mEdgeBoxes: objectness estimation for depth image

Zhiwen Fang; Zhiguo Cao; Yang Xiao; Lei Zhu; Hao Lu

doi:10.1117/12.2205726

14 December 2015 mEdgeBoxes: objectness estimation for depth image

Zhiwen Fang, Zhiguo Cao, Yang Xiao, Lei Zhu, Hao Lu

Proceedings Volume 9813, MIPPR 2015: Pattern Recognition and Computer Vision; 98130A (2015) https://doi.org/10.1117/12.2205726
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

Abstract

Object detection is one of the most important researches in computer vision. Recently, category-independent objectness in RGB images has been a hot field for its generalization ability and efficiency as a pre-filtering procedure of the object detection. Many traditional applications have been transferred from the RGB images to the depth images since the economical depth sensors, such as Kinect, were popularized. The depth data represents the distance information. Because of the special characteristic, the methods of objectness evaluation in RGB images are often invalid in depth images. In this study, we propose mEdgeboxes to evaluate the objectness in depth image. Aside from detecting the edge from the raw depth information, we extract another edge map from the orientation information based on the normal vector. Two kinds of the edge map are integrated and are fed to Edgeboxes1 in order to produce the object proposals. The experimental results on two challenging datasets demonstrate that the detection rate of the proposed objectness estimation method can achieve over 90% with 1000 windows. It is worth noting that our approach generally outperforms the state-of-the-art methods on the detection rate.

Citation Download Citation

Zhiwen Fang, Zhiguo Cao, Yang Xiao, Lei Zhu, and Hao Lu "mEdgeBoxes: objectness estimation for depth image", Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 98130A (14 December 2015); https://doi.org/10.1117/12.2205726

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KEYWORDS

RGB color model

Image analysis

Image segmentation

Image sensors

Computer vision technology

Machine vision

Sensors

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