• Journal of KIISE
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• v.42 no.1
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• pp.138-143
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• 2015

In this paper we develop a novel human action recognition system based on communication between an ear-mounted Bluetooth camera and an action recognition server to aid scene recognition for the blind. First, if the blind capture an image of a specific location using the ear-mounted camera, the captured image is transmitted to the recognition server using a smartphone that is synchronized with the camera. The recognition server sequentially performs human detection, object detection and action recognition by analyzing human poses. The recognized action information is retransmitted to the smartphone and the user can hear the action information through the text-to-speech (TTS). Experimental results using the proposed system showed a 60.7% action recognition performance on the test data captured in indoor and outdoor environments.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.363-372
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• 2017

This paper presents a novel face recognition algorithm based on the deep convolution neural network and key point detection jointed local binary pattern methodology to enhance the accuracy of face recognition. We firstly propose the modified face key feature point location detection method to enhance the traditional localization algorithm to better pre-process the original face images. We put forward the grey information and the color information with combination of a composite model of local information. Then, we optimize the multi-layer network structure deep learning algorithm using the Fisher criterion as reference to adjust the network structure more accurately. Furthermore, we modify the local binary pattern texture description operator and combine it with the neural network to overcome drawbacks that deep neural network could not learn to face image and the local characteristics. Simulation results demonstrate that the proposed algorithm obtains stronger robustness and feasibility compared with the other state-of-the-art algorithms. The proposed algorithm also provides the novel paradigm for the application of deep learning in the field of face recognition which sets the milestone for further research.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.8 no.2
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• pp.3-9
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• 2009

When washing machine is rotated in the laundry, it tends to lean toward one side. This tendency causes a serious vibration. The balance of washing machine plays an important role in order to reduce the vibration by injecting the sand or the salt water into the balance of washing machine. The hot plate welder is used to prevent from outflow of contents. The hot plate welder brings about many problems which is concerned with accidents. The direction recognition and location information of the balance are required in this system. In this paper, the recognition direction of balance in washing machine using machine vision system is studied. The template matching algorithm compares sub-image with original image acquired in real-time to obtain a center point of balance image. The mid points and the edges of balance are estimated by the edge detection and gauging algorithms. The data acquired by these results is used for recognition direction of balance. The automation software for image processing is developed by using LabVIEW.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3488-3500
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• 2020

At the present time, the economy continues to flourish, and private cars have become the means of choice for most people. Therefore, the license plate recognition technology has become an indispensable part of intelligent transportation, with research and application value. In recent years, the convolution neural network for image classification is an application of deep learning on image processing. This paper proposes a strategy to improve the YOLO model by studying the deep learning convolutional neural network (CNN) and related target detection methods, and combines the OpenCV and TensorFlow frameworks to achieve efficient recognition of license plate characters. The experimental results show that target detection method based on YOLO is beneficial to shorten the training process and achieve a good level of accuracy.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.2
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• pp.11-19
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• 2023

In this paper, we propose the methods of invoice document structure recognition and of making a spreadsheet electronic document. The texts and block location information of word blocks are recognized by an optical character recognition engine through deep learning. The word blocks on the same row and same column are found through their coordinates. The document area is divided through arrangement information of the word blocks. The character recognition result is inputted in the spreadsheet based on the document structure. In simulation result, the item placement through the proposed method shows an average accuracy of 92.30%.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.12
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• pp.1122-1129
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• 2015

This paper suggests a method of detecting traffic lights for vehicles by combining the HSV(hue saturation value) color model, morphometric characteristics, and location information appearing on consecutive images in daytime. In order to detect the traffic light, the color corresponding to the signal lights should be explored. It is difficult to detect traffic lights among colors of lights from buildings, taillight of cars, leaves, placards, etc. The proposed algorithm searches for the traffic lights from many candidates using morphometric characteristics and location information in consecutive images. The recognition process is divided into three steps. The first step is to detect candidates after converting RGB channel into HSV color model. The second step is to extract the boundaries between the housing of traffic lights and background by exploiting the assumption that the housing has lower brightness than the surrounding background. The last step is to recognize the signal light after eliminating the false candidates using morphometric characteristics and location information appearing on consecutive images. This paper demonstrates successful detection results of traffic lights from various images captured on the city roads.

