• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.4
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• pp.826-832
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• 2016

In this paper, we propose a tongue diagnosis system for determining the presence of specific taste crack area as a first step in the digital tongue diagnosis system that anyone can use easily without special equipment and expensive digital tongue diagnosis equipment. Training DB was developed by the Haar-like feature, Adaboost learning on the basis of 261 pictures which was collected in Oriental medicine. Tongue candidate regions were detected from the input image by the learning results and calculated the average value of the HUE component to separate only the tongue area in the detected candidate regions. A tongue area is separated through the Connected Component Labeling from the contour of tongue detected. The palate regions were divided by the relative width and height of the tongue regions separated. Image on the taste area is converted to gray image and binarized with each of the average brightness values. A crack in the presence or absence was determined via Connected Component Labeling with binary images.

• Journal of Broadcast Engineering
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• v.21 no.4
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• pp.609-620
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• 2016

Recently, there are active studies on a forward collision warning system to prevent the accidents and improve convenience of drivers. For collision evasion, the vehicle detection system is required. In general, existing learning-based vehicle detection methods use the entire appearance of the vehicles from rear-view images, so that each vehicle types should be learned separately since they have distinct rear-view appearance regarding the types. To overcome such shortcoming, we learn Haar-like features from the lower part of the vehicles which contain tail lights to detect vehicles leveraging the fact that the lower part is consistent regardless of vehicle types. As a verification procedure, we detect tail lights to distinguish actual vehicles and non-vehicles. If candidates are too small to detect the tail lights, we use HOG(Histogram Of Gradient) feature and SVM(Support Vector Machine) classifier to reduce false alarms. The proposed forward vehicle detection method shows accuracy of 95% even in the complicated images with many buildings by the road, regardless of vehicle types.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.2
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• pp.130-137
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• 2006

In this paper, we propose a method of selecting new types of rectangle features that are suitable for facial expression recognition. The basic concept in this paper is similar to Viola's approach, which is used for face detection. Instead of previous Haar-like features we choose rectangle features for facial expression recognition among all possible rectangle types in a 3${\times}$3 matrix form using the AdaBoost algorithm. The facial expression recognition system constituted with the proposed rectangle features is also compared to that with previous rectangle features with regard to its capacity. The simulation and experimental results show that the proposed approach has better performance in facial expression recognition.

• Journal of Korea Multimedia Society
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• v.10 no.1
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• pp.26-35
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• 2007

We present a robust face tracking system from the sequence of video images based on effective detector and Kalman filter. To construct the effective face detector, we extract the face features using the five types of simple Haar-like features. Extracted features are reinterpreted using Principal Component Analysis (PCA), and interpreted principal components are used for Support Vector Machine (SVM) that classifies the faces and non-faces. We trace the moving face with Kalman filter, which uses the static information of the detected faces and the dynamic information of changes between previous and current frames. To make a real-time tracking system, we reduce processing time by adjusting the frequency of face detection. In this experiment, the proposed system showed an average tracking rate of 95.5% and processed at 15 frames per second. This means the system is robust enough to track faces in real-time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3490-3507
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• 2014

License plate detection (LPD) is one of the most important aspects of an automatic license plate recognition system. Although there have been some successful license plate recognition (LPR) methods in past decades, it is still a challenging problem because of the diversity of plate formats and outdoor illumination conditions in image acquisition. Because the accurate detection of license plates under different conditions directly affects overall recognition system accuracy, different methods have been developed for LPD systems. In this paper, we propose a license plate detection method that is rapid and robust against variation, especially variations in illumination conditions. Taking the aspects of accuracy and speed into consideration, the proposed system consists of two stages. For each stage, Haar-like features are used to compute and select features from license plate images and a cascade classifier based on the concatenation of classifiers where each classifier is trained by an AdaBoost algorithm is used to classify parts of an image within a search window as either license plate or non-license plate. And it is followed by connected component analysis (CCA) for eliminating false positives. The two stages use different image preprocessing blocks: image preprocessing without adaptive thresholding for the first stage and image preprocessing with adaptive thresholding for the second stage. The method is faster and more accurate than most existing methods used in LPD. Experimental results demonstrate that the LPD rate is 98.38% and the average computational time is 54.64 ms.

• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.71-80
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• 2012

This paper proposes vehicle detection and tracking algorithm using a CCD camera. The proposed algorithm uses Haar-like wavelet edge detector to detect features of vehicle and estimates vehicle's location using calibration information of an image. After that, extract accumulated vehicle information in continuous k images to improve reliability. Finally, obtained vehicle region becomes a template image to find same object in the next continuous image using SURF(Speeded Up Robust Features). The template image is updated in the every frame. In order to reduce SURF processing time, ROI(Region of Interesting) region is limited on expended area of detected vehicle location in the previous frame image. This algorithm repeats detection and tracking progress until no corresponding points are found. The experimental result shows efficiency of proposed algorithm using images obtained on the road.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.8C
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• pp.1184-1192
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• 2004

A real-time pupil detection system that detects the pupil movement from the real-time video data achieved by the visual light camera for general purpose personal computer is proposed. It is implemented with three steps; at first, face region is detected using the Haar-like feature detection scheme, and then eye region is detected within the face region using the template-based scheme. Finally, pupil movement is detected within the eye region by convolution of the horizontal and vertical histogram profiling and Gaussian filter. As results, we obtained more than 90% of the detection rate from 2375 simulation images and the data processing time is about 160㎳, that detects 7 times per second.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.4
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• pp.50-64
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• 2018

This paper describes a visual object detection approach utilizing ensemble based machine learning. Object detection methods employing 1D features have the benefit of fast calculation speed. However, for real image with complex background, detection accuracy and performance are degraded. In this paper, we propose an ensemble learning algorithm that combines a 1D feature classifier and 2D DNF (Disjunctive Normal Form) classifier to improve the object detection performance in a single input image. Also, to improve the computing efficiency and accuracy, we propose a feature selecting method to reduce the computing time and ensemble algorithm by combining the 1D features and 2D DNF features. In the verification experiments, we selected the Haar-like feature as the 1D image descriptor, and demonstrated the performance of the algorithm on a few datasets such as face and vehicle.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.385-386
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• 2007

In this paper, we present a detection method of facial angle using facial features. First, it finds face image using haar-like feature. After that, it finds eyes and lip in need of compute of face rotation angle. Next, it makes a triangle by using the facial features and computes the inside angle. As a result of experiment on various face images, the proposed method improves the efficiency much better than the conventional methods below $40^{\circ}$.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.12
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• pp.5113-5119
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• 2010

Anthropometry has been broadly explored in various fields including automobile industry, home electronic appliances, medical appliances and sports goods with aiming at reaching satisfaction to consumer's need and efficiency. However, current technologies to measure a human body still have barriers in which the methods mostly seem to be contingent on expensive devices such as scanner and digital measuring instruments and to be directly touchable to the body when obtaining body size.. Therefore, in this paper, we present a general method to automatically extract size of body from a real body image acquired from a camera and to utilize it into recommend systems including clothing and bicycle fitting. At first, Haar-like features and AdaBoost algorithm are employed to detect body position. Then features of body can be recognized using AAM. Finally clothing and bicycle recommending modules have been implemented and experimented to validate the proposed method.