Showing papers on "Human visual system model published in 1987"

Model for the extraction of image flow

Abstract: A model is presented, consonant with current views regarding the neurophysiology and psychophysics of motion perception, that combines the outputs of a set of spatiotemporal motion-energy filters to extract optical flow. The output velocity is encoded as the peak in a distribution of velocity-tuned units that behave much like cells of the middle temporal area of the primate brain. The model appears to deal with the aperture problem as well as the human visual system since it extracts the correct velocity for patterns that have large differences in contrast at different spatial orientations, and it simulates psychophysical data on the coherence of sine-grating plaid patterns.

Visual Languages: A Tutorial and Survey

Abstract: Research into visual and growing in popularity. The concept of generalized icons is a framework for the design of the next genertion of visual languages.

The use of color in computational vision

Abstract: Color has always been part of the visual percept, yet research in computer vision seemed to focus on other properties, mainly because they did not require the additional information color provides. In this thesis, different aspects of color perception and computation are discussed. We start by examining and analyzing biologically motivated models of early chromatic visual processing. Two non-linear models of color measurement are investigated, and tested with different stimuli designed to reveal some of their spatio-chromatic properties. This analysis leads to several speculations about the use and functionality of the models and the operators they employ. One application of the operators previously discussed is to the problem of distinguishing shadow boundaries from material changes. We examine and formulate the behavior of shadows under different illumination conditions through the use of a model of reflection. Based on this analysis, we suggest a technique which makes use of a subset of the operators mentioned earlier which determines which discontinuities in images are material changes and not shadow boundaries. This technique is shown to be more accurate and robust than previous methods reported. Another problem addressed is that of color constancy, which is the perceptual ability of the human visual system to assign the same colors to objects under different lighting conditions. We use a method, based on finite-dimensional linear models of reflectance and illumination, which allows the transformation (R,G,B) images into color constant images. In contrast to previous work, we show that good results can be obtained using a 3-receptor system and statistical measurements of natural materials and illuminants. Finally, we address the problem of identifying of highlights in images through the use of chromatic information. We show that understanding reflection, through the use of existing models, allows us to make predictions about the behavior of highlights. In particular, some observations can be made regarding the shift in color from diffuse to specular reflection. Based on these observations, we developed an algorithm which segments images into regions and looks for shifts in color between adjacent regions, and labels the ones which fit the expected relation between diffuse and specular reflection.

Visual Integration and Detection of Discontinuities: The Key Role of Intensity Edges

Abstract: Integration of several vision modules is likely to be one of the keys to the power and robustness of the human visual system. We suggest that integration is best performed at the location of discontinuities in early processes, such as discontinuities in image brightness, depth, motion, texture, and color. Coupled Markov Random Field models can be used to combine vision modalities with their discontinuities. We derive a scheme to integrate intensity edges with stereo depth and motion field information and show results from a Connection Machine algorithm on synthetic and natural images. The use of intensity edges to integrate other visual cues and to help discover discontinuities emerges as a general and powerful principle.

Selection of image primitives for general-purpose visual processing

Abstract: The results of psychophysical experiments suggest the existence of a hierarchy of visual features based on the relations between image contours. The human visual system appears to be preattentively, selectively sensitive to image contours which contain certain of these features. These results can be used to develop computer vision algorithms for: (1) the selective enhancement of image contours which correlate with their perceptual significance; and (2) the segmentation of boundary images into sets which have a high probability of depicting a single object. The extreme simplicity of the algorithms, as well as their ability to generate perceptually significant results, demonstrate the advantages of using psychophysical results to uncover image invariants which correlate with the structure of the physical world that is of value for visual perception.

Colour graphics—Blessing or Ballyhoo?

Abstract: The human visual system's capacity and capability to process colour can be applied as a design criterion for colour information displays. This paper reviews key elements in the visual domain of colour, encompassing the visual, perceptual, and cognitive modes and develops a series of recommendations for effective colour usage based on these elements.

Segmentation based on measures of contrast, homogeneity, and region size

Abstract: Two algorithms are described for automatic image segmentation using a homogeneity measure and a contrast measure defined on the co-occurrence matrix of the image. The measure of contrast involves the concept of logarithmic response (adaptability with background intensity) of the human visual system. Provisions are made in two different ways to remove the undesirable thresholds. The effectiveness of the algorithms is demonstrated for a set of images having different types of histograms. Their performance is compared to that of existing algorithms.

Plasticity and rigidity in the representation of the human visual field.

