We explore the idea that the visual system uses specialized processes to extract critical information about 3-d motion and structure for visually-guided navigation. We first consider the computation of three essential...
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We explore the idea that the visual system uses specialized processes to extract critical information about 3-d motion and structure for visually-guided navigation. We first consider the computation of three essential properties: the relative 3-ddirection of heading of the observer, the time-to-collision with approaching object surfaces, and the locations of object boundaries defined by motion discontinuities. We then focus on the heading computation, relating current algorithms to the perception of heading direction.
A robust vision model has been developed and implemented with a self-organizing/unsupervised artificial neural network (ANN) classifier-KART, which is a novel hybrid model of a modified Kohonen's feature map and t...
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A robust vision model has been developed and implemented with a self-organizing/unsupervised artificial neural network (ANN) classifier-KART, which is a novel hybrid model of a modified Kohonen's feature map and the Carpenter/Grossberg's ART architecture. The six moment invariants have been mapped onto a 7-dimensional unit hypersphere and have been applied to the KART classifier. In this paper the KART model will be presented. The non-adaptive neural implementations on the image processing and the moment invariant feature extraction will be discussed. In addition, the simulation results that illustrate the capabilities of this model will also be provided.
Our fds-theory accepts context effects in perception. It takes account of early perception phase, by establishing the concept of 'Structural Identity' of a k-norm, 'SkId'. Early perception detects the ...
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Our fds-theory accepts context effects in perception. It takes account of early perception phase, by establishing the concept of 'Structural Identity' of a k-norm, 'SkId'. Early perception detects the 'maximally abstracted' holistic view of an object by representing it in terms of an attributed semantic graph-word. The SkId graph-word, along with the contextual, abstraction-level dependent, description of perceptual characteristics, constitute the 'formal description schema - fds' models of the norm-objects.
There is a growing need for progress in the technology for the management of knowledge derived from sensory data. In this paper we address the structure and operations of a cellular 'Experiential Knowledge Base - ...
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There is a growing need for progress in the technology for the management of knowledge derived from sensory data. In this paper we address the structure and operations of a cellular 'Experiential Knowledge Base - E*KB' system, which acts as a cognitive prosthesis to the decision maker. We concern ourselves with the experiential knowledge representation techniques, and with the methods and tools required for the extraction (relevance filtering), the contextual abstraction (data compression and generalization), the classification and storage of real time sensory knowledge in the cellular architecture of the E*KB.
biological sensor design has long provided inspiration for sensor design in machine vision. However, relatively little attention has been paid to the actual design parameters provided by biological systems, as opposed...
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biological sensor design has long provided inspiration for sensor design in machine vision. However, relatively little attention has been paid to the actual design parameters provided by biological systems, as opposed to the general nature of biologicalvision architectures. In the present paper, we provide a review of current knowledge of primate spatial visiondesign parameters and present recent experimental and modeling work from our lab which demonstrates that a numerical conformal mapping, which is a refinement of our previous complex logarithmic model, provides the best current summary of this feature of the primate visual system.
A multi-task dynamic neural network that can be programmed for sorting, processing and encoding spatio-temporal visual information is presented in this paper. This dynamic neural network, called the PN-network, is com...
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A multi-task dynamic neural network that can be programmed for sorting, processing and encoding spatio-temporal visual information is presented in this paper. This dynamic neural network, called the PN-network, is comprised of numerous densely interconnectedneural subpopulations which reside in one of the two coupled sublayers, P or N. The subpopulations in the P-sublayer transmit an excitatory, or a positive, influence onto all interconnected units, whereas the subpopulations in the N-sublayer transmit an inhibitory, or negative, influence. The dynamical activity generated by each subpopulation is given by a nonlinear first-order system. By varying the coupling strength between these different subpopulations it is possible to generate three distinct modes of dynamical behavior useful for performing vision related tasks. It is postulated that the PN-network can function as a basic programmable processor for novel vision machine systems.
We investigate the use of chromatic information in dense stereo correspondence. Specifically, the chromatic photometric constraint, which is used to specify a mathematical optimality criterion for solving the dense st...
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We investigate the use of chromatic information in dense stereo correspondence. Specifically, the chromatic photometric constraint, which is used to specify a mathematical optimality criterion for solving the dense stereo correspondence problem, is developed. The result is a theoretical construction for developing dense stereo correspondence algorithms which use chromatic information. The efficacy of using chromatic information via this construction is tested by implementing single-and multi-resolution versions of a stereo correspondence algorithm which uses simulated annealing as a means of solving the optimization problem. Results demonstrate that the use of chromatic information can significantly improve the performance of dense stereo correspondence.
For autonomous machines equipped with vision capabilities and in a controlled environment. 3-d model-based object identification methodologies will, in general, solve rigid body recognition problems. In an uncontrolle...
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For autonomous machines equipped with vision capabilities and in a controlled environment. 3-d model-based object identification methodologies will, in general, solve rigid body recognition problems. In an uncontrolled environment, however, several factors pose difficulties for correct identification. We have addressed the problem of 3-d object recognition using a number of methods including neural network classifiers and a Bayesian-like classifier for matching image data with model projection-deriveddata. neural network classifiers used began operation as simple feature vector classifiers. However, unmodelled signal behavior was learned with additional samples yielding great improvement in classification rates. The model analysis drastically shortened training time of both classification systems. In an environment where signal behavior is not accurately modelled, two separate forms of learning give the systems the ability to update estimates of this behavior. Required, of course, are sufficient samples to learn this new information. Given sufficient information and a well-controlled environment, identification of 3-d objects from a limited number of classes is indeed possible.
This paper addresses the problems of how to efficiently extract information from different data sources and how to fuse the information to achieve a more complete and accurate interpretation of the underlying 3-d scen...
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This paper addresses the problems of how to efficiently extract information from different data sources and how to fuse the information to achieve a more complete and accurate interpretation of the underlying 3-d scene. This paper describes an integrated approach to 3-d image interpretation. The approach is to first obtain region information by applying a multi-resolution segmentation technique to a monocular image. The elevation information is then extracted by applying an edge-based matching technique to a stereo pair covering the same scene. The region information, the elevation information, and the a priori knowledge about the scene are then integrated by using a rule-based scheme to classify the various objects in the scene. Results of applying this integrated technique to an overhead visible urban scene are presented.
The reconstruction of curves and surfaces from sparse data is an important task in many applications. In computervision problems the reconstructed curves and surfaces generally represent some physical property of a r...
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The reconstruction of curves and surfaces from sparse data is an important task in many applications. In computervision problems the reconstructed curves and surfaces generally represent some physical property of a real object in a scene. Thus, the characteristics of the reconstruction process differs from straight forward fitting of smooth curves and surfaces to a set of data. Since the collecteddata is represented in an arbitrarily chosen coordinate system, the reconstruction process should be invariant to the choice of the coordinate system (except for the transformation between the two coordinate systems). In this paper, reconstruction algorithms are presented for reconstructing invariant estimates of both curves and surfaces. The reconstruction problem will be cast as an ill-posed inverse problem which must be stablized using a priori information about the constraint formation. Tikhonov regularization is used to form a well-posed mathematical problem statement. Examples of typical reconstructed objects are also given.
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