52results about How to "Good image segmentation" patented technology

The invention discloses a steel rail surface defect image adaptive segmentation method. The method comprises the following steps of S1, extracting a steel rail region by adopting a row grayscale mean successive summation method; S2, preprocessing a steel rail region image; S3, performing structure region and non-structure region division on the steel rail region image; S4, further distinguishing a defective region and a shadow region by utilizing a non-local feature of the image in the structure region; S5, adaptively building a background image model according to different features in the image; S6, performing image difference; and S7, performing dynamic threshold segmentation. The image is divided into the structure region and the non-structure region by utilizing image local information, the size of a pixel neighborhood window is adaptively adjusted by utilizing non-local information to calculate a mean, the accurate background image model is built, and the image difference and the dynamic threshold setting are performed, so that while a defective part of the image is highlighted, the influence of uneven illumination and steel rail surface reflection property on steel rail surface defect detection is effectively reduced, an ideal image segmentation effect is achieved, and the rail surface detection precision is ensured.

The invention discloses a moving object extraction method based on superpixel division and an optical flow method, and mainly solves the problems of more noises, high-frequency information loss, inaccurate boundary and the like of the existing moving object extraction method. The implementation steps of the method are as follows: (1), inputting an image, and pre-dividing the image into a superpixel set S to obtain a mark sheet I 2; (2), taking images of two adjacent frames in a video sequence and determining a rough position of a moving object by a Horn-Schunck optical flow method; (3), using the optical flow method to obtain the speed u in the horizontal direction and the speed v in the vertical direction, wherein V is speed amplitude of the optical flow method; (4) performing median filtering, Gauss filtering, binarization operation and morphology opening and closing operation on the optical flow result V to obtain V4; (5) using a superpixel division result to further correct the optical flow result, and extracting to obtain the accurate moving object. Superpixels belonging to a moving area are extracted accurately. Simulation experiments show that compared with the prior art, the moving object extraction method has the advantages of simple operation, small noise, clear boundary and the like, and can be used for extracting the moving object in the video sequence.

The invention discloses an image division method based on a watershed-quantum evolution clustering algorithm. The method has the following processes: (1) blocking and processing an input image to be divided, and seeking characteristics of a regional block as a clustering dataset; (2) setting population scale, number of distinct categories k and halt conditions, and randomly generating an initial quantum chromosome Q (t) as an initial clustering center; (3) observing the Q(t) to be a binary system chromosome p (t), calculating a fitness function value f<k> of each chromosome and reserving individuals in the current group; (4) carrying out mutation operation on the Q (t) to obtain Q<M>(t); (5) quantum crossover Q<M>(t) to obtain the Q<C>(t); (6) observing the Q<C>(t) to be a binary system chromosome p <c> (t) to obtain offspring chromosomes; (8) judging the halt condition of the offspring chromosomes, dividing an image kind which the chromosome with the highest affinity degree in the offspring chromosomes is corresponding to as output results if the halt condition is met, otherwise returning the process (3). The method has the advantages of good regional consistency, accurate edge preservation and can be used for target recognition in the image processing field.

The invention discloses a fuzzy clustering image segmenting method with transfer learning function. The method adopts the classic fuzzy C fuzzy-means algorithm as the study object and is special for overcoming the shortcoming of the C fuzzy-means algorithm that the low capacity is provided for resisting the noise while facing the image with noise. During processing the new image, the image segmenting method is mainly carried out for the image with noise pollution. With the adoption of the fuzzy clustering image segmenting method disclosed by the invention, the reliable clustering information obtained by summarizing lots of past similar images under the C fuzzy-means algorithm can be effectively learnt and utilized, such information is always considered as the clustering centre; by introducing the reliable information into the current new image segmenting task, the current clustering task can be effectively guided, and the noise resisting effect can be achieved, therefore, more precise clustering centre and more precise image segmenting result can be obtained.

The invention discloses an unsupervised image division method based on a multi-target immune cluster integration technology, which mainly solves the problems of poor global optimization capability, single evaluation index, single division scheme form and difficult selection of a plurality of division schemes in the traditional technology. The method comprises the following steps of: (1) extracting gray scale information and wavelet energy information of an image to be divided; (2) sampling the image by using an area-based sampling policy to generate a test sample set; (3) selecting different characteristic vectors to form a plurality of test sample sets; (3) generating a primary division scheme by using an evolution cluster based on a multi-target immune algorithm; (5) integrating and learning an optimal division scheme in the primary division scheme; (6) marking the class of the pixel points of the image according to the selected division scheme; and (7) outputting the image division result. The invention has advantages of high average accuracy in image division and strong robustness and is applicable to the obtaining of image information and the division of image texture.

The invention belongs to the technical field of image processing, and discloses a porous material hole automatic measurement method based on scanning electron microscope image segmentation, which comprises the following steps of 1, initializing parameters; 2, filtering processing; 3, obtaining a potential clustering number through the characteristic value information; 4, calculating histogram information of the filtered image; 5, completing image segmentation by using a fast fuzzy clustering algorithm; 6, extracting data distribution corresponding to the minimum clustering center as hole output to obtain a binary hole image; 7, performing morphological filling operation on the binary hole image; 8, automatically classifying the holes according to areas by utilizing multi-scale morphological closed reconstruction; and 9, counting the number, the area ratio and the actual average area of holes of different grades in the porous material. According to the method, automatic measurement andclassification of the holes of the porous material are realized, the porous material can be objectively evaluated, and an ideal image segmentation effect can be directly obtained.

