168 results about "Contour segmentation" patented technology

A system for measuring a pulse and respiratory rate from passive thermal video includes contour segmentation and tracking, clustering of informative pixels of interests, and robust dominant frequency component estimation. Contour segmentation is used to locate a blood vessel region to measure, after which all pixels in the nearby region are aligned across frames based on the segmentation's position, and scale in each frame. Spatial filtering is then performed to remove noise not related to heart beat and then non-linear filtering is performed on the temporal signal corresponding to each aligned pixel. The signal spectrum of each pixel is then feed to a clustering algorithm for outlier removal. Pixels in the largest cluster are then used to vote for the dominant frequency, and the median of the dominant frequency is output as the pulse rate.

A method for automatic left ventricle segmentation of cine short-axis magnetic resonance (MR) images that does not require manually drawn initial contours, trained statistical shape models, or gray-level appearance models is provided. More specifically, the method employs a roundness metric to automatically locate the left ventricle. Epicardial contour segmentation is simplified by mapping the pixels from Cartesian to approximately polar coordinates. Furthermore, region growing is utilized by distributing seed points around the endocardial contour to find the LV myocardium and, thus, the epicardial contour. This is a robust technique for images where the epicardial edge has poor contrast. A fast Fourier transform (FFT) is utilized to smooth both the determined endocardial and epicardial contours. In addition to determining endocardial and epicardial contours, the method also determines the contours of papillary muscles and trabeculations.

The invention discloses a digital human serial section image segmentation method based on geometric active contour target tracking, comprising the following steps: 1) inputting a first layer digital human serial section image which includes a target to be segmented; 2) initializing the target contour by intelligent scissors; 3) generating a target contour matching score image; 4) smoothening the target contour matching score image generated in step 3); 5) using a self-adopting geometric active contour model tracking to obtain the target contour of current layer; and 6) storing the target contour segmentation result of current layer, simultaneously taking the result as an initial target contour of next layer and returning to step 3) to continue the target tracking to obtain subsequent target contour. The digital human serial section image segmentation method can accurately and robustly segment the target contour of the digital human serial section image and simultaneously has the advantages of high automation level and processing of topology change of the target contour.

The invention relates to a multi-target picture segmentation based on a level set, and the multi-target picture segmentation based on the level set is applied in the technical field of the image analysis. The method includes the following steps: firstly, drawing one closed curve or more than one closed curves on to-be-segmented images as an initial outline; secondly, utilizing an initiative outline model based on a region, carrying out an iteration revolution on the initiative outline, and obtaining a profile curve of a target. The initiative outline model based on the region fully takes partial gray level information of the image into account. Therefore, the multi-target picture segmentation based on the level set is capable of segmenting images with uneven gray levels, extending the model into multiple orders, and segmenting over the multi-target images. Compared with the prior art, the multi-target picture segmentation based on the level set has the advantages of being insensitive in initialization, quick in calculation speed, strong in anti-noise ability and accurate in segmentation results. Meanwhile, the multi-target picture segmentation based on the level set is capable of segmenting medical images provided with a plurality of different targets.

The invention provides a video image motion target extracting method based on pattern detection and color segmentation. The method comprises the steps of: firstly preprocessing video signals to obtain an RGB (red, green and blue) image, performing k-means image clustering segmentation to the RGB image, and recording color classification number of each pixel point; secondly, performing LBP (local binary pattern) detection according to the gray value of the current frame and the background frame, calculating to obtain the pixel points representing a motion target, and creating a mapping relation between the pixel points and macro blocks to obtain the macro block level motion target; then, according to the color classification number and pixels of the motion target, performing overlapping detection to obtain the initial motion target; and at last, merging the motion targets obtained from the LBP detection and the color segmentation, and filtering to obtain the final motion target. The method has the advantages that the advantages of accurate location in the pattern detection and robustness of illumination variation are kept; and the problem that the motion target is fused in a background by slow movement or in-situ movement can be effectively solved.

