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Method of using fully convolutional neural network to segment human hand area in stop-motion animation

A convolutional neural network and stop-motion animation technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as error-prone

Inactive Publication Date: 2018-05-08
SOUTH CHINA UNIV OF TECH
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The method of identifying skin color also has its obvious limitations, that is, it is easy to make mistakes when the background contains colors similar to the skin

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  • Method of using fully convolutional neural network to segment human hand area in stop-motion animation
  • Method of using fully convolutional neural network to segment human hand area in stop-motion animation
  • Method of using fully convolutional neural network to segment human hand area in stop-motion animation

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Embodiment Construction

[0082] The present invention will be further described below in conjunction with specific examples.

[0083] The method of using the full convolutional neural network provided by this embodiment to automatically segment the hand region of the stop-motion animation is as follows:

[0084] 1) Data set preparation

[0085] Training first requires the establishment of a dataset containing the human hand region. The data set can contain 1500-3000 initial pictures, with different male or female hands as the collection targets, preferably both men and women. The shooting environment of the picture is not limited, such as in the student dormitory and outdoors of the office building, etc. In the picture, the human hand can grab some objects for shooting, for daily necessities, office supplies, etc., or not grab the objects for shooting. You can consider shooting in different brightness environments, for example, the brightness of the pictures taken in the dormitory is relatively dark...

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Abstract

The invention discloses a method of using a fully convolutional neural network to segment a human hand area in a stop-motion animation. The method comprises steps: 1) data inputting is carried out; 2)the fully convolutional network is used for feature extraction and preliminary segmentation; 3) a conditional random field (CRF) algorithm is used to optimize the segmentation effects; 4) the networkmodel is trained; and 5) the model which completes the training is used to segment an inputted picture. The method disclosed in the invention mainly aims to solve the problem that through self building a picture data set containing the human hand area, the network model is built, and the data set is used to train the network model. After training, the network model can carry out high-accuracy segmentation on the human hand area. The method of using the fully convolutional neural network to segment the human hand area in the stop-motion animation has the advantages of high accuracy, good anti-noise performance, simple use, high efficiency, quick speed and the like.

Description

technical field [0001] The invention relates to the fields of computer graphics and stop-motion animation production, in particular to a method for segmenting human hand regions in stop-motion animation using a fully convolutional neural network. Background technique [0002] Stop-motion animation is to shoot animations one by one, and then play these animations to form a continuous animation film. Although more computer-aided design and 3D technology are used in animation generation, stop-motion animation is still a unique branch in the animation industry. It still has an important charm and occupies a certain weight in the industry. The shooting method of stop-motion animation usually requires some kind of support to support the object to be photographed to form various actions, and then use different methods to erase the support in the later stage of animation production. After erasing the support, it can Make objects appear to animate themselves. The support is usually...

Claims

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Application Information

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IPC IPC(8): G06T7/194G06T7/90G06T3/40G06N3/08G06N3/04
CPCG06N3/08G06T3/40G06T7/194G06T7/90G06T2207/20132G06N3/047G06N3/045
Inventor 许家荣李桂清
Owner SOUTH CHINA UNIV OF TECH
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