• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.8
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• pp.1604-1611
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• 2007

In a process of insect footprint recognition, basic footprint segments should be extracted from a whole insect footprint image in order to find out appropriate features for classification. In this paper, we used a clustering method as a preprocessing stage for extraction of basic insect footprint segments. In general, sizes and strides of footprints may be different according to type and sire of an insect for recognition. Therefore we proposed an improved ART2 algorithm for extraction or basic insect footprint segments regardless of size and stride or footprint pattern. In the proposed ART2 algorithm, threshold value for clustering is determined automatically using contour shape of the graph created by accumulating distances between all the spots of footprint pattern. In the experimental results applying the proposed method to two kinds of insect footprint patterns, we could see that all the clustering results were accomplished correctly.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.275-278
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• 2007

In this paper, we proposed a automatic extraction method as a preprocessing stage for extraction of basic insect footprint segments. In general, sizes and strides of footprints may be different according to type and size of an insect for recognition. Therefore we proposed an improved algorithm for extraction of basic insect footprint segments regardless of size and stride of footprint pattern. In the proposed algorithm, threshold value for clustering is determined automatically using contour shape of the graph created by accumulating distances between all the spots of footprint pattern. In the experimental results applying the proposed method, The basic footprint segments should be extracted from a whole insect footprint image using significant information in order to find out appropriate features for classification.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.418-421
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• 2007

In this paper, we proposed a hierarchical nearest-neighbor searching method for deciding fitness of a clustered segment. It is difficult to distinguish the difference between correct spots and atypical noisy spots in footprint patterns. Therefore we could not completely remove unsuitable noisy spots from binarized image in image preprocessing stage or clustering stage. As a preprocessing stage for recognition of insect footprints, this method decides whether a segment is suitable or not, using degree of clustered segment fitness, and then unsuitable segments are eliminated from patterns. Removing unsuitable segments can improve performance of feature extraction for recognition of inset footprints.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.313-316
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• 2008

이 논문에서는 곤충 발자국의 패턴을 인식하기 위해, 인식의 기본 단위인 세그먼트를 자동 추출하는 기법과 Trace transform을 이용하여 발자국 인식에 필요한 특징을 추출하는 기법을 제안하였다. Trace transform 방법을 이용하면 패턴의 크기, 이동, 회전, 반사에 불변하는 특징값을 얻을 수 있다. 이러한 특징값들은 곤충 발자국과 같이 다양한 변형이 존재하는 패턴을 인식하는 데에 적합하다. 특징값을 도출하기 위한 첫 번째 단계로는 추출된 세그먼트에 대한 Trace transform을 통해 새로운 Trace 이미지를 생성시킨다. 그런 다음 병렬로 표현되는 trace-line을 따라 특성 함수에 의해 특징들이 일차적으로 도출되고, 또 다시 도출된 특징들은 diametric, circus 단계의 함수를 거치면서 새로운 특징값으로 재구성된다. 2가지 서로 다른 곤충의 발자국 패턴을 이용하여 실험한 결과 곤충 발자국의 크기, 이동, 회전, 반사에 관계없이 인식에 적합한 특징값들이 추출됨을 확인할 수 있었다.