• Journal of the Korean Society of Industry Convergence
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• v.18 no.1
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• pp.10-17
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• 2015

We describe a research about remote control of mobile robot based on voice command in this paper. Through real-time remote control and wireless network capabilities of an unmanned remote-control experiments and Home Security / exercise with an unmanned robot, remote control and voice recognition and voice transmission are possible to transmit on a PC using a microphone to control a robot to pinpoint of the source. Speech recognition can be controlled robot by using a remote control. In this research, speech recognition speed and direction of self-driving robot were controlled by a wireless remote control in order to verify the performance of mobile robot with two drives.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.941-942
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• 2006

A depth measurement system to recognize 3D shape of objects using single camera, line laser and a rotating mirror has been investigated. The camera and the light source are fixed, facing the rotating mirror. The laser light is reflected by the mirror and projected to the scene objects whose locations are to be determined. The camera detects the laser light location on object surfaces through the same mirror. The scan over the area to be measured is done by mirror rotation. The Segmentation process of object recognition is performed using the depth data of restored 3D data. The Object recognition domain can be reduced by separating area of interest objects from complex background.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.413-414
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• 2007

Successful speech recognition in noisy environments for intelligent robots depends on the performance of preprocessing elements employed. We propose an architecture that effectively combines adaptive beamforming (ABF) and blind source separation (BSS) algorithms in the spatial domain to avoid permutation ambiguity and heavy computational complexity. We evaluated the structure and assessed its performance with a DSP module. The experimental results of speech recognition test shows that the proposed combined system guarantees high speech recognition rate in the noisy environment and better performance than the ABF and BSS system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.396-402
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• 2022

User interactions are being developed in various forms, and in particular, interactions using human gestures are being actively studied. Among them, hand gesture recognition is used as a human interface in the field of realistic media based on the 3D Hand Model. The use of interfaces based on hand gesture recognition helps users access media media more easily and conveniently. User interaction using hand gesture recognition should be able to view images by applying fast and accurate hand gesture recognition technology without restrictions on the computer environment. This paper developed a fast and accurate user hand gesture recognition algorithm using the open source media pipe framework and machine learning's k-NN (K-Nearest Neighbor). In addition, in order to minimize the restriction of the computer environment, a stereoscopic image control system based on user hand gesture recognition was designed and implemented using a web service environment capable of Internet service and a docker container, a virtual environment.

• Journal of Internet Computing and Services
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• v.7 no.5
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• pp.95-108
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• 2006

The digital contents expand into software source code and maintenance of technology and IPR about source code have a very important meaning to international competition, The recognition about software security is very low specially among these Intellectual Property Rights. On occurring disputation property, we have to prove the fact, there is a problem to discriminate the original source code, Also, it is hard to accurate decision that is correct to complexity and the lack of read and understand ability even if software is reproduced. In this paper, we don't enforce distinction about software reproduction by one individual code unit. And we developed digital license prototype of XML that can distinguish reproduction based on structural conformability of whole source codes. Software has Context Free Grammar in structure and presents BNF notation type, it is apt to present hierarchical structure. Then, we can express architecture of software source code by hierarchical structure to discriminate structural conformability. In this paper, we make a study of the digital licence prototype for discriminate the original source code. Reserved words of software source code by parsing express to XML file that have hierarchical structure. Then, we can express architecture of software source code by tree structure form instead of complex source code.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1209-1214
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• 2016

LPR(License plate recognition) is a kind of the most popular surveillance technology based on accompanied by a video and video within the optical character recognition. LPR need a many process. One is a localization of car license plates, license plate of size, space, contrast, normalized to adjust the brightness, another is character division for recognize the character optical character recognition to win the individual characters, character recognition, the other is phrase analysis of the shape, size, position by year, the procedure for the analysis by comparing the database of license plate having a difference by region. In this paper, describing the results of performance of license plate recognition S/W, which was implemented using EmguCV, find the location, using the tesseract OCR, which are well known to an optical character recognition engine of open source, the characters of the license plate image capturing angle of the plate, image size, brightness.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.4 s.316
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• pp.36-44
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• 2007

In this paper, we implement the speech & face recognition system to support various ubiquitous sensor network application services such as switch control, authentication, etc. using wireless audio and image interface. The proposed system is consist of the H/W with audio and image sensor and S/W such as speech recognition algorithm using psychoacoustic model, face recognition algorithm using PCA (Principal Components Analysis) and LDPC (Low Density Parity Check). The proposed speech and face recognition systems are inserted in a HOST PC to use the sensor energy effectively. And improve the accuracy of speech and face recognition, we implement a FEC (Forward Error Correction) system Also, we optimized the simulation coefficient and test environment to effectively remove the wireless channel noises and correcting wireless channel errors. As a result, when the distance that between audio sensor and the source of voice is less then 1.5m FAR and FRR are 0.126% and 7.5% respectively. The face recognition algorithm step is limited 2 times, GAR and FAR are 98.5% and 0.036%.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.6-29
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• 2020

Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics. In this paper, we present a novel multimodal recognition system that trains a deep learning network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denoising auto-encoders to automatically extract robust and non-redundant features. The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Moreover, the proposed technique has proven robust when some of the above modalities were missing during the testing. The proposed system has three main components that are responsible for detection, which consists of modality specific detectors to automatically detect images of different modalities present in facial video clips; feature selection, which uses supervised denoising sparse auto-encoders network to capture discriminative representations that are robust to the illumination and pose variations; and classification, which consists of a set of modality specific sparse representation classifiers for unimodal recognition, followed by score level fusion of the recognition results of the available modalities. Experiments conducted on the constrained facial video dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% Rank-1 recognition rates, respectively. The multimodal recognition accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips even in the situation of missing modalities.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.1
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• pp.1-9
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• 2015

Template matching is used for many applications in image processing. One of the most researched topics is object tracking. Normalized Cross Correlation (NCC) is the basic statistical approach to match images. NCC is used for template matching or pattern recognition. A template can be considered from a reference image, and an image from a scene can be considered as a source image. The objective is to establish the correspondence between the reference and source images. The matching gives a measure of the degree of similarity between the image and the template. A problem with NCC is its high computational cost and occasional mismatching. To deal with this problem, this paper presents an algorithm based on the Sum of Squared Difference (SSD) and an adaptive template matching to enhance the quality of the template matching in object tracking. The SSD provides low computational cost, while the adaptive template matching increases the accuracy matching. The experimental results showed that the proposed algorithm is quite efficient for image matching. The effectiveness of this method is demonstrated by several situations in the results section.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.289-294
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• 2019

As social network service platforms grow and one-person media market expands, people upload their own photos and/or videos through multiple open platforms. However, it can be illegal to upload the digital contents containing the faces of others on the public sites without their permission. Therefore, many people are spending much time and effort in editing such digital contents so that the faces of others should not be exposed to the public. In this paper, we propose an automatic face hiding system called 'autoblur', which detects all the unregistered faces and mosaic them automatically. The system has been implemented using the GitHub MIT open-source 'Face Recognition' which is based on deep learning technology. In this system, two dozens of face images of the user are taken from different angles to register his/her own face. Once the face of the user is learned and registered, the system detects all the other faces for the given photo or video and then blurs them out. Our experiments show that it produces quick and correct results for the sample photos.