41 results about "Histogram matching" patented technology

To employ low resolution, noisy tone mapping operations for high resolution images, at least one raw image frame is converted to a first image at a higher resolution and a second image at a lower resolution. Tone mapping is applied to the second image to derive a third image at the lower resolution. Histogram matching and regularization are performed to determine a lookup table approximating histogram matching of the second image to the third image. A global gain map is derived based on luma for the first image and the lookup table. A local gain map is derived by up-sampling and denoising residual differences between the third image and the lookup table applied to the second image. Based on the global gain map and the local gain map, a total gain map is determined for tone mapping the first image to produce a fourth image at the first resolution.

The invention provides an adaptive weight distribution chromosome stripe contrast enhancement method and system, and the method comprises the steps: firstly improving the significance of a chromosome stripe structure and an edge contour in a chromosome gray image through an edge enhancement convolution operator; carrying out chromosome monomer target detection and instance segmentation on the metaphase chromosome based on an instance segmentation network of deep learning to obtain a chromosome monomer ROI region; then, extracting a structure tensor response of the ROI region of the chromosome monomer, designing an adaptive enhancement weight of a histogram merging interval based on a normalized difference between a response value and global statistical information, and performing reconstruction and interpolation on a histogram of the image of the ROI region of the chromosome monomer; and finally, adaptively enhancing the contrast of the chromosome stripe through iterative histogram matching. According to the method, the original image texture structure is kept, and meanwhile, the definition and the distinguishability of chromosome stripes are effectively and adaptively improved.

A method for detecting defect in image is provided. The method obtains a number of original images, determines a first reference image from the original images, and performs a histogram matching on the original images excluding the first reference image according to the first reference image, to obtain a plurality of matched images. The method further generates a synthesized image according to pixel intensities of the matched images and pixel intensities of the first reference image; and uses the synthesized image as a second reference image to perform an image comparison with a test image, to generate a result of defect detection. A related device and a related non-transitory storage medium are also provided.

The invention relates to the technical field of underwater target detection, and discloses a reverberation suppression method and device based on tensor robust principal component analysis. The method comprises the following steps: arranging frame signals received by a sonar receiving array according to a time sequence to obtain a distance-azimuth echo sequence; performing histogram matching processing on the distance-azimuth echo sequence, aligning the intensity distribution of each frame to a reference frame, and obtaining a distance-azimuth echo sequence after matching processing; after the distance-azimuth echo sequence after matching processing is preprocessed, low-rank component estimation initialization processing is executed; and after the initialization processing is completed, executing an adaptive threshold iterative optimization process, and iteratively solving an iterative formula by using a scale gradient descent method to obtain a low-rank component and a target component. By applying the method, the problems of high target false alarm rate and insufficient detection precision caused by reverberation interference in a complex environment can be solved.

The invention belongs to the field of panoramic stitching digital image processing technologies and camera ISP technologies, and particularly relates to a chromatic aberration consistency control method based on front and rear end collaboration. The method comprises the following steps: 1, acquiring statistical information of multiple cameras; step 2, acquiring a current camera control parameter by adopting a front-end chromatic aberration consistency adjustment algorithm; 3, the current camera control parameters are issued to all the cameras, and all the cameras complete exposure imaging according to the current camera control parameters; step 4, acquiring an overlapped part image in each path of camera imaging; step 5, calculating a statistical histogram cumulative distribution function of overlapped part images in each path of camera imaging; step 6, calculating a histogram matching function according to the cumulative distribution function of the overlapped part images in each path of overlapped camera imaging, so that the histograms of the overlapped parts of the two images tend to be consistent; and step 7, according to the obtained histogram matching function, transforming the brightness channel of each pixel in the corresponding camera imaging to complete color difference consistency correction.

The application discloses a backdoor attack method based on color frequency injection and adaptive local enhancement, and relates to the technical field of machine learning and artificial intelligence security. The method comprises the following steps: introducing low-frequency color offset and weak high-frequency signal into an image in a CIELAB color space to perform global color-frequency injection; using a pre-trained proxy model to locate a high-sensitive perception domain of the model through mixed evaluation of gradients and class activation maps, and generating a binary mask; in an HSV color space, respectively applying nonlinear stretching factors to saturation and brightness of the sensitive domain based on the mask to perform adaptive local enhancement; using Gaussian smoothing, adaptive noise and histogram matching to eliminate edges and statistical abnormalities caused by local enhancement, completing compensation color enhancement to generate a poisoned image; and modeling a trigger core parameter as a constrained optimization problem, and using a particle swarm optimization algorithm to jointly dynamically update the trigger core parameter to obtain an optimal strategy. The application anchors the trigger feature depth in the core semantic area of the model and lurks in the normal data manifold, guarantees a high attack success rate, realizes extreme visual and feature concealment, and has strong anti-defense robustness.

