• 제어로봇시스템학회논문지
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• 제20권6호
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• pp.593-598
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• 2014

Hand gesture recognition based on vision is a challenging task in human-robot interaction. The sign language of finger spelling alphabets has been tested as a kind of hand gesture. In this paper, we test hand gesture recognition by detecting the contour shape and orientation of hand with visual image. The method has three stages, the first stage of finding hand component separated from the background image, the second stage of extracting the contour feature over the hand component and the last stage of comparing the feature with the reference features in the database. Here, finger spelling alphabets are used to verify the performance of our system and our method shows good performance to discriminate finger alphabets.

• 로봇학회논문지
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• 제16권3호
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• pp.270-275
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• 2021

Most of the initial forms of cooperative robots were intended to repeat simple tasks in a given space. So, they showed no significant difference from industrial robots. However, research for improving worker's productivity and supplementing human's limited working hours is expanding. Also, there have been active attempts to use it as a service robot by applying AI technology. In line with these social changes, we produced a mobile manipulator that can improve the worker's efficiency and completely replace one person. First, we combined cooperative robot with mobile robot. Second, we applied speech recognition technology and deep learning based object detection. Finally, we integrated all the systems by ROS (robot operating system). This system can communicate with workers by voice and drive autonomously and perform the Pick & Place task.

• 센서학회지
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• 제30권1호
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• pp.15-19
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• 2021

Image processing is one of the major techniques that are used for computer vision. Nowadays, researchers are using machine learning and deep learning for the aforementioned task. In recent years, digit recognition tasks, i.e., automatic meter recognition approach using electric or water meters, have been studied several times. However, two major issues arise when we talk about previous studies: first, the use of the deep learning technique, which includes a large number of parameters that increase the computational cost and consume more power; and second, recent studies are limited to the detection of digits and not storing or providing detected digits to a database or mobile applications. This paper proposes a system that can detect the digital number of meter readings using a lightweight deep neural network (DNN) for low power consumption and send those digits to an Android mobile application in real-time to store them and make life easy. The proposed lightweight DNN is computationally inexpensive and exhibits accuracy similar to those of conventional DNNs.

• 한국정밀공학회지
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• 제10권4호
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• pp.180-189
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• 1993

In CAD/CAM system, it is required to produce manufacturing information from the deawing output of design system. The most difficult task is to formulate 3-dimentional modeling information utilizing 2-dimentional data. This paper addresses the automatic converting steps of 2-dimentional drawing data to 3-dimentional solid modeling using feature recognition rules as an expert shell. With the standardization of design process and recognition rule as a fundamental steps, the developed system shows a good application tool which can interface the design and manufacturing stage in CAD/CAM system of PC level.

• 국제학술발표논문집
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• The 5th International Conference on Construction Engineering and Project Management
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• pp.587-591
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• 2013

Measuring the process of construction operations for productivity improvement remains a difficult task for most construction companies due to the manual effort required in most activity measurement methods. There are many ways to measuring the process. But past measurement methods was inefficient. Because they needed a lot of manpower and time. So, this article focus on the vision-based object recognition and tracking methods for automated construction. These methods have the advantage of efficient that human intervention was reduced. Therefore, this article is analyzed the performance of vision-based methods in the construction sites and is expected to contribute to selection of vision-based methods.

• 한국항행학회논문지
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• 제17권2호
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• pp.247-254
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• 2013

본 논문에서는 키넥트 센서를 이용한 AdaBoost 기반 제스처 인식에 관한 알고리즘을 제안한다. 최근 스마트 TV에 대한 보급으로 관련 산업이 주목받고 있다. 기존 리모컨을 이용하여 TV를 컨트롤 하던 시대에서 벗어나 제스처를 이용하여 TV를 컨트롤 할 수 있는 새로운 접근을 제안한다. AdaBoost 학습 모델에 신체 정규화 된 시간 간격 특징 벡터의 집합을 특징 패턴으로 하여, 속도가 다른 동작들을 인식할 수 있도록 하였다. 또한 속도가 다른 다양한 제스처를 인식하기 위해 다중 AdaBoost 알고리즘을 적용하였다. 제안된 알고리즘을 실제 동영상 플레이어와 연결하여 적용하였고, 실험 후 좌표 변화를 이용한 알고리즘에 비해 정확도가 향상되었음을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권3호
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• pp.1670-1683
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• 2017

