Narrow Results

Region-of-interest (ROI) image coding is one of the new features included in the JPEG2000 image coding standard. Two methods are defined in the standard: the Maxshift method and the generic scaling based method. In this paper, a new region-of-interest coding method called Contour-based Multi-ROI Multi-quality Image Coding (CMM) is proposed. Unlike other existing methods, the CMM method takes the contour and texture of the whole image as a special ROI, which makes the visually most important parts (in both ROI and Background) to be coded first. Experimental results indicate that the proposed method significantly outperforms the previous ROI coding schemes in the overall ROI coding performance.

Edge detection is a major step in several computer vision applications. Edges define the shape of objects to be used in a recognition system, for example. In this work, we introduce an approach to edge detection inspired by a challenge for artists: the Speed Drawing Challenge. In this challenge, a person is asked to draw the same figure in different times (as 10min, 1min and 10s); at each time, different levels of details are drawn by the artist. In a short time stamp, just the major elements remain. This work proposes a new approach for producing images with different amounts of edges representing different levels of relevance. Our method uses superpixel to suppress image details, followed by Globalized Probability of Boundary (gPb) and Canny edge detection algorithms to create an image containing different number of edges. After that, an edge analysis step detects whose edges are the most relevant for the scene. The results are presented for the BSDS500 dataset and they are compared to other edge and contour detection algorithms by quantitative and qualitative means with very satisfactory results.