Abstract: Neuronal plasticity in the mammalian visual system has been studied with a variety of experimental methods like induction of artificial squint and eye rotation. To investigate neuronal plasticity in the human visual system, we examined a patient with a congenital convergent squint of his left eye, who later suffered a vascular lesion in his left occipital lobe that led to an incomplete hemianopia in his right visual field. The examination revealed that the visual field representation in the striate cortex is rigidly prewired with reference to the anatomical fovea. In contrast, plasticity in the oculomotor system enables the patient to use a functional visual axis that does not correspond to the anatomical fovea. Local alterations of sensitivity within the visual field that indicate interactions among non-corresponding retinal points provide additional evidence of functional plasticity.

Adaptive Enhancement Based On A Visual Model

Abstract: The human visual system is capable of detecting and following the course of striated periodic patterns, even under adverse conditions of poor contrast and low signal-to-noise ratio. Sections of a striated pattern of subthreshold contrast may be detected easily if other parts of the same pattern have suprathreshold contrast. To simulate these capabilities of the visual system, an image processing algorithm was developed using basic "cells" that are well localized in both the space and spatial frequency domains. These band-limiting, orientation-sensitive "fan filters" are similar in their point spread functions to the two-dimensional Gabor functions commonly used to describe responses of visual cortical cells. These filters are used both to detect the orientation of the striated pattern in a small window and to enhance the image in that orientation. The search for local orientation is limited to a small range based on orientations found in neighboring, overlapping windows. The orientation of the maximally responding cell is used for the enhancement. Results of applying the adaptive directional enhancement to nerve fiber layer photographs, finger-prints, and seismic data are presented.

Design and implementation of a colour vision model for computer vision applications

Abstract: In the past, the area of computer vision has predominantly dealt with the processing of either binary or grey-scale images with little emphasis placed on colour. A colour vision model is therefore developed and implemented for computer vision applications. The coordinate system utilized, however, is of paramount importance since all subsequent processing depends on its appropriateness to the problem domain, namely computer vision. To this end, it is argued that a suitable perceptual coordinate system may be found by investigating properties of the human visual system. Experimental data is employed to optimize the parameters of a model of the human system as well as to determine its performance. The coordinate system which follows from this model is also related to the internationally accepted CIE colour system.

Segmentation and aggregation: an approach to figure-ground phenomena

Abstract: We describe a new approach to low-level vision in which the task of image segmentation is to distinguish meaningful relationships between image elements from a background distribution of random alignments Unlike most previous approaches, which start from idealized models of what we wish to detect in the world, this approach is not based on prior world knowledge and uses measurements which can be computed directly from the input signal Groupings of image elements are formed over a wide range of sizes and classes while attempting to make use of all available statistical information at each level of the grouping hierarchy, resulting in far more sensitive discrimination than is possible from just local measurements This paper explores the range of grouping capabilities and discriminations exhibited by the human visual system and discusses the application of the meaningfulness measure to each of them

Statistical mechanics and invariant perception.

Abstract: The problem of discrimination among ensembles of images generated by distortions of a prototype and the addition of noise is considered. As the noise level is increased, the discrimination task becomes qualitatively more difficult in that optimal discrimination requires the computation of increasingly longer-ranged correlations, or the solution of increasingly difficult optimization problems. These results suggest the use of such image ensembles in probing the computational abilities of the human visual system, and possible theoretical implications of such experiments are discussed.

An image coding technique using a human visual system model and image analysis criteria

Abstract: Recently, ways to obtain a new generation of image-coding techniques have been proposed. The incorporation of the human visual system (HVS) models and tools of the image analysis, such as segmentation, are two defining features of these techniques. In this paper, a new image coding technique is presented which utilizes a human visual system model and a image analysis criteria together with the technique of projection onto convex sets (POCS). The human visual system model is used to determine the most probable perceptual information of an image and the removal of irrelevant information. The projection onto convex sets operation is used as a restoration technique aimed at correcting the degradations caused by the conventional coders in the transmitter part of the channel. And finally, the image analysis based technique is used as an interpolator in order to predict the unknown regions of the received image. This new technique can be used in single frame image coding applications to obtain acceptable images at extremely high compression rates.

Visual simulation

Abstract: Vision is the primary sense for derivation of real world data, providing the bulk of our knowledge (>90%) of our environment. Incorporation of visual displays into simulation systems is consequently of extreme importance, particularly in those simulations involving man-in-the-loop interactive systems (flight training, driver training, etc.). Presentation of effective visual displays have been driven by state of the art technology, and, despite massive efforts, has been hampered by a failure to consider the visual parameters of an observer and the mental processes involved in the perception of visual scenes.This paper provides a discussion of the visual parameters of the individual as well as of simulation systems and summarizes the current visual system display efforts.

