51results about How to "Precise Segmentation Effect" patented technology

The invention relates to a convolutional neural network road scene classification and road segmentation method. A dual-task joint structure model based on the convolutional neural network is established.and composed of an encoder and two decoders. Feature information sharing is achieved through end-to-end training.Classification of various road scenes such as urban roads, rural roads and highwaysis completed, road drivable areas in the scenes are segmented, and the dual goals of road scene classification and road area segmentation are achieved. Based on a convolutional neural network, scene classification and road extraction in road scene perception are combined. Real-time output is obtained through an end-to-end training mode, the functions of an intelligent driving assistance system areimproved, and an effective and reliable driving assistance function is provided. A mode of combining a full convolutional network and a conditional random field is adopted for a part extracted from adrivable area in the model, a high-resolution image optimization output result can be generated, and a relatively accurate segmentation effect is achieved.

The invention relates to the field of medical image processing and pattern recognition, and provides a computer-assisted lump detecting method based on a mammary gland magnetic resonance image. The computer-assisted lump detecting method based on the mammary gland magnetic resonance image aims at solving the problems that in the prior art, the lump partition effect is not good, and the accuracy, the sensitivity and the specificity in a classification test are not high. The computer-assisted lump detecting method includes the following steps: S1, extracting an interest area from the mammary gland magnetic resonance image; S2, extracting an initial lump area from the interest area in a separated mode, and determining the contour line of the initial lump area; S3, calculating the weight distribution of characteristic parameters of the initial lump area; S4, selecting the characteristic parameters, with the weight coefficients larger than a standard weight coefficient, of the initial lump area, and carrying out training classifying to obtain optimized characteristic parameters; S5, inputting the optimized characteristic parameters into a classifier, analyzing the optimized characteristic parameters with a support vector machine classification method, determining a final lump area, and displaying the final lump area for a user. The detecting method has the good lump partition effect, the accuracy, the sensitivity and the specificity in the classification test are effectively improved, the detecting result serves as a second opinion to be provided for a radiologist, and the misdiagnosis rate and the missed diagnosis rate of the radiologist can be effectively reduced.

The invention provides a remote sensing image thin and weak target segmentation method. Firstly, data enhancement and corresponding preprocessing are carried out on an original remote sensing image, U-net is improved by means of the dense connection thought of DenseNet, and a Dense-Unet network structure is provided. Dense convolution is used in a network structure, the cascade relation between convolution channels is enhanced, through a symmetric structure and a jump connection thought, the connection between features of all layers is further tighter, and thin and weak target features can belearned more effectively. In order to ensure the real-time performance of final network identification and reduce the parameter quantity, a bottleneck layer and a batch normalization layer are introduced behind each dense block. And the objective function is adjusted by using the cost-sensitive vector weight, so that the problem of unbalanced segmentation target categories is solved, and the segmentation precision is further improved. And finally, a plurality of independent models are trained by using an ensemble learning method, the independent models are combined, and target category information is jointly predicted in the picture.

The invention relates to the technical field of computer vision, image processing, pattern recognition and the like and particularly relates to a multiple foreground object image interactive segmentation method. The image interactive segmentation method includes the steps of (1) constructing an image pixel similarity matrix, (2) acquiring image pixel label information; (3) constructing a spectral clustering segmentation model by combining the image pixel similarity matrix and the image pixel label information and solving to obtain a preliminary segmentation result, (4) constructing spatial smoothing constraint, and (5) constructing a markov random field model by combining the preliminary segmentation result and the spatial smoothing constraint and solving to obtain a final segmentation result. According to the multiple foreground object image interactive segmentation method, advantages of grabcut methods and linear constraint spectral clustering methods are integrated, simultaneously defects of the grabcut methods and the linear constraint spectral clustering methods are overcome, and images with randomly distributed colors and multiple foreground objects can be segmented merely by labeling an extremely small quantity of pixel points.

The invention discloses a partial differential equation-based nano-particle size measurement method. The method includes the following steps that: 1) a nano-particle image I is inputted, pixel-level multiplication is performed on the filtering result of a mean curvature flow model and a PM model, so that a filtered image u can be obtained; 2) a region scalable fitting (RSF) model is adopted to segment the image u; 3) pixel calibration is carried out, the actual size of each pixel in the image can be obtained; 4) inadherent particles are selected out by means of the convexity (Cconv) of a target; and 5) least-square circle fitting is performed on the boundaries of the particles, so that the diameters of spherical nano-particles can be obtained, and the diameter rc of the internally tangent circle of the nano-particles, the diameter ri of the externally tangent circle of the nano-particles, and the sphericity of the nano-particles are obtained, wherein the sphericity of the nano-particles can be represented by an expression S=ri/rc. The method of the invention can be widely applied to the high-tech fields such as catalytic science, medical drugs, new materials, electric power industry and compound materials which require nano-particle size measurement technology.

