Results 91 to 100 of about 1,634 (141)

Robust Fall Army Worm detection in maize using multimodal RGB and thermal image fusion. [PDF]

Sci Rep
Sandhya P, Venkataramana B.
europepmc   +1 more source

LRNet: lightweight attention-oriented residual fusion network for light field salient object detection. [PDF]

Sci Rep
Ma S, Zhu X, Xu L, Zhou L, Chen D.
europepmc   +1 more source

Wavelet-Driven Multi-Band Feature Fusion for RGB-T Salient Object Detection. [PDF]

Sensors (Basel)
Zhao J, Wen X, He Y, Yang X, Song K.
europepmc   +1 more source

A lightweight tri-modal few-shot detection framework for fruit diversity recognition toward digital orchard archiving. [PDF]

Front Plant Sci
Xu H, Li H, Zhao J.
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Dynamic Selective Network for RGB-D Salient Object Detection

IEEE Transactions on Image Processing, 2021
RGB-D saliency detection is receiving more and more attention in recent years. There are many efforts have been devoted to this area, where most of them try to integrate the multi-modal information, i.e. RGB images and depth maps, via various fusion strategies. However, some of them ignore the inherent difference between the two modalities, which leads
Bolun Zheng, Yaoqi Sun, Jiyong Zhang
exaly   +3 more sources

CDNet: Complementary Depth Network for RGB-D Salient Object Detection

IEEE Transactions on Image Processing, 2021
Current RGB-D salient object detection (SOD) methods utilize the depth stream as complementary information to the RGB stream. However, the depth maps are usually of low-quality in existing RGB-D SOD datasets. Most RGB-D SOD networks trained with these datasets would produce error-prone results.
Ming-Ming Cheng, Wenda Jin, Qi Han
exaly   +3 more sources

Calibrated RGB-D Salient Object Detection

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021
Complex backgrounds and similar appearances between objects and their surroundings are generally recognized as challenging scenarios in Salient Object Detection (SOD). This naturally leads to the incorporation of depth information in addition to the conventional RGB image as input, known as RGB-D SOD or depth-aware SOD. Meanwhile, this emerging line of
Wei Ji 0011, Jingjing Li, Shuang Yu, Miao Zhang 0004, Yongri Piao, Shunyu Yao 0004, Qi Bi, Kai Ma 0002, Yefeng Zheng 0001, Huchuan Lu, Li Cheng 0001   +10 more
openaire   +1 more source

Circular Complement Network for RGB-D Salient Object Detection

Neurocomputing, 2021
Abstract With the supplement of texture and geometry cues in depth maps, some difficult scenes of salient object detection (SOD) in 2D images can be overcome. However, some distractors in the depth maps with relatively poor quality may interfere with SOD.
Zhen Bai 0001, Zhi Liu 0003, Gongyang Li, Linwei Ye, Yang Wang 0003   +4 more
openaire   +1 more source

Bifurcation Fusion Network for RGB-D Salient Object Detection

Journal of Circuits, Systems and Computers, 2022
With the rapid development of sensor technology, multi-modal data fusion methods based on deep neural networks provide a reliable guarantee for object recognition and detection in complex scenarios. Most of the existing RGB-D image salient object detection methods improve the salient object detection methods in 2D scenes, which have many problems, such
Zhi-Hua Zhao 0001, Li Chen
openaire   +1 more source