Visual Communications And Image Processing

Abstract: This special issue of Optical Engineering is concerned with visual communications and image processing. The increase in communication of visual information over the past several decades has resulted in many new image processing and visual communication systems being put into service. The growth of this field has been rapid in both commercial and military applications. The objective of this special issue is to intermix advent technology in visual communications and image processing with ideas generated from industry, universities, and users through both invited and contributed papers. The 15 papers of this issue are organized into four different categories: image compression and transmission, image enhancement, image analysis and pattern recognition, and image processing in medical applications.

A second generation image coding technique using human visual system based segmentation

Abstract: A new segmentation method based on the properties of the human visual system is described in this paper. The segmentation method is part of a new second generation image coder. In addition, one of the properties is that it is not necessary to transmit (or store) the visual residual for use in reconstructing the received signal. It is assumed that the characteristics of this visual residual are known at the receiver and can be used in the reconstruction process.

Multiple input adaptive iterative image restoration algorithms

Abstract: In this paper image restoration applications where multiple distorted versions of the same original image are available, are considered. A general adaptive iterative restoration algorithm is derived based on regularization techniques. The adaptivity of the algorithm is introduced in two ways: a) by a constraint operator which incorporates properties of the response of the human visual system into the restoration process, and b) by a weight matrix which assigns greater importance for the deconvolution process to areas of high spatial activity than to areas of low spatial activity. Different degrees of trust are assigned to the various distorted images depending on the amount of noise on each image. The proposed algorithms are general and can be used for any type of linear distortion and constraint operators. It can also be used to restore signals other than images.

Overview of Machine Vision Lighting Techniques

Abstract: As vision system projects move from laboratory investigations to factory floor installations, the absolute necessity of and advantages created by optimized lighting systems is being widely acknowledged. Frequently heard phrases such as "garbage in - garbage out", "the system can be no better than the image it starts with", and "if I could only get a little better contrast in the image, I could ..." attest to this acknowledgement. Furthermore, to quote Douglas Goodman, "In general, illumination is more critical in automated optical inspection than in visual inspection, since detectors and computer programs lack the dynamic range and forgiveness of the human visual system." The Machine Vision Association of SME attests to the interest in illumination by offering an Optics and Lighting session at Vision 86 and an Optics and Lighting hands on workshop.

Computing intrinsic images

Abstract: Low-level modern computer vision is not domain dependent, but concentrates on problems that correspond to identifiable modules in the human visual system. Several theories have been proposed in the literature for the computation of shape from shading, shape from texture, retinal motion from spatiotemporal derivatives of the image intensity function, and the like. The problems with the existing approach are basically the following: (1) The employed assumptions are very strong (they are not present in a large subset of real images), and so most of the algorithms fail when applied to real images. (2) Usually the constraints from the geometry and the physics of the problem are not enough to guarantee uniqueness of the computed parameters. In this case, strong additional assumptions about the world are used, in order to restrict the space of all solutions to a unique value. (3) Even if no assumptions at all are used and the physical constraints are enough to guarantee uniqueness of the computed parameters, then in most cases the resulting algorithms are not robust, in the sense that if there is a slight error in the input (i.e. small amount of noise in the image), this results in a catastrophic error in the output (computed parameters). It turns out that if several available cues are combined, then the above-mentioned problems disappear; the resulting algorithms compute uniquely and robustly the intrinsic parameters (shape, depth, motion, etc.). In this thesis the problem of machine vision is explored from its basics. A low level mathematical theory is presented for the unique and robust computation of intrinsic parameters. The computational aspect of the theory envisages a cooperative highly parallel implementation, bringing in information from five different sources (shading, texture, motion, contour and stereo), to resolve ambiguities and ensure uniqueness and stability of the intrinsic parameters. The problems of shape from texture, shape from shading and motion, visual motion analysis and shape and motion from contour are analyzed in detail.

Segmentation adaptative pour le codage d'images

Abstract: This Ph.D. dissertation analyses the problem of segmenting an image into a set of regions corresponding as much as possible to the real objects of a scene. The goal of this segmentation is to go from a numerical representation of an image to a symbolic one, i.e. the regions and their characteristic features. Starting from the set of picture elements, one reaches a more compact model where region frontiers define the contours of the objects and the signal within each region their texture. Such a representation can find useful applications for scene understanding and image coding. Recent works have shown the potential of a contour-texture model for image coding due to the properties of our human visual system. We have studied here the way of coding a segmented representation of an image when using high order polynomials for approximating the different regions. The proposed segmentation algorithm is adaptive. Given a certain approximation to model the texture within each region, one tries to modify adaptively the region shape and the approximation parameters. To do so, the image is first split into a set of squares of different sires in order to obtain an optimal correspondence between the original signal and its approximation within each square. Then starting from this initial partition, adjacent regions are iteratively merged till one reaches a segmentation with a certain number of regions of any shape. At each step of the merging process, the two most similar regions are associated on the basis of the adequacy of the approximation on the new region. Polynomials of degree 0 to 3 have been used in the approximation process. Once the final segmentation is obtained, frontier information and texture information are coded separately. Performances for redundancy reduction are impressive: acceptable quality images can be obtained with compression ratios of the order of 30 to 1. It is shown how most of the semantics can be preserved with coded pictures at compression ratios ranging from 60 to 1 to 130 to 1. The algorithm has been applied to three different 256x256 natural images quantized with eight bit dynamics.

