Salient object detection from low contrast images based on local contrast enhancing and non-local feature learning

2020 ◽  
Author(s):  
Tengda Guo ◽  
Xin Xu
Keyword(s):  
Object Detection ◽  
Feature Learning ◽  
Salient Object Detection ◽  
Local Feature ◽  
Local Contrast ◽  
Salient Object ◽  
Low Contrast ◽  
Non Local

Infrared salient object detection based on global guided lightweight non-local deep features

2021 ◽  
Vol 115 ◽  
pp. 103672
Author(s):  
Zhaoying Liu ◽  
Xuesi Zhang ◽  
Tianpeng Jiang ◽  
Ting Zhang ◽  
Bo Liu ◽  
...  
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Salient Object ◽  
Non Local

scholarly journals Pyramid Constrained Self-Attention Network for Fast Video Salient Object Detection

2020 ◽  
Vol 34 (07) ◽  
pp. 10869-10876 ◽  
Author(s):  
Yuchao Gu ◽  
Lijuan Wang ◽  
Ziqin Wang ◽  
Yun Liu ◽  
Ming-Ming Cheng ◽  
...  
Keyword(s):  
Object Detection ◽  
Short Term Memory ◽  
Salient Object Detection ◽  
Object Motion ◽  
Salient Object ◽  
Motion Cues ◽  
Spatiotemporal Information ◽  
Local Mechanism ◽  
Non Local ◽  
Pyramid Structures

Spatiotemporal information is essential for video salient object detection (VSOD) due to the highly attractive object motion for human's attention. Previous VSOD methods usually use Long Short-Term Memory (LSTM) or 3D ConvNet (C3D), which can only encode motion information through step-by-step propagation in the temporal domain. Recently, the non-local mechanism is proposed to capture long-range dependencies directly. However, it is not straightforward to apply the non-local mechanism into VSOD, because i) it fails to capture motion cues and tends to learn motion-independent global contexts; ii) its computation and memory costs are prohibitive for video dense prediction tasks such as VSOD. To address the above problems, we design a Constrained Self-Attention (CSA) operation to capture motion cues, based on the prior that objects always move in a continuous trajectory. We group a set of CSA operations in Pyramid structures (PCSA) to capture objects at various scales and speeds. Extensive experimental results demonstrate that our method outperforms previous state-of-the-art methods in both accuracy and speed (110 FPS on a single Titan Xp) on five challenge datasets. Our code is available at https://github.com/guyuchao/PyramidCSA.

Salient object detection using a covariance-based CNN model in low-contrast images

2017 ◽  
Vol 29 (8) ◽  
pp. 181-192 ◽  
Author(s):  
Nan Mu ◽  
Xin Xu ◽  
Xiaolong Zhang ◽  
Hong Zhang
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Salient Object ◽  
Low Contrast

scholarly journals Hybrid Local and Global Deep-Learning Architecture for Salient-Object Detection

2020 ◽  
Vol 10 (23) ◽  
pp. 8754
Author(s):  
Wajeeha Sultan ◽  
Nadeem Anjum ◽  
Mark Stansfield ◽  
Naeem Ramzan
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
State Of The Art ◽  
Salient Object Detection ◽  
Salient Object ◽  
Global Level ◽  
Low Contrast ◽  
Qualitative And Quantitative ◽  
Exact Boundary ◽  
Quantitative Analyses

Salient-object detection is a fundamental and the most challenging problem in computer vision. This paper focuses on the detection of salient objects, especially in low-contrast images. To this end, a hybrid deep-learning architecture is proposed where features are extracted on both the local and global level. These features are then integrated to extract the exact boundary of the object of interest in an image. Experimentation was performed on five standard datasets, and results were compared with state-of-the-art approaches. Both qualitative and quantitative analyses showed the robustness of the proposed architecture.

scholarly journals Salient Object Detection by Lossless Feature Reflection

2018 ◽  
Author(s):  
Pingping Zhang ◽  
Wei Liu ◽  
Huchuan Lu ◽  
Chunhua Shen
Keyword(s):  
Object Detection ◽  
Large Scale ◽  
Structural Information ◽  
Saliency Detection ◽  
Feature Learning ◽  
Salient Object Detection ◽  
Superior Performance ◽  
Salient Object ◽  
Convolutional Network ◽  
Performance Improvements

Salient object detection, which aims to identify and locate the most salient pixels or regions in images, has been attracting more and more interest due to its various real-world applications. However, this vision task is quite challenging, especially under complex image scenes. Inspired by the intrinsic reflection of natural images, in this paper we propose a novel feature learning framework for large-scale salient object detection. Specifically, we design a symmetrical fully convolutional network (SFCN) to learn complementary saliency features under the guidance of lossless feature reflection. The location information, together with contextual and semantic information, of salient objects are jointly utilized to supervise the proposed network for more accurate saliency predictions. In addition, to overcome the blurry boundary problem, we propose a new structural loss function to learn clear object boundaries and spatially consistent saliency. The coarse prediction results are effectively refined by these structural information for performance improvements. Extensive experiments on seven saliency detection datasets demonstrate that our approach achieves consistently superior performance and outperforms the very recent state-of-the-art methods.

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