Results 21 to 30 of about 18,850,036 (270)
Remote Sensing, 2023 Multi-modal feature fusion and effectively exploiting high-level semantic information are critical in salient object detection (SOD). However, the depth maps complementing RGB image fusion strategies cannot supply effective semantic information when the ...
Zhihong Zeng +3 more
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RGB-D salient object detection: A survey. [PDF]
Comput Vis Media (Beijing), 2021 Salient object detection (SOD), which simulates the human visual perception system to locate the most attractive object(s) in a scene, has been widely applied to various computer vision tasks. Now, with the advent of depth sensors, depth maps with affluent spatial information that can be beneficial in boosting the performance of SOD, can easily be ...
Zhou T +4 more
europepmc +6 more sources
Advancing in RGB-D Salient Object Detection: A Survey
Applied SciencesThe human visual system can rapidly focus on prominent objects in complex scenes, significantly enhancing information processing efficiency. Salient object detection (SOD) mimics this biological ability, aiming to identify and segment the most prominent ...
Ai Chen +4 more
doaj +2 more sources
Study on salient object segmentation based on depth information guidance and SAM low-rank adaptation fine-tuning. [PDF]
PLoS ONEAccurate segmentation of salient objects is crucial for various computer vision applications including image editing, autonomous driving, and object detection. While research on using depth information (RGB-D) in saliency detection is gaining significant
Weiping M A
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Lightweight multi-level feature difference fusion network for RGB-D-T salient object detection
Journal of King Saud University: Computer and Information Sciences, 2023 In recent years, bimodal salient object detection has developed rapidly. In view of the advanced performance of their robustness to extreme situations such as background similarity and illumination variation, researchers began to focus on RGB-Depth ...
Kechen Song +5 more
doaj +2 more sources
RGB-D Saliency Detection via Depth Quality Perception and Hierarchical Feature Guidance [PDF]
Jisuanji gongcheng, 2023 Existing fusion-based RGB-D saliency object detection methods ignore the differences between RGB and depth map features when fusing cross-modal features.The problems from fusing unbalanced cross-modal features makes the model insufficiently leverage ...
SONG Mengke, ZHENG Yuanchao, CHEN Chenglizhao
doaj +1 more source
RGB-D Salient Object Detection Using Saliency and Edge Reverse Attention
IEEE Access, 2023 RGB-D salient object detection is a task to detect visually significant objects in an image using RGB and depth images. Although many useful CNN-based methods have been proposed in the past, there are some problems such as blurring of object boundaries ...
Tomoki Ikeda, Masaaki Ikehara
doaj +1 more source
Self-Supervised Pretraining for RGB-D Salient Object Detection
Proceedings of the AAAI Conference on Artificial Intelligence, 2022 Existing CNNs-Based RGB-D salient object detection (SOD) networks are all required to be pretrained on the ImageNet to learn the hierarchy features which helps provide a good initialization. However, the collection and annotation of large-scale datasets are time-consuming and expensive. In this paper, we utilize self-supervised representation learning
Xiaoqi Zhao 0003 +4 more
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Employing Bilinear Fusion and Saliency Prior Information for RGB-D Salient Object Detection [PDF]
, 2022 Multi-modal feature fusion and saliency reasoning are two core sub-tasks of RGB-D salient object detection. However, most existing models employ linear fusion strategies (e.g., concatenation) for multi-modal feature fusion and use a simple coarse-to-fine
Zhang, Dingwen +9 more
core +1 more source
HOSO: Histogram of Surface Orientation for RGB-D Salient Object Detection
, 2022 Salient object detection using RGB-D data is an emerging field in computer vision. Salient regions are often characterized by an unusual surface orientation profile with respect to the surroundings.
Barnes, Nick, Feng, David, You, Shaodi
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