Image Pattern Recognition Using Phase-based Local Features and Their
Flexible Spatial Configuration
by Gustavo Carneiro
We propose a new image pattern recognition system that is applicable to several computer vision tasks, such as long range motion matching and object recognition. The main strength of our system is its ability to handle substantial image deformations without significantly sacrificing the expressiveness of the model representation. This system is divided into three steps, namely: a) feature extraction, b) similarity search, and c) hypothesis verification. The phase-based local feature proposed for step (a) is shown to be distinctive and robust to 2-D rigid deformations and severe brightness changes. The step (b) pairs similar model and test image features, producing the correspondence set, which is usually densely populated with outliers. Hence, the rejection of outliers from this set is necessary to reduce the number of hypotheses to be verified in step (c). We propose two methods to reject outliers that are robust to rigid and non-rigid deformations. Quantitative evaluations for both the local feature extractor and the outlier rejection methods are also provided. Comparison results produced by these evaluations show that our feature is more robust and distinctive than state-of-the-art features proposed in the literature, and our methods to reject outliers are more robust to 3-D rigid and non-rigid deformations than the Hough transform, which is a common method used to reject outliers. Finally, our last contribution is a probabilistic verification for step (c) that uses local and semi-local similarities between test and model images. The effectiveness of our system is tested in several recognition problems.
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