This paper presents a new method for using texture and color to visualize multivariate data elements arranged on an underlying height field. We combine simple texture patterns with perceptually uniform colors to increase the number of attribute values we can display simultaneously. Our technique builds multicolored perceptual texture elements (or pexels) to represent each data element. Attribute values encoded in an element are used to vary the appearance of its pexel. Texture and color patterns that form when the pexels are displayed can be used to rapidly and accurately explore the dataset. Our pexels are built by varying three separate texture dimensions: height, density, and regularity. Results from computer graphics, computer vision, and human visual psychophysics have identified these dimensions as important for the formation of perceptual texture patterns. The pexels are colored using a selection technique that controls color distance, linear separation, and color category. Prop...

In this paper we describe a technique for choosing multiple colours for use during data visualization. Our goal is a systematic method for maximizing the total number of colours available for use, while still allowing an observer to rapidly and accurately search a display for any one of the given colours. Previous research suggests that we need to consider three separate effects during colour selection: colour distance, linear separation, and colour category. We describe a simple method for measuring and controlling all of these effects. Our method was tested by performing a set of target identification studies; we analysed the ability of thirty-eight observers to find a colour target in displays that contained differently coloured background elements. Results showed our method can be used to select a group of colours that will provide good differentiation between data elements during data visualization. CR Descriptors: H.5.2 [Information Interfaces and Presentation]: User Interfaces- ergonomics, screen design (graphics,

Spatial attention: Visual selection and deployment over space The attentional spotlight and spatial cueing Attentional shifts, splits, and resolution Object-based Selection The visual search paradigm Top-down and bottom-up control of attention Inhibitory mechanisms of attention Invalid cueing Negative priming Inhibition of return Temporal attention: Visual selection and deployment over time Single target search Attentional blink and attentional dwell time Repetition blindness NEURAL MECHANISMS OF SELECTION Single-cell physiological method Event-related potentials Functional imaging: PET and fMRI

Integration of goal-driven, top-down attention and image-driven, bottom-up attention is crucial for visual search. Yet, previous research has mostly focused on models that are purely top-down or bottom-up. Here, we propose a new model that combines both. The bottom-up component computes the visual salience of scene locations in different feature maps extracted at multiple spatial scales. The topdown component uses accumulated statistical knowledge of the visual features of the desired search target and background clutter, to optimally tune the bottom-up maps such that target detection speed is maximized. Testing on 750 artificial and natural scenes shows that the model’s predictions are consistent with a large body of available literature on human psychophysics of visual search. These results suggest that our model may provide good approximation of how humans combine bottom-up and top-down cues such as to optimize target detection speed. 1.

In order to establish a search asymmetry, one must run an experiment with a symmetric design and get asymmetric results. Given an asymmetric design, one expects asymmetric results and such results do not imply an asymmetry in the search mechanisms. In this paper I argue that a number of experiments purporting to show search asymmetries contain built-in design asymmetries. A saliency model of visual search predicts the results of these experiments using only a simple measure of target-distractor similarity, without reliance on asymmetric search mechanisms. These results have implications for search mechanisms and for other experiments purporting to show search asymmetries.

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This paper presents a simple method for segmenting colour regions into categories like red, green, blue, and yellow. We are interested in studying how colour categories influence colour selection during scientific visualization. The ability to name individual colours is also important in other problem domains like real-time displays, user-interface design, and medical imaging systems. Our algorithm uses the Munsell and CIE LUV colour models to automatically segment a colour space like RGB or CIE XYZ into ten colour categories. Users are then asked to name a small number of representative colours from each category. This provides three important results: a measure of the perceptual overlap between neighbouring categories, a measure of a category’s strength, and a user-chosen name for each strong category. We evaluated our technique by segmenting known colour regions from the RGB, HSV, and CIE LUV colour models. The names we obtained were accurate, and the boundaries between different colour categories were well defined. We concluded our investigation by conducting an experiment to obtain user-chosen names and perceptual overlap for ten colour categories along the circumference of a colour wheel in CIE LUV.

A theory is presented that attempts to answer two questions. What visual contents can an observer consciously access at one moment? Answer: only one feature value (e.g., green) per dimension, but those feature values can be associated (as a group) with multiple spatially precise locations (comprising a single labeled Boolean map). How can an observer voluntarily select what to access? Answer: in one of two ways: (a) by selecting one feature value in one dimension (e.g., selecting the color red) or (b) by iteratively combining the output of (a) with a preexisting Boolean map via the Boolean operations of intersection and union. Boolean map theory offers a unified interpretation of a wide variety of visual attention phenomena usually treated in separate literatures. In so doing, it also illuminates the neglected phenomena of attention to structure.

This talk describes our investigation of methods for choosing color, texture, orientation, shape, and other features to visualize certain types of large, multidimensional datasets. These datasets are becoming more and more common; examples include scientific simulation results, geographic information systems, satellite images, and biomedical scans. The overwhelming amount of information contained in these datasets makes them difficult to analyze using traditional mathematical or statistical techniques. It also makes them difficult to visualize in an efficient or useful manner. The size of a dataset can be divided into three separate characteristics: the number of elements in the dataset, the number of attributes or dimensions embedded in each element, and the range of values possible for each attribute. All three characteristics may need to be considered during visualization. Many of our techniques make explicit use of the way viewers perceive information in an image. Our visualization systems display data in a manner that takes advantage of the low-level human visual system. Offloading the majority of the analysis task on the low-level visual system allows users to perform exploratory visualization very rapidly and accurately on large multidimensional datasets. Trends and relationships, unexpected patterns or results, and other areas of interest can be quickly identified within the dataset. These data subsets can then be further visualized or analyzed as required. Preattentive Processing

Intuitively, dynamic visual stimuli, such as moving objects or flashing lights, attract attention. Visual search tasks have revealed that dynamic targets among static distractors can indeed efficiently guide attention. The present study shows that the reverse case, a static target among dynamic distractors, allows for relatively efficient selection in certain but not all cases. A static target was relatively efficiently found among distractors that featured apparent motion, corroborating earlier findings. The important new finding was that static targets were equally easily found among distractors that blinked on and off continuously, even when each individual item blinked at a random rate. However, search for a static target was less efficient when distractors abruptly varied in luminance but did not completely disappear. The authors suggest that the division into the parvocellular pathway dealing with static visual information, on the one hand, and the magnocellular pathway common to motion and new object onset detection, on the other hand, allows for efficient filtering of dynamic and static information.