• Journal of Information Processing Systems
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• 제13권2호
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• pp.370-384
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• 2017

The image segmentation is the most important operation in an image processing system. It is located at the joint between the processing and analysis of the images. Unsupervised segmentation aims to automatically separate the image into natural clusters. However, because of its complexity several methods have been proposed, specifically methods of optimization. In our work we are interested to the technique SFLA (Shuffled Frog-Leaping Algorithm). It's a memetic meta-heuristic algorithm that is based on frog populations in nature searching for food. This paper proposes a new approach of unsupervised image segmentation based on SFLA method. It is implemented and applied to different types of images. To validate the performances of our approach, we performed experiments which were compared to the method of K-means.

• 한국정밀공학회지
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• 제16권10호
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• pp.25-34
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• 1999

In this paper, an algorithm is presented to recognize lane and obstacles based on highway road image. The road images obtained by a video camera undergoes a pre-processing that includes filtering, edge detection, and identification of lanes. After this pre-processing, a part of image is grouped into 27 sub-windows and fed into a three-layer feed-forward neural network. The neural network is trained to indicate the road direction and the presence of absence of an obstacle. The proposed algorithm has been tested with the images different from the training images, and demonstrated its efficacy for recognizing lane and obstacles. Based on the test results, it can be said that the algorithm successfully combines the traditional image processing and the neural network principles towards a simpler and more efficient driver warning of assistance system

• 한국축산시설환경학회지
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• 제3권2호
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• pp.105-113
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• 1997

The environment for pig production should be controlled according to a criterion based on the pig's thermoregulartory behavior. Quantifying the pig's thermoregulatroy behavior was needed to prepare a criterion based on the pig's thermoregulatory behavior. Therefore, this study was conducted to quantify the pig's thermoregulatory behavior. The raw images were acquired according to the pig's thermoregulatory behavior and they were processed to binary images. The mean deviations of x and y coordinates of pig's images in a binary image were computed and they were multiplied. The values computed in this manner showed very wide differences according to the pig's thermoregulatory behavior. Therefore, the image processing and mean deviation can be certainly used as a method for classifying the pig's thermoregulatory behavior.

• 한국컴퓨터정보학회논문지
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• 제21권9호
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• pp.45-49
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• 2016

In this study, an image processing algorithm was developed to increase readability of the images of specific parts of a KTX train acquired by using a mobile digital radiographic testing device in a situation where a running train is stopped. The image processing algorithm was realized by using a Visual C++ development tool. The algorithm developed in this study allows to select an interested region in the acquired images when the interested region is suspected to cause a problem, and applies a thinning process based Sobel operators to the selected region. The experimental results show that the readability of defect parts that are not visible to naked eyes was increased through edge detector. Application of the algorithm developed in this study may help to accurately read non-destructive inspection images.

• 한국가시화정보학회지
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• 제18권1호
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• pp.44-49
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• 2020

Neutron radiography has been widely used in many research areas due to its different characteristics from X-rays. Neutron tomography is a powerful tool because it can clearly show the inside of an object that the eye cannot see. However, generally, commercial software is used for the reconstruction of neutron tomography. It means that maintenance costs are incurred and analysis is inefficient in some cases. In this respect, our own image processing code is required to reconstruct neutron images efficiently. In this study, an image processing code is developed for reconstruction of cross-sectional images from neutron radiography taken from the side of the object. Using the developed code, cross-sectional images of the sample are successfully reconstructed.

• Journal of Information Processing Systems
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• 제16권4호
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• pp.795-808
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• 2020

Due to the poor evaluation indexes such as detection accuracy and recall rate when Yolov3 network detects aircraft in remote sensing images, in this paper, we propose a remote sensing image aircraft detection method based on machine vision. In order to improve the target detection effect, the Inception module was introduced into the Yolov3 network structure, and then the data set was cluster analyzed using the k-means algorithm. In order to obtain the best aircraft detection model, on the basis of our proposed method, we adjusted the network parameters in the pre-training model and improved the resolution of the input image. Finally, our method adopted multi-scale training model. In this paper, we used remote sensing aircraft dataset of RSOD-Dataset to do experiments, and finally proved that our method improved some evaluation indicators. The experiment of this paper proves that our method also has good detection and recognition ability in other ground objects.

• Journal of Information Processing Systems
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• 제19권1호
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• pp.33-45
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• 2023

This paper proposes an algorithm that automatically converts images into Pipo, painting images using OpenCV-based image processing technology. The existing "purity," "palm," "puzzling," and "painting," or Pipo, painting image production method relies on manual work, so customized production has the disadvantage of coming with a high price and a long production period. To resolve this problem, using the OpenCV library, we developed a technique that automatically converts an image into a Pipo painting image by designing a module that changes an image, like a picture; draws a line based on a sector boundary; and writes sector numbers inside the line. Through this, it is expected that the production cost of customized Pipo painting images will be lowered and that the production period will be shortened.

• 대한인간공학회지
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• 제8권2호
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• pp.35-41
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• 1989

In this paper a new method is proposed to create 3-dimensional coordinate values from two 2- dimensional images (side and front image of objects) using image processing system and two video cameras. This method is task requiring measurements of camera lense distortion, calibrations and conversin 2-dimensional images into 3-dimensional images. This system provides 3-dimensional me- asurement error of +5mm for about 2m length objects.

• Journal of Information Processing Systems
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• 제12권1호
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• pp.129-148
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• 2016

Acute myocardial infarction (AMI) is one of the three emergency diseases that require urgent diagnosis and treatment in the golden hour. It is important to identify the status of the coronary artery in AMI due to the nature of disease. Therefore, multi-modal medical images, which can effectively show the status of the coronary artery, have been widely used to diagnose AMI. However, the legacy system has provided multi-modal medical images with flat and unstructured data. It has a lack of semantic information between multi-modal images, which are distributed and stored individually. If we can see the status of the coronary artery all at once by integrating the core information extracted from multi-modal medical images, the time for diagnosis and treatment will be reduced. In this paper, we analyze semantic relations between multi-modal medical images based on coronary anatomy for AMI. First, we selected a coronary arteriogram, coronary angiography, and echocardiography as the representative medical images for AMI and extracted semantic features from them, respectively. We then analyzed the semantic relations between them and defined the convergence data model for AMI. As a result, we show that the data model can present core information from multi-modal medical images and enable to diagnose through the united view of AMI intuitively.

• 한국측량학회지
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• 제36권5호
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• pp.413-422
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• 2018

Many applications using satellite data from high-resolution multispectral sensors require an image fusion step, known as pansharpening, before processing and analyzing the multispectral images when spatial fidelity is crucial. Image fusion methods are to improve images with higher spatial and spectral resolutions by reducing spectral distortion, which occurs on image fusion processing. The image fusion methods can be classified into MRA (Multi-Resolution Analysis) and CSA (Component Substitution Analysis) approaches. To suggest the efficient image fusion method for Pleiades and KOMPSAT (Korea Multi-Purpose Satellite) 3 satellites, this study will evaluate image fusion methods for multispectral and panchromatic images. HPF (High-Pass Filtering), SFIM (Smoothing Filter-based Intensity Modulation), GS (Gram Schmidt), and GSA (Adoptive GS) were selected for MRA and CSA based image fusion methods and applied on multispectral and panchromatic images. Their performances were evaluated using visual and quality index analysis. HPF and SFIM fusion results presented low performance of spatial details. GS and GSA fusion results had enhanced spatial information closer to panchromatic images, but GS produced more spectral distortions on urban structures. This study presented that GSA was effective to improve spatial resolution of multispectral images from Pleiades 1A and KOMPSAT 3.