• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.222-224
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• 2009

This paper presents an algorithm that extracts road lane's specific information by using HSI color information and performance enhancement of lane detection base on vision processing of drive assist. As a preprocessing for high speed lane detection, the optimal extraction of region of interest for lane boundary(ROI-LB) can be processed to reduction of detection region in which high speed processing is enabled and it also increases reliabilities by deleting edges those are misrecognized. Road lane is extracted with simultaneous processing of noise reduction and edge enhancement using the Laplacian filter, the reliability of feature extraction can be increased for various road lane patterns. Since noise can be removed by using saturation and brightness of HSI color model. Also it searches for the road lane's color information and extracts characteristics. The real road experimental results are presented to evaluate the effectiveness of the proposed method.

• Journal of Positioning, Navigation, and Timing
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• v.5 no.3
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• pp.119-129
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• 2016

This paper presents an integrated navigation system for accurate navigation solution-based safety and convenience services in the vehicular augmented reality (AR)-head up display (HUD) system. For lane-level guidance service, especially, an accurate navigation system is essential. To achieve this, an inertial navigation system (INS)/global positioning system (GPS)/vision/digital map (IGVM) integrated navigation system has been developing. In this paper, the concept of the integrated navigation system is introduced and is implemented based on a multi-model switching filter and vehicle status decided by using the GPS data and inertial measurement unit (IMU) measurements. The performance of the implemented navigation system is verified experimentally.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.250-256
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• 2015

We present a novel method to model the body posture of a worm for vision-based automatic monitoring and analysis. The worm considered in this study is a Caenorhabditis elegans (C. elegans), which is popularly used for research in biological science and engineering. We model the posture by an open chain of a few curved or rigid line segments, in contrast to previously published approaches wherein a large number of small rigid elements are connected for the modeling. Each link segment is represented by only two parameters: an arc angle and an arc length for a curved segment, or an orientation angle and a link length for a straight line segment. Links in the proposed method can be readily related using the Denavit-Hartenberg convention due to similarities to the kinematics of an articulated manipulator. Our method was tested with real worm images, and accurate results were obtained.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.199-202
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• 2002

We described a real-time 3D computer vision system called MIMIC(Motion interface f Motion information Capture system) that can capture and save motion of an actor. This system analyzes input images from vision sensors and searches feature information like a head, hands, and feet. Moreover, this estimates intermediated joints as an elbow and hee using feature information and makes 3D human model having 20 joints. This virtual human model mimics the motion of an actor in real-time. Therefore this system can realize the movement of an actor unaffectedly because of making intermediated joint for complete human body contrary to other marker-free motion capture system.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.239-242
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• 2002

The rapid expansion of the Internet in the past few years has rapidly increased the availability of digital data such as audio, images and videos to the public. Therefore, The need for copyright protect of digital data are increasing in the internet. In this paper, Color image(RGB model) is transformed into LUV model, it includes the characteristics of, human vision and then the U or V component is transformed into 3-level wavelet transform. we can insert watermark to several objects of an image separately The experimental results showed that the proposed watermarking algorithm was better than to other RGB watermarking algorithm.

• Journal of the Semiconductor & Display Technology
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• v.10 no.1
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• pp.83-89
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• 2011

In the case of the tracking system with an active camera, it is very difficult to guarantee real-time processing due to the attribute of vision system which handles large amounts of data at once and has time delay to process. The reliability of the processed result is also badly influenced by the slow sampling time and uncertainty caused by the image processing. In this paper, we figure out dynamic characteristics of pixels reflected on the image plane and derive the mathematical model of the vision tracking system which includes the actuating part and the image processing part. Based on this model, we find a controller that stabilizes the system and enhances the tracking performance to track a target rapidly. The centroid is used as the position index of moving object and the DC motor in the actuating part is controlled to keep the identified centroid at the center point of the image plane.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.9
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• pp.843-849
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• 2006

Intelligent Space defines an environment where many intelligent devices, such as computers and sensors, are distributed. As a result of the cooperation between smart devices, intelligence emerges from the environment. In such scheme, a crucial task is to obtain the global location of every device in order to of for the useful services. Some tracking systems often prepare the models of the objects in advance. It is difficult to adopt this model-based solution as the tracking system when many kinds of objects exist. In this paper the location is achieved with no prior model, using color properties as information source. Feature vectors of multiple objects using color histogram and tracking method are described. The proposed method is applied to the intelligent environment and its performance is verified by the experiments.

• Journal of Sensor Science and Technology
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• v.30 no.5
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• pp.286-289
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• 2021

Image classification is one of the fundamental applications of computer vision. It enables a system to identify an object in an image. Recently, image classification applications have broadened their scope from computer applications to edge devices. The convolutional neural network (CNN) is the main class of deep learning neural networks that are widely used in computer tasks, and it delivers high accuracy. However, CNN algorithms use a large number of parameters and incur high computational costs, which hinder their implementation in edge hardware devices. To address this issue, this paper proposes a lightweight image classifier that provides good accuracy while using fewer parameters. The proposed image classifier diverts the input into three paths and utilizes different scales of receptive fields to extract more feature maps while using fewer parameters at the time of training. This results in the development of a model of small size. This model is tested on the CIFAR-10 dataset and achieves an accuracy of 90% using .26M parameters. This is better than the state-of-the-art models, and it can be implemented on edge devices.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.3
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• pp.102-109
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• 1996

In this paper, we present a new approach for the automation of deburring process. An algorithm for teaching skills of a human expert to a robot manipulator is developed. This approach makes use of TSK fuzzy mode that can wxpress a highly nonlinear functional relation with small number of rules. Burr features such as height, width, area, grinding area are extracted from image processing by use of the vision system. Grinding depth, repetitive number and normal grinding force are chosen as control signals representing actions of the human expert. It is verified that our proposed fuzzy model can accurately express the skills of human experts for the deburring process.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.4
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• pp.324-329
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• 1996

In this paper, we present a vision algorithm to extract the tire information markings on the sidewall of tires. Since the appearance of tire marks is the same as its background, a primary feature to distinguish tire marks from their background is the roughness. Generally, the roughness of tire marks is different from that of its bakground: the surface of tire marks is smoother than the backgrounds. Light incident on the tire surface is reflected differently according to the roughness. For smoother surfaces, the surface irradiance is much stronger than that of rough surfaces. Based on these phenomena and observation, we propose an optimal illumination condition based on Torrance-Sparrow reflection model. We also develop an efficient reflectance-ratio based operator to extract the boundary of tire marks. Even with a very simple masking operation, we were able to obtain remarkable boundary extraction results from real experiments using many tires. By explicitly using the surface reflection model to explain the intensity variation on the black tire surface, we demonstrate that a physics-based vision method is powerful and feasible in extracting surface markings on tires.