chromatic aberration and geometric structure characteristics to construct inter-class characteristics, fusing the inter-class characteristics with texture characteristics serving as intra-class characteristics to obtain a comprehensive score, and judging whether the coating is uniform or not according to the comprehensive score. According to the method, the segmentation weight is adjusted in a self-adaptive manner, and the weak chromatic aberration is amplified in a nonlinear manner, so that accurate segmentation of a tiny defect region is realized; geometric irregularity, color mutation and texture anomaly features are integrated, detection of various tiny coating defects is achieved, and the evaluation accuracy is improved.

The invention relates to a laser radar point cloud densification method and device fusing image information, and a medium. The method comprises the following steps: fixing a visual camera and a laser radar on a rigid tool, and carrying out joint calibration and data alignment; a laser radar is used to collect three-dimensional point cloud data, and a visual camera is used to shoot a scene; performing super-pixel segmentation on the image by using an image brightness linear iterative clustering algorithm; mapping the three-dimensional point cloud data into a segmented two-dimensional image superpixel pattern spot region by using a jointly calibrated camera model conversion relationship to realize feature clustering of the original three-dimensional point cloud of the laser radar; performing curved surface fitting on a clustered result by using a random sampling consistency algorithm; and linear interpolation is carried out on the fitted curved surface, dense points which are not covered by the original point cloud of the laser radar are supplemented and generated, and densification of the point cloud of the laser radar is realized. According to the method, the point cloud densification precision and practicability are improved, and technical support is provided for automatic driving, robot navigation and three-dimensional reconstruction.

The invention discloses an underwater hyperspectral image clustering method based on a multi-scale anchor image, and belongs to the technical field of underwater image processing. The method mainly comprises the following steps: carrying out superpixel segmentation on an underwater hyperspectral image; performing noise removal processing on the segmented hyperspectral image; learning a multi-scale anchor image of the denoised hyperspectral image; matrix decomposition is carried out on the multi-scale anchor images, and soft labels of super-pixel points are obtained; stacking the soft label matrixes into a tensor, and introducing a tensor Schatten-p norm to obtain a high-dimensional space structure of the multi-scale anchor map; constructing an optimization objective function; updating the target function variable by using an iterative updating strategy; and carrying out adaptive fusion on the learned soft label matrix. According to the method, pixel point soft labels are obtained mainly through multi-scale anchor image decomposition, label matrixes are stacked into tensors to explore a high-dimensional space structure of data, finally, self-adaptive weighted summation is conducted on the soft labels of the multi-scale anchor images to obtain a label result used for clustering analysis, and the clustering precision is remarkably improved.

The invention discloses a power equipment identification and segmentation method adapted to a dark light environment. The method comprises the following steps: constructing a bimodal collaborative perception basic model comprising a power equipment visible light detection model and an infrared image semantic segmentation model; establishing a cross-modal mapping relation between the visible light image and the infrared thermal image; collecting a visible light image and an infrared thermal image of the to-be-detected area under the same visual angle, and performing dark light enhancement on the visible light image of the to-be-detected area; performing target detection on the enhanced visible light image of the to-be-detected area to generate structured guide information; obtaining an infrared thermal image with a target bounding box based on the cross-modal mapping relation and the structured guidance information; and based on the infrared thermal image semantic segmentation model and the superpixel segmentation strategy, performing segmentation processing on the infrared thermal image with the target bounding box to obtain an infrared thermal image of the target power equipment. According to the method, the missing detection rate and the false detection rate are reduced, and the precision and the stability of infrared thermal image segmentation are improved.