The invention relates to the technical field of three-dimensional image generation, and specifically relates to a three-dimensional modeling method of strawberry plants. The method comprises the following steps: calculating a central point of vector field convolution, selecting a multi-objective initial contour control point, and applying an active contour model of a parameter to intensity image over-segmentation; and adopting a labeling method to convert a segmented contour into a point cloud set, taking the point cloud set as a unit to construct a plane fitting selection mechanism, and finally finishing modeling and display of a three-dimensional model of the elevated in-situ strawberry, and carrying out an exploratory research for the construction of a local vision scene for an agricultural intelligent robot. The method of the invention can perform three-dimensional modeling on in-situ plants, which effectively overcomes the limitation that the traditional modeling method cannot build a three-dimensional model of a whole plant in a greenhouse environment, and improves the utilization ratio of collected data.

A system and method for active contour segmentation. An image of a first and at least a second structure, a first position for the first structure, and at least a second position for the at least second structure are received, the first and the at least second positions are set as a first and an at least second initial contour, and the first and the at least second initial contours are concurrently and iteratively deformed to respectively expand into a first and at least a second expanded contour matching a shape of the first and the at least second structure by applying constraints to each point of the first and the at least second initial contour, comprising a constraint for preventing the first and at least one of the at least second initial contour from intersecting one another upon being deformed, and the constraints are updated after each iteration.

A method is provided for segmenting a moving object immersed in a background, comprising: obtaining a time-varying autoregressive model of prior motion of the object to predict future motion of the object; predicting a subsequent contour of the object from the background using the obtaining time-varying autoregressive model comprising using the obtained time-varying autoregressive model to initialize and/or constrain segmentation of the object from the background, and segmenting the object using the predicted subsequent contour and updating the autoregressive model while tracking of the segmented object.

The invention provides a hybrid level set based three-dimensional tooth modeling method. The method comprises the following multiple steps: 1) selecting an initial slice and initializing a level set; 2) segmenting a tooth root slice image by utilizing a single-phase hybrid level set model constructed by combining priori shape constraint energy, Flux model based edge energy and priori gray based local region energy; 3) segmenting a tooth crown layer slice image by a dual-phase hybrid level set model in combination with regional competition constraint; and 4) reconstructing a contour segmentation based three-dimensional tooth model. According to the method, the hybrid level set model can effectively solve the problems of inaccuracy for edge positioning, incapability of processing an image, uneven gray level and the like in a conventional segmentation model, and an independent three-dimensional model of each tooth can be quickly and accurately constructed, so that a solid foundation is laid for making oral repair plans, biomechanical analysis and the like.

The invention discloses a method and a system for building an image database. The method comprises the following steps of utilizing keywords to automatically download images from the Internet, carrying out object detection on the downloaded images, carrying out preliminary filtering, carrying out fine contour segmentation on detected objects to obtain segmentation pixels, and filtering the obtained segmentation pixels based on contour match to form the image database. The database can generally achieve 80-90 percent of tagging accuracy, and a user can quickly carry out content-based retrieval on the image database in modes such as draft drawing or framework adjustment so as to quickly find the needed image synthesis and creating resources.

The invention belongs to the field of automatic recognition and classification of underwater targets, in particular to an automatic classification method of side scan sonar image targets based on transfer learning and depth learning. The method includes acquiring a conventional optical image data set with segmentation annotations; carrying out the contour segmentation by using the annotated imagescorresponding to each image in the data set; selecting a convolution neural network structure for training to obtain the source domain target classification network; fozening the parameters of the front part of the fully trained source domain target classification network, and setting the parameters of the back part of the classification network to the trainable state; continuing to train the setclassification network using the training set; when the training is complete, using the verification set to evaluate the performance of the classification network. The method uses the transfer learning method to transfer the convolution neural network trained with non-side scan sonar images, and pre-processes the source domain data set according to the similarity principle, so as to improve the transfer learning efficiency and prevent the negative transfer phenomenon.