The invention discloses a satellite remote sensing image fusion method based on Wrapping-Curvelet transformation, and the method comprises the steps: carrying out the spatial registration of a multispectral image and a panchromatic image, carrying out the resampling of the multispectral image according to bilinear interpolation, obtaining a multispectral image with the same pixel size as the panchromatic image, and carrying out the fusion of the multispectral image and the panchromatic image. The method comprises the following steps of: respectively performing histogram matching on a panchromatic image and three wave bands of a multispectral image to be fused to obtain a new panchromatic image, taking the three wave bands of the multispectral image and the corresponding panchromatic image as examples, respectively performing Wrapping-Curvelet transformation on the three wave bands of the multispectral image and the corresponding panchromatic image to obtain respective Curvelet coefficients, fusing the coefficients of each layer, and then performing inverse Wrapping-Curvelet transformation on the fused layers to obtain a new panchromatic image; and obtaining a fused image. The blocking effect caused by blocking is avoided, the number of parameters of the implementation method is small, redundancy is reduced, and the algorithm is simple, convenient and rapid.

The invention provides a general plane graph color migration method and system based on a depth diffusion model, and the method comprises the following steps: (1) carrying out the semantic segmentation of a black and white general plane line draft graph, and generating a semantic mask graph; (2) extracting global style features of the color general plane reference image and local color distribution features of different semantic regions; (3) inputting color features of the line draft image, the semantic mask image and the reference image into a depth diffusion model, restraining the structure of the generated image to be consistent with that of the line draft through a ControlNet module, and migrating the color features of the reference image to a corresponding position of the line draft according to a semantic region through a region perception attention mechanism; and (4) in the de-noising process of the diffusion model, combining the style consistency loss function and the regional color histogram matching loss to generate an image after color migration. According to the method, the color of the reference picture is migrated to the black-and-white line draft according to the semantic region, meanwhile, the line draft structure is kept complete, and the requirement for rapid personalized drawing can be met.

The application provides a style transfer strong similarity-based generation method, device, equipment and medium, the method comprising: S10, redrawing a style reference map, outputting a clear style map; S20, inputting the clear style map into a monocular depth estimation model, outputting a single-channel depth map; S30, inputting the single-channel depth map into an enhanced image generation model for structure optimization, generating a redrawing sketch; S40, using a visual feature extraction model and a deep learning model to perform style fusion and rendering on the redrawing sketch, generating a preliminary style map; S50, pre-training a LoRA model, using the LoRA model to adjust the preliminary style map, outputting an intermediate style map; S60, after upsampling the intermediate style map, performing block optimization, outputting an optimized style map; S70, inputting the optimized style map into a Lab color space, performing histogram matching, and generating a final style map.

The application discloses a kind of micro-light image enhancement methods, including obtaining target micro-light image;Based on gamma correction, RTV correction and histogram matching scheme, the structure diagram and texture diagram of target micro-light image are calculated;According to the structure diagram and texture diagram obtained, based on Retinex model and structure / texture guide scheme, the brightness diagram and reflection diagram of target micro-light image are calculated;The obtained brightness diagram is adjusted and adaptively corrected;Based on RTV correction scheme, the enhanced image of target micro-light image is calculated to complete the image enhancement of target micro-light image.The application also discloses a kind of mine image enhancement method comprising the micro-light image enhancement method.The application not only realizes the enhancement of micro-light image and mine image, but also has higher reliability, better accuracy and better enhancement effect.