The invention belongs to the field of medical image processing and especially relates to a nucleus segmentation method based on white blood cell detection. The method comprises the following steps: to begin with, summarizing the size of diameter of each kind of white blood cell to set a plurality of sizes of sliding windows; then, selecting a candidate window for each sliding window according to gradient information of each white blood cell picture; calculating characteristic value of each candidate window according to the color contrast information, boundary density contrast information and gradient contrast information of the white blood cell pictures, and inputting the characteristic values to an epsilon-SVR model, and selecting a final detection window; intercepting the region of the final detection window of each white blood cell picture as a final positioning subgraph; and then, selecting threshold segmentation nucleus through a subgraph gray statistical chart polynomial curve fitting method, and then, carrying out hole filling and isolated point removal through morphologic processing to enable the segmentation result to be more accurate.

The invention discloses an electronic connector detection method based on machine vision, and belongs to the technical field of computer program design comprising image acquisition, analysis and processing. Actual targeted images are processed by various methods of image smoothing, image segmentation, image matching, edge extraction and color recognition, complete geometrical characteristics of defects of the electronic connector are acquired, and automatic detection can be realized. According to experiments, system detecting results is consistent with visual detecting results, accuracy of the detection is guaranteed, and detecting efficiency of the electronic connector is improved greatly.

The invention belongs to the technical field of computer vision, image treatment and pattern recognition, and particularly relates to a video image splitting method based on restrain spectral clustering and a markov random field. According to the method, a similarity matrix between two pixels is calculated based on the static characteristics of images, and the motion characteristic is added in a spectral clustering framework to be used as a restrain. In comparison with the conventional simple weighting method, two pieces of information with different reliability degrees are utilized well in the treatment method. Besides, as motion information coding is used as a restrain, only sparse point motion trains are required, so that reliable long-time motion information can be utilized to obtain an accurate and dense splitting result. Further, space smooth information of each pixel is coded into corresponding restrains by constructing a markov random field model, so that the video image splitting effect is relatively precise.

The invention relates to an image segmentation method based on an image data field. Conventional monochromatic image segmentation methods are generally based on the discontinuity and similarity of a pixel gray value; and a conventional segmentation algorithm is difficult to get the ideal segmentation effect under the condition of non-uniform illumination. Firstly, the input image is subjected to pre-processing filtering and then segmented by a watershed algorithm based on the eight-direction gradient, and then the average gray value of the edge point is obtained by the edge detection of the segmented image and is set as the seed value, and meanwhile, an image data filed is constructed, and the potential value function of the pixel point is calculated, and at the end, based on the seed value and the image data field, the feed filed automatic growth is carried out. The segmentation effect and the segmentation time of the image being nonuniform in illumination are greatly improved, and the demand of image identification can be met.

The invention relates to a probability graphic model image segmentation method based on flag fusion. The method comprises the steps of (1) acquiring a target image and multiple training images, conducting registration on the target image and the training images, and obtaining a corresponding deformation field; (2) acquiring the manual segmentation flag image of each training image subjected to registration, and obtaining a corresponding candidate flag according to deformation field reflection; (3) establishing a probability graphic model according to the training images and target image subjected to registration; (4) calculating the flag value of each pixel point in the target image by means of the local weighted voting flag fusion algorithm and the maximum posterior probability according to the probability graphic model, so that segmentation of the target image is achieved. Compared with the prior art, the method has the advantages that segmentation can be achieved automatically and segmentation precision is high.

The invention discloses a vegetable leaf disease image segmentation method and system and a computer readable storage medium. The vegetable leaf disease image segmentation method includes the following steps that 1, superpixel clustering processing is conducted on vegetable leaf images to obtain superpixel segmentation images; 2, significance region detection processing is conducted on the superpixel segmentation images to obtain lesion significance images; 3, the lesion significance images are processed to obtain non-lesion pixel sets; 4, region growing an region merging are conducted on pixel points in the non-lesion pixel sets to obtain lesion areas and normal areas of the vegetable leaf images. By the adoption of the vegetable leaf disease image segmentation method and system and the computer readable storage medium, leaf diseases in greenhouses can be rapidly and effectively detected and extracted under the conditions that no chemical reagents are in use and diseased vegetable leaves are not damaged, and therefore the vegetable leaf disease image segmentation method and system and the computer readable storage medium can be well applied in monitoring greenhouse vegetable diseases.

