マルメ ヘイゴウホウ オ モチイタ HOUGH ヘンカン ニヨル イン ノ ケンシュツ

Hough transfom is one of the most stabilized methods for feature extraction from digital images. At first, its main purpose was line detection. But nowadays it is known that Hough transform can also be used for detection of circles, ellipses and other general figures. This detection method is called generalized Hough transform. One of the problems of generalized Hough transform for practical use is that is requires very large memory for parameter space. If very high precision about those parameters is required, the parameter space grows very large. In this paper, a method for generalized Hough transform computation without using a parameter space is proposed and the result of its experiment is shown. This method is named Round and Merge (RM) method. In this experiment, all circles in the picture can be correctly detected with smaller memory and with shorter CPU time than usual computation method. By RM method, high precision generalized Hough transform computation can be executed with so small memory

パソコン ガゾウ ショリ エキスパート システム EXWIPER ノ シサク

Recent extreme advance of personal computers has made it possible to execute image processing programs which have been executed only by main frame computers. Nowadays many researchers or engineers in various fields require image processing techniques by a computer, but it is not so easy for beginners to make or to use image processing programs with a personal computer. Some kinds of image processing software libraries have been developed and used, but they are all developed for main frame computers with FORTRAN language and not necessarily suitable for personal computers. This paper has two main subjects. First, a new image processing software library named WIPER for personal computers with BASIC language is proposed. Second, an expert system for image processing instruction for beginners named EXWIPER is proposed. EXWIPER is developped with an expert shell and runs with WIPER. Beginners can learn fundamental image processing techniques by interactive operation with EXWIPER

テガキ カンジ データ ベース ノ データ アッシュク ジッケン

At the study of recognition of handwritten KANJI characters, it is also necessary to evaluate the recognition efficiency of the developed method. The common data base is necessary when we compare the various algorithms. So, the handwritted KANJI data base ETL-8 (B2) is made and open to public by Electrotechnical Laboratory in Japan. This data base is stored in 3 open reel magnetic tapes because of its large volume.In this paper, we converted this data base into the data base on MS-DOS and showed the results of data compression experiments with it made for the purpose of using it. on a personal computer or a work-station

ニューラル ネットワーク オ モチイタ ホウコウ セグメント トクチョウ ニヨル テガキ ルイジ カンジ ノ ニンシキ ジッケン

In this paper, a new KANJI recognition method by directional segment features using neural networks is proposed. Neural networks enable the directional segment features to obtain higher recognition rate. In the proposed method, the neural networks consisting of three layers are used. As this method uses the initial value of synapse coupling coefficient 1.0 for interesting regions in the KANJI pattern, partial patterns can be learned from the whole KANJI pattern without decomposition to partial patterns. The forward learning is used and the system knows what kind of partial patterns to learn from the teacher\u27s signal. The results of fundamental recognition experiment using similar handwritten KANJI characters by both the usual recognition method and the proposed method are reported. By usual method, the average recognition rate of 70.5% for learning samples and 66.5% for unknown samples are obtained. By the proposed method, 100% for learning samples and 90.7% for unknown samples are obtained

タジュウ ジショ ルイジドホウ ニヨル テガキ ルイジ カンジ ノ シキベツ ジッケン

The results of discrimination experiment of similar handprinted KANJI characters by multidictionary templet matching method is presented. This recognition method is one of the applications of templet matching method and uses multiple temlets for each category. To examine the discrimination ability of this method, three experimental parameters are used. The first is the number of multiple templets for each category, the second is the number of training samples which make the templets and the last is the picture size of character patterns. The handprinted KANJI character data base ETL-9(B2) made at Electrotechnical Laboratory is used as the test data

ジドウシャ ガゾウ カラノ ナンバー プレート ノ チュウシュツ ト ニンシキ

Car license number recognition by a computer is a valuable work is highly motorized society. Japanese car license number plate containts four kinds of characters, KANJI, small numeral, HIRAGANA, and large numerals. Most former researches don\u27t have dealt with the KANJI recognition. In this paper, a new method for car license number recognition containing KANJI is proposed. Devised Hough transform is used for a plate extraction in a car image, and location features of each character on the plate are used for character segmentation. As the experimental results using 160 samples, 87.5% of whole character recognition rate can be obtained

シカク ショウガイシャ ノ タメノ ブンコボン ショウセツ ノ ジドウ テンジ ホンヤク システム ノ コウチク

In this paper, a recognition system of novel books written in Japanese language for blind persons using a personal computer is proposed. This system consists of two units, a docu-ment image processing unit and a character recognition unit. In the former unit, normalization of the document image and extraction of the character patterns are carried out. In the latter unit, pre-classification and recognition of character patterns are carried out. As the result of the experiment with 56,054 characters of 93 pages in the novel books, average recognition rate of 98.15% was obtained. Average processing time was 3.1 sec/character

ニューラル ネットワーク オ モチイタ ダクテン ハンダクテン ノ ウム ニヨル テガキ ヒラガナ ノ ブンルイ

Handwritten HIRAGANA characters are classified according to whether they have DAKUTEN (or HAN-DAKUTEN) or not by the 3-layer neural networks. The input data to the networks is 25-dimensional local mesh-feature extracted from an original character pattern. The numbers of units of three layers are 25 (input-layer), 25 (hidden-layer) and 2 (output-layer). The numbers of training and unknown samples used in a classification experiment are 6900 and 5000,respectively. The average classification rate of 94(%) for the unknown samples is obtained