The invention relates to the technical field of biological detection, and discloses an intestinal digestion flora sampling and classification detection method and system, and the method comprises the steps: analyzing a feature image, extracting high-frequency texture features and low-frequency texture features, obtaining a frequency domain feature parameter set, extracting an RGB color histogram from an original flora microscopic image, and obtaining a frequency domain feature parameter set; obtaining a color feature parameter set; according to the method, self-adaptive double-threshold edge fusion and three-dimensional skeleton topology analysis are adopted, so that the quantification precision of morphological characteristics is greatly improved, thallus contour fracture and measurement errors are reduced, and the reliability of a result is improved; secondly, the dynamic adaptability of fractal feature extraction is improved, the classification requirements of new flora can be effectively met, and the classification accuracy is improved; besides, through HSV channel strengthening and multi-channel histogram matching, color feature quantification is objectified, subjective errors in traditional manual interpretation are eliminated, and color feature recognition is more accurate.

The application discloses a high-resolution satellite image urban area shadow-removed ARD production method and device, and belongs to the technical field of remote sensing image processing. The method comprises the following steps: firstly, screening out images covering urban areas; performing radiation calibration on high-resolution satellite images covering urban areas to obtain apparent radiance images; based on the apparent radiance images, performing preliminary shadow detection by using a pre-trained deep learning model; using a near-infrared band of an atmospheric-corrected ground reflectivity image, performing false detection elimination on the preliminary detection result by adaptive threshold segmentation to obtain a final shadow mask; performing histogram matching processing on the shadow area based on the shadow mask on the atmospheric-corrected ground reflectivity image to realize shadow removal; and finally, outputting the shadow-removed ARD product. The application significantly improves the shadow detection precision and the quantitative reliability of the ARD product, and is suitable for large-scale ARD production of high-resolution satellite images in urban areas.

The invention provides an inclined image dodging processing method and device, and is applied to the technical field of aerial remote sensing. The method comprises the following steps: carrying out observation geometric calculation and illumination geometric calculation according to imaging parameters and position attitude data of an inclined camera; determining atmospheric influence parameters from the atmospheric correction parameter lookup table according to a calculation result, determining theoretical radiance of each pixel in the original inclined image based on the atmospheric influence parameters, determining standard brightness, and unifying a brightness reference of the theoretical radiance based on the standard brightness; calculating a cosine quadruple proportion coefficient of an actual correction angle of oblique observation of the camera, and correcting the theoretical radiance of each pixel based on the cosine quadruple proportion coefficient; and converting the processed original inclined image and the reference image from a first color space to a second color space, performing histogram matching on each channel, and then turning back to the first color space to obtain a uniform light normalized image.

The invention discloses an image sequence flicker elimination method and system based on clustering and multi-scale histogram matching, and relates to the technical field of image processing, and the method comprises the steps: carrying out the denoising processing of an image through employing a non-local mean value denoising algorithm, obtaining a denoised image sequence, calculating the histogram features, and carrying out the calculation of the histogram features; grouping the images in the de-noised image sequence by adopting a K-Means clustering algorithm so as to determine a category label of each frame of image; for the de-noised image sequence, taking an image of which the category label is a standard category as a reference, performing multi-scale histogram matching on an image of which the category label is an abnormal category, and performing weighted fusion on optimized images under all scales through a Gaussian kernel function to obtain an optimized image sequence; and for the optimized image sequence, carrying out time sequence smooth transition processing at the image junction of different types of labels by adopting a cross gradual change technology so as to obtain a final image sequence. The flicker of the image sequence can be effectively eliminated, and the visual experience is improved.

The application discloses a method and system for automatically correcting the angle of head data in a medical image, and the method comprises the following steps: setting a reference picture, inputting a picture sequence to be corrected, wherein the input picture sequence to be corrected is a medical picture or picture sequence obtained after CT or nuclear magnetic resonance scanning and reconstruction of a patient; searching for a similar slice: using a histogram matching method or other algorithms based on a deep neural network to search for a similar slice in the input sequence which is most similar to the reference picture; head correction: registering the found similar slice picture with the reference picture to obtain correction parameters, including a rotation angle and horizontal offset in x and y directions, and then processing the picture sequence to be corrected to realize head correction. The application automatically corrects the image, automatically corrects the position of the head of the patient in the input data by using an image registration method, and replaces three-dimensional direct registration by a two-dimensional registration method, so that the calculation amount is greatly reduced and the accuracy is increased.

