29 research outputs found
Light Field Salient Object Detection: A Review and Benchmark
Salient object detection (SOD) is a long-standing research topic in computer
vision and has drawn an increasing amount of research interest in the past
decade. This paper provides the first comprehensive review and benchmark for
light field SOD, which has long been lacking in the saliency community.
Firstly, we introduce preliminary knowledge on light fields, including theory
and data forms, and then review existing studies on light field SOD, covering
ten traditional models, seven deep learning-based models, one comparative
study, and one brief review. Existing datasets for light field SOD are also
summarized with detailed information and statistical analyses. Secondly, we
benchmark nine representative light field SOD models together with several
cutting-edge RGB-D SOD models on four widely used light field datasets, from
which insightful discussions and analyses, including a comparison between light
field SOD and RGB-D SOD models, are achieved. Besides, due to the inconsistency
of datasets in their current forms, we further generate complete data and
supplement focal stacks, depth maps and multi-view images for the inconsistent
datasets, making them consistent and unified. Our supplemental data makes a
universal benchmark possible. Lastly, because light field SOD is quite a
special problem attributed to its diverse data representations and high
dependency on acquisition hardware, making it differ greatly from other
saliency detection tasks, we provide nine hints into the challenges and future
directions, and outline several open issues. We hope our review and
benchmarking could help advance research in this field. All the materials
including collected models, datasets, benchmarking results, and supplemented
light field datasets will be publicly available on our project site
https://github.com/kerenfu/LFSOD-Survey
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
Salient Object Detection Techniques in Computer Vision-A Survey.
Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning-based. In this survey, most of the influential advances in image-based SOD from both conventional as well as deep learning-based categories have been reviewed in detail. Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. Results are presented for various challenging cases for some large-scale public datasets. Different metrics considered for assessment of the performance of state-of-the-art salient object detection models are also covered. Some future directions for SOD are presented towards end
Single image defocus estimation by modified gaussian function
© 2019 John Wiley & Sons, Ltd. This article presents an algorithm to estimate the defocus blur from a single image. Most of the existing methods estimate the defocus blur at edge locations, which further involves the reblurring process. For this purpose, existing methods use the traditional Gaussian function in the phase of reblurring but it is found that the traditional Gaussian kernel is sensitive to the edges and can cause loss of edges information. Hence, there are more chances of missing spatially varying blur at edge locations. We offer the repeated averaging filters as an alternative to the traditional Gaussian function, which is more effective, and estimate the spatially varying defocus blur at edge locations. By using repeated averaging filters, a blur sparse map is computed. The obtained sparse map is propagated by integration of superpixels segmentation and transductive inference to estimate full defocus blur map. Our adopted method of repeated averaging filters has less computational time of defocus blur map estimation and has better visual estimates of the final defocus recovered map. Moreover, it has surpassed many previous state-of-the-art proposed systems in terms of quantative analysis