Mitotic event recognition is a crucial and challenging task in biomedical applications. In this paper, we introduce the slow feature analysis and propose a fully-automated mitotic event recognition method for cell populations imaged with time-lapse phase contrast microscopy. The method includes three steps. First, a candidate sequence extraction method is utilized to exclude most of the sequences not containing mitosis. Next, slow feature is learned from the candidate sequences using slow feature analysis. Finally, a hidden conditional random field (HCRF) model is applied for the classification of the sequences. We use a supervised SFA learning strategy to learn the slow feature function because the strategy brings image content and discriminative information together to get a better encoding. Besides, the HCRF model is more suitable to describe the temporal structure of image sequences than nonsequential SVM approaches. In our experiment, the proposed recognition method achieved 0.93 area under curve (AUC) and 91% accuracy on a very challenging phase contrast microscopy dataset named C2C12.

• 말소리와 음성과학
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• 제7권1호
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• pp.3-10
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• 2015

In this paper, different frequency scales in cepstral feature extraction are evaluated for the text-independent speaker recognition. To this end, mel-frequency cepstral coefficients (MFCCs), linear frequency cepstral coefficients (LFCCs), and bilinear warped frequency cepstral coefficients (BWFCCs) are applied to the speaker recognition experiment. In addition, the spectro-temporal features extracted by the cepstral-time matrix (CTM) are examined as an alternative to the delta and delta-delta features. Experiments on the NIST speaker recognition evaluation (SRE) 2004 task are carried out using the Gaussian mixture model-universal background model (GMM-UBM) method and the joint factor analysis (JFA) method, both based on the ALIZE 3.0 toolkit. Experimental results using both the methods show that BWFCC with appropriate warping factor yields better performance than MFCC and LFCC. It is also shown that the feature set including the spectro-temporal information based on the CTM outperforms the conventional feature set including the delta and delta-delta features.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권9호
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• pp.4510-4526
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• 2017

Most available methods of facial gender recognition work well under a constrained situation, but the performances of these methods have decreased significantly when they are implemented under unconstrained environments. In this paper, a method via low-rank and collaborative representation is proposed for facial gender recognition in the wild. Firstly, the low-rank decomposition is applied to the face image to minimize the negative effect caused by various corruptions and dynamical illuminations in an unconstrained environment. And, we employ the collaborative representation to be as the classifier, which using the much weaker $l_2-norm$ sparsity constraint to achieve similar classification results but with significantly lower complexity. The proposed method combines the low-rank and collaborative representation to an organic whole to solve the task of facial gender recognition under unconstrained environments. Extensive experiments on three benchmarks including AR, CAS-PERL and YouTube are conducted to show the effectiveness of the proposed method. Compared with several state-of-the-art algorithms, our method has overwhelming superiority in the aspects of accuracy and robustness.

• 보건의료산업학회지
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• 제14권1호
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• pp.103-110
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• 2020

Objectives: In this paper, we propose the ethical education direction by analyzing the personal information recognition and practice of music therapists. Methods: For the analyses, we selected 60 music therapists who answered a questionnaire from members of K Music Therapy Association, and analyzed task recognition and practice ask performance using IPA method. Results: In the IPA table, the areas of high recognition and practice (1) are the areas of personal information protection information management. In the IPA table, the areas of low awareness and high practice (2) are areas of privacy communication for those who have completed ethics education. In the IPA table, the areas of low awareness and low practice (3) are areas of privacy communication when ethics education is not completed. In the IPA table, areas of high awareness and low levels of practice (4) are areas of privacy protection. Conclusions: Continuing education should be provided to improve the curriculum on the protection of personal information for music therapists, thereby raising the awareness and practice of privacy.