Visual Attention for Robotic Cognition: A Survey <s> 5) Space-and Object-Based Analysis: <s> A new hypothesis about the role of focused attention is proposed. The feature-integration theory of attention suggests that attention must be directed serially to each stimulus in a display whenever conjunctions of more than one separable feature are needed to characterize or distinguish the possible objects presented. A number of predictions were tested in a variety of paradigms including visual search, texture segregation, identification and localization, and using both separable dimensions (shape and color) and local elements or parts of figures (lines, curves, etc. in letters) as the features to be integrated into complex wholes. The results were in general consistent with the hypothesis. They offer a new set of criteria for distinguishing separable from integral features and a new rationale for predicting which tasks will show attention limits and which will not. <s> BIB001 </s> Visual Attention for Robotic Cognition: A Survey <s> 5) Space-and Object-Based Analysis: <s> Medical Research Council, Applied Psychology Unit, Cambridge, England Theories of visual attention deal with the limit on our ability to see (and later report) several things at once. These theories fall into three broad classes. Objectbased theories propose a limit on the number of separate objects :that can be perceived simultaneously. Discrimination-based theories propose a limit on the number of separate discriminations that can be made. Space-based theories propose a limit on the spatial area from which information can be taken up. To distinguish these views, the present experiments used small (< 1 °), brief, foveal displays, each consisting of two overlapping objects (a box with a line struck through it). It was found that two judgments that concern the same object can be made simultaneously without loss of accuracy, whereas two judgments that concern different objects cannot. Neither the similarity nor the difficulty of required discriminations, nor the spatial distribution of information, could account for the results. The experiments support a view in which parallel, preattentive processes serve to segment the field into separate objects, followed by a pfocess of focal attention that deals with only one object at a time. This view is also able to account for results taken to support both discrimination-based and space-based theories. Object-Based Theories of Visual Attention Theories of visual attention are concerned with the limit on our ability to see (and later report) several things at once. This article deals with what I call object-based theories (e.g., Neisser, 1967), which propose that this limit concerns the number of separate objects that can be seen. Here some predictions of this view are tested, and object-based theories are contrasted with discrimination-based theories (e.g., Allport, 1971, 1980) and with spaced-based theories (e.g., Hoffman & Nelson, 1981; Posner, Snyder, & Davidson, 1980). The work of Neisser (1967) illustrates the object-based approach. Neisser (1967) proposed that perceptual analysis of the visual world takes place in two successive stages. The first, preattentive, stage segments the field into separate objects on the basis of such Gestalt properties as spatial proximity, continuity of contour, shared color or movement, and so on. The second stage, focal attention, analyzes a particular object in more detail. Neisser (1967) supposed that, whereas preat <s> BIB002 </s> Visual Attention for Robotic Cognition: A Survey <s> 5) Space-and Object-Based Analysis: <s> A number of psychophysical studies concerning the detection, localization and recognition of objects in the visual field have suggested a two-stage theory of human visual perception. The first stage is the “preattentive” mode, in which simple features are processed rapidly and in parallel over the entire visual field. In the second, “attentive” mode, a specialized processing focus, usually called the focus of attention, is directed to particular locations in the visual field. The analysis of complex forms and the recognition of objects are associated with this second stage.1 The computational justification for such a hypothesis comes from the realization that while it is possible to imagine specific algorithms performing tasks such as shape analysis and recognition at specific locations, it is difficult to imagine these algorithms operating in parallel over the whole visual scene, since such an approach will quickly lead to a combinatorial explosion in terms of required computational resources.2 This is essentially the major critique of Minsky and Papert to a universal application of perceptrons in visual perception.3 Taken together, these empirical and theoretical studies suggest that beyond a certain preprocessing stage, the analysis of visual information proceeds in a sequence of operations, each one applied to a selected location (or locations). <s> BIB003 </s> Visual Attention for Robotic Cognition: A Survey <s> 5) Space-and Object-Based Analysis: <s> Subjects searched sets of items for targets defined by conjunctions of color and form, color and orientation, or color and size. Set size was varied and reaction