Results 31 to 40 of about 18,850,036 (270)

All in One: RGB, RGB-D, and RGB-T Salient Object Detection [PDF]

, 2023
Salient object detection (SOD) aims to identify the most attractive objects within an image. Depending on the type of data being detected, SOD can be categorized into various forms, including RGB, RGB-D (Depth), RGB-T (Thermal) and light field SOD ...
Zhao, Zhongqiu, Zhang, Zhao, Jia, Xingzhao, Dongye, Changlei   +3 more
core  

RGB-D datasets using microsoft kinect or similar sensors: a survey [PDF]

, 2016
RGB-D data has turned out to be a very useful representation of an indoor scene for solving fundamental computer vision problems. It takes the advantages of the color image that provides appearance information of an object and also the depth image that ...
Cai, Ziyun, Liu, Li, Li Liu, Han, Jungong, Shao, Ling, Jungong Han, Ling Shao, Ziyun Cai   +7 more
core   +2 more sources

LIANet: Layer Interactive Attention Network for RGB-D Salient Object Detection

IEEE Access, 2022
RGB-D salient object detection (SOD) usually describes two modes’ classification or regression problem, namely RGB and depth. The existing RGB-D SOD methods use depth hints to increase the detection performance, meanwhile they focus on the quality
Yibo Han, Liejun Wang, Anyu Du, Shaochen Jiang   +3 more
doaj   +1 more source

TANet: Transformer‐based asymmetric network for RGB‐D salient object detection

IET Computer Vision, 2023
Existing RGB‐D salient object detection methods mainly rely on a symmetric two‐stream Convolutional Neural Network (CNN)‐based network to extract RGB and depth channel features separately.
Chang Liu, Gang Yang, Shuo Wang, Hangxu Wang, Yunhua Zhang, Yutao Wang   +5 more
doaj   +1 more source

Learning RGB-D salient object detection using background enclosure, depth contrast, and top-down features

, 2022
In human visual saliency, top-down and bottom-up information are combined as a basis of visual attention. Recently, deep Convolutional Neural Networks (CNN) have demonstrated strong performance on RGB salient object detection, providing an effective ...
Barnes, Nick, Feng, David, Shigematsu, Riku, You, Shaodi   +3 more
core   +1 more source

Three‐stream RGB‐D salient object detection network based on cross‐level and cross‐modal dual‐attention fusion

IET Image Processing, 2023
The effective integration of RGB and depth map features to improve the performance of RGB‐D salient object detection (SOD) has garnered significant research interest.
Lingbing Meng, Mengya Yuan, Xuehan Shi, Qingqing Liu, Fei Cheng, Lingli Li   +5 more
doaj   +1 more source

Bi-directional Progressive Guidance Network for RGB-D Salient Object Detection [PDF]

, 2022
Most existing RGB-D salient detection models pay more attention to the quality of the depth images, while in some special cases, the quality of RGB images may even have greater impacts on saliency detection, which has long been ignored and underestimated.
Luo, Yongjiang, Han, Jungong, Liu, Yi, Zhang, Qiang, Qin, Qi, Yang, Yang, Qi Qin, Yongjiang Luo, Qiang Zhang, Jungong Han, Yang Yang, Yi Liu   +11 more
core   +1 more source

Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images

, 2022
Training deep models for RGB-D salient object detection (SOD) often requires a large number of labeled RGB-D images. However, RGB-D data is not easily acquired, which limits the development of RGB-D SOD techniques.
Wang, X, Li, P, Zhu, Lei, Wang, Xiaoqiang, Fu, Huazhu, Li, Ping, Yang, Y, Wu, F, Zhuang, Yueting, Tang, Siliang, Zhu, L, Fu, H, Tang, S, Zhuang, Y, Wu, Fei, Yang, Yi   +15 more
core   +1 more source

COTS: A Multipurpose RGB-D Dataset for Saliency and Image Manipulation Applications

IEEE Access, 2021
Many modern computer vision systems include several modules that perform different processing operations packaged as a single pipeline architecture. This generally introduces a challenge in the evaluation process since most datasets provide evaluation ...
Dylan Seychell, Carl James Debono, Mark Bugeja, Jeremy Borg, Matthew Sacco   +4 more
doaj   +1 more source

Local Background Enclosure for RGB-D Salient Object Detection [PDF]

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016
Recent work in salient object detection has considered the incorporation of depth cues from RGB-D images. In most cases, depth contrast is used as the main feature. However, areas of high contrast in background regions cause false positives for such methods, as the background frequently contains regions that are highly variable in depth.
David Feng 0002, Nick Barnes, Shaodi You, Chris McCarthy   +3 more
openaire   +2 more sources