Results 211 to 220 of about 40,656 (261)

Reflection Removal Using RGB-D Images

2018 25th IEEE International Conference on Image Processing (ICIP), 2018
This paper proposes a novel reflection removal method for RGB-D images that achieve reflection removal and depth map recovery simultaneously. In general, there is a strong structure correlation between an RGB image and a depth map in gradient domain.
Toshihiro Shibata, Yuji Akai, Ryo Matsuoka   +2 more
openaire   +1 more source

Unsupervised Segmentation of RGB-D Images

2015
While unsupervised segmentation of RGB images has never led to results comparable to supervised segmentation methods, a surprising message of this paper is that unsupervised image segmentation of RGB-D images yields comparable results to supervised segmentation.
Zhuo Deng, Longin Jan Latecki
openaire   +1 more source

Image retargeting using RGB-D camera

Multimedia Tools and Applications, 2014
Forimage retargeting, most approaches only use color information to tackle this problem. In this paper, we analyze both color and depth information captured by a RGB-D camera to maintain the structure and preserve important regions. Particularly, we present a content-aware image retargeting algorithm based on depth information.
Wei-Yang Lin, Chih-Fong Tsai, Pei-Chen Wu, Bo-Rong Chen   +3 more
openaire   +1 more source

An Implementation of ResNet on the Classification of RGB-D Images

2020
Facial recognition is to identify human faces from an image. It is becoming more and more important these days as it can be applied in multiple industries, such as bank, airport, e-business, etc. Because of the broad application prospects, face recognition is actively developed and researched by many people, companies and academic organizations.
Tongyan Gong, Huiqian Niu
openaire   +1 more source

Saliency Cuts on RGB-D Images

2018
Saliency cuts aims to segment salient objects from a given saliency map. The existing saliency cuts methods focus on dealing with RGB images and videos, but ignore the exploration of depth cue, which limit their performance on RGB-D images. In this paper, we propose a novel saliency cuts method on RGB-D images, which utilizes both color and depth cues ...
Yuantian Wang, Lei Huang 0004, Tongwei Ren, Yunfei Zhang   +3 more
openaire   +1 more source

Structured Images for RGB-D Action Recognition

2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017
This paper presents an effective yet simple video representation for RGB-D based action recognition. It proposes to represent a depth map sequence into three pairs of structured dynamic images at body, part and joint levels respectively through bidirectional rank pooling.
Pichao Wang, Shuang Wang 0001, Zhimin Gao, Yonghong Hou, Wanqing Li 0001   +4 more
openaire   +1 more source

Accelerated DNA-SLAM for RGB-D images

Proceedings of the 2018 International Conference on Image and Graphics Processing, 2018
In the highly active research field of Simultaneous Localization And Mapping (SLAM), RGB-D images have been a major interest to use. Real-time SLAM for RGB-D images is of great importance since dense methods using all the depth and intensity values showed superior performance in the past.
Mina Ameli, Oliver Wasenmüller, Mohammad Reza Soheili, Jamshid Shanbehzadeh, Didier Stricker   +4 more
openaire   +1 more source

Efficient image segmentation of RGB-D images

2017 12th International Conference on Computer Engineering and Systems (ICCES), 2017
Image segmentation is a fundamental problem in computer vision. With the current advent of depth sensors, it is gradually becoming a research focus on how to utilize the depth information to improve image segmentation. This paper proposes an automatic RGB-D image segmentation method in which the depth and RGB images are separately segmented and the ...
Islam I. Fouad, Sherine Rady, Mostafa G. M. Mostafa   +2 more
openaire   +1 more source

3D Texture Recognition for RGB-D Images

2015
In this paper, we present a novel 3D object recognition system. In this system, we capture both the color and depth information of 3D objects using Kinect, and represent them in RGB-D images. To alleviate the deformations and partial defects of the obtained 3D surface textures, 3D texture reconstruction techniques are applied.
Guoqiang Zhong 0001, Xin Mao, Yaxin Shi, Junyu Dong   +3 more
openaire   +1 more source

Edge-Aware Convolution for RGB-D Image Segmentation

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ), 2020
Convolutional Neural Networks using RGB-D images as input have shown superior performance in recent research in the field of semantic segmentation. In RGB-D data, the depth channel encodes information from the 3D spatial domain, which has an inherent difference with the color channels.
Rongsen Chen, Fang-Lue Zhang, Taehyun Rhee   +2 more
openaire   +1 more source