times (RT) were measured. For many unpracticed subjects, the slopes of the resulting RT X Set Size functions are too shallow to be consistent with Treisman's feature integration model, which proposes serial, self-terminating search for conjunctions. Searches for triple conjunctions (Color X Size X Form) are easier than searches for standard conjunctions and can be independent of set size. A guided search model similar to Hoffman's (1979) two-stage model can account for these data. In the model, parallel processes use information about simple features to guide attention in the search for conjunctions. Triple conjunctions are found more efficiently than standard conjunctions because three parallel processes can guide attention more effectively than two. <s> BIB004 </s> Visual Attention for Robotic Cognition: A Survey <s> 5) Space-and Object-Based Analysis: <s> This article presents a theory that integrates space-based and object-based approaches to visual attention. The theory puts together M.P. van Oeffelen and P.G. Vos's (1982, 1983) COntour DEtector (CODE) theory of perceptual grouping by proximity with C. Bundesen's (1990) theory of visual attention (TVA). CODE provides input to TVA, accounting for spatially based between-object selection, and TVA converts the input to output, accounting for feature- and category-based within-object selection. CODE clusters nearby items into perceptual groups that are both perceptual objects and regions of space, thereby integrating object-based and space-based approaches to attention. The combined theory provides a quantitative account of the effects of grouping by proximity and distance between items on reaction time and accuracy data in 7 empirical situations that shaped the current literature on visual spatial attention. <s> BIB005 </s> Visual Attention for Robotic Cognition: A Survey <s> 5) Space-and Object-Based Analysis: <s> The model presented here is an attempt to explain the results from a number of different studies in visual attention, including parallel feature searches and serial conjunction searches, variations in search slope with variations in feature contrast and individual subject differences, attentional gradients triggered by cuing, feature-driven spatial selection, split attention, inhibition of distractor locations, and flanking inhibition. The model is implemented in a neural network consisting of a hierarchy of spatial maps. Attentional gates control the flow of information from each level of the hierarchy to the next. The gates are jointly controlled by a Bottom-Up System favoring locations with unique features and a Top-Down System favoring locations with features designated as target features. Because the gating of each location depends on the features present there, the model is called FeatureGate. <s> BIB006 </s> Visual Attention for Robotic Cognition: A Survey <s> 5) Space-and Object-Based Analysis: <s> Most models of visual search, whether involving overt eye movements or covert shifts of attention, are based on the concept of a saliency map, that is, an explicit two-dimensional map that encodes the saliency or conspicuity of objects in the visual environment. Competition among neurons in this map gives rise to a single winning location that corresponds to the next attended target. Inhibiting this location automatically allows the system to attend to the next most salient location. We describe a detailed computer implementation of such a scheme, focusing on the problem of combining information across modalities, here orientation, intensity and color information, in a purely stimulus-driven manner. The model is applied to common psychophysical stimuli as well as to a very demanding visual search task. Its successful performance is used to address the extent to which the primate visual system carries out visual search via one or more such saliency maps and how this can be tested. <s> BIB007 </s> Visual Attention for Robotic Cognition: A Survey <s> 5) Space-and Object-Based Analysis: <s> What are the units of attention? In addition to standard models holding that attention can select spatial regions and visual features, recent work suggests that in some cases attention can directly select discrete objects. This paper reviews the state of the art with regard to such ‘object-based’ attention, and explores how objects of attention relate to locations, reference frames, perceptual groups, surfaces, parts, and features. Also discussed are the dynamic aspects of objecthood, including the question of how attended objects are individuated in time, and the possibility of attending to simple dynamic motions and events. The final sections of this review generalize these issues beyond vision science, to other modalities and fields such as auditory objects of attention and the infant’s ‘object concept’. q 2001 Elsevier Science B.V. All rights reserved. <s> BIB008 </s> Visual Attention for Robotic Cognition: A Survey <s> 5) Space-and Object-Based Analysis: <s> In this paper, a novel model of object-based visual attention extending Duncan's Integrated Competition Hypothesis [Phil. Trans. R. Soc. London B 353 (1998) 1307-1317] is presented. In contrast to the attention mechanisms used in most previous machine vision systems