239 research outputs found

    Computational principles for an autonomous active vision system

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    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

    Computational Models of Perceptual Organization and Bottom-up Attention in Visual and Audio-Visual Environments

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    Figure Ground Organization (FGO) - inferring spatial depth ordering of objects in a visual scene - involves determining which side of an occlusion boundary (OB) is figure (closer to the observer) and which is ground (further away from the observer). Attention, the process that governs how only some part of sensory information is selected for further analysis based on behavioral relevance, can be exogenous, driven by stimulus properties such as an abrupt sound or a bright flash, the processing of which is purely bottom-up; or endogenous (goal-driven or voluntary), where top-down factors such as familiarity, aesthetic quality, etc., determine attentional selection. The two main objectives of this thesis are developing computational models of: (i) FGO in visual environments; (ii) bottom-up attention in audio-visual environments. In the visual domain, we first identify Spectral Anisotropy (SA), characterized by anisotropic distribution of oriented high frequency spectral power on the figure side and lack of it on the ground side, as a novel FGO cue, that can determine Figure/Ground (FG) relations at an OB with an accuracy exceeding 60%. Next, we show a non-linear Support Vector Machine based classifier trained on the SA features achieves an accuracy close to 70% in determining FG relations, the highest for a stand-alone local cue. We then show SA can be computed in a biologically plausible manner by pooling the Complex cell responses of different scales in a specific orientation, which also achieves an accuracy greater than or equal to 60% in determining FG relations. Next, we present a biologically motivated, feed forward model of FGO incorporating convexity, surroundedness, parallelism as global cues and SA, T-junctions as local cues, where SA is computed in a biologically plausible manner. Each local cue, when added alone, gives statistically significant improvement in the model's performance. The model with both local cues achieves higher accuracy than those of models with individual cues in determining FG relations, indicating SA and T-Junctions are not mutually contradictory. Compared to the model with no local cues, the model with both local cues achieves greater than or equal to 8.78% improvement in determining FG relations at every border location of images in the BSDS dataset. In the audio-visual domain, first we build a simple computational model to explain how visual search can be aided by providing concurrent, co-spatial auditory cues. Our model shows that adding a co-spatial, concurrent auditory cue can enhance the saliency of a weakly visible target among prominent visual distractors, the behavioral effect of which could be faster reaction time and/or better search accuracy. Lastly, a bottom-up, feed-forward, proto-object based audiovisual saliency map (AVSM) for the analysis of dynamic natural scenes is presented. We demonstrate that the performance of proto-object based AVSM in detecting and localizing salient objects/events is in agreement with human judgment. In addition, we show the AVSM computed as a linear combination of visual and auditory feature conspicuity maps captures a higher number of valid salient events compared to unisensory saliency maps

    Hypothesis-based image segmentation for object learning and recognition

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    Denecke A. Hypothesis-based image segmentation for object learning and recognition. Bielefeld: Universität Bielefeld; 2010.This thesis addresses the figure-ground segmentation problem in the context of complex systems for automatic object recognition as well as for the online and interactive acquisition of visual representations. First the problem of image segmentation in general terms and next its importance for object learning in current state-of-the-art systems is introduced. Secondly a method using artificial neural networks is presented. This approach on the basis of Generalized Learning Vector Quantization is investigated in challenging scenarios such as the real-time figure-ground segmentation of complex shaped objects under continuously changing environment conditions. The ability to fulfill these requirements characterizes the novelty of the approach compared to state-of-the-art methods. Finally our technique is extended towards online adaption of model complexity and the integration of several segmentation cues. This yields a framework for object segmentation that is applicable to improve current systems for visual object learning and recognition

    Neural dynamics of invariant object recognition: relative disparity, binocular fusion, and predictive eye movements

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    How does the visual cortex learn invariant object categories as an observer scans a depthful scene? Two neural processes that contribute to this ability are modeled in this thesis. The first model clarifies how an object is represented in depth. Cortical area V1 computes absolute disparity, which is the horizontal difference in retinal location of an image in the left and right foveas. Many cells in cortical area V2 compute relative disparity, which is the difference in absolute disparity of two visible features. Relative, but not absolute, disparity is unaffected by the distance of visual stimuli from an observer, and by vergence eye movements. A laminar cortical model of V2 that includes shunting lateral inhibition of disparity-sensitive layer 4 cells causes a peak shift in cell responses that transforms absolute disparity from V1 into relative disparity in V2. The second model simulates how the brain maintains stable percepts of a 3D scene during binocular movements. The visual cortex initiates the formation of a 3D boundary and surface representation by binocularly fusing corresponding features from the left and right retinotopic images. However, after each saccadic eye movement, every scenic feature projects to a different combination of retinal positions than before the saccade. Yet the 3D representation, resulting from the prior fusion, is stable through the post-saccadic re-fusion. One key to stability is predictive remapping: the system anticipates the new retinal positions of features entailed by eye movements by using gain fields that are updated by eye movement commands. The 3D ARTSCAN model developed here simulates how perceptual, attentional, and cognitive interactions across different brain regions within the What and Where visual processing streams interact to coordinate predictive remapping, stable 3D boundary and surface perception, spatial attention, and the learning of object categories that are invariant to changes in an object's retinal projections. Such invariant learning helps the system to avoid treating each new view of the same object as a distinct object to be learned. The thesis hereby shows how a process that enables invariant object category learning can be extended to also enable stable 3D scene perception