The invention relates to the technical field of remote sensing image processing, in particular to a multi-temporal remote sensing image change detection method and system based on a space-time diagram neural network, and the method comprises the steps: carrying out the geometric registration, radiation correction and superpixel segmentation of an image, and outputting a segmented superpixel region set; taking the superpixel of each time phase as a node, constructing a space-time diagram structure fusing the spatial adjacency relation and the multi-step time association, and generating a normalized space-time Laplacian matrix; spatial structure features and multi-temporal evolution features are extracted through a double-branch graph neural network, spatial branch and time branch output are fused based on a gating mechanism, and node embedding is generated; constructing positive and negative sample pairs based on node embedding, and optimizing a feature space by comparing a loss function; and calculating the Euclidean distance of node embedding at adjacent moments, and outputting a change detection result in combination with a dynamic threshold. The method effectively reduces the false alarm rate and omission rate, greatly improves the reasoning speed, and is suitable for the scenes of urban expansion monitoring, dynamic disaster evaluation and the like.

The invention belongs to the technical field of image processing, and particularly relates to a pet behavior analysis method based on image recognition, and the method comprises the steps: 1, obtaining a video frame sequence, carrying out the superpixel segmentation of each frame of image in the video frame sequence, and obtaining a superpixel image composed of a plurality of superpixel blocks; step 2, constructing a Poisson multi-Bernoulli hybrid tracking link by taking the region association graph as input, and segmenting a trajectory entity set into a time sequence fragment set; 3, constructing a skeleton * object * scene hypergraph by taking the time sequence fragment set as input; and 4, finally outputting a behavior analysis result carrying an object participation type and a scene type according to a feeding action segment and a water drinking action segment in the strong recognition initial result of the interactive behavior. According to the invention, unified modeling and interpretable behavior identification of pets, objects and scenes are realized, and the method has the advantages of high robustness, high precision and real-time performance.

The invention belongs to the technical field of image processing, and particularly relates to a monitoring method and system for hazardous waste disposal, and the method comprises the steps: obtaining a continuous image sequence of a spraying system, and carrying out the preprocessing of images in the image sequence; analyzing the preprocessed image based on super-pixel segmentation and a density clustering algorithm, and generating instantaneous hole confidence, thereby forming an instantaneous hole confidence graph sequence corresponding to the image sequence; fusing the instantaneous cavity confidence map sequence, and calculating a time accumulation stability index representing the continuity of the cavity state of each pixel point in the time dimension for each pixel point so as to generate a time accumulation stability map; and calculating based on the time accumulation stability graph to obtain a final cavity confidence coefficient, and judging the final cavity confidence coefficient based on a preset threshold value to identify a spraying fault. According to the invention, the instantaneous disturbance interference is effectively inhibited, the false alarm rate is obviously reduced, and the accurate identification of the real spraying fault is realized.

The invention relates to the field of computer vision and image processing, in particular to a three-dimensional matching method based on wavelet transform and superpixel segmentation, which can improve the problems of poor depth estimation precision, blurred depth map contour and failure in multi-texture area depth estimation of the existing three-dimensional matching method. According to the method, different frequency domain sub-band information of the image is obtained by introducing a wavelet transform image processing method, the guiding effect of a low-frequency structure is enhanced, and the cost body aggregation process is participated; and meanwhile, fine modeling is carried out on the boundary of an image structure by combining a superpixel segmentation technology so as to enhance the integrity of an object contour in a depth image and the accuracy of parallax estimation. The method shows higher matching precision and boundary definition in a complex scene, has good calculation efficiency and deployment feasibility, and can provide reliable support for an embedded stereoscopic vision system with high precision and low resource consumption.

The invention belongs to the technical field of cross-seasonal remote sensing image change detection, and particularly relates to a cross-seasonal remote sensing image domain adaptive change detection method based on an improved CycleGAN. According to the method, through segmentation of all models and boundary constraint multi-scale super-pixel segmentation, the source domain image and the target domain image are kept consistent in object-level structure, the problems of ground feature breakage, texture dislocation and sample alignment irregularity caused by seasonal differences are effectively reduced, pre-training ViT-B high-dimensional semantic features and a density clustering algorithm are introduced, and the accuracy and the robustness of the method are improved. Noise samples such as mixed ground features, shadows and illumination anomalies in a complex remote sensing scene can be automatically recognized, a source domain training set is made to be purer, the stability of CycleGAN style migration training is improved, a generator fuses a multi-scale residual block and a self-attention module, a migrated image is made to be close to a target domain in the aspects of color, texture and seasonal features, and the image migration efficiency is improved. And meanwhile, the boundary and the structure of the ground object are kept not to be damaged through semantic consistency constraint, so that the problem of false change in cross-seasonal change detection is fundamentally solved.

