Generalized pattern matching using orbit decomposition

Image Processing and Computer Vision applications often require finding a particular pattern in a set of images. The task involves finding appearances of a given pattern in an image under various transformations and at various locations. This process is of very high time complexity since a search must be implemented both in the transformation domain and in the spatial domain. Contributing to this complexity is the chosen distance metric that measures the similarity between patterns. The Euclidean distance, for example, may change drastically when a small transformation is applied to the pattern. Applying a different metric distance might be advantageous, though at the expense of loosing the norm structure of the Euclidean space. In this work we present a new method for fast search in the transformation domain which can also be applied in metric spaces. The method is based on recursive decomposition of the transformation domain, and a rejection scheme which enables the process to quick...

• Type: Conference
• Year: 2003
• Where: ICIP
• Authors: Yacov Hel-Or, Hagit Hel-Or
• Keywords: Euclidean Distance, ICIP 2003, Image Processing, Irrelevant Large Percentages, Metric Spaces, Small Transformation, Transformation Domain