• IEIE Transactions on Smart Processing and Computing
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• v.1 no.1
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• pp.17-26
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• 2012

This paper presents an image processing-based validation method for unrecognizable numbers in severely distorted license plate images which have been degraded by various factors including low-resolution, low light-level, geometric distortion, and periodic noise. Existing vehicle license plate recognition (LPR) methods assume that most of the image degradation factors have been removed before performing the recognition of printed numbers and letters. If this is not the case, conventional LPR becomes impossible. The proposed method adopts a novel approach where a set of reference number images are intentionally degraded using the same factors estimated from the input image. After a series of image processing steps, including geometric transformation, super-resolution, and filtering, a comparison using cross-correlation between the intentionally degraded reference and the input images can provide a successful identification of the visually unrecognizable numbers. The proposed method makes it possible to validate numbers in a license plate image taken under low light-level conditions. In the experiment, using an extended set of test images that are unrecognizable to human vision, the proposed method provides a successful recognition rate of over 95%, whereas most existing LPR methods fail due to the severe distortion.

• Journal of the Korean Society of Industry Convergence
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• v.17 no.2
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• pp.77-83
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• 2014

This paper describes a new technology to develop the character recognition technology based on pattern recognition for non-contacting inspection optical lens slant or precision parts, and including external form state of lens or electronic parts for the performance verification, this development can achieve badness finding. And, establish to existing reflex data because inputting surface badness degree of scratch's standard specification condition directly, and error designed to distinguish from product more than schedule error to badness product by normalcy product within schedule extent after calculate the error comparing actuality measurement reflex data and standard reflex data mutually. Developed system to smallest 1 pixel unit though measuring is possible 1 pixel as $37{\mu}m{\times}37{\mu}m$ ($0.1369{\times}10-4mm^2$) the accuracy to $1.5{\times}10-4mm$ minutely measuring is possible performance verification and trust ability through an experiment prove.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.688-693
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• 2010

Vision based object recognition in mobile robots has many issues for image analysis problems with neighboring elements in dynamic environments. SURF(Speeded Up Robust Features) is the local feature extraction method of the image and its performance is constant even if disturbances, such as lighting, scale change and rotation, exist. However, it has a difficulty of real-time processing caused by representation of high dimensional vectors. To solve th problem, execution of SURF in GPU(Graphics Processing Unit) is proposed and implemented using CUDA of NVIDIA. Comparisons of recognition rates and processing time for SURF between CPU and GPU by variation of robot velocity and image sizes is experimented.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.13-23
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• 2001

Most current machine vision systems for industrial inspection were developed with one specific task in mind. Hence, these systems are inflexible in the sense that they cannot easily be adapted to other applications. In this paper, a general vision system framework has been developed that can be easily adapted to a variety of industrial web inspection problems. The objective of this system is to automatically locate and identify \\\"defects\\\" on the surface of the material being inspected. This framework is designed to be robust, to be flexible, and to be as computationally simple as possible. To assure robustness this framework employs a combined strategy of top-down and bottom-up control, hierarchical defect models, and uncertain reasoning methods. To make this framework flexible, a modular Blackboard framework is employed. To minimize computational complexity the system incorporates a simple multi-thresholding segmentation scheme, a fuzzy logic focus of attention mechanism for scene analysis operations, and a partitioning if knowledge that allows concurrent parallel processing during recognition.cognition.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.41-43
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• 2021

Robot Operating System (ROS) has been a prominent and successful framework used in robotics business and academia.. However, the framework has long been focused and limited to navigation of robots and manipulation of objects in the environment. This focus leaves out other important field such as speech recognition, vision abilities, etc. Our goal is to take advantage of ROS capacity to integrate additional libraries of programming functions aimed at real-time computer vision with a depth-image camera. In this paper we will focus on the implementation of an upgraded vision with the help of a depth camera which provides a high quality data for a much enhanced and accurate understanding of the environment. The varied data from the cameras are then incorporated in ROS communication structure for any potential use. For this particular case, the system will use OpenCV libraries to manipulate the data from the camera and provide a face-detection capabilities to the robot, while navigating an indoor environment. The whole system has been implemented and tested on the latest technologies of Turtlebot3 and Raspberry Pi4.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.687-690
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• 2000

