• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.510-513
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• 2016

Detecting of the object in image processing is substantial but it depends on the object itself and the environment. An object can be detected either by its shape or color. Color is an essential for pattern recognition and computer vision. It is an attractive feature because of its simplicity and its robustness to scale changes and to detect the positions of the object. Generally, color of an object depends on its characteristics of the perceiving eye and brain. Physically, objects can be said to have color because of the light leaving their surfaces. Here, we conducted experiment in the aquarium fish tank. Different color of fish robots are mimic the natural swim of fish. Unfortunately, in the underwater medium, the colors are modified by attenuation and difficult to identify the color for moving objects. We consider the fish motion as a moving object and coordinates are found at every instinct of the aquarium to detect the position of the fish robot using OpenCV color detection. In this paper, we proposed to identify the position of the fish robot by their color and use the position data to control the fish robot gathering in one point in the fish tank through serial communication using RF module. It was verified by the performance test of detecting the position of the fish robot.

• The Journal of the Korea Contents Association
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• v.12 no.12
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• pp.63-70
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• 2012

In medical image processing field, object recognition is usually processed based on region segmentation algorithm. Region segmentation in the computing field is carried out by computerized processing of various input information such as brightness, shape, and pattern analysis. If the information mentioned does not make sense, however, many limitations could occur with region segmentation during computer processing. Therefore, this paper suggests effective region segmentation method based on R2-map information within the magnetic resonance (MR) theory. In this study, the experiment had been conducted using images including the liver region and by setting up feature points of R2-map as seed points for 2D region growing and final boundary correction to enable region segmentation even when the border line was not clear. As a result, an average area difference of 7.5%, which was higher than the accuracy of conventional exist region segmentation algorithm, was obtained.

• Journal of Digital Contents Society
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• v.14 no.2
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• pp.275-282
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• 2013

In medical image processing field, object recognition is usually carried out by computerized processing of various input information such as brightness, shape, and pattern. If the information mentioned does not make sense, however, many limitations could occur with object recognition during computer processing. Therefore, this paper suggests effective object recognition method based on the magnetic resonance (MR) theory to resolve the basic limitations in computer processing. We propose the efficient method of robust gray-white matter segmentation by texture analysis through the Susceptibility Weighted Imaging (SWI) for contrast enhancement. As a result, an average area difference of 5.2%, which was higher than the accuracy of conventional region segmentation algorithm, was obtained.

• International Journal of Control, Automation, and Systems
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• v.2 no.4
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• pp.509-515
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• 2004

In this paper we present the linear combination of a fuzzy opening and closing filter with locally adaptive structuring elements that can preserve the geometrical features of an image. Based on the adaptation algorithm of linear combination of the fuzzy opening and closing filter, the optimal structuring element for image representation is obtained. The optimal structuring element is an indicator of the shape and direction of an object's image, which is useful in filtering, multi resolution, segmentation, and recognition of an image.

• The Journal of Korea Robotics Society
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• v.14 no.3
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• pp.228-235
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• 2019

This paper presents a multiple DoFs (degrees-of-freedom) prosthetic forearm and sEMG (surface electromyogram) pattern recognition and motion intent classification of forearm amputee. The developed prosthetic forearm has 9 DoFs hand and single-DoF wrist, and the socket is designed considering wearability. In addition, the pattern recognition based on sEMG is proposed for prosthetic control. Several experiments were conducted to substantiate the performance of the prosthetic forearm. First, the developed prosthetic forearm could perform various motions required for activity of daily living of forearm amputee. It was able to control according to shape and size of the object. Additionally, the amputee was able to perform 'tying up shoe' using the prosthetic forearm. Secondly, pattern recognition and classification experiments using the sEMG signals were performed to find out whether it could classify the motions according to the user's intents. For this purpose, sEMG signals were applied to the multilayer perceptron (MLP) for training and testing. As a result, overall classification accuracy arrived at 99.6% for all participants, and all the postures showed more than 97% accuracy.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.101-106
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• 2023

