High-Pass Learning Machine: An Edge Detection Approach

This paper describes an approach using a neural network structure which is able to generalize the detection of high frequency transitions in non-noisy signals and noisy signals in a linearly separable problem. The neural network performs an adaptive filtering on the signal acting as a high-pass filter for edge detection from linear environment, and estimates its training patterns from user-defined parameters. Such generalization is possible due to the hidden layers use a parametric approximation of the input vector, so that it is approximate knowledge of the patterns of the network, i.e., the training patterns that were estimated by the neural network. Once the network has learned through these patterns, the parametric approach held causes the input vector can be generalized in a linear environment. Considering the accuracy, f-score, PNSR and SSIM, our proposal achieved results quite similar to their counterparts methods, Prewitt, Sobel and Canny filters, using different settings. Thus, our proposal can replace traditional edge detectors in their applications in a optimization way due to the flexibility in a range of applications.