The invention relates to the technical field of medical image processing and particularly relates to a cell counting method based on skeleton extraction, which comprises the following steps: S1, obtaining a to-be-processed histopathology image, carrying out graying processing on each to-be-processed image, and then carrying out image segmentation on each grayed image to obtain a cell binary imageof each to-be-processed image; S2, performing morphological processing on each cell binary image to obtain a cell binary image with intracellular hole filling and cell edge noise and impurity noise point removal; And S3, performing cell skeletonization processing on each morphologically processed cell binary image to obtain the number of cell skeletonization nodes in each image, and obtaining a cell counting result of each to-be-processed image according to the relationship between the number of cell skeletonization nodes in each image and the number of cells. The obtained cell counting resultis good in accuracy, and the method is high in operation efficiency.

The invention provides an immunity cloning and multitarget optimizing texture image segmentation method. The method is mainly used for solving the problem in the prior art that the segmentation effect is poor caused by the fact that only spatial separation degree or category compactness is optimized. The method comprises the implementation steps: (1) reading a texture image and extracting a characteristic matrix G from the texture image; (2) generating an initial antibody group V(t) and carrying out initial setting; (3) calculating a clustering objective function f1 and a categorization objective function f2 according to the characteristic matrix G and the antibody group V(t); (4) carrying out immunity cloning operation on the antibody group V(t) so as to obtain a cloned antibody group Vc(t); (5) carrying out non-uniform mutation operation on the cloned antibody group Vc(t) so as to obtain an antibody group Vm(t) subjected to non-uniform mutation; (6) carrying out population updating operation on the antibody group Vm(t) subjected to non-uniform mutation so as to obtain an updated antibody group Vm(t+1); and (7) calculating the categories of all pixels in the texture image according to the updated antibody group Vm(t+1) and the characteristic matrix G. The method has the advantages of high segmentation efficiency and good image segmentation effect and can be used for extracting and obtaining detailed information on the texture image.

The invention discloses a texture image segmentation method based on Lamarck multi-target immune algorithm, mainly aiming at solving the problems of high operational data quantity, weak global optimization capability, one-sided evaluation index and poorer local searching capability in the prior art. The texture image segmentation method comprises the steps of: (1) extracting image grey-scale information and image small wave energy information; (2) based on watershed pre-segmentation, generating a test sample set for image sampling; (3) using the Lamarck multi-target immune algorithm for carrying out data clustering on the test sample set, and generating a data clustering scheme sets (4) according to the Minkowski index value, selecting the most satisfied data clustering scheme; (5) according to the selected data clustering scheme, marking image pixel point category attribution; and (6) outputting the image segmentation result. The texture image segmentation method has the advantages of low operational data quantity, lower calculation complexity, high image segmentation average accuracy rate and excellent segmentation result, and can be used for image information acquisition and image texture partition.

The invention discloses an image segmentation method and system based on dynamic multi-objective optimization. The method comprises the steps of: constructing a multi-objective function by using a K-Means algorithm and a Fuzzy C-means (FCM) algorithm; defining an environmental change rule by using a background difference method; constructing a self-adaptive inertial dynamic factor; optimizing a timely mutation factor; based on the self-adaptive inertial dynamic factor and the timely mutation factor, dynamically optimizing the multi-objective function by using a multi-objective optimization particle swarm method. The K-Means and the FCM are optimized by dynamically optimizing a particle swarm algorithm, thus a good aggregation result can be acquired; the problem of error segmentation of pixels or edge blur is avoided, thus a good image segmentation effect can be achieved; the image segmentation is high in accuracy rate; the image segmentation method and system based on dynamic multi-objective optimization can provide high-quality result data and technical reference for image recognition.

The invention discloses an image segmentation method based on a structure tensor and an image segmentation model, and belongs to the field of digital image processing. The method comprises the steps: taking an image as a hypersurface of a three-dimensional Euclidean space according the viewpoint of Riemannian geometry, and obtaining a classic ST (structure tensor); combining the obtained ST with the color information of the image, and obtaining an EST (extended structure tensor); carrying out the dimension reduction of the obtained EST through employing PCA, and obtaining a constrictive CST; carrying out the non-linear diffusion of the obtained CST through employing a vectorization mode of a PM equation; calculating the KL distance of two tensor spaces; substituting the obtained distance into a GrabCut model, and completing the segmentation of the image. The method is small in number of parameters, is simple in calculation, is high processing speed, is good in image segmentation effect, and is suitable for a condition that a to-be-segmented object is highly similar to the background.

The invention discloses a fast superpixel segmentation method which is a top-down iterative segmentation method. The divisibility of the region is judged by analyzing the color consistency of the regional pixel set according to the method. The method comprises the following steps that step 1: the corresponding grayscale image I<Gray> of the original color image is acquired according to the formula(1); I<Gray>(x,y)=[I<R>(x,y)+ I<G>(x,y)+ I(x,y)]/3, wherein I<Gray>(x,y) refers to the pixel grayscale of the coordinates (x,y) on the image I<Gray>, I<R>(x,y) refers to the pixel grayscale of thecoordinates (x,y) on the image I<R>, I<G>(x,y) refers to the pixel grayscale of the coordinates (x,y) on the image I<G>, and I(x,y) refers to the pixel grayscale of the coordinates (x,y) on the image I; and I<R>, I<G> and I are the red, green and blue channel images of the original color image; and step 2: the superpixel region set S is calculated according to the grayscale image I<Gray>.