/pdf/feeling-and-thinking-preferences-need-no-inferences-q4mw2hj8rx.pdf

Feeling and thinking : Preferences need no inferences

This paper presents a theoretical model of situation awareness based on its role in dynamic human decision making in a variety of domains. Situation awareness is presented as a predominant concern in system operation, based on a descriptive view of decision making. The relationship between situation awareness and numerous individual and environmental factors is explored. Among these factors, attention and working memory are presented as critical factors limiting operators from acquiring and interpreting information from the environment to form situation awareness, and mental models and goal-directed behavior are hypothesized as important mechanisms for overcoming these limits. The impact of design features, workload, stress, system complexity, and automation on operator situation awareness is addressed, and a taxonomy of errors in situation awareness is introduced, based on the model presented. The model is used to generate design implications for enhancing operator situation awareness and future directio...

Toward a Theory of Situation Awareness in Dynamic Systems

A theory of edge detection is presented. The analysis proceeds in two parts. (1) Intensity changes, which occur in a natural image over a wide range of scales, are detected separately at different scales. An appropriate filter for this purpose at a given scale is found to be the second derivative of a Gaussian, and it is shown that, provided some simple conditions are satisfied, these primary filters need not be orientation-dependent. Thus, intensity changes at a given scale are best detected by finding the zero values of delta 2G(x,y)*I(x,y) for image I, where G(x,y) is a two-dimensional Gaussian distribution and delta 2 is the Laplacian. The intensity changes thus discovered in each of the channels are then represented by oriented primitives called zero-crossing segments, and evidence is given that this representation is complete. (2) Intensity changes in images arise from surface discontinuities or from reflectance or illumination boundaries, and these all have the property that they are spatially. Because of this, the zero-crossing segments from the different channels are not independent, and rules are deduced for combining them into a description of the image. This description is called the raw primal sketch. The theory explains several basic psychophysical findings, and the operation of forming oriented zero-crossing segments from the output of centre-surround delta 2G filters acting on the image forms the basis for a physiological model of simple cells (see Marr & Ullman 1979).

http://www.hms.harvard.edu/bss/neuro/bornlab/qmbc/beta/day4/marr-hildreth-edge-prsl1980.pdf

Theory of Edge Detection

An important component of routine visual behavior is the ability to find one item in a visual world filled with other, distracting items. This ability to performvisual search has been the subject of a large body of research in the past 15 years. This paper reviews the visual search literature and presents a model of human search behavior. Built upon the work of Neisser, Treisman, Julesz, and others, the model distinguishes between a preattentive, massively parallel stage that processes information about basic visual features (color, motion, various depth cues, etc.) across large portions of the visual field and a subsequent limited-capacity stage that performs other, more complex operations (e.g., face recognition, reading, object identification) over a limited portion of the visual field. The spatial deployment of the limited-capacity process is under attentional control. The heart of the guided search model is the idea that attentional deployment of limited resources isguided by the output of the earlier parallel processes. Guided Search 2.0 (GS2) is a revision of the model in which virtually all aspects of the model have been made more explicit and/or revised in light of new data. The paper is organized into four parts: Part 1 presents the model and the details of its computer simulation. Part 2 reviews the visual search literature on preattentive processing of basic features and shows how the GS2 simulation reproduces those results. Part 3 reviews the literature on the attentional deployment of limited-capacity processes in conjunction and serial searches and shows how the simulation handles those conditions. Finally, Part 4 deals with shortcomings of the model and unresolved issues.

/pdf/guided-search-2-0-a-revised-model-of-visual-search-1qi4v6qhev.pdf

Guided Search 2.0 A revised model of visual search

Let's read! We will often find out this sentence everywhere. When still being a kid, mom used to order us to always read, so did the teacher. Some books are fully read in a week and we need the obligation to support reading. What about now? Do you still love reading? Is reading only for you who have obligation? Absolutely not! We here offer you a new book enPDFd the perception of the visual world to read.

The Perception of the Visual World. By James J. Gibson. U.S.A.: Houghton Mifflin Company, 1950 (George Allen & Unwin, Ltd., London). Price 35s.

PART I: INTRODUCTION PART II: ADAPTION PART III: SUMMATION PART IV: UNCERTAINTY PART V: IDENTIFICATION PART VI: MULTIPLE DIMENSIONS PART VII: EPILOGUE

Visual Pattern Analyzers

Probability summation and regional variation in contrast sensitivity across the visual field.

https://www.cns.nyu.edu/~msl/courses/2223/Readings/grahamnachmias71.pdf

Detection of grating patterns containing two spatial frequencies: A comparison of single-channel and multiple-channels models

Grating summation in fovea and periphery

What is visual texture, and how might a study of the visual perception of texture help us to better understand human vision? In this chapter we will attempt to give the reader a feel for how the study of texture perception is useful both in understanding the impact of texture itself, as well as in providing a better understanding of basic visual mechanisms that respond not only to texture but to all visual stimuli. This review will be relatively brief and, of necessity, incomplete. We hope to give an overview of the different research areas concerned with texture perception and of the current issues. For a longer early review, we refer the reader to Bergen (1991). Consider the scene in Fig. 1. The border between the sky and the trees/grass involves a difference in luminance, one that would easily be signaled by a linear mechanism such as a simple cell in primary visual cortex. The boundary between the zebras and the background also involves a change in chromaticity (although not visible in the black-and-white image in Fig. 1), which might be signaled by color-opponent mechanisms. But, the borders between pairs of zebras involve neither a difference in color nor in average luminance. These borders include stretches of boundary that are black on one side and white on the other, stretches where the colors are reversed, and stretches where there is no local visual information to signal the boundary (where black abuts black, or white abuts white). Nevertheless, we perceive a smooth, continuous occlusion boundary at the edge of each animal. It is as if the visual system possesses the capability of segmenting regions of the image based on a local textural property, such as separating " vertical stuff " from " horizontal stuff. " Thus, texture is a property that is statistically defined. A uniformly textured region might be described as " predominantly vertically oriented " , " predominantly small in scale " , " wavy " , " stubbly " , " like wood grain " or " like water. " As Adelson and Bergen (1991) put it, texture is a property of Landy/Graham 3 " stuff " in the image, in contradistinction to visual features such as lines and edges, the " things " in the image (analogous to the linguistic difference between mass nouns like " water " and count nouns like " mouse "). Another way …

/pdf/visual-perception-of-texture-4lgxxzlwwo.pdf

Norma Graham

Papers

Visual Pattern Analyzers

Probability summation and regional variation in contrast sensitivity across the visual field.

Detection of grating patterns containing two spatial frequencies: A comparison of single-channel and multiple-channels models

Grating summation in fovea and periphery

Visual perception of texture