which drive attention based on the spatial location hypothesis, the mechanisms which direct visual attention in our system are object-driven as well as feature-driven. The competition to gain visual attention occurs not only within an object but also between objects. For this purpose, two new mechanisms in the proposed model are described and analyzed in detail. The first mechanism computes the visual salience of objects and groupings; the second one implements the hierarchical selectivity of attentional shifts. The results of the new approach on synthetic and natural images are reported. <s> BIB009 </s> Visual Attention for Robotic Cognition: A Survey <s> 5) Space-and Object-Based Analysis: <s> A computational model of object-based selective visual attention is introduced. What are the units of selective attention is always the focus of selective attention. Most existing computational model of selective attention is space-based, but more and more neuroscience experiments indicate that it is object-based. For object-based attention, how to define "object" is the biggest difficulty and it is generally accepted that the "object" refers to "perceptual object" which is created by Gestalt rules. However, Gestalt rules are very difficult to be calculated in images. The model presented in this paper is comprised of two independent components. One component is object-based segmentation by using perceptual color cues, the other is the generation of a object-based saliency-map in terms of the inhibition of return mechanism. Lastly, the results of experiments show that the model is valid. <s> BIB010 </s> Visual Attention for Robotic Cognition: A Survey <s> 5) Space-and Object-Based Analysis: <s> When a monkey searches for a colour and orientation feature conjunction target, the scan path is guided to target coloured locations in preference to locations containing the target orientation [Vision Res. 38 (1998b) 1805]. An active vision model, using biased competition, is able to replicate this behaviour. As object-based attention develops in extrastriate cortex, featural information is passed to posterior parietal cortex (LIP), enabling it to represent behaviourally relevant locations [J. Neurophysiol. 76 (1996) 2841] and guide the scan path. Attention evolves from an early spatial effect to being object-based later in the response of the model neurons, as has been observed in monkey single cell recordings. This is the first model to reproduce these effects with temporal precision and is reported here at the systems level allowing the replication of psychophysical scan paths. <s> BIB011 </s> Visual Attention for Robotic Cognition: A Survey <s> 5) Space-and Object-Based Analysis: <s> One of the first steps of any visual system is that of locating suitable interest points, "salient regions", in the scene, to detect events, and eventually to direct gaze toward these locations. In the last few years, object-based visual attention models have received an increasing interest in the literature, the problem, in this case, being that of creating a model of "objecthood" that eventually guides a saliency mechanism. We propose here an object-based model of visual attention and show its instantiation on a humanoid robot. The robot employs action to learn and define its own concept of objecthood. <s> BIB012 </s> Visual Attention for Robotic Cognition: A Survey <s> 5) Space-and Object-Based Analysis: <s> This paper argues that the object- and space-based modes of visual attention can be naturally integrated in a common mathematical framework. In an earlier work we have proposed a mathematical model of visual attention for robotic system exploiting the knowledge of visual attention mechanism of the primates. This paper investigates on the validity of the proposed model for robotic systems through experimentation on a real robot. The paper sheds light on a number of real world issues involved with the design of visual attention system for physically embodied robots and explains how the proposed Bayesian model of visual attention addresses these issues. The object- and space-based modes of visual attention are naturally integrated in the model and is reflected in the sequential Monte Carlo implementation of the model on a real robot. <s> BIB013
Inspired by the early psychophysical theories of attention BIB001 , BIB004 , majority of the computer vision models hypothesize "space" as the elemental unit of attention selection BIB003 - BIB006 ,  ,  , BIB007 . Accordingly, saliency and task-relevance are investigated at the pixel level without considering the concept of an object. Increasing evidence, in the psychology and cognitive neuroscience, of "object" being one of the elemental units of attention selection BIB005 , BIB002 ,  - BIB008 has influenced the recent computer vision models of visual attention. Many of the recent models perform object-based analysis for selective attention BIB009 , BIB012 , BIB010 . There are, however, only a few efforts which integrate spaceand object-based analysis in the same framework  , BIB011 ,  , BIB013 . Although common in almost every computational model, the way these characteristics are achieved differs in different attention models and hence, the variation in performance.