The application discloses a kind of based on 4D millimeter wave radar / inertial sensor tightly coupled navigation positioning method, comprising: the original point cloud is based on the dynamic point rejection of Doppler consistency, obtain first point cloud, then carry out noise point rejection and obtain second point cloud;Weighted Doppler velocity factor residual corresponding to single frame point cloud is constructed based on Doppler velocity weight;Adjacent two frames of second point cloud are histogram matched, obtain interframe point pair set, and interframe point pair factor residual is constructed based on interframe point pair set;Joint weighted Doppler velocity factor residual, interframe point pair factor residual and inertial pre-integral factor residual, construct tightly coupled nonlinear factor graph optimization model, and the navigation positioning information of carrier is obtained by executing nonlinear factor graph optimization.The application is applied to navigation positioning field, faces extreme dynamic scene, the characteristics of dynamic, static target are jointly analyzed, and positioning precision and robustness in dynamic scene are significantly improved.

The application discloses a kind of mass remote sensing image automatic color correction methods of sub-meter satellite.Belongs to the technical field of mass sub-meter satellite remote sensing image automatic color correction, specifically relates to a kind of mass remote sensing image automatic color correction methods of sub-meter satellite technical field, by training improved ResNet50 model, obtain mass sub-meter high-resolution paired data, then combined with histogram matching method, realize mass sub-meter satellite remote sensing image automatic color correction.The method comprises the following steps: obtaining pre-correction sub-meter remote sensing image, and performing histogram matching on pre-correction sub-meter remote sensing image;Pre-process the pre-correction sub-meter remote sensing image and corresponding RGB histogram;ResNet50 model is constructed and improved: after the full connection layer of ResNet50 model, increase full connection module;Sub-meter remote sensing image is corrected by ResNet50 improved model.

This application provides an anti-interference texture enhancement localization method, system, and medium suitable for low-texture visual scenes. The method includes: acquiring an original image; preprocessing the original image; extracting the texture response of the preprocessed image in different directions and frequency dimensions based on multi-scale Gabor filtering to obtain multiple texture response maps; dividing the preprocessed image into multiple image partitions based on a partitioned LED array and calculating the local gradient contrast of each image partition; independently adjusting the light source brightness of different image partitions to obtain an illumination-optimized image; performing feature fusion processing on the multiple texture response maps and the illumination-optimized image to extract feature points for visual localization; improving the consistency of images acquired at different time periods by normalizing the frequency response of the original image, histogram matching, and adaptive brightness restoration; and improving the local contrast by using a partitioned LED array with spatial illumination modulation to improve visual localization accuracy.

This invention discloses an adaptive enhancement method for low-light images based on physical prior guidance and spatiotemporal decoupling, belonging to the fields of computer vision and autonomous driving perception technology. The method first converts the original low-light image to YUV space to extract the brightness channel and performs inverse mapping. A pseudo-dehazing physical model is used to inversely solve for the transmittance map as the structural prior matrix. Subsequently, a mask decomposition network decouples the original image features into a reflection component containing local texture and an initial illumination component containing global brightness distribution. This invention constructs a dual-branch parallel enhancement architecture: the first branch uses an enhanced Transformer network for detail restoration and introduces a color histogram matching loss function to ensure color fidelity; the second branch uses an adaptive Gamma U-Net to predict pixel-level gamma parameter matrices for brightness compensation and suppresses overexposure through a regularized loss function with a range penalty term. Finally, the enhanced image is reconstructed based on Retinex theory.

The invention discloses a low-light-level image enhancement method. The method comprises the following steps: acquiring a target low-light-level image; based on gamma correction, RTV correction and a histogram matching scheme, calculating to obtain a structure diagram and a texture diagram of the target low-light image; according to the obtained structure diagram and texture diagram, based on a Retinex model and a structure / texture guiding scheme, calculating to obtain a brightness diagram and a reflection diagram of the target low-light image; performing adjustment and adaptive correction on the obtained brightness map; and based on the RTV correction scheme, calculating to obtain an enhanced image of the target low-light-level image so as to complete image enhancement of the target low-light-level image. The invention further discloses a mine image enhancement method comprising the low-light-level image enhancement method. According to the method, the enhancement of the low-light image and the mine image is realized, the reliability is higher, the accuracy is better, and the enhancement effect is better.