A depth information static gesture segmentation method includes the steps of converting a depth image into a depth grey-scale image of a same size, determining the gray scale of a gesture area in the depth grey-scale image, converting the depth grey-scale image into a binary image, conducting smooth processing for the binary image to obtain a mask image, determining a brightness component image, and segmenting the gesture area. The gesture area image segmented is accurate, and no segmentation problem exists. The influence of factors including non-uniform illumination, race difference, other human body parts and similar color background on gesture segmentation is prevented. The method is simple and rapid, and provides technical foundation for man-machine interaction works including gesture identification, control and medical surgeries.

The invention provides a large-scale train shift fault detection method and system based on deep learning. The detection method comprises the steps that of collecting and transmitting a train part shifting image is collected and transmitted into a deep learning framework to train a train part classification model, and obtaining a part shear map is obtained; marking the shear map and transmitting the shear map into a deep learning framework to train a component contour segmentation model; collecting a to-be-detected image, transmitting the to-be-detected image into the train part classification model for prediction, automatically detecting parts according to classification and positioning, and shearing; transmitting the shear map into a component contour segmentation model to segment inner and outer contours of a component, and obtaining relative position information; and setting a threshold value of the relative position information, carrying out logic judgment according to the inner and outer contour relative position information, and judging whether the train part has a displacement fault. Compared with the prior art, automatic detection of mass train part data is effectively achieved, the detection efficiency and accuracy are improved, and the manpower and material resource cost is reduced.

The invention discloses a CT image computer-aided diagnosis system and method for the liver disease based on data mining. The system comprises an input module, a texture feature extraction module, a classification diagnosis identification module and an output module. The method adopts the system and comprises the following steps of: segmenting and extracting the contour of an abdominal CT image inadvance, marking the normal liver CT, the liver cyst or the liver cancer, and importing the preprocessed image into the system; analyzing the image texture of the imported image to obtain the 13-dimensional gray-scale co-occurrence texture feature in the CT image by the image texture feature extraction module; according to a database, establishing a diagnosis model, and substituting the input liver CT image texture feature into a diagnosis model classifier for processing to obtain a diagnosis result and accuracy, wherein the database is stored in the classification diagnosis module and is composed of liver cancer and liver cyst image texture features and normal liver image texture features.

The invention is suitable for the field of the medical image technology, and provides a magnetic resonance image-based bone segmentation method and a system thereof. The method comprises the following steps: calculating step: calculating the Hessian matrix of each pixel and a characteristic value thereof in a first magnetic resonance image; extracting step: extracting the flake structure of the first magnetic resonance image and performing gray level enhancement on the flake structure of the first magnetic resonance image to obtain a second magnetic resonance image; optimization step: performing threshold connected calculation on the second magnetic resonance image, extracting a bone contour of the second magnetic resonance image, and carrying out smooth optimization to obtain a bone contour segmentation result of the second magnetic resonance image. Therefore by applying the magnetic resonance image-based bone segmentation method and the system thereof, a bone needing to be segmented is completely and independently segmented from the magnetic resonance image.

The invention discloses a 3D vertebra CT image active contour segmentation method fusing weighted random forest, and relates to the field of medical image processing. For the problem of sensitivity ofa vertebra CT image segmentation method to an initial contour, a method for automatically locating a vertebra and segmenting a vertebra CT image is proposed. The method comprises the steps of firstly, proposing a weighted random regression and classification forest algorithm to determine a vertebra center; secondly, putting an initial contour ball of active contour segmentation in the vertebra center, and segmenting out the vertebra in the image by adopting a 3D active contour segmentation method in combination with an energy function; and finally, performing combination output on trained models to obtain a complete vertebra CT image segmentation model. A spinal CT segmentation model proposed in the method can automatically locate the vertebra center and can perform automatic 3D segmentation on the vertebra, so that the spinal CT image segmentation steps and processes are simplified.