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603results about How to "Improve segmentation" patented technology

Automatic object extraction

ActiveUS7085401B2Poor exposureQuality degradationImage enhancementImage analysisHigh rateBinary image
A method for automatic, stable and robust object extraction of moving objects in color video frames, achieved without any prior knowledge of the video content. For high rate video, the method includes providing at least a first and a second high frame rate video frames, performing a reciprocal illumination correction of the first and second video frames to yield respective first and second smoothed frames, performing a change detection operation between the first and second smoothed frames to obtain a difference image, and performing a local adaptive thresholding operation on the difference image to generate a binary image containing extracted objects, the local thresholding operation using a weight test to determine a boundary of each of the extracted objects. For an extracted object with a fragmented boundary, the method further comprises re-unifying the boundary. For low rate video, additional steps include: an edge correction applied on the first image to yield a first edge-corrected image, a global thresholding applied to the first edge-corrected image to yield a first binary edge image, and an ANDing operation on the difference image and the first binary edge image to generate a second binary image which is fed to the local adaptive thresholding operation.
Owner:F POSZAT HU

System and method for semantic segmentation using hybrid dilated convolution (HDC)

A system and method for semantic segmentation using hybrid dilated convolution (HDC) are disclosed. A particular embodiment includes: receiving an input image; producing a feature map from the input image; performing a convolution operation on the feature map and producing multiple convolution layers; grouping the multiple convolution layers into a plurality of groups; applying different dilation rates for different convolution layers in a single group of the plurality of groups; and applying a same dilation rate setting across all groups of the plurality of groups.
Owner:TUSIMPLE INC

Semi-supervised multi-spectral remote sensing image segmentation method based on spectral clustering

The invention discloses a semi-supervised multi-spectral remote sensing image segmentation method based on spectral clustering; the segmentation process includes that: (1) the characteristics inputted to the multi-spectral sensing image are extracted; (2) N points without labels and M points with labels are randomly and evenly sampled from a multi-spectral sensing image with S pixel points to form a set n which is the summation of N and M, wherein M points with labels are used for creating pairing limit information Must-link and Cannot-link sets; (3) the sampled point set is analyzed through semi-supervised spectral clustering to obtain the class labels of the n (n=N+M) points; (4) the sampled n (n=N+M) points are used as the training sample to classify the rest (S-N-M) points through nearest-neighbor rule, each pixel point is assigned with a class label according to the class of the pixel point and is used as the segmentation result of the inputted image. Compared with prior art, the invention has good image segmentation effect, strong operability, improves the classification accuracy, avoids searching the optimum parameters through repeated test, has small limit on image size and is better applicable to the segmentation of multi-class multi-spectral sensing images.
Owner:XIDIAN UNIV

Brain glioma segmentation based on cascaded convolutional neural network

The invention discloses a brain glioma segmentation method based on a cascaded convolutional neural network, and the method comprises the steps: carrying out the primary coarse segmentation of a braintumor region, and extracting the approximate position information of a tumor; expanding 10 pixels for each dimension on the basis of coarse segmentation and taking the 10 pixels as input of a fine segmentation network; improviing the fine segmentation network, so as to enable the fine segmentation network to combine the advantages of dense connection, an improved loss function and multi-dimensional model integration; designing an integrated model of three directions (2D, 2.5 D and 3DCNN models), and respectively considering all information of different resolutions corresponding to each direction; integrating post-processing operation condition random fields in a segmentation algorithm, and optimizing continuity of segmentation results in appearance and spatial positions. According to themethod, the brain glioma is segmented through the two-step cascaded convolutional neural network, the advantages of dense connection, a new loss function and multi-dimensional model integration are combined, an integration model in multiple directions is designed, and finally a segmentation result is optimized through a conditional random field.
Owner:NANJING UNIV OF AERONAUTICS & ASTRONAUTICS

Semantic image segmentation method based on multichannel convolutional neural network

The invention provides a semantic image segmentation method based on a multichannel convolutional neural network. A provided network model comprises 6 channels; fusion of shallow and deep features isrealized through addition operation of outputs of the channels; and compared with a single-channel network, the structure can improve segmentation performance of semantic images. The whole network model structure improves a receptive field by combining an a'trous algorithm, so that the captured global information is allowed to be richer; and in the test phase, the segmentation result is subjectedto optimization through a full connection condition random field, so that the segmentation performance of the semantic images can be further improved.
Owner:CHINA UNIV OF MINING & TECH
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