the junction of the vegetation and the mountains), which limit their practical application. However, these methods struggle in 1) efficiently generating camouflage images using foreground and background with arbitrary structure 2) camouflaging foreground objects to regions with multiple appearances (e.g.

Previous work has typically been implemented by an iterative optimization process. Ĭamouflage is a common visual phenomenon, which refers to hiding the foreground objects into the background images, making them briefly invisible to the human eye. All the materials including models, datasets, benchmarking results, and supplemented light field datasets are publicly available at. We provide nine observations on challenges and future directions, and outline several open issues. Lastly, light field SOD is a specialised problem, because of its diverse data representations and high dependency on acquisition hardware, so it differs greatly from other saliency detection tasks. Our supplemental data make a universal benchmark possible. Due to the inconsistency of current datasets, we further generate complete data and supplement focal stacks, depth maps, and multi-view images for them, making them consistent and uniform. 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, providing insightful discussions and analyses, including a comparison between light field SOD and RGB-D SOD models. Existing datasets for light field SOD are also summarized. Firstly, we introduce 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, a comparative study, and a brief review. This paper provides the first comprehensive review and a benchmark for light field SOD, which has long been lacking in the saliency community. Since light fields record comprehensive information of natural scenes that benefit SOD in a number of ways, using light field inputs to improve saliency detection over conventional RGB inputs is an emerging trend. Salient object detection (SOD) is a long-standing research topic in computer vision with increasing interest in the past decade. The results on the publicly available benchmark datasets prove that our Q-DMD outperforms the exact DMD method, and experiment results also demonstrate that the performance of our approach is comparable to that of the state-of-the-art ones. We exploit the standard eigenvalues of the quaternion matrix to compute its spectral decomposition and calculate the corresponding Q-DMD modes and eigenvalues. In this study, we propose a quaternion-based DMD (Q-DMD), which extends the DMD by quaternion matrix analysis, so as to ultimately preserve the inherent color structure of the color image and the color video. However, this method needs to convert the color image into a grayscale image for processing, which leads to the neglect of the coupling information between the three channels of the color image. Dynamic mode decomposition (DMD) is a recently proposed method to robustly decompose a video sequence into the background model and the corresponding foreground part.

Scene Background Initialization (SBI) is one of the challenging problems in computer vision.

Dmd panorama github