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.

A Model for Studying Display Methods of Statistical Graphics

Abstract: A method of statistical graphics consists of two parts: a selection of statistical information to be displayed and a selection of a visual display method to encode the information. Some display methods lead to efficient, accurate visual decoding of encoded information, and others lead to inefficient, inaccurate decoding. It is only through rigorous studies of visual decoding that informed judgments can be made about how to choose display methods. A model has been developed to provide a framework for the study of visual decoding. The model consists of three parts: (1) a two-way classification of information on displays—quantitative-scale, quantitative-physical, categorical-scale, and categorical-physical; (2) a division of the visual processing of graphical displays into pattern perception and table look-up; (3) a specification of visual operations that are employed to carry out pattern perception and table look-up. Display methods are assessed by studying the visual operations to which they lead....

Image coding ringing artifact reduction using morphological post-filtering

Abstract: Ringing is an annoying artifact frequently encountered in low bit-rate transform and subband decomposition based compression of different media such as image, intra frame video and graphics. A mathematical morphology based post-processing algorithm is presented in this paper for image ringing artifact suppression. First, we use binary morphological operators to isolate the regions of an image where the ringing artifact is most prominent to the human visual system (HVS) while preserving genuine edges and other (high-frequency) fine details present in the image. Then, a gray-level morphological nonlinear smoothing filter is applied to the unmasked regions of the image under the filtering mask to eliminate ringing within this constraint region. To gauge the effectiveness of this approach, we propose an HVS compatible objective measure of the ringing artifact. Preliminary simulations indicate that the proposed method is capable of significantly reducing the ringing artifact on both subjective and objective basis.

An Efficient Infrared Small Target Detection Method Based on Visual Contrast Mechanism

Abstract: Robust and efficient detection of an infrared (IR) small target is very important in the IR search and track system. Based on the contrast mechanism of the human visual system, an IR small target detection method with high detection rate, low false alarm rate, and short processing time is proposed in this letter. This method consists of two stages. At the first stage, with the top-hat filter and an adaptive threshold operation based on the constant false alarm rate applied to the original image, the suspicious target regions are obtained. In this way, the computing time of the following steps would be reduced a lot; meanwhile, the desired and predictable detection probability with the constant false alarm probability is maintained. At the second stage, we first define a new efficient local contrast measure between the target and the background, and the local self-similarity of an image is introduced to calculate the local saliency map. With the combination of the local self-similarity and local contrast, an efficient saliency map is obtained, which cannot only increase the signal-to-clutter ratio but also suppress residual clutter simultaneously. Then, a simple threshold operation on the saliency map is used to get the true targets. Experimental results indicate that the proposed method is superior in detection rate, false alarm rate, and processing time compared with the contrast algorithms, and it is an efficient method for IR small target detection in a complex background.