Early Stage Object Recognition Using Neural Networks
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چکیده
Object recognition has been the focus of much research in the photogrammetric and image processing communities. The goal of this research is often the quantitative three-dimensional measurement of objects found in two-dimensional image space. Although a multitude of different approaches to the automation of this problem have been developed and thoroughly tested, few could claim that the process is more than semi-automated even in cases that involve a specialised application. One reason for this could be the reliance of these techniques on recognition-by-reconstruction. In this approach, images are processed to extract edges or homogeneous regions. These edges are combined using geometric and/or perceptual rules to complete the object description. In some cases, the edges are matched to models of generic objects. The recognition of the object occurs as a result of the reconstruction phase. This suggests a cognitive approach to vision, where the recognition task is largely performed as a cognitive rather than a visual process. This paper reports on an investigation into the use of neural network approaches for the initial recognition of objects within images. This research considers the initial identification of the objects as a visual rather than cognitive process. It is analogous to the classification problem in image processing and requires that characteristic image signatures are identified for particular object classes. The research focuses on the identification of image patches containing buildings as the first stage in the reconstruction of the building geometry. The general applicability of the method is still to be determined but the initial results are promising.
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تاریخ انتشار 2011