Human-Shape Image Segmentation Method
First Claim
1. A human-shape image segmentation method, characterized by comprising:
- step S1;
extracting multi-scale context information for all first pixel points for training a human-shape image;
step S2;
sending image blocks of all scales of all the first pixel points into a same convolution neural network to form a multi-channel convolutional neural network group, wherein each channel corresponds to image blocks of one scale;
step S3;
training the neural network group using a back propagation algorithm to obtain human-shape image segmentation training model data;
step S4;
extracting multi-scale context information for all second pixels points for testing the human-shape image;
step S5;
sending image blocks of different scales of each of the second pixel points into a neural network channel corresponding to the human-shape image segmentation training model, wherein all of said neural network channels are merged together in a full-connected layer, a first value representing a first probability of said second pixel points belonging to the human-shape region is output at a first node of the last layer of the full-connected layer, and a second value representing a probability of said second pixel points being outside of the human-shape region is output at a second node of the last layer of the full-connected layer;
if said first probability is larger than said second probability, the second pixel points belong to the human-shape region, otherwise, the second pixel points are outside of the human-shape region.
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Abstract
A human-shape image segmentation method comprising: extracting multi-scale context information for all first pixel points for training a human-shape image; sending image blocks of all scales of all the first pixel points into a same convolution neural network to form a multi-channel convolutional neural network group, wherein each channel corresponds to image blocks of one scale; training the neural network group using a back propagation algorithm to obtain human-shape image segmentation training model data; extracting multi-scale context information for all second pixels points for testing the human-shape image; sending image blocks of different scales of each of the second pixel points into a neural network channel corresponding to the human-shape image segmentation training model, wherein if said first probability is larger than said second probability, the second pixel points belong to the human-shape region, otherwise, the second pixel points are outside of the human-shape region. The human-shape image segmentation method is fast in image segmentation speed and high in accuracy.
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Citations
4 Claims
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1. A human-shape image segmentation method, characterized by comprising:
-
step S1;
extracting multi-scale context information for all first pixel points for training a human-shape image;step S2;
sending image blocks of all scales of all the first pixel points into a same convolution neural network to form a multi-channel convolutional neural network group, wherein each channel corresponds to image blocks of one scale;step S3;
training the neural network group using a back propagation algorithm to obtain human-shape image segmentation training model data;step S4;
extracting multi-scale context information for all second pixels points for testing the human-shape image;step S5;
sending image blocks of different scales of each of the second pixel points into a neural network channel corresponding to the human-shape image segmentation training model, wherein all of said neural network channels are merged together in a full-connected layer, a first value representing a first probability of said second pixel points belonging to the human-shape region is output at a first node of the last layer of the full-connected layer, and a second value representing a probability of said second pixel points being outside of the human-shape region is output at a second node of the last layer of the full-connected layer;
if said first probability is larger than said second probability, the second pixel points belong to the human-shape region, otherwise, the second pixel points are outside of the human-shape region. - View Dependent Claims (2, 3, 4)
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Specification