The invention discloses an eyelid topology morphological feature extraction method based on deep learning. The method specifically comprises the following steps: collecting an electronic digital photo of a normal person, processing the electronic digital photo, constructing an ROI image training set, and inputting the ROI image training set into a to-be-trained convolutional neural network to obtain a trained convolutional neural network; positioning an eye region-of-interest (ROI) position by using a facial recognition method for a to-be-detected electronic digital photo to obtain a to-be-detected ROI image; inputting the to-be-detected ROI image into the trained convolutional neural network to output an image with an eyelid contour line and a cornea contour line, determining a circular scale and a pupil center of the to-be-detected electronic digital photo, and extracting eyelid topology morphological characteristics of a single eye. According to the method, the eyelid and cornea structures are segmented by using the convolutional neural network; after the center of the pupil is determined by using Mean Shift clustering, the parameters of the related structure of the eyelid are automatically calculated, so that the accuracy equivalent to that of manual measurement is obtained.

The invention belongs to the technical field of cervical cancer cell screening, adn discloses an intelligent screening system and method for cervical cancer cell pathology negative exclusion. The intelligent screening system for the cervical cancer cell pathology negative exclusion comprises an image acquisition module, an input module, a main control module, an image processing module, a filtering module, an identification module, a data storage module and a display module. By means of the image processing module segmentation method from coarse to fine, on one hand, the processing speed is guaranteed, and on the other hand, an accurate segmentation effect is obtained. Meanwhile, the artificial recognition pressure is reduced through the recognition module, and the cost for artificially judging the probability of suffering from cervical cancer is also reduced. The identification efficiency of the image block (TCT digital slice) of the TCT slide scanning image is improved; evaluation iscarried out through a statistical machine learning model, cost is saved, and convenience and effectiveness are achieved.

The invention provides a malignant nodule edge detection image processing method based on a benign thyroid template. The method comprises the steps: learning an obtained thyroid three-dimensional image data set through a neural network, and forming a parameterized benign thyroid template; matching with the benign thyroid template according to the labeling information and the image information of the thyroid nodule ultrasonic image to be recognized, and performing coarse positioning on the nodule by combining the three-dimensional image and the feature information of the benign thyroid templateto obtain an initial contour curve of the nodule; adopting a local binary fitting model to iterate and evolve the initial contour curve to obtain an evolved contour curve; and performing effective contour selection on the evolved contour curve to obtain a final peripheral contour curve and a segmentation result, and performing fine positioning on the position of the nodule. The method is higher in autonomous recognition capability, better in adaptability to input of different thyroid images, better in segmentation effect, more accurate in numerical value implementation and higher in segmentation adaptability to images with uneven gray levels.

The invention discloses a three-dimensional CNV growth prediction method and device and a quantitative analysis method, and the method comprises the steps: sending an original OCT sequence into a three-dimensional segmentation model, and segmenting an original CNV region; selecting a registration reference, sequentially pairing the registration reference with the original OCT sequence, sending theregistration reference into a three-dimensional registration model, and obtaining a registration parameter sequence; sequentially carrying out image transformation on the original OCT sequence by using the registration parameter sequence to obtain a registered OCT sequence; and sending the registered OCT sequence into a three-dimensional CNV growth prediction model to obtain a three-dimensional CNV prediction area. The three-dimensional CNV region can be accurately segmented, the CNV region can be accurately predicted, and the CNV growth region can be quantitatively analyzed and predicted.

The invention provides an image segmentation model training method, an image segmentation method and an image segmentation device. The method comprises the steps that firstly, a sample image library is acquired, each group of sample images in the sample image library is used for training an image segmentation model until it is determined that image segmentation model training is completed, and theexecuted training process comprises the steps that pixel values of pixel points of the sample images in the sample image library are adjusted, and a first image corresponding to the sample images isobtained; inputting the first image into an image segmentation model, and outputting a second image after the first image is segmented; calculating a loss value of image segmentation model training according to the second image and the mark image; when the loss value meets the preset condition, it is determined that training of the image segmentation model is completed, and through the method, theimage segmentation accuracy is improved.