• Korean Journal of Cognitive Science
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• v.25 no.1
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• pp.51-71
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• 2014

This paper presents an image enhancement method in real road traffic scenes. The images captured by the camera on the car cannot keep the color constancy as illumination or weather changes. In the real environment, these problems are more worse at back light conditions and at night that make more difficult to the applications of the vision-based intelligent vehicles. Using the existing image enhancement methods without considering the position and intensity of the light source and their geometric relations the image quality can even be deteriorated. Thus, this paper presents a fast and effective method for image enhancement resembling human cognitive system which consists of 1) image preprocessing, 2) color-contrast evaluation, 3) alpha blending of over/under estimated image and preprocessed image. An input image is first preprocessed by gamma correction, and then enhanced by an Automatic Color Enhancement(ACE) method. Finally, the preprocessed image and the ACE image are blended to improve image visibility. The proposed method shows drastically enhanced results visually, and improves the performance in traffic sign detection of the vision based intelligent vehicle applications.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.1166-1169
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• 2011

In this paper, we design a Circle dot Code, In our scheme, we design a dot patterns for increasing maximum capacity and also for increasing robustness to Affine Transformation. Our code Can be extended according number of data circle. We use three data circle vision code. In this type code, after acquiring camera images for the Circle dot Codes, and perform error correction decoding using four position symbols and six CRC symbols. We perform graph based dot code analysis which determines the topological distance between dot pixels. Our code can be bridged the real world and ubiquitous computing environment.

• Journal of the Korea Society of Computer and Information
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• v.11 no.3
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• pp.31-39
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• 2006

This paper presents a novel approach to real-time recognition of 3D environment and objects for various applications such as intelligent robots, intelligent vehicles, intelligent buildings,..etc. First, we establish the three fundamental principles that humans use for recognizing and interacting with the environment. These principles have led to the development of an integrated approach to real-time 3D recognition and modeling, as follows: 1) It starts with a rapid but approximate characterization of the geometric configuration of workspace by identifying global plane features. 2) It quickly recognizes known objects in environment and replaces them by their models in database based on 3D registration. 3) It models the geometric details the geometric details on the fly adaptively to the need of the given task based on a multi-resolution octree representation. SIFT features with their 3D position data, referred to here as stereo-sis SIFT, are used extensively, together with point clouds, for fast extraction of global plane features, for fast recognition of objects, for fast registration of scenes, as well as for overcoming incomplete and noisy nature of point clouds.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.1
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• pp.24-30
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• 2016

The accuracy of small and low-cost CCD cameras is insufficient to provide data for precisely tracking unmanned aerial vehicles (UAVs). This study shows how a quad rotor UAV can hover on a human targeted tracking object by using data from a CCD camera rather than imprecise GPS data. To realize this, quadcopter UAVs need to recognize their position and posture in known environments as well as unknown environments. Moreover, it is necessary for their localization to occur naturally. It is desirable for UAVs to estimate their position by solving uncertainty for quadcopter UAV hovering, as this is one of the most important problems. In this paper, we describe a method for determining the altitude of a quadcopter UAV using image information of a moving object like a walking human. This method combines the observed position from GPS sensors and the estimated position from images captured by a fixed camera to localize a UAV. Using the a priori known path of a quadcopter UAV in the world coordinates and a perspective camera model, we derive the geometric constraint equations that represent the relation between image frame coordinates for a moving object and the estimated quadcopter UAV's altitude. Since the equations are based on the geometric constraint equation, measurement error may exist all the time. The proposed method utilizes the error between the observed and estimated image coordinates to localize the quadcopter UAV. The Kalman filter scheme is applied for this method. Its performance is verified by a computer simulation and experiments.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.5
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• pp.325-333
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• 2015

Vision based night-time vehicle detection has been an emerging research field in various advanced driver assistance systems(ADAS) and automotive vehicle as well as automatic head-lamp control. In this paper, we propose night-time vehicle detection method based on multi-class support vector machine(SVM) that consists of thresholding, labeling, feature extraction, and multi-class SVM. Vehicle light candidate blobs are extracted by local mean based thresholding following by labeling process. Seven geometric and stochastic features are extracted from each candidate through the feature extraction step. Each candidate blob is classified into vehicle light or not by multi-class SVM. Four different multi-class SVM including one-against-all(OAA), one-against-one(OAO), top-down tree structured and bottom-up tree structured SVM classifiers are implemented and evaluated in terms of vehicle detection performances. Through the simulations tested on road video sequences, we prove that top-down tree structured and bottom-up tree structured SVM have relatively better performances than the others.

