Human visual system model

About: Human visual system model is a research topic. Over the lifetime, 8697 publications have been published within this topic receiving 259440 citations.

Lossless watermarking considering the Human Visual System

Abstract: Due to quantization error, bit-replacement, or truncation, most data embedding techniques proposed so far lead to distortions in the original image. These distortions create problems in some areas such as medical, astronomical, and military imagery. Lossless watermarking is an exact restoration approach for recovering the original image from the watermarked image. In this paper we present a novel reversible watermarking technique with higher embedding capacity considering the Human Visual System (HVS). During embedding we detect the textured blocks, extract LSBs of the pixel-values from these textured blocks considering the HVS and concatenate the authentication information with the compressed bit-string. We then replace the LSBs of the textured blocks considering the HVS with this bit-string. Since we consider the HVS while extracting LSBs and embedding the payload, the distortions in the resulting watermarked image are completely reversible and imperceptible. We present experimental results to demonstrate the utility of our proposed algorithm.

Genetic object recognition using combinations of views

Abstract: Investigates the application of genetic algorithms (GAs) for recognizing real 2D or 3D objects from 2D intensity images, assuming that the viewpoint is arbitrary. Our approach is model-based (i.e. we assume a pre-defined set of models), while our recognition strategy relies on the theory of algebraic functions of views. According to this theory, the variety of 2D views depicting an object can be expressed as a combination of a small number of 2D views of the object. This implies a simple and powerful strategy for object recognition: novel 2D views of an object (2D or 3D) can be recognized by simply matching them to combinations of known 2D views of the object. In other words, objects in a scene are recognized by "predicting" their appearance through the combination of known views of the objects. This is an important idea, which is also supported by psychophysical findings indicating that the human visual system works in a similar way. The main difficulty in implementing this idea is determining the parameters of the combination of views. This problem can be solved either in the space of feature matches among the views ("image space") or the space of parameters ("transformation space"). In general, both of these spaces are very large, making the search very time-consuming. In this paper, we propose using GAs to search these spaces efficiently. To improve the efficiency of genetic searching in the transformation space, we use singular value decomposition and interval arithmetic to restrict the genetic search to the most feasible regions of the transformation space. The effectiveness of the GA approaches is shown on a set of increasingly complex real scenes where exact and near-exact matches are found reliably and quickly.

Rapid Serial Visual Presentation in dynamic graph visualization

Abstract: Rapid Serial Visual Presentation is an effective approach for browsing and searching large amounts of data. By presenting subsequent images at high frequency, we utilize the perceptual abilities of the human visual system to rapidly process certain visual features. While this concept is successfully used in video and image browsing, we demonstrate how it can be applied to dynamic graph visualization. In this paper, we introduce a visualization technique for time-varying graphs that is scalable with respect to the number of time steps. The graph visualization is based on the Parallel Edge Splatting technique, which employs a space-efficient display of a sequence of dynamically changing graphs. To illustrate the usefulness of our approach we analyzed method call graphs recorded during the execution of the open source software system JHotDraw. Furthermore, we studied a time-varying social network representing researchers and their dynamic communication structure while attending the ACM Hypertext 2009 conference.

Real-time foveation techniques for low bit rate video coding

Abstract: Lossy video compression methods often rely on modeling the abilities and limitations of the intended receiver, the human visual system (HVS), to achieve the highest possible compression with as little effect on perceived quality as possible. Foveation, which is non-uniform resolution perception of the visual stimulus by the HVS due to the non-uniform density of photoreceptor cells in the eye, has been demonstrated to be useful for reducing bit rates beyond the abilities of uniform resolution video coders. In this work, we present real-time foveation techniques for low bit rate video coding. First, we develop an approximate model for foveation. Then, we demonstrate that foveation, as described by this model, can be incorporated into standard motion compensation and discrete cosine transform (DCT)-based video coding techniques for low bit rate video coding, such as the H.263 or MPEG-4 video coding standards, without incurring prohibitive complexity overhead. We demonstrate that foveation in the DCT domain can actually result in computational speed-ups. The techniques presented can be implemented using the baseline modes in the video coding standards and do not require any modification to, or post-processing at, the decoder.