We develop a new method for weighted least squares 2D linear-phase FIR filter design. It poses the problem of filter design as the problem of projecting the desired frequency response onto the subspace spanned by an appropriate orthonormal basis. We show how to compute the orthonormal basis efficiently in the cases of quadrantally symmetric filter design and centro-symmetric filter design. The design examples show that the proposed method is faster than a conventional weighted least squares filter design method. Also, the amount of storage required to compute the filter coefficients is greatly reduced.

Cork is a natural material produced in the Mediterranean countries. Cork stoppers are used to seal wine bottles, Cork stopper quality classification is a practical pattern classification problem. The cork stoppers are grouped into eight classes according to the degree of defects on the cork surface. These defects appear in the form of random-shaped holes, cracks, and others. As a result, the classification cork stopper is not a simple object recognition problem. This is because the pattern features are not specifically defined to a particular shape or size. Thus, a complex classification form is involved, Furthermore, there is a need to build a standard quality control system in order to reduce the classification problems in the cork stopper industry. The solution requires factory automation meeting low time and reduced cost requirements. This paper describes a cork stopper quality classification system using morphological filtering and contour extraction and following (CEF) as the feature extraction method, and a fuzzy-neural network as a classifier. This approach will be used on a daily basis. A new adaptive image thresholding method using iterative and localized scheme is also proposed, A fully functioning prototype of the system has been built and successfully tested. The test results showed a 6.7% rejection ratio, It is compared with the 40% counterpart provided by traditional systems. The human experts in the cork stopper industry rated this proposed classification approach as excellent.

In the technical discipline of computer vision, the concept of using stereo cameras for depth perception has been motivated by the fact that, in human vision, one percept can arise from two retinal images as a result of the process of “fusion” in the visual cortex. If mankind wants to apply this concept to computer controlled machines it must be inferred that knowledge of the psychological process is sufficient to emulate, at least in a weak sense, the human sensory phenomena called vision. Typically, models for this emulation process have been computationally intensive. They contained or required massive amounts of data and thus resulted in very time consuming analytic solutions for depth or range information. The visual process, however complex, does not operate independently of other brain functions. For instance, the superior colliculus of the brain is used to generate information about visual field motion and many visual reflexes. The superior colliculus is also a multi-modal sensory area which includes auditory and somaethetic information [1]. This visual system-brain function connection must be kept in mind when one is attempting to utilize general visual system models to control manipulative processes in order to accomplish a task. In this paper we investigate the utilization of binocular fusion to determine range and heading angle for the specialized control task of guidance for an autonomous vehicle in a convoy following application. It is therefore necessary to develop the vision model, to define the decision space and to use multi-modal information when appropriate. Both parallel and convergent vision models have been utilized for this task. A comparison of calibration results, the mathematical advantages of a log-polar decision space and the resulting real-time structure for controlling a vehicle in a convoy mode are given from actual experimental evaluation.

The problem of collision-free navigation and guidance for mobile robots is essential to the survival of unmanned terrain vehicles. This communication adds complexity to the guidance problem by introducing moving objects that may cross the desired navigational path of an autonomous vehicle. The vehicle is equipped with sensors that report locations of the moving objects. Speed and direction of each moving object are modeled as random walk processes. An optimization approach that provides for the acceleration or deceleration of the vehicle is developed, as is the duration of the time for which this control must be applied to avoid a collision with moving objects. The constraints on the objective function are given in terms of collision probabilities, and simulation results are presented for a collision-free environment. Implications of this approach for autonomous vehicles are given in terms of the flexibility of path planning.>

This research is concerned with investigating the problem of data compression utilizing an unsupervised estimation algorithm. This extends previous work utilizing a hybrid source coder which combines an orthogonal transformation with differential pulse code modulation (DPCM). The data compression is achieved in the DPCM loop, and it is the quantizer of this scheme which is approached from an unsupervised learning procedure. The distribution defining the quantizer is represented as a set of separable Laplacian mixture densities for two-dimensional images. The condition of identifiability is shown for the Laplacian case and a decision directed estimate of both the active distribution parameters and the mixing parameters are discussed in view of a Bayesian structure. The decision directed estimators, although not optimum, provide a realizable structure for estimating the parameters which define a distribution which has become active. These parameters are then used to scale the optimum (in the mean square error sense) Laplacian quantizer. The decision criteria is modified to prevent convergence to a single distribution which in effect is the default condition for a variance estimator. This investigation was applied to a test image and the resulting data demonstrate improvement over other techniques using fixed bit assignments and ideal channel conditions.