JOURNAL ARTICLE

Saliency detection for RGBD image using optimization

Abstract

Saliency detection in images attracts much research attention for its usage in numerous multimedia applications. In this paper, we propose a saliency detection method based on optimization for RGBD images. With RGBD images, our method utilizes the depth channel to enhance the identification of background and foreground regions. We firstly generate new depth image by using non-linear transformation and outstand object region. Then, we introduce saliency optimization framework to integrate the depth cue and other low-level cues to obtain the final saliency map. The experimental results demonstrate that our method performs better in saliency detection for RGBD Images.

Keywords:
Computer science Artificial intelligence Computer vision Image (mathematics) Kadir–Brady saliency detector Transformation (genetics) Channel (broadcasting) Object detection Identification (biology) Object (grammar) Pattern recognition (psychology) Saliency map

Metrics

3
Cited By
0.25
FWCI (Field Weighted Citation Impact)
30
Refs
0.58
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Visual Attention and Saliency Detection
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Image and Video Quality Assessment
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Olfactory and Sensory Function Studies
Life Sciences →  Neuroscience →  Sensory Systems

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