• IEIE Transactions on Smart Processing and Computing
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• v.3 no.4
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• pp.226-233
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• 2014

This paper presents an external light evasion method that rectifies the problem of misrecognition due to external lighting. The fundamental concept underlying the proposed method involves recognition of the differences between two images and elimination of the desynchronized external light by synchronizing the image sensor and inner light source of the optical touch screen. A range of artificial indoor light sources and natural sunlight are assessed. The proposed system synchronizes with a Vertical Synchronization (VSYNC) signal and the light source drive signal of the image sensor. Therefore, it can display synchronized light of the acquired image through the image sensor and remove external light that is not from the light source. A subtraction operation is used to find the differences and the absolute value of the result is utilized; hence, the order is irrelevant. The resulting image, which displays only a touched blob on the touchscreen, was created after image processing for coordination recognition and was then supplied to a coordination extraction algorithm.

• Speech Sciences
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• v.12 no.1
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• pp.135-142
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• 2005

An important aspect of video indexing is the ability to segment video into meaningful segments, i.e., content-based video segmentation. Since the audio signal in the sound track is synchronized with image sequences in the video program, a speech signal in the sound track can be used to segment video into meaningful segments. In this paper, we propose a new approach to content-based video segmentation. This approach uses closed caption to construct a recognition network for speech recognition. Accurate time information for video segmentation is then obtained from the speech recognition process. For the video segmentation experiment for TV news programs, we made 56 video summaries successfully from 57 TV news stories. It demonstrates that the proposed scheme is very promising for content-based video segmentation.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.3
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• pp.221-225
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• 2010

This paper presents the preliminary study of an interactive rehabilitation support system for both dementia patients and their caregivers, the goal of which is to improve the quality of life(QOL) of the patients suffering from dementia through virtual interaction. To achieve the virtual interaction, three kinds of recognition modules for speech, facial image and pen-mouse gesture are studied. The results of both practical tests and questionnaire surveys show that the proposed system had to be further improved, especially in both speech recognition and user interface for real-world applications. The surveys also revealed that the pen-mouse gesture recognition, as one of possible interactive aids, show us a probability to support weakness of speech recognition.

• Journal of the Society of Disaster Information
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• v.12 no.1
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• pp.47-53
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• 2016

Techniques to be developed in this study is intended to apply to an existing integrated control system to "A Study on the multiple Texture Rendering system for three-dimensional Image Signal Recognition" technology or become a center of the building control system in real time video. so, If the study plan multi-image mapping system developed, CCTV camera technology and network technology alone that is, will be a number of security do not have to build a linked system personnel provide services that control while the actual patrol, the other if necessary systems and linked to will develop a system that can reflect the intention Ranger.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.48-54
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• 2023

Nowadays, a blind man finds it very difficult to cross the roads. They should be very vigilant with every step they take. To resolve this problem, Convolutional Neural Networks(CNN) is a best method to analyse the data and automate the model without intervention of human being. In this work, a traffic signal recognition system is designed using CNN for the visually impaired. To provide a safe walking environment, a voice message is given according to light state and timer state at that instance. The developed model consists of two phases, in the first phase the CNN model is trained to classify different images captured from traffic signals. Common Objects in Context (COCO) labelled dataset is used, which includes images of different classes like traffic lights, bicycles, cars etc. The traffic light object will be detected using this labelled dataset with help of object detection model. The CNN model detects the color of the traffic light and timer displayed on the traffic image. In the second phase, from the detected color of the light and timer value a text message is generated and sent to the text-to-speech conversion model to make voice guidance for the blind person. The developed traffic light recognition model recognizes traffic light color and countdown timer displayed on the signal for safe signal crossing. The countdown timer displayed on the signal was not considered in existing models which is very useful. The proposed model has given accurate results in different scenarios when compared to other models.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.1
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• pp.155-161
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• 2014

This study presents the real-time emotion image contents player to induce the user's emotion efficiently. The emotion image contents player was designed to efficiently induce by giving a change in the color, brightness, saturation of image contents corresponded to the user's emotion. In the emotion recognition module, physiological signal of pulse, skin temperature, skin resistance which based on autonomic nervous system were used. The emotion recognition part used physiological signal of pulse, skin temperature, skin resistance based on autonomic nervous system. The image as emotional contents was used with the 9 kinds emotion area classified in international affective picture system(IAPS). As experimental results, the use's emotion that match the image's emotion with the emotion image contents player was derived 10% more accurately. The emotion contents player is expected to increase emotional feeling between users's emotion and contents emotion duo to the real-time emotion reflection.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.11-14
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• 2007

인간과 컴퓨터간의 상호교류 하는데 있어서 감정 인식은 필수라 하겠다. 그래서 본 논문에서는 음성 신호 및 얼굴 영상을 BL(Bayesian Learning)과 PCA(Principal Component Analysis)에 적용하여 5가지 감정 (Normal, Happy, Sad, Anger, Surprise) 으로 패턴 분류하였다. 그리고 각각 신호의 단점을 보완하고 인식률을 높이기 위해 결정 융합 방법과 특징 융합 방법을 이용하여 감정융합을 실행하였다. 결정 융합 방법은 각각 인식 시스템을 통해 얻어진 인식 결과 값을 퍼지 소속 함수에 적용하여 감정 융합하였으며, 특정 융합 방법은 SFS(Sequential Forward Selection)특정 선택 방법을 통해 우수한 특정들을 선택한 후 MLP(Multi Layer Perceptron) 기반 신경망(Neural Networks)에 적용하여 감정 융합을 실행하였다.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.585-590
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• 2000

Recently, It is gradually raised necessity that thickness of thin film is measured accuracy and managed in industrial circles and medical world. Ultrasonic Signal processing method is likely to become a very powerful method for NDE method of detection of microdefects and thickness measurement of thin film below the limit of Ultrasonic distance resolution in the opaque materials, provides useful information that cannot be obtained by a conventional measuring system. In the present research, considering a thin film below the limit of ultrasonic distance resolution sandwiched between three substances as acoustical analysis model, demonstrated the usefulness of ultrasonic Signal processing technique using information of ultrasonic frequency for NDE of measurements of thin film thickness, sound velocity, and step height, regardless of interference phenomenon. Numeral information was deduced and quantified effective information from the image. Also, pattern recognition of a defected input image was performed by neural network algorithm. Input pattern of various numeral was composed combinationally, and then, it was studied by neural network. Furthermore, possibility of pattern recognition was confirmed on artifical defected input data formed by simulation. Finally, application on unknown input pattern was also examined.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.2
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• pp.15-22
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• 2002

In this paper, we proposed the face recognition algorithm which can be used for a security system. The distance and angle of the face features are used in the conventional method, but the proposed method used the genetic algorithm which selects image to best fit the input image in the database images. The performance of proposed algorithm is verified through the simulation. The results of proposed method show good performance.

• Journal of the Korean Society of Safety
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• v.23 no.6
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• pp.164-170
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• 2008

This research was verified detection way of intercept vehicles and performance evaluation after system installation using image detector as detection way of ground installation. By image recognition algorithm was on the trace of moving orbit of violation vehicles for detection way of intercept vehicles. When moving orbit is located special site, utilized geometric image calibration and DC-notch filter. These are cognitive system of license plate by making signal. Then, Bright Evidence Detection and Dark Evidence Detection were applied to after mixing. It is applied to way of Backward tracking for detection way of intercept vehicles. After the field evaluation of developed system, it should be analyzed the more high than recognition rate of minimum standards 80%. It should rise in the estimation of the site applicability is highly from now.