Aspects of multi-resolutional foveal images for robot vision

Imperial Users onl

A Survey of Multimedia Technologies and Robust Algorithms

Multimedia technologies are now more practical and deployable in real life, and the algorithms are widely used in various researching areas such as deep learning, signal processing, haptics, computer vision, robotics, and medical multimedia processing. This survey provides an overview of multimedia technologies and robust algorithms in multimedia data processing, medical multimedia processing, human facial expression tracking and pose recognition, and multimedia in education and training. This survey will also analyze and propose a future research direction based on the overview of current robust algorithms and multimedia technologies. We want to thank the research and previous work done by the Multimedia Research Centre (MRC), the University of Alberta, which is the inspiration and starting point for future research.Comment: arXiv admin note: text overlap with arXiv:2010.1296

The hierarchical sparse selection model of visual crowding

Because the environment is cluttered, objects rarely appear in isolation. The visual system must therefore attentionally select behaviorally relevant objects from among many irrelevant ones. A limit on our ability to select individual objects is revealed by the phenomenon of visual crowding: an object seen in the periphery, easily recognized in isolation, can become impossible to identify when surrounded by other, similar objects. The neural basis of crowding is hotly debated: while prevailing theories hold that crowded information is irrecoverable – destroyed due to over-integration in early stage visual processing – recent evidence demonstrates otherwise. Crowding can occur between high-level, configural object representations, and crowded objects can contribute with high precision to judgments about the “gist” of a group of objects, even when they are individually unrecognizable. While existing models can account for the basic diagnostic criteria of crowding (e.g., specific critical spacing, spatial anisotropies, and temporal tuning), no present model explains how crowding can operate simultaneously at multiple levels in the visual processing hierarchy, including at the level of whole objects. Here, we present a new model of visual crowding—the hierarchical sparse selection (HSS) model, which accounts for object-level crowding, as well as a number of puzzling findings in the recent literature. Counter to existing theories, we posit that crowding occurs not due to degraded visual representations in the brain, but due to impoverished sampling of visual representations for the sake of perception. The HSS model unifies findings from a disparate array of visual crowding studies and makes testable predictions about how information in crowded scenes can be accessed

Space-variant picture coding

PhDSpace-variant picture coding techniques exploit the strong spatial non-uniformity of the human visual system in order to increase coding efficiency in terms of perceived quality per bit. This thesis extends space-variant coding research in two directions. The first of these directions is in foveated coding. Past foveated coding research has been dominated by the single-viewer, gaze-contingent scenario. However, for research into the multi-viewer and probability-based scenarios, this thesis presents a missing piece: an algorithm for computing an additive multi-viewer sensitivity function based on an established eye resolution model, and, from this, a blur map that is optimal in the sense of discarding frequencies in least-noticeable- rst order. Furthermore, for the application of a blur map, a novel algorithm is presented for the efficient computation of high-accuracy smoothly space-variant Gaussian blurring, using a specialised filter bank which approximates perfect space-variant Gaussian blurring to arbitrarily high accuracy and at greatly reduced cost compared to the brute force approach of employing a separate low-pass filter at each image location. The second direction is that of artifi cially increasing the depth-of- field of an image, an idea borrowed from photography with the advantage of allowing an image to be reduced in bitrate while retaining or increasing overall aesthetic quality. Two synthetic depth of field algorithms are presented herein, with the desirable properties of aiming to mimic occlusion eff ects as occur in natural blurring, and of handling any number of blurring and occlusion levels with the same level of computational complexity. The merits of this coding approach have been investigated by subjective experiments to compare it with single-viewer foveated image coding. The results found the depth-based preblurring to generally be significantly preferable to the same level of foveation blurring

Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet

Neurophysiological evidence for invariant representations of objects and faces in the primate inferior temporal visual cortex is described. Then a computational approach to how invariant representations are formed in the brain is described that builds on the neurophysiology. A feature hierarchy model in which invariant representations can be built by self-organizing learning based on the temporal and spatial statistics of the visual input produced by objects as they transform in the world is described. VisNet can use temporal continuity in an associative synaptic learning rule with a short-term memory trace, and/or it can use spatial continuity in continuous spatial transformation learning which does not require a temporal trace. The model of visual processing in the ventral cortical stream can build representations of objects that are invariant with respect to translation, view, size, and also lighting. The model has been extended to provide an account of invariant representations in the dorsal visual system of the global motion produced by objects such as looming, rotation, and object-based movement. The model has been extended to incorporate top-down feedback connections to model the control of attention by biased competition in, for example, spatial and object search tasks. The approach has also been extended to account for how the visual system can select single objects in complex visual scenes, and how multiple objects can be represented in a scene. The approach has also been extended to provide, with an additional layer, for the development of representations of spatial scenes of the type found in the hippocampus