    Change blindness: eradication of gestalt strategies

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    Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

    Visual Cortex

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    The neurosciences have experienced tremendous and wonderful progress in many areas, and the spectrum encompassing the neurosciences is expansive. Suffice it to mention a few classical fields: electrophysiology, genetics, physics, computer sciences, and more recently, social and marketing neurosciences. Of course, this large growth resulted in the production of many books. Perhaps the visual system and the visual cortex were in the vanguard because most animals do not produce their own light and offer thus the invaluable advantage of allowing investigators to conduct experiments in full control of the stimulus. In addition, the fascinating evolution of scientific techniques, the immense productivity of recent research, and the ensuing literature make it virtually impossible to publish in a single volume all worthwhile work accomplished throughout the scientific world. The days when a single individual, as Diderot, could undertake the production of an encyclopedia are gone forever. Indeed most approaches to studying the nervous system are valid and neuroscientists produce an almost astronomical number of interesting data accompanied by extremely worthy hypotheses which in turn generate new ventures in search of brain functions. Yet, it is fully justified to make an encore and to publish a book dedicated to visual cortex and beyond. Many reasons validate a book assembling chapters written by active researchers. Each has the opportunity to bind together data and explore original ideas whose fate will not fall into the hands of uncompromising reviewers of traditional journals. This book focuses on the cerebral cortex with a large emphasis on vision. Yet it offers the reader diverse approaches employed to investigate the brain, for instance, computer simulation, cellular responses, or rivalry between various targets and goal directed actions. This volume thus covers a large spectrum of research even though it is impossible to include all topics in the extremely diverse field of neurosciences

    Cortico-spinal modularity in the parieto-frontal system: a new perspective on action control

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    : Classical neurophysiology suggests that the motor cortex (MI) has a unique role in action control. In contrast, this review presents evidence for multiple parieto-frontal spinal command modules that can bypass MI. Five observations support this modular perspective: (i) the statistics of cortical connectivity demonstrate functionally-related clusters of cortical areas, defining functional modules in the premotor, cingulate, and parietal cortices; (ii) different corticospinal pathways originate from the above areas, each with a distinct range of conduction velocities; (iii) the activation time of each module varies depending on task, and different modules can be activated simultaneously; (iv) a modular architecture with direct motor output is faster and less metabolically expensive than an architecture that relies on MI, given the slow connections between MI and other cortical areas; (v) lesions of the areas composing parieto-frontal modules have different effects from lesions of MI. Here we provide examples of six cortico-spinal modules and functions they subserve: module 1) arm reaching, tool use and object construction; module 2) spatial navigation and locomotion; module 3) grasping and observation of hand and mouth actions; module 4) action initiation, motor sequences, time encoding; module 5) conditional motor association and learning, action plan switching and action inhibition; module 6) planning defensive actions. These modules can serve as a library of tools to be recombined when faced with novel tasks, and MI might serve as a recombinatory hub. In conclusion, the availability of locally-stored information and multiple outflow paths supports the physiological plausibility of the proposed modular perspective

    Neural models of inter-cortical networks in the primate visual system for navigation, attention, path perception, and static and kinetic figure-ground perception

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    Vision provides the primary means by which many animals distinguish foreground objects from their background and coordinate locomotion through complex environments. The present thesis focuses on mechanisms within the visual system that afford figure-ground segregation and self-motion perception. These processes are modeled as emergent outcomes of dynamical interactions among neural populations in several brain areas. This dissertation specifies and simulates how border-ownership signals emerge in cortex, and how the medial superior temporal area (MSTd) represents path of travel and heading, in the presence of independently moving objects (IMOs). Neurons in visual cortex that signal border-ownership, the perception that a border belongs to a figure and not its background, have been identified but the underlying mechanisms have been unclear. A model is presented that demonstrates that inter-areal interactions across model visual areas V1-V2-V4 afford border-ownership signals similar to those reported in electrophysiology for visual displays containing figures defined by luminance contrast. Competition between model neurons with different receptive field sizes is crucial for reconciling the occlusion of one object by another. The model is extended to determine border-ownership when object borders are kinetically-defined, and to detect the location and size of shapes, despite the curvature of their boundary contours. Navigation in the real world requires humans to travel along curved paths. Many perceptual models have been proposed that focus on heading, which specifies the direction of travel along straight paths, but not on path curvature. In primates, MSTd has been implicated in heading perception. A model of V1, medial temporal area (MT), and MSTd is developed herein that demonstrates how MSTd neurons can simultaneously encode path curvature and heading. Human judgments of heading are accurate in rigid environments, but are biased in the presence of IMOs. The model presented here explains the bias through recurrent connectivity in MSTd and avoids the use of differential motion detectors which, although used in existing models to discount the motion of an IMO relative to its background, is not biologically plausible. Reported modulation of the MSTd population due to attention is explained through competitive dynamics between subpopulations responding to bottom-up and top- down signals
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