• Journal of Advanced Navigation Technology
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• v.15 no.5
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• pp.839-844
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• 2011

Various technologies such as infrared light, ultrasonic waves, RFID, GPS, UWB, and signal indicators have been incorporated in the location tracking system. However, such pre-existing systems require location recognition in shadow areas. This study proposes a location tracking system that utilizes Cooperative Spectrum Sensing. Cooperative Spectrum Sensing is not only able to track the location and path of moving objects but also recognize when objects breakaway from the path set by sensors and guide them back. In addition, it has the advantage of being more efficient in terms of frequency usage. It is able to automatically fix power transmission and frequency modulation for transmission cognitive users to an optimum level within the range that does not cause interference for primary users.

• The KIPS Transactions:PartB
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• v.16B no.2
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• pp.93-100
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• 2009

In this paper, we propose a Difference Image Entropy based gaze direction recognition system. The Difference Image Entropy is computed by histogram levels using the acquired difference image of current image and reference images or average images that have peak positions from $-255{\sim}+255$ to prevent information omission. There are two methods about the Difference Image Entropy based gaze direction. 1) The first method is to compute the Difference Image Entropy between an input image and average images of 45 images in each location of gaze, and to recognize the directions of user's gaze. 2) The second method is to compute the Difference Image Entropy between an input image and each 45 reference images, and to recognize the directions of user's gaze. The reference image is created by average image of 45 images in each location of gaze after receiving images of 4 directions. In order to evaluate the performance of the proposed system, we conduct comparison experiment with PCA based gaze direction system. The directions of recognition left-top, right-top, left-bottom, right-bottom, and we make an experiment on that, as changing the part of recognition about 45 reference images or average image. The experimental result shows that the recognition rate of Difference Image Entropy is 97.00% and PCA is 95.50%, so the recognition rate of Difference Image Entropy based system is 1.50% higher than PCA based system.

• Journal of Korea Multimedia Society
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• v.15 no.3
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• pp.336-344
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• 2012

As an important part of the Computer Vision, the object recognition and tracking function has infinite possibilities range from motion recognition to aerospace applications. One of methods to improve accuracy of the object recognition, are uses colors which have robustness of orientation, scale and occlusion. Computational cost for extracting features can be reduced by using color. Also, for fast object recognition, predicting the location of the object recognition in a smaller area is more effective than lowering accuracy of the algorithm. In this paper, we propose a method that uses SURF descriptors which applied with color model for improving recognition accuracy and combines with Kalman filter which is Motion estimation algorithm for fast object tracking. As a result, the proposed method classified objects which have same patterns with different colors and showed fast tracking results by performing recognition in ROI which estimates future motion of an object.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.1
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• pp.185-196
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• 1999

In this dissertation, a new off-line recognition system is proposed using a subgraph pattern, neural network. After thinning is applied to input characters, balance having a noise elimination function on location is performed. Then as the first step for recognition procedure, circular elements are extracted and recognized. From the subblock HT, space feature points such as endpoint, flex point, bridge point are extracted and a subgraph pattern is formed observing the relations among them. A region where vowel can exist is allocated and a candidate point of the vowel is extracted. Then, using the subgraph pattern dictionary, a vowel is recognized. A same method is applied to extract horizontal vowels and the vowel is recognized through a simple structural analysis. For verification of recognition subgraph in this paper, experiments are done with the most frequently used Myngjo font, Gothic font for printed characters and handwritten characters. In case of Gothic font, character recognition rate was 98.9%. For Myngjo font characters, the recognition rate was 98.2%. For handwritten characters, the recognition rate was 92.5%. The total recognition rate was 94.8% with mixed handwriting and printing characters for multi-font recognition.