Learning RGB-D Salient Object Detection using background enclosure, depth contrast, and top-down features

Recently, deep Convolutional Neural Networks (CNN) have demonstrated strong performance on RGB salient object detection. Although, depth information can help improve detection results, the exploration of CNNs for RGB-D salient object detection remains limited. Here we propose a novel deep CNN architecture for RGB-D salient object detection that exploits high-level, mid-level, and low level features. Further, we present novel depth features that capture the ideas of background enclosure and depth contrast that are suitable for a learned approach. We show improved results compared to state-of-the-art RGB-D salient object detection methods. We also show that the low-level and mid-level depth features both contribute to improvements in the results. Especially, F-Score of our method is 0.848 on RGBD1000 dataset, which is 10.7% better than the second place

RGB-D Salient Object Detection: A Survey

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 captured. Although various RGB-D based SOD models with promising performance have been proposed over the past several years, an in-depth understanding of these models and challenges in this topic remains lacking. In this paper, we provide a comprehensive survey of RGB-D based SOD models from various perspectives, and review related benchmark datasets in detail. Further, considering that the light field can also provide depth maps, we review SOD models and popular benchmark datasets from this domain as well. Moreover, to investigate the SOD ability of existing models, we carry out a comprehensive evaluation, as well as attribute-based evaluation of several representative RGB-D based SOD models. Finally, we discuss several challenges and open directions of RGB-D based SOD for future research. All collected models, benchmark datasets, source code links, datasets constructed for attribute-based evaluation, and codes for evaluation will be made publicly available at https://github.com/taozh2017/RGBDSODsurveyComment: 24 pages, 12 figures. Has been accepted by Computational Visual Medi

RGB-D Scene Representations for Prosthetic Vision

This thesis presents a new approach to scene representation for prosthetic vision. Structurally salient information from the scene is conveyed through the prosthetic vision display. Given the low resolution and dynamic range of the display, this enables robust identification and reliable interpretation of key structural features that are missed when using standard appearance-based scene representations. Specifically, two different types of salient structure are investigated: salient edge structure, for depiction of scene shape to the user; and salient object structure, for emulation of biological attention deployment when viewing a scene. This thesis proposes and evaluates novel computer vision algorithms for extracting salient edge and salient object structure from RGB-D input. Extraction of salient edge structure from the scene is first investigated through low-level analysis of surface shape. Our approach is based on the observation that regions of irregular surface shape, such as the boundary between the wall and the floor, tend to be more informative of scene structure than uniformly shaped regions. We detect these surface irregularities through multi-scale analysis of iso-disparity contour orientations, providing a real time method that robustly identifies important scene structure. This approach is then extended by using a deep CNN to learn high level information for distinguishing salient edges from structural texture. A novel depth input encoding called the depth surface descriptor (DSD) is presented, which better captures scene geometry that corresponds to salient edges, improving the learned model. These methods provide robust detection of salient edge structure in the scene. The detection of salient object structure is first achieved by noting that salient objects often have contrasting shape from their surroundings. Contrasting shape in the depth image is captured through the proposed histogram of surface orientations (HOSO) feature. This feature is used to modulate depth and colour contrast in a saliency detection framework, improving the precision of saliency seed regions and through this the accuracy of the final detection. After this, a novel formulation of structural saliency is introduced based on the angular measure of local background enclosure (LBE). This formulation addresses fundamental limitations of depth contrast methods and is not reliant on foreground depth contrast in the scene. Saliency is instead measured through the degree to which a candidate patch exhibits foreground structure. The effectiveness of the proposed approach is evaluated through both standard datasets as well as user studies that measure the contribution of structure-based representations. Our methods are found to more effectively measure salient structure in the scene than existing methods. Our approach results in improved performance compared to standard methods during practical use of an implant display

Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks

The use of RGB-D information for salient object detection has been extensively explored in recent years. However, relatively few efforts have been put towards modeling salient object detection in real-world human activity scenes with RGBD. In this work, we fill the gap by making the following contributions to RGB-D salient object detection. (1) We carefully collect a new SIP (salient person) dataset, which consists of ~1K high-resolution images that cover diverse real-world scenes from various viewpoints, poses, occlusions, illuminations, and backgrounds. (2) We conduct a large-scale (and, so far, the most comprehensive) benchmark comparing contemporary methods, which has long been missing in the field and can serve as a baseline for future research. We systematically summarize 32 popular models and evaluate 18 parts of 32 models on seven datasets containing a total of about 97K images. (3) We propose a simple general architecture, called Deep Depth-Depurator Network (D3Net). It consists of a depth depurator unit (DDU) and a three-stream feature learning module (FLM), which performs low-quality depth map filtering and cross-modal feature learning respectively. These components form a nested structure and are elaborately designed to be learned jointly. D3Net exceeds the performance of any prior contenders across all five metrics under consideration, thus serving as a strong model to advance research in this field. We also demonstrate that D3Net can be used to efficiently extract salient object masks from real scenes, enabling effective background changing application with a speed of 65fps on a single GPU. All the saliency maps, our new SIP dataset, the D3Net model, and the evaluation tools are publicly available at https://github.com/DengPingFan/D3NetBenchmark.Comment: Accepted in TNNLS20. 15 pages, 12 figures. Code: https://github.com/DengPingFan/D3NetBenchmar