Mr. Ingo Bax Profile

Ingo Bax

at Univ Bielefeld

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SPIE Journal Paper | 1 June 2006

Hierarchical feed-forward network for object detection tasks

Ingo Bax, Gunther Heidemann, Helge Ritter

OE, Vol. 45, Issue 06, 067203, (June 2006) https://doi.org/10.1117/12.10.1117/1.2209948

KEYWORDS: Image classification, Optical engineering, Machine learning, Convolution, Image processing, Feature extraction, Detection and tracking algorithms, Image segmentation, Independent component analysis, Computing systems

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Recent research on neocognitron-like neural feed-forward architectures, which have formerly been successfully applied to the recognition of artificial stimuli such as paperclip objects, now also opens up application to more natural stimuli. Such networks exhibit high-recognition performance with respect to translation, rotation, scaling and cluttered surroundings. In this contribution, we introduce a new type of hierarchical model, which is trained using a non-negative matrix factorization algorithm. In contrast to previous work, our approach cannot only classify objects but is also capable of rapid object detection in natural scenes. Thus, the time-consuming and conceptually unsatisfying split-up into a localization stage (e.g., using segmentation) and a subsequent classification can be avoided. The network consists of alternating layers of simple and complex cell planes and incorporates nonlinear processing schemes that have been proposed in recent literature. Learning of receptive field profiles for the lower layers of the network takes place by unsupervised learning whereas a final classification layer is trained supervised. This final layer is then utilized for detection. We test the classification performance of the network on images of natural objects which are systematically distorted. To test the ability to detect objects, cluttered natural background is used.

Proceedings Article | 28 March 2005 Paper

A hierarchical feed-forward network for object detection tasks

Ingo Bax, Gunther Heidemann, Helge Ritter

Proceedings Volume 5818, (2005) https://doi.org/10.1117/12.605958

KEYWORDS: Image classification, Machine learning, Detection and tracking algorithms, Image processing, Visualization, Computing systems, Independent component analysis, Image compression, Feature extraction, Convolution

Read Abstract +

Recent research on Neocognitron-like neural feed-forward architectures, which have formerly been successfully applied to recognition of artifical stimuli like paperclip objects, is promising application to more natural stimuli. Several authors have shown high recognition performance of such networks with respect to translation, rotation, scaling and cluttered surroundings. In this contribution, we introduce a variation of existing hierarchical models, that is trained using a non-negative matrix factorization algorithm. In contrast to previous work, our approach can not only classify objects but is also capable of rapid object detection in natural scenes. Thus, the time-consuming and conceptually unsatisfying split-up into a localization stage (e.g. using segmentation) and a subsequent classification can be avoided. Though in principle an exhaustive search by classification of every sub-window of an image is performed, the process is nevertheless highly efficient. The network consists of alternating layers of simple and complex cell planes and incorporates nonlinear processing schemes that have been proposed in recent literature. Learning of receptive field profiles for the lower layers of the network takes place by unsupervised learning whereas a final classification layer is trained supervised. Detection is achieved by attaching an additional network layer, whose simple cell profiles are learned from the final classification units that were acquired during the training phase. We test the classification performance of the network on images of natural objects which are systematically distorted. To test the ability to detect objects, cluttered natural background is used.

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