• ETRI Journal
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• v.35 no.6
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• pp.1021-1028
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• 2013

Automatic facial expression recognition is a widely studied problem in computer vision and human-robot interaction. There has been a range of studies for representing facial descriptors for facial expression recognition. Some prominent descriptors were presented in the first facial expression recognition and analysis challenge (FERA2011). In that competition, the Local Gabor Binary Pattern Histogram Sequence descriptor showed the most powerful description capability. In this paper, we introduce hybrid facial representations for facial expression recognition, which have more powerful description capability with lower dimensionality. Our descriptors consist of a block-based descriptor and a pixel-based descriptor. The block-based descriptor represents the micro-orientation and micro-geometric structure information. The pixel-based descriptor represents texture information. We validate our descriptors on two public databases, and the results show that our descriptors perform well with a relatively low dimensionality.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2010-2014
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• 2005

Object detection and parameter estimation in point cloud data is a relevant subject to robotics, reverse engineering, computer vision, and sport mechanics. In this paper a software is presented for fully-automatic object detection and parameter estimation in unordered, incomplete and error-contaminated point cloud with a large number of data points. The software consists of three algorithmic modules each for object identification, point segmentation, and model fitting. The newly developed algorithms for orthogonal distance fitting (ODF) play a fundamental role in each of the three modules. The ODF algorithms estimate the model parameters by minimizing the square sum of the shortest distances between the model feature and the measurement points. Curvature analysis of the local quadric surfaces fitted to small patches of point cloud provides the necessary seed information for automatic model selection, point segmentation, and model fitting. The performance of the software on a variety of point cloud data will be demonstrated live.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.504-509
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• 1994

Recently SMT is used widely to place the SMD on bare board which is very small and highly integrated. And that is one of the issue directly in the electric products assembly process and especiallly in the field of optimizing electric product's performance, automatically tuning method which is highly demanded in the electronics industry. To tune product's performance, variable resistances's resistivity should be changed until it has good performance characteristics. In this paper to automatically regulate the 8mm camcoder's performance, it is proposed variable resistence's center position and orientation detection algorithm by image processing, which has very precise and accurate result. And we found optimal conditions which can have effects on image acquisition process. And real time processing is done by DSP to detect vr's center and orientation. This results make it possible to utilize proposed image processing algorithm and system directly in electronics industry.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.7
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• pp.127-137
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• 1996

The 3-dimensional measurement using stereo vision system must achieve a camera calibration. So far, the 3-dimensional calibration technique that uses two-dimensional grid papar and a non-linear least square method has been developed and tested. But, this method is inefficient because it has many calculation procedure and a non-linear analysis. Therefore, this paper proposed the projective geometric method which produced the calibration parameter by vanishing point. The vanishing point is producted by a cross ratio and a parallel line pairs. The results of the computer simulation show utility of the proposed method.

• Korean Journal of Computational Design and Engineering
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• v.15 no.1
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• pp.33-42
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• 2010

In this study, new medial surface calculation methods using Voronoi diagrams are investigated for the point samples extracted on closed surface models. The medial surface is defined by the closure of all points having more than one closest point on the shape boundary. It is a one of essential geometric information in 3D and can be used in many areas such as 3D shape analysis, dimension reduction, freeform shape deformation, image processing, computer vision, FEM analysis, etc. In industrial parts, the idealized solid parts and shell shapes including sharp edges and vertices are frequently used. Other medial surface extraction methods using Voronoi diagram have inherent separation and branch problems, so that they are not appropriate to the sharp edged objects and have difficulties to be applied to industrial parts. In addition, the branched surfaces on sharp edges in shell shapes should be eliminated to obtain representative medial shapes. In order to avoid separation and branch problems, the new approach by analyzing the shapes and specially sampling on surfaces has been developed.