With the recent surge in YouTube usage, there has been a proliferation of user-generated videos where individuals evaluate cosmetics. Consequently, many companies are increasingly utilizing evaluation videos for their product marketing and market research. However, a notable drawback is the manual classification of these product review videos incurring significant costs and time. Therefore, this paper proposes a deep learning-based cosmetics search algorithm to automate this task. The algorithm consists of two networks: One for detecting candidates in images using shape features such as circles, rectangles, etc and Another for filtering and categorizing these candidates. The reason for choosing a Two-Stage architecture over One-Stage is that, in videos containing background scenes, it is more robust to first detect cosmetic candidates before classifying them as specific objects. Although Two-Stage structures are generally known to outperform One-Stage structures in terms of model architecture, this study opts for Two-Stage to address issues related to the acquisition of training and validation data that arise when using One-Stage. Acquiring data for the algorithm that detects cosmetic candidates based on shape and the algorithm that classifies candidates into specific objects is cost-effective, ensuring the overall robustness of the algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.1
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• pp.69-76
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• 2010

The segmentation of moving object in video sequence is a core technique of intelligent image processing system such as video surveillance, traffic monitoring and human tracking. A typical method to segment a moving region from the background is the background subtraction. The steps of background subtraction involve calculating a reference image, subtracting new frame from reference image and then thresholding the subtracted result. One of famous background modeling is Gaussian mixture model (GMM). Even though the method is known efficient and exact, GMM suffers from a problem that includes false pixels in ROI (region of interest), specifically shadow pixels. These false pixels cause fail of the post-processing tasks such as tracking and object recognition. This paper presents a method for removing false pixels included in ROT. First, we subdivide a ROI by using shape characteristics of detected objects. Then, a method is proposed to classify pixels from using histogram characteristic and comparing difference of energy that converts the color value of pixel into grayscale value, in order to estimate whether the pixels belong to moving object area or shadow area. The method is applied to real video sequence and the performance is verified.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.44-46
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• 2022

Various object recognition using artificial intelligence basically shows planar results. It is based on classifying objects or identifying what objects are on the image. However, the original object has a three-dimensional shape, not a plane, and although the perception to obtain only simple results from the image does not matter, there is a lot of information that is insufficient when used in various fields. In this paper, checks the method of generating data in various fields of objects and whether it is meaningful by utilizing the characteristics of Layer that generates intermediate results with respect to image generation based on the GAN algorithm. It solves some of the problems in the hardware and collection process for generating existing multi-faceted data, and confirms that it can be utilized after data generation on several limited objects.

• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1881-1892
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• 1997

This paper proposes the method for recognition of printed chinese characters by probabilistic VQ networks and multi-stage recognizer has hierarchical structure. We use modular neural networks, because it is difficult to construct a large-scale neural network. Problems in this procedure are replaced by probabilistic neural network model. And, Confused Characters which have significant ratio of miss-classification are reclassified using the entropy theory. The experimental object consists of 4,619 chinese characters within the KSC5601 code except the same shape but different code. We have 99.33% recognition rate to the training data, and 92.83% to the test data. And, the recognition speed of system is 4-5 characters per second. Then, these results demonstrate the usefulness of our work.

• Journal of Korea Multimedia Society
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• v.12 no.6
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• pp.794-803
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• 2009

In this paper, we will present a container auto-landing system, the system use the stereo camera to measure the container depth information. And the container region can be detected by using its hough line feature. In the line feature detection algorithm, we will detect the parallel lines and perpendicular lines which compose the rectangle region. Among all the candidate regions, we can select the region with the same aspect-ratio to the container. The region will be the detected container region. After having the object on both left and right images, we can estimate the distance from camera to object and container dimension. Then all the detect dimension information and depth inform will be applied to reconstruct the virtual environment of crane which will be introduce in this paper. Through the simulation result, we can know that, the container detection rate achieve to 97% with simple background. And the estimation algorithm can get a more accuracy result with a far distance than the near distance.