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

The invention provides a GrabCut texture image segmentation method based on Gabor transform. The method comprises the following steps that: transforming a two-dimensional image into frequency domain space representation through Gabor wavelet transform, calculating the parametric transform of a frequency spectrum space two-dimensional Gabor filter group to obtain multi-scale multi-direction texture features, constructing a multi-scale texture energy generic function, calculating the GMM (Gaussian Mixture Model) probability distribution model of the texture features, and introducing the GMM of the texture features into GrabCut for image segmentation. The GrabCut texture image segmentation method based on the Gabor transform has the characteristics of being simple in artificial interaction, high in processing speed, good in image segmentation effect and the like.

A bacteria microscopic image segmentation method based on a deep learning network comprises the following steps: 1) culturing bacteria, shooting a group of bacteria growth pictures under a microscope according to a fixed time interval, carrying out image preprocessing, constructing a training set, a verification set and a test set which are not intersected with one another, wherein the training set comprises original images and corresponding label images, and the verification set and the test set only comprise the original images respectively; (2) building a U-Net + + model, wherein the U-Net + + model is provided with an encoder module and a decoder module, the encoder module carries out feature extraction, the decoder module carries out feature reduction decoding to obtain the size of an original image; inputting the training set into the U-Net + + model for training, then inputting the verification set into the trained U-Net + + model for verification, and obtaining the trained U-Net + + model; and 3) inputting the test set into the trained U-Net + + model, and outputting a binary segmentation image. According to the method, the bacterial microscopic image can be automatically segmented quickly and accurately, too many complex image preprocessing links in the early stage are omitted, and time is saved.

The invention relates to a segmentation method based on a reaction diffusion equation, characterized in that under a level set framework, based on a reaction diffusion mechanism, utilizing a diffusion term to smoothen a level set function; and through analyzing image properties and characteristics, constructing a reaction term having opposite signs inside and outside an image target. The equation can be realized based on a simple finite difference method; in addition, the level set function can be initialized to be any bounded function, thereby well solving the problem of contour initialization. The segmentation method can obtain sound image segmentation effects, and possesses the advantages of simple implementation and fast operation speed.

The invention discloses an artificial intelligence global optimization method based on the QUATRE architecture, including: S1, establishing a quasi-affine transformation evolution architecture; S2, setting an evaluation function of the quasi-affine transformation evolution architecture, a global optimal value, an iteration threshold, and calling The maximum number of evaluation functions and the conditions for iteration, and initialize the evolutionary architecture; S3, judge whether the conditions for iteration are met, if yes, continue to perform the following steps, if not, stop; S4, calculate the variation matrix B, and According to the quasi-affine transformation evolution framework, the next-generation particle population XG+1 of the particle population XG is obtained; S5, updating the number of calls to the evaluation function and the next-generation particle population; S6, obtaining the Boolean evolution matrix M and the associated evolution matrix from the evaluation function The optimal value is the current optimal particle of the particle population in G+1 iterations and G=G+1, then return to step S3 for judgment. The invention has few parameters and high efficiency.

The embodiment of the invention discloses an image segmentation method and device, electronic equipment, a processor and a computer readable storage medium. The method comprises the steps that U-shaped expansion is conducted on a preset YOLOv3 model, an improved YOLOv3 model containing a U-shaped sampling unit is obtained, the U-shaped sampling unit comprises a down-sampling network and an up-sampling network, the down-sampling network is composed of a Darknet53 network of the YOLOv3 model, and the up-sampling network is formed by connecting a plurality of up-sampling modules in the YOLOv3 model in series; training the improved YOLOv3 model by using a sample image of a preset training sample set to obtain an image segmentation model; and performing image segmentation processing on the to-be-processed image by using the image segmentation model. According to the embodiment, the Darknet53 is adopted as the down-sampling network, the depth of the sampling network is large, the model expression ability is high, the whole model is easy to train, and then the method can be suitable for complex image segmentation scenes.

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 divisionresult. 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 proposes an image edge detection method based on threshold value segmentation, which is a new threshold value segmentation algorithm for taking threshold values ​​one by one for all points in the image and classifying them. Using the statistical information of the mean value of the gray value of the pixel neighborhood in the image as the threshold setting standard of the point, and introducing the variance of the gray value of the pixel in the neighborhood of the point as an additional judgment condition, so as to realize the global binarization that comprehensively considers the regional information, so that The extracted target points are the edges of the image. Moreover, the size of the structural elements for calculating the mean and variance can be adjusted, and structural elements of different sizes can be selected according to different needs. This method sets a threshold value for each pixel, so as to realize the global binarization comprehensively considering the area information, the image segmentation effect is good, and the edge location is accurate.