We describe a model of visual processing in which feedback connections from a higher- to a lower-order visual cortical area carry predictions of lower-level neural activities, whereas the feedforward connections carry the residual errors between the predictions and the actual lower-level activities. When exposed to natural images, a hierarchical network of model neurons implementing such a model developed simple-cell-like receptive fields. A subset of neurons responsible for carrying the residual errors showed endstopping and other extra-classical receptive-field effects. These results suggest that rather than being exclusively feedforward phenomena, nonclassical surround effects in the visual cortex may also result from cortico-cortical feedback as a consequence of the visual system using an efficient hierarchical strategy for encoding natural images.

Computer vision is embracing a new research focus in which the aim is to develop visual skills for robots that allow them to interact with a dynamic, realistic environment. To achieve this aim, new kinds of vision algorithms need to be developed which run in real time and subserve the robot's goals. Two fundamental goals are determining the location of a known object. Color can be successfully used for both tasks.This article demonstrates that color histograms of multicolored objects provide a robust, efficient cue for indexing into a large database of models. It shows that color histograms are stable object representations in the presence of occlusion and over change in view, and that they can differentiate among a large number of objects. For solving the identification problem, it introduces a technique called Histogram Intersection, which matches model and image histograms and a fast incremental version of Histogram Intersection, which allows real-time indexing into a large database of stored models. For solving the location problem it introduces an algorithm called Histogram Backprojection, which performs this task efficiently in crowded scenes.

The Hough Transform is a method for detecting curves by exploiting the duality between points on a curve and parameters of that curve. The initial work showed how to detect both analytic curves [Duda and Hart, 1972;Hough, 1962] and non-analytic curves [Merlin and Farber, 1975], but these methods were restricted to binary edge images. This work was generalized to the detection of some analytic curves in grey level images, specifically lines [OIGorman and Clowes, 1973] and circles [Kimrne et al., 1975] and parabolas [Wechsler and Sklansky, 1975]. The line detection case is the most well known and has been ingeniously exploited in several applications [Dudani and Lik, 1977; Fenema and Thompson, 1978; Kender, 1979].We show how the boundaries of an arbitrary non-analytic shape can be used to construct a mapping between image space and Hough Transform space.Such a mapping can be exploited to detect instances of that particular s.hape in an image'. Furthermore, variations in the shape such as rotations, scale changes or figure-ground reversals, correspond to straightforward transfor mations of this mapping. However, the most remarkable property is that such mappings can-be composed to build mappings for complex shapes from the mappings of simpler, component shapes. This makes the generalized Hough a kind of universal transform which can be used to find arbitrarily complex shapes.

No abstract

Models of gaze allocation in complex scenes are derived mainly from studies of static picture viewing. The dominant framework to emerge has been image salience, where properties of the stimulus play a crucial role in guiding the eyes. However, salience-based schemes are poor at accounting for many aspects of picture viewing and can fail dramatically in the context of natural task performance. These failures have led to the development of new models of gaze allocation in scene viewing that address a number of these issues. However, models based on the picture-viewing paradigm are unlikely to generalize to a broader range of experimental contexts, because the stimulus context is limited, and the dynamic, task-driven nature of vision is not represented. We argue that there is a need to move away from this class of model and find the principles that govern gaze allocation in a broader range of settings. We outline the major limitations of salience-based selection schemes and highlight what we have learned from studies of gaze allocation in natural vision. Clear principles of selection are found across many instances of natural vision and these are not the principles that might be expected from picture-viewing studies. We discuss the emerging theoretical framework for gaze allocation on the basis of reward maximization and uncertainty reduction.