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

Behind the Machine's Gaze: Biologically Constrained Neural Networks Exhibit Human-like Visual Attention

By and large, existing computational models of visual attention tacitly assume perfect vision and full access to the stimulus and thereby deviate from foveated biological vision. Moreover, modelling top-down attention is generally reduced to the integration of semantic features without incorporating the signal of a high-level visual tasks that have shown to partially guide human attention. We propose the Neural Visual Attention (NeVA) algorithm to generate visual scanpaths in a top-down manner. With our method, we explore the ability of neural networks on which we impose the biological constraints of foveated vision to generate human-like scanpaths. Thereby, the scanpaths are generated to maximize the performance with respect to the underlying visual task (i.e., classification or reconstruction). Extensive experiments show that the proposed method outperforms state-of-the-art unsupervised human attention models in terms of similarity to human scanpaths. Additionally, the flexibility of the framework allows to quantitatively investigate the role of different tasks in the generated visual behaviours. Finally, we demonstrate the superiority of the approach in a novel experiment that investigates the utility of scanpaths in real-world applications, where imperfect viewing conditions are given

Computational principles for an autonomous active vision system

Vision research has uncovered computational principles that generalize across species and brain area. However, these biological mechanisms are not frequently implemented in computer vision algorithms. In this thesis, models suitable for application in computer vision were developed to address the benefits of two biologically-inspired computational principles: multi-scale sampling and active, space-variant, vision. The first model investigated the role of multi-scale sampling in motion integration. It is known that receptive fields of different spatial and temporal scales exist in the visual cortex; however, models addressing how this basic principle is exploited by species are sparse and do not adequately explain the data. The developed model showed that the solution to a classical problem in motion integration, the aperture problem, can be reframed as an emergent property of multi-scale sampling facilitated by fast, parallel, bi-directional connections at different spatial resolutions. Humans and most other mammals actively move their eyes to sample a scene (active vision); moreover, the resolution of detail in this sampling process is not uniform across spatial locations (space-variant). It is known that these eye-movements are not simply guided by image saliency, but are also influenced by factors such as spatial attention, scene layout, and task-relevance. However, it is seldom questioned how previous eye movements shape how one learns and recognizes an object in a continuously-learning system. To explore this question, a model (CogEye) was developed that integrates active, space-variant sampling with eye-movement selection (the where visual stream), and object recognition (the what visual stream). The model hypothesizes that a signal from the recognition system helps the where stream select fixation locations that best disambiguate object identity between competing alternatives. The third study used eye-tracking coupled with an object disambiguation psychophysics experiment to validate the second model, CogEye. While humans outperformed the model in recognition accuracy, when the model used information from the recognition pathway to help select future fixations, it was more similar to human eye movement patterns than when the model relied on image saliency alone. Taken together these results show that computational principles in the mammalian visual system can be used to improve computer vision models

Foveated object recognition by corner search

textHere we describe a gray scale object recognition system based on foveated corner finding, the computation of sequential fixation points, and elements of Lowe’s SIFT transform. The system achieves rotational, transformational, and limited scale invariant object recognition that produces recognition decisions using data extracted from sequential fixation points. It is broken into two logical steps. The first is to develop principles of foveated visual search and automated fixation selection to accomplish corner search. The result is a new algorithm for finding corners which is also a corner-based algorithm for aiming computed foveated visual fixations. In the algorithm, long saccades move the fovea to previously unexplored areas of the image, while short saccades improve the accuracy of putative corner locations. The system is tested on two natural scenes. As an interesting comparison study we compare fixations generated by the algorithm with those of subjects viewing the same images, whose eye movements are being recorded by an eyetracker. The comparison of fixation patterns is made using an information-theoretic measure. Results show that the algorithm is a good locator of corners, but does not correlate particularly well with human visual fixations. The second step is to use the corners located, which meet certain goodness criteria, as keypoints in a modified version of the SIFT algorithm. Two scales are implemented. This implementation creates a database of SIFT features of known objects. To recognize an unknown object, a corner is located and a feature vector created. The feature vector is compared with those in the database of known objects. The process is continued for each corner in the unknown object until enough information has been accumulated to reach a decision. The system was tested on 78 gray scale objects, hand tools and airplanes, and shown to perform well.Electrical and Computer Engineerin

A computer vision model for visual-object-based attention and eye movements

This is the post-print version of the final paper published in Computer Vision and Image Understanding. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2008 Elsevier B.V.This paper presents a new computational framework for modelling visual-object-based attention and attention-driven eye movements within an integrated system in a biologically inspired approach. Attention operates at multiple levels of visual selection by space, feature, object and group depending on the nature of targets and visual tasks. Attentional shifts and gaze shifts are constructed upon their common process circuits and control mechanisms but also separated from their different function roles, working together to fulfil flexible visual selection tasks in complicated visual environments. The framework integrates the important aspects of human visual attention and eye movements resulting in sophisticated performance in complicated natural scenes. The proposed approach aims at exploring a useful visual selection system for computer vision, especially for usage in cluttered natural visual environments.National Natural Science of Founda- tion of Chin

Bio-inspired foveal and peripheral visual sensing for saliency-based decision making in robotics

Computer vision is an area of research that has grown at immense speed in the last few decades, tackling problems towards scene understanding from very diverse fronts, such as image classification, object detection, localization, mapping and tracking. It has also been long understood that there are very valuable lessons to learn from biology and to be applied to this research field, where the human visual system is very likely the most studied brain mechanism. The eye foveation system is a very good example of such lessons, since both machines and animals often face a similar dilemma; to prioritize visual areas of interest to faster process information, given limited computing power and from a field of view that is too wide to be simultaneously attended. While extensive models of artificial foveation have been presented, the re-emerging area of machine learning with deep neural networks has opened the question into how these two approaches can contribute to each other. Novel deep learning models often rely on the availability of substantial computing power, but areas of application face strict constraints, a good example are unmanned aerial vehicles, which in order to be autonomous should lift and power all their computing equipment. In this work it is studied how applying a foveation principle to down-scale images can be used to reduce the number of operations required for object detection, and compare its effect to normally down-sampled images, given the prevalent number of operations by Convolutional Neural Network (CNN) layers. Foveation requires prior knowledge of regions of interest to center the fovea, this point in question is addressed by a merging of bottom-up saliency and top-down feedback of objects that the CNN has been trained to detect. Albeit saliency models have also been studied extensively in the last couple of decades, most often comparing their performance to human observer datasets, the question remains open into how they fit in wider information processing paradigms and into functional representations of the human brain. It is proposed here an information flow scheme that encompasses these principles. Finally, to give to the model the capacity to operate coherently in the time domain, it adapts a representation of a well-established theory of the decision-making process that takes place in the basal ganglia region of the brain. The behaviour of this representation is then tested against human observer's data in an omnidirectional field of view, where the importance of selecting the most contextually relevant region of interest in each time-step is highlighted

Methods and Apparatus for Autonomous Robotic Control

Sensory processing of visual, auditory, and other sensor information (e.g., visual imagery, LIDAR, RADAR) is conventionally based on "stovepiped," or isolated processing, with little interactions between modules. Biological systems, on the other hand, fuse multi-sensory information to identify nearby objects of interest more quickly, more efficiently, and with higher signal-to-noise ratios. Similarly, examples of the OpenSense technology disclosed herein use neurally inspired processing to identify and locate objects in a robot's environment. This enables the robot to navigate its environment more quickly and with lower computational and power requirements