NDLI: Adaptive integration of depth and color for objectness estimation

Content Provider	IEEE Xplore Digital Library
Author	Xiangyang Xu Ling Ge Tongwei Ren Gangshan Wu
Copyright Year	2015
Description	Author affiliation: Collaborative Innovation Center of Novel Software Technol. & Industrialization, Nanjing Univ., Nanjing, China (Xiangyang Xu; Ling Ge; Tongwei Ren; Gangshan Wu)
Abstract	The goal of objectness estimation is to predict a moderate number of proposals of all possible objects in a given image with high efficiency. Most existing works solve this problem solely in conventional 2D color images. In this paper, we demonstrate that the depth information could benefit the estimation as a complementary cue to color information. After detailed analysis of depth characteristics, we present an adaptively integrated description for generic objects, which could take full advantages of both depth and color. With the proposed objectness description, the ambiguous area, especially the highly textured regions in original color maps, can be effectively discriminated. Meanwhile, the object boundary areas could be further emphasized, which leads to a more powerful objectness description. To evaluate the performance of the proposed approach, we conduct the experiments on two challenging datasets. The experimental results show that our proposed objectness description is more powerful and effective than state-of-the-art alternatives.
Starting Page	1
Ending Page	6
File Size	1523828
Page Count	6
File Format	PDF
ISBN	9781479970827
DOI	10.1109/ICME.2015.7177498
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-29
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Collaboration Manuals generic object description Objectness estimation object proposal depth map
Content Type	Text
Resource Type	Article

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