Depth Matters: Influence of Depth Cues on Visual Saliency

By Congyan Lang, Tam V. Nguyen, Harish Katti, Karthik Yadati, Mohan Kankanhalli, Shuicheng Yan. European Conference on Computer Vision, 2012

297 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Sensory Systems

View Paper: https://www.semanticscholar.org/paper/6166927f1fc3eea4b20a280ed898359e87eb332d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Ming-Ming Cheng
2. Deng-Ping Fan
3. Ling Shao
4. Jianbing Shen
5. Ming-Hsuan Yang
6. Wenguan Wang
7. Luc Van Gool
8. Dacheng Tao

In Sensory Systems

1. Deng-Ping Fan
2. Jianbing Shen
3. Ling Shao
4. Ming-Ming Cheng
5. Huan Fu
6. Qingming Huang
7. Tao Zhou
8. Ali Borji

Citations in the corpus

• Salient object detection: A survey (2014, background): “[111] through experimental analysis of the importance of depth cues for eye fixation prediction.”
• Review of Visual Saliency Detection With Comprehensive Information (2018, background): “devices, the depth map can be acquired conveniently, which lays the data foundation for RGBD saliency detection [51].”
• RGB-D salient object detection: A survey (2020, methodology): “[46], the first work on RGB-D based SOD, utilized Gaussian mixture models to model the distribution”
• Attentive Systems: A Survey (2017, methodology): “Li et al. (2011) use graph cuts (Kolmogorov and Zabih 2004; Boykov and Kolmogorov 2004) to find global optimal segmentation of an n-dimensional image.”
• DeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations (2015, background): “Recently, several other works have employed more complex features maps such as isocentric curvedness [24], regional histograms [25], depth cues [26] etc. for estimating saliency.”
• Salient Object Detection: A Discriminative Regional Feature Integration Approach (2013, methodology): “Recent developments include using isocentric curvedness and color [44], adopting image histogram [32], quaternion-based spectral analysis [40], utilizing depth cues [30], multitask sparsity pursuit [29], statistically modeling [42], exploring patch rarities [6], combing bottom-up and top-down featur”
• RGBD Salient Object Detection via Deep Fusion (2016, methodology): “In addition to RGB information, depth has been shown to be a practical cue for extracting saliency [21], [22], [23], [24].”
• Salient Region Detection by UFO: Uniqueness, Focusness and Objectness (2013, unknown): “[23].”
• Depth saliency based on anisotropic center-surround difference (2014, methodology): “We choose three state-of-the-art methods work on color images namely CNTX [9], RC [10], PCA [11] and three depth saliency methods namely CURV [16], SS [13], DP [14] for comparison.”
• Saliency Detection on Light Field (2017, unknown): “Lang et al. [28] used a Kinect sensor to capture the scene depth.”
• Cluster-based Co-saliency Detection (2013, unknown): “multiplication [35] of static salient features.”
• Depth Enhanced Saliency Detection Method (2014, background): “Moreover, both of their works [9, 6] focus only on the depth (or stereo) rather than the entire 3D saliency.”
• RGBD Salient Object Detection: A Benchmark and Algorithms (2014, background): “(2) Rather than directly combining depthinduced saliency with color-produced saliency via simple fusion strategies [25][13], the proposed RGBD saliency model simultaneously takes account of depth and appearance information from multiple layers.”