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.

Extended visual cryptography for natural images

Abstract: Extended Visual Cryptography[Ateni01] is a type of cryptography which encodes a number of images in the way that when the images on transparencies are stacked together, the hidden message appears without a trace of original images The decryption is done directly by the human visual system with no special cryptographic calculations This paper presents a system which takes three pictures as an input and generates two images which correspond to two of the three input pictures The third picture is reconstructed by printing the two output images onto transparencies and stacking them together While the previous researches basically handle only binary images, this paper establishes the extended visual cryptography scheme suitable for natural images Generally, visual cryptography suffers from the deterioration of the image quality This paper also describes the method to improve the quality of the output images The trade-off between the image quality and the security are discussed and assessed by observing the actual results of this method Furthermore, the optimization of the image quality is discussed

A no-reference perceptual image sharpness metric based on a cumulative probability of blur detection

Abstract: In this paper, a no-reference objective sharpness metric based on a cumulative probability of blur detection is proposed. The metric is evaluated by taking into account the Human Visual System (HVS) response to blur distortions. The perceptual significance of the metric is validated through subjective experiments. It is shown that the proposed metric results in a very good correlation with subjective scores especially for images with varying foreground and background perceived blur qualities. This is accomplished with a significantly lower computational complexity as compared to existing methods that take into account the visual attention information.

Color and luminance information in natural scenes

Abstract: The spatial filtering applied by the human visual system appears to be low pass for chromatic stimuli and band pass for luminance stimuli. Here we explore whether this observed difference in contrast sensitivity reflects a real difference in the components of chrominance and luminance in natural scenes. For this purpose a digital set of 29 hyperspectral images of natural scenes was acquired and its spatial frequency content analyzed in terms of chrominance and luminance defined according to existing models of the human cone responses and visual signal processing. The statistical 1/f amplitude spatial-frequency distribution is confirmed for a variety of chromatic conditions across the visible spectrum. Our analysis suggests that natural scenes are relatively rich in high-spatial-frequency chrominance information that does not appear to be transmitted by the human visual system. This result is unlikely to have arisen from errors in the original measurements. Several reasons may combine to explain a failure to transmit high-spatial-frequency chrominance: (a) its minor importance for primate visual tasks, (b) its removal by filtering applied to compensate for chromatic aberration of the eye's optics, and (c) a biological bottleneck blocking its transmission. In addition, we graphically compare the ratios of luminance to chrominance measured by our hyperspectral camera and those measured psychophysically over an equivalent spatial-frequency range.

An empirical approach to grouping and segmentation

Abstract: This thesis presents a novel dataset of 12,000 segmentations of 1,000 natural images by 30 human subjects. The subjects marked the locations of objects in the images, providing ground truth data for learning grouping cues and benchmarking grouping algorithms. We feel that the data-driven approach is critical for two reasons: (1) the data reflects “ecological statistics” that the human visual system has evolved to exploit, and (2) innovations in computational vision should be evaluated quantitatively. We develop a battery of segmentation comparison measures that we use both to validate the consistency of the human data and to provide approaches for evaluating grouping algorithms. In conjunction with the segmentation dataset, the various measures provide “micro-benchmarks” for boundary detection algorithms and pixel affinity functions, as well a benchmark for complete segmentation algorithms. Using these performance measures, we can systematically improve grouping algorithms with the human ground truth as our goal. Starting at the lowest level, we present local boundary models based on brightness, color, and texture cues, where the cues are individually optimized with respect to the dataset and then combined in a statistically optimal manner with classifiers. The resulting detector is shown to significantly outperform prior state-of-the-art algorithms. Next, we learn from data how to combine the boundary model with patch-based features in a pixel affinity model to settle long-standing debates in computer vision with empirical results: (1) brightness boundaries are more informative than patches, and vice versa for color; (2) texture boundaries and patches are the two most powerful cues; (3) proximity is not a useful cue for grouping, it is simply a result of the process; and (4) both boundary-based and region-based approaches provide significant independent information for grouping.