The invention provides a geographic information surveying and mapping data processing method and system, and the method comprises the steps: obtaining terrain superpixels, carrying out the segmentation operation of the terrain superpixels, obtaining terrain superpixel blocks, carrying out the pulse frequency coding operation of the terrain superpixel blocks, obtaining a pulse frequency sequence, and carrying out the pulse delay coding operation of the terrain superpixel blocks. The method comprises the steps of obtaining a pulse frequency sequence, obtaining a pulse delay sequence, adjusting pulse density through terrain superpixels, obtaining pulse density, carrying out feature integration on the pulse frequency sequence, the pulse delay sequence and the pulse density, and obtaining a structured feature tensor. The method comprises the following steps: obtaining original point cloud data, carrying out position calibration on the original point cloud data through equipment movement track data, carrying out feature extraction on original point cloud according to a structured feature tensor, obtaining a terrain block map with a classification label, and improving the accuracy of geographic information surveying and mapping data while ensuring terrain continuity.

The invention discloses a sandstone slice image component identification method based on a GL-SLIC algorithm and semi-supervised learning. The method comprises the following steps: firstly, acquiring a sandstone slice image amplified by 200 times under single polarization, converting an RGB image into a CIE-Lab color space, and extracting a GLBP texture feature vector; then a GL-SLIC superpixel segmentation algorithm is constructed, and adaptive segmentation based on texture and color features is realized in combination with a Gabor filter and a local binary pattern; then implementing a region merging algorithm to generate complete mineral particles and pore fragments; finally, a classifier based on VGG16 and a discriminator based on ResNet18 are constructed, a semi-supervised self-training framework is adopted, a model is initialized by using about 6% of manual labeling samples, a high-confidence-coefficient pseudo-label extension training data set is generated through iteration, and automatic recognition of sandstone components such as quartz, pores, kaolinite, rock debris and a matrix is achieved. According to the method, the recognition accuracy of 96.3% on a test set is achieved, compared with a traditional method, the segmentation precision and the recognition accuracy are remarkably improved, the data labeling cost is greatly reduced, and an efficient technical scheme is provided for automatic analysis of geological images.

The invention discloses an ultrahigh-speed imaging method, device and system of an event camera, and belongs to the technical field of image processing. The method comprises the following steps: acquiring a superpixel segmentation mask of a motion area according to an event trigger position and an image appearance-position feature, selecting event points in the superpixel segmentation area for continuous time point tracking, and calculating an optical flow field with continuous time and dense space by combining a point tracking time sequence track and the spatial superpixel mask. An imaging result of any to-be-imaged moment t between the two frames is calculated by using the optical flow field, and high-precision imaging of an ultra-high-speed motion scene can be realized.

The invention discloses a ship target detection method based on linear non-iterative clustering. The method comprises the following steps: collecting data of a radar image ship target; performing speckle noise filtering on the data; performing superpixel segmentation on the image, performing superpixel segmentation by using an improved SNIC algorithm, and merging isolated superpixels; and carrying out CFAR detection on the superpixels to obtain a ship target, selecting background superpixels of the superpixels to be detected, assuming that the background superpixels conform to truncated GAMMA distribution and estimating parameters, and completing CFAR detection. A radar image is input into an algorithm for testing, and it is proved that the method can effectively improve the target detection accuracy of a radar image ship.