Theoretical And Practical Super-Resolution Performance In Simple Processing Of Local Form, Motion And Stereo Disparity.

Abstract: It is widely known that the human visual system is able to achieve super-resolution under ideal viewing conditions in its sensing of local form, local motion and stereo disparity. The practical limits appear to be something less than 0.1 receptor spacings (pixels) of spatial displacement and better than 1 degree in local orientation. Yet it seems to achieve this performance instantaneously and with utmost ease. Classical computer vision is unable to compare either in absolute capability or apparent simplicity. We have constructed a computer simulation of early human vision with the emphasis being on our particular interpretation of the important neural components and on simplicity. This simulation has now been working well and achieving super-resolution on practical images for some time. This paper presents the theory and practice of performance limits for this simulation. It is shown that theoretical and practical performance limits are in good agreement. Practical performance includes pixel by pixel edge location to within 0.03 pixels, local motion and disparity measurement between pairs of sampled frames pixel by pixel to better than 3% and pixel by pixel orientation measurement to better than 1 degree. All these are achieved by direct pipeline processing with no iterative procedures. They compare very closely with observed limits of human visual performance.© (1987) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

An Eye Motion Dependent Interactive Display

Abstract: Using readily available equipment, we assembled a digital graphics system to investigate several facets of how the human visual system gathers information. Of particular interest is the relationship and interaction between central and peripheral visual processes, investigated by selectively prohibiting foveal or peripheral visual access to pertinent details in a display. This process requires realtime display modification in response to eye movements and is accomplished by utilizing the capabilities of a graphics array processor to manipulate the 512x512x8 bit images at TV line rates. An infra-red limbus reflection technique is used to track the observer's gaze. Our applications involved radiologists looking at x-ray images of the human chest. We describe both hardware and software aspects of our interactive visual display system and mention its applications in our laboratory.

The Need For A Quantitative Model Of Human Preattentive Vision

Abstract: The goal of every computer vision system is to at least match human perceptual abilities at performing a desired task. Hence the need to have an interest in and be knowledgeable of human perceptual abilities is implicit to the discipline of computer vision. If one desires to create a general purpose computer vision system the best place to look to gain general insights into how to construct such a system would seemingly be to examine what is known about human perceptual mechanisms. With this in mind the general scene analysis and image analysis strategies of the human visual system will be reviewed. Based on an analysis of these strategies it will be argued that what is needed to create a general purpose vision system is a quantitative model of human preattentive vision. Further it will be argued that this quantitative model should be based on what has been classically called texture operators.

A Connectionist Architecture For Computing Textural Segmentation

Abstract: This project examines some parallel architectures designed for image processing, and then addresses their applicability to the problem of image segmentation by texture analysis. Using this information, and research into the structure of the human visual system, an architecture for textural segmentation is proposed. The underlying premise is that textural segmentation can be achieved by recognizing local differences in texture elements (texels). This approach differs from most of the previous work where the differences in global, second-order statistics of the image points are used as the basis for segmentation. A realistic implementation of this approach requires a parallel computing architecture which consists of a hierarchy of functionally different nodes. First, simple features are extracted from the image. Second, these simple features are linked together to form more complex texels. Finally, local and more global differences in texels or their organization are enhanced and linked into boundaries.

Automated Fake Color Separation: Combining Computer Vision And Computer Graphics

Abstract: A system is described for the automation of the color separation process. In current color separation systems, humans must visually segment line-art images, and using pen and ink, delineate the segments in a manner that enables a computer graphics system to be used interactively to color in each segment. The goal of this research was to remove the labor-intensive human visual segmentation, by adding rudimentary visual processing capabilities to the computer graphics system. This is possible through the use of computer vision algorithms which incorporate general knowledge about line-art, and are based on image features that are used by the human visual system in the early stages of visual processing. A major color separation company is planning the hardware implementation of a vision-graphics system based on these algorithms, and the State University of New York is applying for two patents based on this research.