Lately, many studies have been progressed for the protection human's lives and property as holding in check accidents happened by human's carelessness or mistakes. One part of these is the development of an autonomouse vehicle. General control method of vision-based autonomous vehicle system is to determine the navigation direction by analyzing lane images from a camera, and to navigate using proper control algorithm. In this paper, characteristic points are abstracted from lane images using lane recognition algorithm with sobel operator. And then the vehicle is controlled using two proposed auto-steering algorithms. Two steering control algorithms are introduced in this paper. First method is to use the geometric relation of a camera. After transforming from an image coordinate to a vehicle coordinate, a steering angle is calculated using Ackermann angle. Second one is using a neural network algorithm. It doesn't need to use the geometric relation of a camera and is easy to apply a steering algorithm. In addition, It is a nearest algorithm for the driving style of human driver. Proposed controller is a multilayer neural network using Levenberg-Marquardt backpropagation learning algorithm which was estimated much better than other methods, i.e. Conjugate Gradient or Gradient Decent ones.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.5
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• pp.490-498
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• 2011

In this paper, we present a two-stage vision-based approach to detect multi views of pedestrian in road scene images. The first stage is HG (Hypothesis Generation), in which potential pedestrian are hypothesized. During the hypothesis generation step, we use a vertical, horizontal edge map, and different colors between road background and pedestrian's clothes to determine the leg position of pedestrian, then a novel symmetry peaks processing is performed to define how many pedestrians is covered in one potential candidate region. Finally, the real candidate region where pedestrian exists will be constructed. The second stage is HV (Hypothesis Verification). In this stage, all hypotheses are verified by Support Vector Machine for classification, which is robust for multi views of pedestrian detection and recognition problems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.29-34
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• 2024

Since the outbreak of COVID-19, there has been a surge in sports conducted through online platforms due to the increase in remote and non-contact activities. Billiards, being suitable for online platforms, has received much attention, leading to research on detecting the position and trajectory of balls. In this paper, we propose a new method utilizing machine vision to detect the position of the balls accurately. The proposed method detects the outline of the ball using the Canny edge detection and then employs simple correlation to determine its position. This correlation-based approach offers satisfactory system performance and is easily applicable in practical systems due to its low implementation complexity and robustness to noise.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.791-798
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• 2024

Construction industry records poor safety records annually due to a large number of injuries and accidents on construction jobsite. In order to improve existing safety performance, object detection approaches have been extensively studied using vision-sensing techniques and deep learning algorithms. Unfortunately, an insufficient number of datasets (e.g., images) and challenges that reside in manually collecting quality datasets constitute a significant hurdle in fully deploying object recognition approaches in real construction sites. Although advanced technologies (e.g., virtual reality) have attempted to address such challenges, they have achieved limited success because they still rely on labor-intensive work. A promising alternative is to adopt generative AI-based data augmentation methods attributed to their efficiency in creating realistic visual datasets and proven performance. However, there remain critical knowledge gaps on how such alternatives can be effectively employed by safety managers on real construction sites in terms of practicability and applications. In this context, this study establishes a framework that can identify effective strategies for improving object detection performance (e.g., accuracy) using generative AI technologies. The outcome of this study will contribute to providing guidelines and best practices for practitioners as well as researchers by exploring different generative AI-driven augmentation approaches and comparing the corresponding results in a quantitative manner.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.256-261
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• 2007

Electroencephalographic(EEG) is used to record activities of human brain in the area of psychology for many years. As technology developed, neural basis of functional areas of emotion processing is revealed gradually. So we measure fundamental areas of human brain that controls emotion of human by using EEG. Hands gestures such as shaking and head gesture such as nodding are often used as human body languages for communication with each other, and their recognition is important that it is a useful communication medium between human and computers. Research methods about gesture recognition are used of computer vision. Many researchers study Emotion Recognition method which uses one of EEG signals and Gestures in the existing research. In this paper, we use together EEG signals and Gestures for Emotion Recognition of human. And we select the driver emotion as a specific target. The experimental result shows that using of both EEG signals and gestures gets high recognition rates better than using EEG signals or gestures. Both EEG signals and gestures use Interactive Feature Selection(IFS) for the feature selection whose method is based on the reinforcement learning.