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 Exploring Image Enhancement for Salient Object Detection in Low Light Images

2021 ◽  
Vol 17 (1s) ◽  
pp. 1-19
Author(s):  
Xin Xu ◽  
Shiqin Wang ◽  
Zheng Wang ◽  
Xiaolong Zhang ◽  
Ruimin Hu
Keyword(s):  
Object Detection ◽  
Image Enhancement ◽  
Ground Truth ◽  
Salient Object Detection ◽  
Salient Object ◽  
Low Light ◽  
Low Contrast ◽  
Local Content ◽  
The Difference ◽  
Public Datasets

Low light images captured in a non-uniform illumination environment usually are degraded with the scene depth and the corresponding environment lights. This degradation results in severe object information loss in the degraded image modality, which makes the salient object detection more challenging due to low contrast property and artificial light influence. However, existing salient object detection models are developed based on the assumption that the images are captured under a sufficient brightness environment, which is impractical in real-world scenarios. In this work, we propose an image enhancement approach to facilitate the salient object detection in low light images. The proposed model directly embeds the physical lighting model into the deep neural network to describe the degradation of low light images, in which the environment light is treated as a point-wise variate and changes with local content. Moreover, a Non-Local-Block Layer is utilized to capture the difference of local content of an object against its local neighborhood favoring regions. To quantitative evaluation, we construct a low light Images dataset with pixel-level human-labeled ground-truth annotations and report promising results on four public datasets and our benchmark dataset.

Saliency detection in deep learning era: trends of development

2019 ◽  
pp. 10-36 ◽  
Author(s):  
M. N. Favorskaya ◽  
L. C. Jain
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Event Detection ◽  
Visual Analysis ◽  
Saliency Detection ◽  
Salient Object Detection ◽  
Public Image ◽  
Detection Methods ◽  
Salient Object ◽  
Salient Event

Introduction:Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection.Purpose:A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing.Results:A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation.Practical relevance:This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.

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