RGBD Salient Object Detection via Deep Fusion
نویسندگان
چکیده
منابع مشابه
RGBD Salient Object Detection: A Benchmark and Algorithms
Although depth information plays an important role in the human vision system, it is not yet well-explored in existing visual saliency computational models. In this work, we first introduce a large scale RGBD image dataset to address the problem of data deficiency in current research of RGBD salient object detection. To make sure that most existing RGB saliency models can still be adequate in R...
متن کاملSelf-explanatory Deep Salient Object Detection
Salient object detection has seen remarkable progress driven by deep learning techniques. However, most of deep learning based salient object detection methods are black-box in nature and lacking in interpretability. This paper proposes the first self-explanatory saliency detection network that explicitly exploits lowand high-level features for salient object detection. We demonstrate that such...
متن کاملSalient Object Detection via Objectness Proposals
Salient object detection has gradually become a popular topic in robotics and computer vision research. This paper presents a real-time system that detects salient object by integrating objectness, foreground and compactness measures. Our algorithm consists of four basic steps. First, our method generates the objectness map via object proposals. Based on the objectness map, we estimate the back...
متن کاملSalient Object Detection via Saliency Spread
Salient object detection aims to localize the most attractive objects within an image. For such a goal, accurately determining the saliency values of image regions and keeping the saliency consistency of interested objects are two key challenges. To tackle the issues, we first propose an adaptive combination method of incorporating texture with the dominant color, for enriching the informativen...
متن کاملSalient Object Detection via Augmented Hypotheses
In this paper, we propose using augmented hypotheses which consider objectness, foreground and compactness for salient object detection. Our algorithm consists of four basic steps. First, our method generates the objectness map via objectness hypotheses. Based on the objectness map, we estimate the foreground margin and compute the corresponding foreground map which prefers the foreground objec...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Image Processing
سال: 2017
ISSN: 1057-7149,1941-0042
DOI: 10.1109/tip.2017.2682981