Fundamentals of digital image processing
Fundamentals of digital image processing
An Efficiently Computable Metric for Comparing Polygonal Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine vision
Convexity rule for shape decomposition based on discrete contour evolution
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Similarity Measure Based on Correspondence of Visual Parts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Shape Classification Using the Inner-Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant Description and Retrieval of Planar Shapes Using Radon Composite Features
IEEE Transactions on Signal Processing - Part I
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In this paper we propose a new approach for image classification by simplifying contour of shape and making use of the tangent function as image feature. We firstly extract shapes from a sample image and connecting pixels of its contour. The extracted contour is simplified by our algorithm and converted into tangent function which is regarded as a feature. The tangent function represented a shape is input into classified system and compared with tangent function from existed classes by computing their distance. The input sample image will finally be classified into a class that has minimum distance with it. The experimental results show the proposed method can achieve high accuracy.