The paper deals with dynamic acquisition of range data using a multiple proximity sensor system located in a robot gripper for the purpose of pose estimation of 3-d regular objects. A sensor structure is proposed and ...
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ISBN:
(纸本)0819404497
The paper deals with dynamic acquisition of range data using a multiple proximity sensor system located in a robot gripper for the purpose of pose estimation of 3-d regular objects. A sensor structure is proposed and an algorithm of dynamic sensing presented. Pose estimation of regular objects using Newton's method and cylindrical objects is described and results of experiments are presented anddiscussed.
We advance new associative processor (AP) synthesis algorithms and performance measures. We compare 10 different 1:1 APs (where each input key vector is associated with a different output recollection vector). We find...
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ISBN:
(纸本)0819404497
We advance new associative processor (AP) synthesis algorithms and performance measures. We compare 10 different 1:1 APs (where each input key vector is associated with a different output recollection vector). We find that unless new output recollection vector encoding techniques are used, APs are not competitive. We find the robust Ho Kashyap-2 CAAP (content addressable AP) to be preferable and that the σsyn parameter used in it should not be chosen larger than necessary.
In this paper, a stereo vision matching algorithm, implemented via a neural network architecture, is described. The stereo matching problem, that is, finding the correspondence of features between two images, can be c...
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ISBN:
(纸本)0819404497
In this paper, a stereo vision matching algorithm, implemented via a neural network architecture, is described. The stereo matching problem, that is, finding the correspondence of features between two images, can be cast as a constraint satisfaction problem. The algorithm uses image edge features and assumes a parallel-axis camera geometry such that the corresponding image points must lie in the same scanline. Intra-scanline constraints are used to perform multiple-constraint satisfaction searches for the correct match. Further, inter-scanline constraints are used to enforce consistent matches by eliminating those that are not getting enough support from the neighboring scanlines. The inter-scanline constraints are implemented in a 3-d neural network which is formed by a stack of 2-d neuron layers. First, a mulilayerednetwork is designed to extract the features points for matching using a static neural network. A similarity measure is defined for each pair of feature point matches which are then passed on to the second stage of the algorithm. The purpose of the second stage is to turn the difficult correspondence problem into a constraint satisfaction problem by imposing relational constraints. The result of computer simulations are presented to demonstrate the effectiveness of the approach.
This paper deals with quantitative aspects of camera fixation for reconstruction of a static scene. In general, when the camera undergoes translation and rotation, there is an infinite number of points that produce eq...
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ISBN:
(纸本)0819404497
This paper deals with quantitative aspects of camera fixation for reconstruction of a static scene. In general, when the camera undergoes translation and rotation, there is an infinite number of points that produce equal optical flow for any instantaneous point in time. For the case where the rotation axis of the camera is perpendicular to the instantaneous translation vector, these points form a circle (called the Equal Flow Circle or simply EFC) and a line. A special case of the EFCs is the Zero Flow Circle (ZFC) where both components of the optical flow are equal to zero. A fixation point is the intersection of all the ZFCs. Points inside and outside the ZFC are quantitatively mapped using the EFCs. We show how to find the exact location of points in space during fixation.
Insects use a relatively simple visual system to navigate and avoid obstacles. In particular, they use self motion to determine the range to objects by the angular velocities of the contrasts across the retina array. ...
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ISBN:
(纸本)0819404497
Insects use a relatively simple visual system to navigate and avoid obstacles. In particular, they use self motion to determine the range to objects by the angular velocities of the contrasts across the retina array. Adopting principles learnt from studying insect behaviour and neurophysiology we have modelled aspects of the motion detection mechanism of insect visual system into a means of categorising edges and computing their motion and thus determining range. Copying insect motion perception, a camera is scanned across a scene and a temporal sequence of line images captured. The 8-bit grey scale image is immediately reduced to a log23 = 1.6 bit image by saturating the contrast. Behind each pixel, one state is formed by increasing intensity, one by decreasing intensity and a third is indeterminate. Pairs of receptors at two consecutive times, forming a 2 by 2 template in space-time, give a finite number of combinations, of which it is found that only a small subset provide useful motion information. Combinations of selected templates results in a distribution of template responses that is amenable to analysis by the Hough transform. Running the model on real scenes reveals the value of lateral inhibition as well as insights into the effect of different edge types and the use of parallax. The model suggests a possible new neurophysiological construction that can be copied in hardware to provide a fast means of inferring 3-d structure in a scene where the observer is moving with a known velocity.
The aim of this paper is to propose a neural network architecture as an approach to the feature matching problem in stereo vision. The model is based on the principle of shunting feedback competitive equations studied...
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The aim of this paper is to propose a neural network architecture as an approach to the feature matching problem in stereo vision. The model is based on the principle of shunting feedback competitive equations studied in depth by Grossberg and his colleagues. Psychophysical constraints utilized in the early computational models of Marr-Poggio-Grimson, Pollard-Mayhew-Frisby and Prazdny, serve as basis for the architecture design of our network and for the selection of candidate matches. Competition and cooperation take place among the candidate matches and provide a strong and natural disambiguation power.
This paper introduces an understanding of the role of certain types of neural cells in the vertebrate retina as a process for edge detection and localization. A design of an electronic neural edge detector is proposed...
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This paper introduces an understanding of the role of certain types of neural cells in the vertebrate retina as a process for edge detection and localization. A design of an electronic neural edge detector is proposed and analyzed. Our hope is that this and similar efforts will eventually lead to the formation of engineering principles that will assist developments in the science and technology of the hardware electronic vision and machine perception. The use of neural networks is the optimal choice for the hardware implementation of the edge detector because of parallel processing is satisfied. This is similar to the role played by the physical neurons in vertebrate retina while processing the images. A computer simulation is used to test the performance of this approach.
The brain can perform the tasks of associative recall, detection, recognition, and optimization. In this paper, space-time system field models of the brain are introduced. They are called the space-time maximum likeli...
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The brain can perform the tasks of associative recall, detection, recognition, and optimization. In this paper, space-time system field models of the brain are introduced. They are called the space-time maximum likelihood associative memory system (ST-ML-AMS) and the space-time adaptive learning system (ST-ALS). Performance of the system is analyzed using the probability of error in memory recall (PEMR) and the space-time neural capacity (ST-NC).
There is growing interest in using the complex logarithmic mapping for depth determination in motion stereo applications. This has lead to a need for a comprehensive error analysis. Rather than just giving an analytic...
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There is growing interest in using the complex logarithmic mapping for depth determination in motion stereo applications. This has lead to a need for a comprehensive error analysis. Rather than just giving an analytic description of the errors inherent in the approach, an attempt will be made to characterize the errors that occur when using the mapping with real images. Techniques to reduce the impact of these errors will also be discussed.
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.
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