8,060 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
Bottom-up visual attention model for still image: a preliminary study
The philosophy of human visual attention is scientifically explained in the field of cognitive psychology and neuroscience then computationally modeled in the field of computer science and engineering. Visual attention models have been applied in computer vision systems such as object detection, object recognition, image segmentation, image and video compression, action recognition, visual tracking, and so on. This work studies bottom-up visual attention, namely human fixation prediction and salient object detection models. The preliminary study briefly covers from the biological perspective of visual attention, including visual pathway, the theory of visual attention, to the computational model of bottom-up visual attention that generates saliency map. The study compares some models at each stage and observes whether the stage is inspired by biological architecture, concept, or behavior of human visual attention. From the study, the use of low-level features, center-surround mechanism, sparse representation, and higher-level guidance with intrinsic cues dominate the bottom-up visual attention approaches. The study also highlights the correlation between bottom-up visual attention and curiosity
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