Saliency Detection on Light Field

Li, Nianyi; Ye, Jinwei; Ji, Yu; Ling, Haibin; Yu, Jingyi

doi:10.1109/tpami.2016.2610425

Cited by 103 publications

(76 citation statements)

References 36 publications

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“…In some methods, it is also named SSB. LFSD [22]: contains 100 image pairs captured by a Lytro camera. RGBD135 [8]: includes 135 indoor images collected by the Microsoft Kinect.…”

Section: Experiments 41 Datasetmentioning

confidence: 99%

Feature Reintegration over Differential Treatment: A Top-down and Adaptive Fusion Network for RGB-D Salient Object Detection

Zhang

Piao

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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Most methods for RGB-D salient object detection (SOD) utilize the same fusion strategy to explore the cross-modal complementary information at each level. However, this may ignore di erent feature contributions from two modalities on di erent levels towards prediction. In this paper, we propose a novel top-down multi-level fusion structure where di erent fusion strategies are utilized to e ectively explore the low-level and high-level features. This is achieved by designing the interweave fusion module (IFM) to effectively integrate the global information and designing the gated select fusion module (GSFM) to discriminatively select useful local information by ltering out the unnecessary one from RGB and depth data. Moreover, we propose an adaptive fusion module (AFM) to reintegrate the fused cross-modal features of each level to predict a more accurate result. Comprehensive experiments on 7 challenging benchmark datasets demonstrate that our method achieves the competitive performance over 14 state-of-the-art RGB-D alternative methods. CCS CONCEPTS • Computing methodologies → Interest point and salient region detections.

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“…In some methods, it is also named SSB. LFSD [22]: contains 100 image pairs captured by a Lytro camera. RGBD135 [8]: includes 135 indoor images collected by the Microsoft Kinect.…”

Section: Experiments 41 Datasetmentioning

confidence: 99%

Feature Reintegration over Differential Treatment: A Top-down and Adaptive Fusion Network for RGB-D Salient Object Detection

Zhang

Piao

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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show abstract

“…The depth cue is leveraged for saliency analysis derived from stereopsic image pairs in [57] and a depth camera (e.g., Kinect) in [58]. Li et al [59] adopt the light field camera for salient object detection. Besides, spectral analysis in the frequency domain is used to detect salient regions [13].…”

Section: Related Workmentioning

confidence: 99%

Salient Object Detection: A Discriminative Regional Feature Integration Approach

et al. 2016

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Abstract-Salient object detection has been attracting a lot of interest, and recently various heuristic computational models have been designed. In this paper, we formulate saliency map computation as a regression problem. Our method, which is based on multi-level image segmentation, utilizes the supervised learning approach to map the regional feature vector to a saliency score. Saliency scores across multiple layers are finally fused to produce the saliency map. The contributions lie in two-fold. One is that we propose a discriminate regional feature integration approach for salient object detection. Compared with existing heuristic models, our proposed method is able to automatically integrate high-dimensional regional saliency features and choose discriminative ones. The other is that by investigating standard generic region properties as well as two widely studied concepts for salient object detection, i.e., regional contrast and backgroundness, our approach significantly outperforms state-of-the-art methods on six benchmark datasets. Meanwhile, we demonstrate that our method runs as fast as most existing algorithms.

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“…Dense stereo matching solutions have exploited unique properties, e.g., spatial and angular coherence [3], ray geometric constraints [4], [5], [6], focal symmetry [7], and defocus blurs [8]. In addition to 3D reconstruction, LF stereo matching can also address traditionally challenging problems, e.g., transparent object reconstruction [9], saliency detection [10] and scene classification [11].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Light Field Stereo Matching

Zhu

Xue²,

et al. 2019

IEEE Trans. Pattern Anal. Mach. Intell.

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In this paper, we describe how scene depth can be extracted using a hyperspectral light field capture (H-LF) system. Our H-LF system consists of a array of cameras, with each camera sampling a different narrow band in the visible spectrum. There are two parts to extracting scene depth. The first part is our novel cross-spectral pairwise matching technique, which involves a new spectral-invariant feature descriptor and its companion matching metric we call bidirectional weighted normalized cross correlation (BWNCC). The second part, namely, H-LF stereo matching, uses a combination of spectral-dependent correspondence and defocus cues that rely on BWNCC. These two new cost terms are integrated into a Markov Random Field (MRF) for disparity estimation. Experiments on synthetic and real H-LF data show that our approach can produce high-quality disparity maps. We also show that these results can be used to produce the complete plenoptic cube in addition to synthesizing all-focus and defocused color images under different sensor spectral responses.

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Saliency Detection on Light Field

Cited by 103 publications

References 36 publications

Feature Reintegration over Differential Treatment: A Top-down and Adaptive Fusion Network for RGB-D Salient Object Detection

Feature Reintegration over Differential Treatment: A Top-down and Adaptive Fusion Network for RGB-D Salient Object Detection

Salient Object Detection: A Discriminative Regional Feature Integration Approach

Hyperspectral Light Field Stereo Matching

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