The invention provides a fan blade background region adaptive segmentation method and system oriented to a complex environment. The fan blade background region adaptive segmentation method comprises the steps of extracting channels and edge feature operators of various color spaces; establishing illumination non-deformation transformation; detecting a sky area through a superpixel segmentation method, and performing adaptive adjustment; generating a blade probability graph by integrating multiple features; segmenting the blade probability graph to obtain a preliminary segmentation result; optimizing initial segmentation result energy by minimizing an energy function; and introducing a sub-pixel positioning method to perform edge refinement on the optimized preliminary segmentation result, and outputting a segmentation result. According to the invention, high-precision detection of the foreground and background areas of the fan blade is realized under the working conditions of a field open environment, various light environments, complex blade surface textures and the like; the problems that the background segmentation precision of an existing method in a complex field environment is insufficient, and the contradiction exists between the amplification factor and the small-size defect distinguishing capacity in fan blade non-stop detection are solved.

The embodiment of the invention discloses an automatic image segmentation method and device based on semantic segmentation and superpixel fusion, and the method comprises the steps: carrying out the image feature extraction of a target image, carrying out the semantic region division through a semantic segmentation module, and outputting a tongue region target image after region division; the method comprises the following steps: inputting a target image of a tongue region into a clustering engine for iterative calculation to obtain a super-pixel block set, setting a segmentation threshold value, performing pixel proportion calculation on the target image of the tongue region to determine a target affiliation category of the super-pixel block, and determining affiliation division of a tongue edge in the target image of the tongue region; according to the embodiment of the invention, semantic segmentation and pixel clustering are fused, an approximate tongue region is firstly positioned, and then the edge of the tongue is finely corrected, so that the edge blurring of pure semantic segmentation is avoided, and the semantic deviation of pure super-pixel segmentation is solved; meanwhile, a low segmentation threshold value is set to solve the under-segmentation problem of an edge fuzzy region, and the whole process is automatic without manual intervention.

The invention discloses a low-rank multi-modal remote sensing image clustering method and device on a superpixel manifold and a storage medium, and relates to the technical field of multi-modal remote sensing image clustering. The method comprises the following steps: splicing a multi-modal remote sensing image along a channel direction, segmenting the spliced image into a plurality of sub-regions through superpixel segmentation, and solving a mean value for each sub-region to obtain a multi-modal superpixel; embedding the Laplacian matrix of the multi-modal superpixels into manifold regularization about the superpixel clustering matrix, and capturing a local manifold structure of the multi-modal superpixels; under the constraint of manifold regularization, constructing a low-rank reconstruction model of a product of a single-mode clustering matrix and a unified clustering matrix; initializing and alternately optimizing the single-mode clustering matrix and the unified clustering matrix by using fuzzy clustering; and analyzing the super-pixel clustering result, and mapping the super-pixel clustering result into a clustering result of the original image. According to the invention, the accuracy and efficiency of remote sensing image clustering are improved.

The application provides a kind of unsupervised hyperspectral image classification method based on mixed space spectrum information, comprising the following steps: S1, obtains binary segmentation graph by entropy rate superpixel segmentation algorithm, applies binary segmentation graph on original hyperspectral image to obtain segmented superpixel block, converts input hyperspectral image into multiple homogeneous regions based on superpixel segmentation, removes redundant information and guides data purification;S2, optimize the redundant information in principal component domain by two-dimensional singular spectrum analysis method, enhance spatial spectral feature;S3, realize the unsupervised classification of large-scale hyperspectral image by anchor point graph clustering unsupervised classification method.The application is closer to actual engineering application compared with existing supervised classification method, can process larger image scale compared with existing unsupervised classification method, has the advantages of not needing prior information reference, high classification precision, fast classification speed and the like.

The present application 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 present application obtains a plurality of superpixel blocks of a satellite remote sensing image; according to the elevation information distribution of different pixel points in each superpixel block in a digital elevation model, the terrain height variation degree of each superpixel block is obtained; according to the elevation information difference between each pixel point in each superpixel 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, the overall water flow direction value of each superpixel block is obtained; and then the merging possibility between different superpixel blocks is obtained; the superpixel optimal block is obtained, and the consolidation boundary line of the land region to be divided is divided. The present application accurately obtains the superpixel segmentation process of the land region to be divided, and improves the accuracy of the consolidation boundary line division.