Unsupervised learning of overlapping image components using divisive input modulation

This paper demonstrates that nonnegative matrix factorisation is mathematically related to a class of neural networks that employ negative feedback as a mechanism of competition. This observation inspires a novel learning algorithm which we call Divisive Input Modulation (DIM). The proposed algorithm provides a mathematically simple and computationally efficient method for the unsupervised learning of image components, even in conditions where these elementary features overlap considerably. To test the proposed algorithm, a novel artificial task is introduced which is similar to the frequently-used bars problem but employs squares rather than bars to increase the degree of overlap between components. Using this task, we investigate how the proposed method performs on the parsing of artificial images composed of overlapping features, given the correct representation of the individual components; and secondly, we investigate how well it can learn the elementary components from artificial training images. We compare the performance of the proposed algorithm with its predecessors including variations on these algorithms that have produced state-of-the-art performance on the bars problem. The proposed algorithm is more successful than its predecessors in dealing with overlap and occlusion in the artificial task that has been used to assess performance

Robust Object Recognition under Partial Occlusions Using NMF

In recent years, nonnegative matrix factorization (NMF) methods of a reduced image data representation attracted the attention of computer vision community. These methods are considered as a convenient part-based representation of image data for recognition tasks with occluded objects. A novel modification in NMF recognition tasks is proposed which utilizes the matrix sparseness control introduced by Hoyer. We have analyzed the influence of sparseness on recognition rates (RRs) for various dimensions of subspaces generated for two image databases, ORL face database, and USPS handwritten digit database. We have studied the behavior of four types of distances between a projected unknown image object and feature vectors in NMF subspaces generated for training data. One of these metrics also is a novelty we proposed. In the recognition phase, partial occlusions in the test images have been modeled by putting two randomly large, randomly positioned black rectangles into each test image

Beleuchtungsverfahren zur problemspezifischen Bildgewinnung für die automatische Sichtprüfung

Der Beleuchtungsentwurf in der automatischen Sichtprüfung ist von zentraler Bedeutung und hat enormen Einfluss auf die Leistungsfähigkeit eines Sichtprüfsystems. In dieser Arbeit wird ein neuartiger problemspezifischer Beleuchtungsentwurf vorgestellt, der durch eine optische, in die physikalische Bildgewinnung vorgelagerte, Merkmalsextraktion motiviert ist. Der Ansatz wird auf Grundlage eines physikalisch begründeten Kamera- und Beleuchtungsmodells signaltheoretisch analysiert sowie im Rahmen verschiedener Anwendungsszenarien experimentell evaluiert