Multiresolution neural networks for image edge detection and restoration

One of the methods for building an automatic visual system is to borrow the properties of the human visual system (HVS). Artificial neural networks are based on this doctrine and they have been applied to image processing and computer vision. This work focused on the plausibility of using a class of Hopfield neural networks for edge detection and image restoration. To this end, a quadratic energy minimization framework is presented. Central to this framework are relaxation operations, which can be implemented using the class of Hopfield neural networks. The role of the uncertainty principle in vision is described, which imposes a limit on the simultaneous localisation in both class and position space. It is shown how a multiresolution approach allows the trade off between position and class resolution and ensures both robustness in noise and efficiency of computation. As edge detection and image restoration are ill-posed, some a priori knowledge is needed to regularize these problems. A multiresolution network is proposed to tackle the uncertainty problem and the regularization of these ill-posed image processing problems. For edge detection, orientation information is used to construct a compatibility function for the strength of the links of the proposed Hopfield neural network. Edge detection 'results are presented for a number of synthetic and natural images which show that the iterative network gives robust results at low signal-to-noise ratios (0 dB) and is at least as good as many previous methods at capturing complex region shapes. For restoration, mean square error is used as the quadratic energy function of the Hopfield neural network. The results of the edge detection are used for adaptive restoration. Also shown are the results of restoration using the proposed iterative network framework

Internal Defect Detection in Hardwood Logs With Fast Magnetic Resonance Imaging.

Identification of defects such as knots in logs before the cutting operation would allow lumber mills to maximize the value of lumber from each log. This dissertation presented images obtained from scanning an oak log with magnetic resonance imaging (MRI). The unique characteristics of MRI images of hardwood logs were noted and were used to derive a quick algorithm to isolate defects. Defect regions had some pixels that varied considerably in intensity from their neighborhood, providing a seed for initiating the defect region. There was an overlap between the pixel gray level of the defects and clear wood. Therefore, traditional thresholding techniques did not cleanly separate these regions. In this study, region-growing methods were used to extract the defects. The algorithm grew the defect region seed until the border-pixel gray levels approached the average level of the neighborhood. The region-growing methods obtained more accurate defect regions than thresholding methods because of the simultaneous consideration of gray level and adjacency information. Two methods of MRI imaging were considered: spin-echo and echo-planar. Spin-echo imaging provided clear, detailed images but required about 20 seconds of acquisition time, which was too slow to be used in a production environment. Echo-planar images could be acquired in about 1/2 second, which was fast enough for production, but the images were fuzzy and noisy. The dissertation presented an algorithm that found the defect regions in spin-echo images. Region-growing methods use a number of parameters and the best parameters were unique for each image. However, common image statistics could be used to predict the proper parameters. The dissertation also presented an algorithm that found most of the defect regions in echo-planar images. Enhancing the echo-planar images using common general-purpose image-enhancement techniques failed because the lack of discrimination allowed the process to smooth image structures as well as noise. By taking advantage of the structure of a tree, smoothing between MRI frames accomplished the goal of smoothing along homogeneous areas and not across image structures. This z-axis smoothing enhanced the echo-planar image visually and reduced the number of false alarm defect regions