Optical character recognition (OCR) algorithms typically start from a binary label image. The need for a binary image is complicated by the fact that most imaging devices usually produce multiply valued data: a grey scale image. The problem then becomes
how to extract the meaningful character data from the grey scale image. Image artifacts such as dirt, variations in background intensity, and imaging noise complicate the character extraction. When inspecting packages moving on a conveyor belt, we have control over the optical parameters of the system. Via autofocus and controlled lighting, parameters such as the optical path length, field of view, and illumination intensity may be adjusted. However no control can be placed on labels. The label reading system is totally subject to the package sender's whimsy. We describe the development of a recurrent neural network to segment grey scale label images into binary label images. To determine a pixel label, the neural network takes into account three sources of information: pixel intensities, correlations between neighboring labels, and edge gradients. These three sources of information are succinctly combined via the network's energy function. By changing its label state to minimize the energy function, the network satisfies constraints imposed by the input image and the current label values. The network has no knowledge of shape. Information on what comprises a desirable shape is probably unwarranted at the earliest stage of image processing. Although significant image filtering could be performed by a network that knows what characters should look like, such knowledge is unavoidably font specific. Further there is the problem of teaching the network about shapes. The neural network does not need to be taught. Learning is typically extremely time consuming. To be mappable to analog hardware, it is desirable that the neural equations be deterministic. Two deterministic networks are developed and compared...
This paper describes the development of a recurrent neural network to segment gray scale label images into binary label images. To determine a pixel label, the neural network takes into account three sources of information: pixel intensities, correlations between neighboring labels, and edge gradients. These three sources of information are succinctly combined via the network's energy function. By changing its label state to minimize the energy function, the network satisfies constraints imposed by the input image and the current label values. To be mappable to analog hardware, it is desirable that the neural equations be deterministic. Two deterministic networks are developed and compared. The first operates at the zero temperature limit, the original Hopfield network. The second employs the mean field annealing algorithm. It is shown that with only a moderate increase in computational requirements, the mean field approach produces far superior results.
One of the fundamental problems of machine vision is the estimation of object depth from perceived images. This paper describes both an apparatus and the corresponding algorithms for the passive extraction of object depth. Here passive extraction implies the processing of images acquired using only the existing illumination, in this case uniform white light. Depth from defocus algorithms are extremely sensitive to image variations. Regularization, the application of a priori constraints, is employed to improve the accuracy of the range measurements. When the camera's point spread function is shift invariant, an adaptive algorithm is developed in the frequency domain. When the camera's point spread function is shift varying, an adaptive algorithm is developed in the spatial domain. Data is acquired from line scan cameras. Only a single range measurement or a single depth profile is extracted.
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