The objective of this study is to produce a system that would allow music symbols to be written by hand using a pen-based computer that would simulate the feeling of writing on sheets of paper and that would also accurately recognize the music symbols. To accomplish these objectives, the following methods are proposed: (1) Two features, time-series data and an image of a handwritten stroke, are used to recognize strokes; and (2) The strokes are combined, as efficiently as possible, and outputted automatically as a music symbol. As a result, recognition rates of 97.60% and 98.80% were obtained in tests with strokes and music symbols, respectively.
In this paper, we describe a range sensing method by projecting a single pattern of multiple slits. To obtain 3D data by projecting a single pattern, certain codes for identifying each slit must be contained in the patten. In our method, random dots are used to identify each slit. The random dots are given as randomly distributed cuts on each slit. Thus, each slit is divided into many small line segments and using these segments as features, stereo matching is carried out to obtain 3D data. Using adjacent relations among slit -segments, the false matches are reduced and segment pairs, whose adjacent segments also correspond with each other, are extracted and considered to be correct matches. Then, from the resultant matches, the correspondence is propagated by utilizing the adjacency relationships to get an entire range image. ABSTRACTIn this paper, we describe a range sensing method by projecting a single pattern of multiple slits. To obtain 3D data by projecting a single pattern, certain codes for identifying each slit must be contained in the patten. In our method, random dots are used to identify each slit The random dots are given as randomly distributed cuts on each slit. Thus, each slit is divided into many small line segments and using these segments as features, stereo matching is carried out to obtain 3D data. Using adjacent relations among slit-segments, the false matches are reduced and segment pairs, whose adjacent segments also correspond with each other, are extracted and considered to be correct matches. Then, from the resultant matches, the correspondence is propagated by utilizing the adjacency relationships to get an entire range image.
This paper proposes a method to improve off-line character classifiers learned from examples using virtual examples synthesized from an on-line character database. To obtain good classifiers, a large database which contains a large enough number of variations of handwritten characters is usually required. However, in practice, collecting enough data is time-consuming and costly. In this paper, we propose a method to train SVM for off-line character recognition based on artificially augmented examples using on-line characters. In our method, virtual examples are synthesized from on-line characters by the following two steps: (1) applying affine transformation to each stroke of "real" characters, and (2) applying affine transformation to each stroke of artificial characters, which are synthesized on the basis of PCA. SVM classifiers are trained by using the training samples containing artificially generated patterns and real characters. We examine the effectiveness of the proposed method with respect to the recognition rates and number of support vectors of SVM through experiments involving the handwritten Japanese Hiragana character classification.
Status of this Memo This memo provides information for the Internet community. This memo does not specify an Internet standard of any kind. Distribution of this memo is unlimited. Authors' Note This memo documents a multiple access protocol for transmission of network-protocol datagrams, encapsulated in High-Level Data Link Control (HDLC) frames, over SONET/SDH. This document is NOT the product of an IETF working group nor is it a standards track document. It has not necessarily benefited from the widespread and in depth community review that standards track documents receive.
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