Systems and methods for mitigating shadow segments from images are provided. One example computer-implemented method includes accessing an original image of a geospatial location including a first shadow segment and generating, using a model architecture, a matte for the original image. The method also includes generating a first histogram of tones of shadow pixels of the first shadow segment in the original image, generating a second histogram of tones of an adjacent region of the original image, which is proximate to the first shadow segment yet outside a boundary of the first shadow segment, defining a lookup table based on histogram matching between the first histogram and the second histogram, and relighting the original image based on the lookup table.

The application discloses a discharge detection method and system of ultraviolet and visible light image adaptive registration, and belongs to the technical field of image processing. The imaging module is used for capturing ultraviolet images and visible light images of a discharge point area of a power equipment respectively. The registration module is used for calculating motion vectors of the visible light image sequence according to optical flow motion estimation and mapping to the ultraviolet image for displacement compensation in a motion scene, and determining translation compensation by histogram matching in a static scene. According to the compensated images, the ultraviolet image is scaled by a scaling ratio calculated by focal length and a center point of the ultraviolet image, and then the scaled ultraviolet image is scaled to a target size. The discharge detection module detects the discharge of the discharge point area according to the registered images. The application improves robustness, avoids boundary offset and nonlinear distortion, realizes edge linear fitting while ensuring accurate alignment of the center point, and significantly improves the calculation efficiency by cooperating with integer operation.

The invention provides an infrared image enhancement method and device based on histogram matching and curvelet transformation. The method comprises the following steps: acquiring a to-be-enhanced infrared image and a reference image; respectively calculating a gray average value and a gray standard deviation of the infrared image and the reference image; calculating a correction coefficient and an average correction value based on the gray average value and the gray standard deviation; generating a histogram matching enhanced image of the infrared image based on the correction coefficient and the average correction value; curvelet transform decomposition is carried out on the histogram matching enhanced image and the infrared image to obtain corresponding multi-scale and multi-direction sub-band coefficients, and fusion is carried out on the corresponding sub-band coefficients under the same scale and the same direction to obtain a fusion sub-band coefficient; and performing curvelet inverse transformation on the fusion sub-band coefficient to obtain a fusion enhanced image. According to the invention, through the cooperation of the global gray correction of histogram matching and the local detail fusion of curvelet transformation, the contrast, definition and detail information of the infrared image are synchronously improved.

The invention discloses a tight coupling navigation positioning method based on a 4D millimeter wave radar / inertial sensor, and the method comprises the steps: carrying out the Doppler consistency-based dynamic point elimination of an original point cloud, obtaining a first point cloud, and carrying out the noise elimination to obtain a second point cloud; constructing a weighted Doppler velocity factor residual error corresponding to the single-frame point cloud based on the Doppler velocity weight; performing histogram matching on two adjacent frames of second point clouds to obtain an inter-frame point pair set, and constructing an inter-frame point pair factor residual based on the inter-frame point pair set; and combining the weighted Doppler velocity factor residual error, the inter-frame point pair factor residual error and the inertia pre-integration factor residual error to construct a tight coupling nonlinear factor graph optimization model, and executing nonlinear factor graph optimization to obtain the navigation positioning information of the carrier. The method is applied to the field of navigation positioning, is oriented to extreme dynamic scenes, jointly analyzes the characteristics of dynamic and static targets, and remarkably improves the positioning precision and robustness in the dynamic scenes.

The invention discloses a multispectral global remote sensing data-oriented color uniformizing method and a multispectral global remote sensing data-oriented color uniformizing system. The method comprises the following steps: firstly, constructing a global reflectivity reference base map, and then acquiring a corresponding position color-uniformizing base map according to a geographic position for global remote sensing data to be subjected to color uniformizing; thirdly, further segmenting the color-uniformizing base map and the data to be subjected to color uniformizing into a plurality of small image blocks with overlapped areas, and respectively calculating brightness characteristics of the small image blocks on the basis of tasseled cap transformation coefficients or equal weight transformation coefficients; adjusting the brightness distribution of the blocks of the image to be subjected to color homogenization according to the brightness characteristics and the one-dimensional histogram, adjusting all wave band values, and finally performing seam processing and re-splicing to obtain data with good color homogenization. According to the method, the color difference expansion phenomenon caused by a local color homogenizing method is effectively avoided, the influence of cloud layer coverage on the overall picture brightness can be avoided through picture blocking processing, the whole set of scheme is very suitable for large-scale parallel computing, and a reliable technical basis is provided for global high-resolution remote sensing image visualization and subsequent computing.

