Guest Editorial: Visual Information Processing and Perception

Abstract

The goal of this special issue was to bring together experts working on applying models, principles, and knowledge of human audio-visual perception and cognition to optimize video processing algorithms and applications. This is an area where our deeper understanding of visual perception to video processing will lead to new breakthroughs in visual information processing. Attentional focus is an important aspect of perception that helps understand user interest and intent. Determining points of saliency is computationally complex. Compressed-domain methods thus become valuable tools for developing computationally efficient and practical solutions. In BCompressed-Domain Correlates of Human Fixations in Dynamic Scenes^ (10. 1007/s11042-015-2802-3) authors present a method for detecting points of fixation in H.264/ AVC video using motion vectors, block coding modes and coded residuals parsed from a H. 164/AVC bitstream. Egocentric videos are captured usingwearable cameras and used to detect a wearer’s point of view. Amount of video captured using wearable cameras is increasing and saliency detection in such videos enables applications such as efficient video summarization. In BGeometrical Cues in Visual Saliency Models for Active Object Recognition in Egocentric Videos^ (10.1007/ s11042-015-2803-2) authors use geometrical cues to improve saliency detection in videos. Action recognition from videos is a challenging task with many applications including surveillance and social behavior understanding. Inspired by models of neural response to visual input, the paper entitled BDeep Learning Human Actions from Video via Sparse Filtering and Locally Competitive Algorithms^ (10.1007/s11042-015-2808-x) presents a method that combines sparse filtering with locally competitive algorithms for applications in action recognition. Multimed Tools Appl (2015) 74:10053–10056 DOI 10.1007/s11042-015-2957-y