• Journal of Korea Society of Industrial Information Systems
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• v.15 no.5
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• pp.159-166
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• 2010

From public insecurity, access of wealth, alteration of population structure, and changes of security recognition, physical security has been continuously developed and changed. In these days, typical systems for physical security are unmanned security system using telephone network and security equipment, image recognition system using DVR and camera, and access control system by finger print recognition and RFID cards. However, physical security system is broadening its domain towards ICT based convergence with networked camera, biometrics, individual authentication, and LBS services. This paper proposes main technical trends and various security convergences for future physical security services by classifying the security categories into 3 parts; Individual security for personal protection, IT Convergence for large buildings, and Homeland Security for omni-directional security.

• Journal of Korea Multimedia Society
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• v.20 no.12
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• pp.1890-1900
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• 2017

Tangram game consists of seven pieces of polygons such as triangle, square, and parallelogram. Typical methods of image processing for object recognition may suffer from the existence of side thickness and shadow of the puzzle pieces that are dependent on the pose of 3D-shaped puzzle pieces and the direction of light sources. In this paper, we propose an image processing method that recognizes simple convex polygon-shaped objects irrespective of thickness and pose of puzzle objects. Our key algorithm to remove the thick side of piece of puzzle objects is based on morphological operations followed by logical operations with edge image and background image. By using the proposed object recognition method, we are able to implement a stable tangram game applications designed for tablet computers with front camera. As the experimental results, recognition rate is about 86 percent and recognition time is about 1ms on average. It shows the proposed algorithm is fast and accurate to recognize tangram blocks.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.252-263
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• 2016

Road sign is an important street furniture that gives some information such as road conditions, driving direction and condition for a driver. Thus, road sign is a major target of image recognition for self-driving car, ADAS(autonomous vehicle and intelligent driver assistance systems), and ITS(intelligent transport systems). In this paper, an enhanced road sign recognition system is proposed for MMS(Mobile Mapping System) using the single camera and GPS. For the proposed system, first, a road sign recognition scheme is proposed. this scheme is composed of detection and classification step. In the detection step, object candidate regions are extracted in image frames using hybrid road sign detection scheme that is based on color and shape features of road signs. And, in the classification step, the area of candidate regions and road sign template are compared. Second, a Geo-marking scheme for geo-content that is consist of road sign image and coordinate value is proposed. If the serious situation such as car accident is happened, this scheme can protect geographical information of road sign against illegal users. By experiments with test video set, in the three parts that are road sign recognition, coordinate value estimation and geo-marking, it is confirmed that proposed schemes can be used for MMS in commercial area.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.783-788
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• 2003

In this paper, we present a new approach to detect and recognize human face in the image from vision camera equipped on the mobile robot platform. Due to the mobility of camera platform, obtained facial image is small and pose-various. For this condition, new algorithm should cope with these constraints and can detect and recognize face in nearly real time. In detection step, ‘coarse to fine’ detection strategy is used. Firstly, region boundary including face is roughly located by dual ellipse templates of facial color and on this region, the locations of three main facial features- two eyes and mouth-are estimated. For this, simplified facial feature maps using characteristic chrominance are made out and candidate pixels are segmented as eye or mouth pixels group. These candidate facial features are verified whether the length and orientation of feature pairs are suitable for face geometry. In recognition step, pseudo-convex hull area of gray face image is defined which area includes feature triangle connecting two eyes and mouth. And random lattice line set are composed and laid on this convex hull area, and then 2D appearance of this area is represented. From these procedures, facial information of detected face is obtained and face DB images are similarly processed for each person class. Based on facial information of these areas, distance measure of match of lattice lines is calculated and face image is recognized using this measure as a classifier. This proposed detection and recognition algorithms overcome the constraints of previous approach [15], make real-time face detection and recognition possible, and guarantee the correct recognition irregardless of some pose variation of face. The usefulness at mobile robot application is demonstrated.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.12
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• pp.1100-1108
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• 2015

A camera-based real-time hand posture and gesture recognition system is proposed for controlling various devices inside automobiles. It uses an imaging system composed of a camera with a proper filter and an infrared lighting device to acquire images of hand-motion sequences. Several steps of pre-processing algorithms are applied, followed by a background normalization process before segmenting the hand from the background. The hand posture is determined by first separating the fingers from the main body of the hand and then by finding the relative position of the fingers from the center of the hand. The beginning and ending of the hand motion from the sequence of the acquired images are detected using pre-defined motion rules to start the hand gesture recognition. A set of carefully designed features is computed and extracted from the raw sequence and is fed into a decision tree-like decision rule for determining the hand gesture. Many experiments are performed to verify the system. In this paper, we show the performance results from tests on the 550 sequences of hand motion images collected from five different individuals to cover the variations among many users of the system in a real-time environment. Among them, 539 sequences are correctly recognized, showing a recognition rate of 98%.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.65-72
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• 2006

