• 정보처리학회논문지:소프트웨어 및 데이터공학
• /
• 제5권10호
• /
• pp.497-502
• /
• 2016

Shape Contexts Recognition(SCR)은 도형이나 사물 등의 모양을 인식하는 기술로 문자인식, 모션인식, 얼굴인식, 상황인식 등의 기반이 되는 기술이다. 하지만 일반적인 SCR은 Shape의 모든 contour에 대해 히스토그램을 만들고 Shape A, B 비교를 위해 추출된 contour를 1:1 개수대로 매핑함으로써 처리속도가 느리다는 단점이 있다. 따라서 본 논문에서는 Shape 모양에 따라 윤곽선을 찾고 개량 DP 알고리즘 및 해리스코너 검출기를 이용하여 contour를 최적화시킴으로써 간략하면서도 더 효과적인 알고리즘을 만들었다. 이렇게 개선된 방법을 사용함으로써 기존방법보다 처리 수행속도가 빨라짐을 확인하였다.

• Journal of Information Processing Systems
• /
• 제16권2호
• /
• pp.343-359
• /
• 2020

In this paper, a new algorithm is proposed for three-dimensional (3D) shape recognition using local features of model views and its sparse representation. The algorithm starts with the normalization of 3D models and the extraction of 2D views from uniformly distributed viewpoints. Consequently, the 2D views are stacked over each other to from view cubes. The algorithm employs the descriptors of 3D local features in the view cubes after applying Gabor filters in various directions as the initial features for 3D shape recognition. In the training stage, we store some 3D local features to build the prototype dictionary of local features. To extract an intermediate feature vector, we measure the similarity between the local descriptors of a shape model and the local features of the prototype dictionary. We represent the intermediate feature vectors of 3D models in the sparse domain to obtain the final descriptors of the models. Finally, support vector machine classifiers are used to recognize the 3D models. Experimental results using the Princeton Shape Benchmark database showed the average recognition rate of 89.7% using 20 views. We compared the proposed approach with state-of-the-art approaches and the results showed the effectiveness of the proposed algorithm.

• 한국공작기계학회:학술대회논문집
• /
• 한국공작기계학회 2003년도 추계학술대회
• /
• pp.271-276
• /
• 2003

In this study, we compared backpropagation neural network(BPNN) with probabilistic neural network(PNN) as shape recognition algorithm of welding flaws. For this purpose, variables are applied the same to two algorithm. Here, feature variable is composed of time domain signal itself and frequency domain signal itself, Through this process, we comfirmed advantages/disadvantages of two algorithms and identified application methods of two algorithms.

• 전자공학회논문지B
• /
• 제31B권7호
• /
• pp.108-118
• /
• 1994

By using a concept of interrelation vector between line segments a new algorithm for partial shape recognition of two-dimensional objects is introduced. The interrelation vector which is invariant under translation rotation and scaling of a pair of line segments is used as a feature information for polygonal shape recognition. Several useful properties of the interrelation vector are also derived in relation to efficient partial shape recognition. The proposed algorithm requires only small space of storage and is shown to be computationally simple and efficient.

• 로봇학회논문지
• /
• 제4권3호
• /
• pp.185-191
• /
• 2009

This paper presents a new shape-based algorithm using affine category shape model for object category recognition and model learning. Affine category shape model is a graph of interconnected nodes whose geometric interactions are modeled using pairwise potentials. In its learning phase, it can efficiently handle large pose variations of objects in training images by estimating 2-D homography transformation between the model and the training images. Since the pairwise potentials are defined on only relative geometric relationship betweenfeatures, the proposed matching algorithm is translation and in-plane rotation invariant and robust to affine transformation. We apply spectral matching algorithm to find feature correspondences, which are then used as initial correspondences for RANSAC algorithm. The 2-D homography transformation and the inlier correspondences which are consistent with this estimate can be efficiently estimated through RANSAC, and new correspondences also can be detected by using the estimated 2-D homography transformation. Experimental results on object category database show that the proposed algorithm is robust to pose variation of objects and provides good recognition performance.

• 한국공작기계학회:학술대회논문집
• /
• 한국공작기계학회 2004년도 춘계학술대회 논문집
• /
• pp.287-292
• /
• 2004

In this study, we made a comparative study of backpropagation neural network and probabilistic neural network and bayesian classifier and perceptron as shape recognition algorithm of welding flaws. For this purpose, variables are applied the same to four algorithms. Here, feature variable is composed of time domain signal itself and frequency domain signal itself, Through this process, we confirmed advantages/disadvantages of four algorithms and identified application methods of few algorithms.

• International journal of advanced smart convergence
• /
• 제8권4호
• /
• pp.104-112
• /
• 2019

This study proposed a robust detection algorithm. It detects face more stably with respect to changes in light and rotation forthe identification of a face shape. The proposed algorithm uses face shape asinput information in a single camera environment and divides only face area through preprocessing process. However, it is not easy to accurately recognize the face area that is sensitive to lighting changes and has a large degree of freedom, and the error range is large. In this paper, we separated the background and face area using the brightness difference of the two images to increase the recognition rate. The brightness difference between the two images means the difference between the images taken under the bright light and the images taken under the dark light. After separating only the face region, the face shape is recognized by using the self-organization feature map (SOFM) algorithm. SOFM first selects the first top neuron through the learning process. Second, the highest neuron is renewed by competing again between the highest neuron and neighboring neurons through the competition process. Third, the final top neuron is selected by repeating the learning process and the competition process. In addition, the competition will go through a three-step learning process to ensure that the top neurons are updated well among neurons. By using these SOFM neural network algorithms, we intend to implement a stable and robust real-time face shape recognition system in face shape recognition.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2002년도 ITC-CSCC -2
• /
• pp.995-998
• /
• 2002

In this paper, we present a fast algorithm for hand gesture recognition of a human from an image by using the directivity information of the fingers. To implement a fast recognition system, we applied the morphological shape decomposition. A proposed gesture recognition algorithm has been tested on the 300 ${\times}$ 256 digital images. Our experiments using image acquired image camera have shown that the proposed hand gesture recognition algorithm is effective.

• 지구물리
• /
• 제7권4호
• /
• pp.247-253
• /
• 2004

3차원 형상화를 통한 분석이 많은 분야에서 연구 및 적용되고 있다. 3차원 형상화는 사진영상의 중첩에서 (3차원)레이저 스캐닝(laser scanning)으로 발전을 하여 가고 있으며, 각 방법이 각기 그 자체로서 발전을 해가고 있는 추세이다. 본 연구에서는 물체에 대한 데이터베이스를 구축하여 대상 이미지에 대하여 기하학적 패턴 매칭(patter matching)을 기반으로 한 인지(인식) 알고리즘을 도입하여 3차원 형상화를 통한 지질 및 지반조사를 위한 기초 기술로 활용하고자 하였다. 물체의 외형적인 성질에 기반하며 특별한 광원없이 물체를 인지할 수 있는 3차원 형상화 알고리즘은 지질 및 지반조사 분야 외에서도 많은 도움이 될 것이다.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 1999년도 하계종합학술대회 논문집
• /
• pp.555-558
• /
• 1999

In this paper, Ire present the result of feature points recognition and classification of radial pulse by the shape of pulse wave. The recognition algorithm use the method which runs in parallel with both the data of ECG and differential pulse simultaneously to recognize the feature points. Also we specified 3-time elements of pulse wave as main parameters for diagnosis and measured them by execution of algorithm. then we classify the shape of radial pulse by existence and position of feature points.