Guest Editorial: Scenes, Images and Objects

Abstract

An important aspect of current computer vision research is the analysis of scenes, and particularly the extraction of their 3D structure and their segmentation into objects. Linked to that is the interpretation of the images and/or recognition of types of images, either from the output of the 3D reconstruction and segmentation or directly from the images. Solving such problems is often accomplished by combining a variety of methods and/or attributes of the images, as the papers in this special issue show. Ladicky et al. provide us with a framework to unify dense stereo reconstruction and object segmentation, where both are formulated as Random Field labelling which are jointly optimized. Evaluation is done on an enhanced Leuven data set, which is publicly available. The paper by Hwang and Grauman provides an enhanced image search methodology by incorporating high-level human scene perception aspects, such as the order of associated key-words, into the visual representation used. Sun et al. present a system that iteratively combines object detection, 3D scene layout estimation, and segmentation of the objects’ support region. As knowledge of the scene becomes available from the layout estimation, it can be used to improve the confidence in the object detection and so remove false detections. The results are demonstrated on the authors’ own dataset and two publicly available datasets.