The traffic lights detection and recognition system is an essential module of the driver warning and assistance system. A method which is a color vision-based real time detection and recognition of traffic lights is presented in this paper This method has four main modules : traffic signals lights detection module, traffic lights boundary candidate determination module, boundary detection module and recognition module. In traffic signals lights detection module and boundary detection module, the color thresholding and the subtraction value of saturation and intensity in HSI color space and detection probability mask for lights detection are used to segment the image. In traffic lights boundary candidate determination module, the detection mask of traffic lights boundary is proposed. For the recognition module, the AND operator is applied to the results of two detection modules. The input data for this method is the color image sequence taken from a moving vehicle by a color video camera. The recorded image data was transformed by zooming function of the camera. And traffic lights detection and recognition experimental results was presented in this zoomed image sequence.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.4
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• pp.948-953
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• 2015

Recently, the finger vein recognition based on NIR and CCD sensor camera is investigating the technology to identify a personal using by biometrics. The performance difference of finger vein recognition is generated according to methods that are to separate the vein and background from noises such as finger thickness, ambient light, skin temperature, etc. To improve these problems, in this study, we are proposing the methods for rotation, ROI extraction, and enhancement of vein image captured by NIR LED and CCD camera, and were evaluated performances of these methods. In results of the experiment, the accuracy of the proposed method for image rotation and ROI extraction was 99.8%. And the proposed filter bank method in vein enhancement has shown better performance than retinex algorithm. The proposed method for results of these experimentations will provide better recognition rate when applied to the preprocessing of finger vein recognition.

• Journal of Auto-vehicle Safety Association
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• v.12 no.3
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• pp.43-51
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• 2020

Currently, the second level of the six stages of self-driving technology, as defined by SAE, is commercialized, and the third level is preparing for commercialization. The purpose of ACC is to be evaluated as a system useful for preventing and preventing accidents by minimizing driver fatigue through longitudinal speed control and relative distance control of the vehicle. In this regard, for the study of safety assessment methods in the practical environment of ACC. Distance measurement method using monocular camera and data acquisition equipment such as DGPS are utilized. Based on the evaluation scenario considering the domestic road environment proposed by the preceding study, the relative distance obtained from equipment such as DPGS and the relative distance using a monocular camera in the actual test is verified by comparing and analyzing the safety assessment. The comparison by scenario results showed a minimum error rate of 3.83% in Scenario 1 and a maximum of 14.61% in Scenario 6. The cause of the maximum error is that the lane recognition is not accurate in the camera image and irregular operation conditions such as rushing in or exiting the surrounding area from the walkway. It is expected that safety evaluation using a monocular camera will be possible for other ADAS systems in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2585-2598
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• 2015

Hand posture recognition has played a very important role in Human Computer Interaction (HCI) and Computer Vision (CV) for many years. The challenge arises mainly due to self-occlusions caused by the limited view of the camera. In this paper, a robust hand posture recognition approach based on 3D point cloud from two RGB-D sensors (Kinect) is proposed to make maximum use of 3D information from depth map. Through noise reduction and registering two point sets obtained satisfactory from two views as we designed, a multi-viewed hand posture point cloud with most 3D information can be acquired. Moreover, we utilize the accurate reconstruction and classify each point cloud by directly matching the normalized point set with the templates of different classes from dataset, which can reduce the training time and calculation. Experimental results based on posture dataset captured by Kinect sensors (from digit 1 to 10) demonstrate the effectiveness of the proposed method.

• Journal of the Ergonomics Society of Korea
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• v.19 no.1
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• pp.91-100
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• 2000

In this paper the intent recognition system which recognizes the human's head movements as a simple non-verbal intent representation is presented. The system recognizes five basic intent representations. i.e., strong/weak affirmation. strong/weak negation, and ambiguity by image processing of nodding or shaking movements of head. The vision system for tracking the head movements is composed of CCD camera, image processing board and personal computer. The modified template matching method which replaces the reference image with the searched target image in the previous step is used for the robust tracking of the head movements. For the improvement of the processing speed, the searching is performed in the pyramid representation of the original image. By inspecting the variance of the head movement trajectories. we can recognizes the two basic intent representations - affirmation and negation. Also, by focusing the speed of the head movements, we can see the possibility which recognizes the strength of the intent representation.