This paper describes a new method of image registration using distortion-tolerant template matching via multi-scale subwindow search. Here, we make full use of the GPT (Global Projection Transformation) correlation technique that maximizes a normalized cross-correlation value between an optimally 2D projection transformed template and a subwindow area of an input image. In particular, we propose to adaptively change the shape of the subwindow area from an original rectangle to its 2D projection transformed one through iterative matching process via the GPT correlation. We name this algorithm: adaptive subwindow control. Experiments made on the well-known datasets, Graﬃti and Boat, show that the proposed method achieves a far superior ability of image registration under varying zoom, rotation, and viewpoints to the well-known feature-point based technique: a combination of ASIFT (Aﬃne Scale-Invariant Feature Transform) and RANSAC (Random Sample Consensus).
This paper proposes new features for recognizing handwritten Japanese Kanji characters. Many feature extraction methods have been studied for Kanji. In particular, stroke directional features are effective if the Kanji are well formed. Directional features are local shape descriptions of individual strokes and so are not robust against shape distortion, in particular, slanting, rotation, and the fluctuation in stroke direction seen in freely handwritten characters. Against this distortion, the 2D relative arrangement of constituent strokes is rather effective as a structural and global shape description. We focus on this fact and derive new features for measuring the 2D relationship between strokes. We derive new measures that express the 2D relationship from directional features of adjacent strokes, and use these as new features. The new features express the relative angle and the relative position of adjacent strokes as a structural and global shape description. Experiments show that the proposed new measures achieve very high recognition rates of about 95 percent for a data set in the square style and about 80 percent for a data set in the free style. These represent a reduction of about 20 percent in the error rates for both data sets achieved with the original directional features.