Three-dimensional image visual saliency detection method based on local comparison and global guidance

A technology of three-dimensional images and detection methods, which is applied in computer parts, character and pattern recognition, instruments, etc., to achieve the effect of improving detection performance and reducing difficulty

Active Publication Date: 2019-12-10
ZHEJIANG UNIVERSITY OF SCIENCE AND TECHNOLOGY
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This may ignore the intrinsic relationship between color features and deep features

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  • Three-dimensional image visual saliency detection method based on local comparison and global guidance
  • Three-dimensional image visual saliency detection method based on local comparison and global guidance
  • Three-dimensional image visual saliency detection method based on local comparison and global guidance

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Embodiment Construction

[0030] The present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments.

[0031] Stereoscopic image visual saliency detection method of the present invention comprises two processes of training phase and testing phase:

[0032] The specific steps of the described training phase process:

[0033] Step 1_1: First, select the left viewpoint image, depth image and corresponding real subjective visual saliency map of N original stereoscopic images, and form a training set, and record the left viewpoint image of the nth original stereoscopic image in the training set as The depth image of the original stereo image is denoted as {D n (x, y)}, the real human gaze image is denoted as {G n (x, y)}, where, 1≤x≤W, 1≤y≤H, W means The width, H means the height of, express The pixel value of the pixel point whose coordinate position is (x, y), D n (x, y) means {D n The pixel value of the pixel whose coordinate positio...

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Abstract

The invention discloses a stereoscopic image visual saliency detection method based on local comparison and global guidance. The method comprises the following steps of selecting a left viewpoint image, a depth image and a corresponding subjective visual saliency map of the original three-dimensional image to form a training set; constructing a neural network, inputting the training set into a neural network model for training, and calculating a loss function value between each saliency prediction graph and a corresponding subjective visual saliency graph in the training set; and utilizing thetrained convolutional neural network to perform prediction processing on a to-be-detected three-dimensional image to obtain a visual saliency map, thereby realizing saliency detection of the image. Features of an RGB image and a depth image are extracted by using the convolutional neural network. A global up-sampling block is used for extracting global information, and the up-sampling block is used for learning local comparison features and gradually fusing comparison information. Meanwhile, the latest progress in the aspect of deep learning, such as an attention model, is utilized, so that the network pays more attention to a remarkable target, and a deeper network architecture is allowed to realize more accurate detection.

Description

technical field [0001] The invention relates to a visual salience detection method, in particular to a stereoscopic image visual salience detection method with local comparison and global guidance. Background technique [0002] Visual saliency is an important feature of the human visual system in processing visual information. It is the cognitive process of selecting relevant regions while obtaining the most important visual information from a visual scene. As an important and challenging problem in computer vision, saliency detection has attracted a large number of researchers in the past few decades because it can be used in various multimedia processing applications such as object recognition, image retargeting, Image compression, object tracking, defect detection, abnormal event detection and identification and other tasks. Saliency detection methods are generally divided into human gaze prediction methods and salient object detection methods. The first goal is to det...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/32G06K9/62G06N3/04
CPCG06V10/25G06N3/045G06F18/214
Inventor 周武杰吕营雷景生钱亚冠王海江何成
Owner ZHEJIANG UNIVERSITY OF SCIENCE AND TECHNOLOGY
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