The invention discloses an epilepsy lesion state classification method based on a double-flow complementary potential diffusion model. The method comprises the following steps: training a hidden space in a health data set; the MR I of the T1 mode and the FLAIR mode is input into an encoder of the VQVAE, and T1 and FLAIR hidden space images are obtained; respectively training two diffusion models by taking T1 and FLAIR hidden space images as conditions; obtaining an original MRI image, inputting the original MRI image into the diffusion model, and in each step of the diffusion model, fusing and denoising noises predicted by the two diffusion models with T1 and FLAIR as conditions; the denoised FLAIR hidden space image obtained through the diffusion model is input into a decoder of the VQVAE, and a reconstructed pseudo-health image is obtained; performing histogram matching on the reconstructed pseudo health image and the original MRI image, and optimizing the reconstructed image; and calculating a difference chart of the reconstructed image and the original image, and classifying epilepsy lesion areas through threshold segmentation.

The application relates to the technical field of image processing, in particular to a multi-source ecological remote sensing data fusion method and system. The method comprises the following steps: collecting multi-band images and panchromatic images; obtaining a closed operation image after edge detection and closed operation of the images, and dividing the images into a plurality of partitions based on the closed operation image; respectively performing interpolation on different layer images of the panchromatic images, and correcting the interpolation based on the gradient deflection direction to obtain interpolation correction values; obtaining principal component images of the panchromatic images after the interpolation is completed, performing histogram matching on the principal component images and the panchromatic images, and adjusting the matching values based on the gray scale change and the quantity change to obtain matching adjustment values; and comparing the partition images after the matching with the multi-band images and the panchromatic images to obtain fusion values of edge pixel points, and obtaining a fusion image based on the fusion values. The application improves the definition and color performance of the fusion image.

The application discloses a kind of based on clustering and multi-scale histogram matching image sequence flicker elimination method and system, it is related to image processing technical field, method includes: using non-local mean denoising algorithm to execute denoising processing to image and obtain denoised image sequence, then calculate histogram feature, using K-Means clustering algorithm to group image in denoised image sequence, to determine the class label of each frame image;For denoised image sequence, image with class label as standard class is regarded as reference, multi-scale histogram matching is executed to image with class label as abnormal class, and all scales under optimization image are weighted fusion by Gaussian kernel function to obtain optimized image sequence;For optimized image sequence, cross-fade technology is used to carry out time sequence smooth transition processing at the junction of image of different class labels, to obtain final image sequence.The application can effectively eliminate the flicker of image sequence, and improve visual experience.

This invention provides an automatic detection method for solid waste from drones based on feature fusion, comprising: acquiring images containing solid waste and generating an image set; using the image set as training samples for a solid waste CNN feature extraction network, and training the convolutional template parameters in the feature extraction network; training classification, proposal regression, and LFA mask networks using feature maps; segmenting morphological images; and introducing a histogram matching overlay analysis module to fuse the CNN and morphological solid waste detection results. This invention proposes an automatic detection method for solid waste from drones based on feature fusion, optimizing existing CNN structures, constructing a layer feature aggregation module based on multi-scale feature integration to fully utilize the image structure and deep semantic information of the target; designing an overlay analysis module to fuse CNN solid waste detection results, utilizing image gradient information and the spectral heterogeneity of solid waste to achieve accurate solid waste detection and edge adjustment; and constructing an automatic solid waste detection network through an improved feature fusion watershed mask CNN structure.

The application provides a finished product camera video real-time splicing method and system based on heterogeneous computing, which comprises the following steps: processing double-channel fisheye video streams through offline calibration to achieve global coarse alignment, and obtaining double-channel equirectangular projection video images containing target video images and reference video images; performing feature matching and inter-frame jitter suppression processing based on optical flow estimation on the target video images and the reference video images to obtain a pixel set matching point set; sequentially performing local homography matrix solving based on grid division and image warping processing on the pixel set matching point set to obtain a warped image; performing image fusion on the warped image and the reference video image through brightness matching and distance weight processing to obtain a fused image; and obtaining a fused video stream through the fused image. The application realizes pixel-level weighted fusion by defining the range through an overlapping area mask, correcting color brightness deviation through histogram matching, and generating dynamic weight coefficients based on distance transformation.