Light Field Segmentation From Super-pixel Graph Representation
This work addresses the problem of handling large and redundant light field data for segmentation, which is important for applications in computer vision and graphics, but it appears incremental as it builds on existing graph-cut methods with a new representation.
The paper tackles the challenge of efficient and accurate light field segmentation by proposing a novel graph representation based on light field super-pixels (LFSP), which reduces graph size while maintaining redundancy, leading to superior accuracy and efficiency compared to previous methods.
Efficient and accurate segmentation of light field is an important task in computer vision and graphics. The large volume of input data and the redundancy of light field make it an open challenge. In the paper, we propose a novel graph representation for interactive light field segmentation based on light field super-pixel (LFSP). The LFSP not only maintains light field redundancy, but also greatly reduces the graph size. These advantages make LFSP useful to improve segmentation efficiency. Based on LFSP graph structure, we present an efficient light field segmentation algorithm using graph-cuts. Experimental results on both synthetic and real dataset demonstrate that our method is superior to previous light field segmentation algorithms with respect to accuracy and efficiency.