• Journal of Korea Multimedia Society
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• v.19 no.4
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• pp.736-746
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• 2016

In this paper, we propose an automatic prostate cancer detection method using position, signal intensity and texture feature based on SVM in multi-parametric MR images. First, to align the prostate on DWI and ADC map to T2wMR, the transformation parameters of DWI are estimated by normalized mutual information-based rigid registration. Then, to normalize the signal intensity range among inter-patient images, histogram stretching is performed. Second, to detect prostate cancer areas in T2wMR, SVM classification with position, signal intensity and texture features was performed on T2wMR, DWI and ADC map. Our feature classification using multi-parametric MR imaging can improve the prostate cancer detection rate on T2wMR.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3534-3549
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• 2020

The traditional method of smoke image recognition has low accuracy. For this reason, we proposed an algorithm based on the good group of IMFOA which is GMFOA to optimize the parameters of SVM. Firstly, we divide the motion region by combining the three-frame difference algorithm and the ViBe algorithm. Then, we divide it into several parts and extract the histogram of oriented gradient and volume local binary patterns of each part. Finally, we use the GMFOA to optimize the parameters of SVM and multiple kernel learning algorithms to Classify smoke images. The experimental results show that the classification ability of our method is better than other methods, and it can better adapt to the complex environmental conditions.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.157-160
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• 2021

프레임 단위 LiDAR (Light Detection And Ranging) 기반의 포인트 클라우드는 프레임 간 상관 관계가 높기 때문에 프레임 사이의 예측 기법을 사용하여 더 높은 압축 효율을 얻을 수 있으며, 이를 위해 MPEG의 G-PCC는 Inter-EM (Inter-Exploratory Model)의 표준화를 진행하고 있다. 특히, Inter-EM은 LiDAR 기반 포인트 클라우드의 이러한 특성을 효율적으로 압축하기 위해 전역 및 지역 움직임을 모두 고려하여 압축하는 구조로 설계되었다. 이 중 전역 움직임은 LiDAR 센서가 장착된 차량의 움직임으로 인해 발생되므로, 포인트 클라우드 내 모든 물체들이 동일한 움직임을 나타낼 것으로 예상된다. 하지만, LiDAR 기반 포인트 클라우드는 포인트 클라우드 내 점들의 특성에 따라서 전역 움직임이 나타나는 양상이 다르다. 본 논문은 이러한 LiDAR 기반 포인트 클라우드의 특성을 설명하고, LiDAR 기반 포인트 클라우드 압축 시 전역 움직임 압축을 위한 포인트 클라우드 분할 방안에 대해 제안한다. 본 논문에서 제안하는 포인트 클라우드 분할 방안을 활용한 전역 움직임 압축 시 기존 Inter-EM 대비 더 효율적인 압축이 가능하다.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.129-134
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• 2024

Object Detection is an emerging technology in the field of Computer Vision and Image Processing that deals with detecting objects of a particular class in digital images. It has considered being one of the complicated and challenging tasks in computer vision. Earlier several machine learning-based approaches like SIFT (Scale-invariant feature transform) and HOG (Histogram of oriented gradients) are widely used to classify objects in an image. These approaches use the Support vector machine for classification. The biggest challenges with these approaches are that they are computationally intensive for use in real-time applications, and these methods do not work well with massive datasets. To overcome these challenges, we implemented a Deep Learning based approach Convolutional Neural Network (CNN) in this paper. The Proposed approach provides accurate results in detecting objects in an image by the area of object highlighted in a Bounding Box along with its accuracy.

• Journal of the Korea Society of Computer and Information
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• v.18 no.5
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• pp.35-42
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• 2013

Shot boundary detection is an essential step for efficient browsing, sorting, and classification of video data. Robust shot detection method should overcome the disturbances caused by pixel brightness and object movement between frames. In this paper, two shot boundary detection methods are presented to address these problem by using segmentation, object movement, and pixel brightness. The first method is based on the histogram that reflects object movements and the morphological dilation operation that considers pixel brightness. The second method uses the pixel brightness information of segmented and whole blocks between frames. Experiments on digitized video data of National Archive of Korea show that the proposed methods outperforms the existing pixel-based and histogram-based methods.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.40-48
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• 2021

Content-Based Image Retrieval (CBIR) system plays a vital role to retrieve the relevant images as per the user perception from the huge database is a challenging task. Images are represented is to employ a combination of low-level features as per their visual content to form a feature vector. To reduce the search time of a large database while retrieving images, a novel image retrieval technique based on feature dimensionality reduction is being proposed with the exploit of metaheuristic optimization techniques based on Genetic Algorithm (GA), Extended Binary Cuckoo Search (EBCS) and Whale Optimization Algorithm (WOA). Each image in the database is indexed using a feature vector comprising of fuzzified based color histogram descriptor for color and Median binary pattern were derived in the color space from HSI for texture feature variants respectively. Finally, results are being compared in terms of Precision, Recall, F-measure, Accuracy, and error rate with benchmark classification algorithms (Linear discriminant analysis, CatBoost, Extra Trees, Random Forest, Naive Bayes, light gradient boosting, Extreme gradient boosting, k-NN, and Ridge) to validate the efficiency of the proposed approach. Finally, a ranking of the techniques using TOPSIS has been considered choosing the best feature selection technique based on different model parameters.

• Journal of KIBIM
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• v.6 no.2
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• pp.39-46
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• 2016

While institutional matters such as improvement on Basic Guidelines for Construction Safety are greatly concerned to reduce falling accidents at construction sites, there are short of studies on how to practically predict accident signs at construction sites and to preemptively prevent them. As one of existing accident prevention methods, it was attempted to build the early warning system based on standardized accident scenarios to control the situations. However, the investment cost was too high depending on the site situation, and it did not help construction workers directly since it was developed to mainly provide support operational work support to safety managers. In the long run, it would be possible to develop the augmented reality based accident prevention method from the worker perspective by extracting product information from BIM, visually rendering it along with site installation materials term and comparing it with the site situation. However, to make this method effective, the BIM model should be implemented first and the technology that can promptly process site situations should be introduced. Accordingly, it is necessary to identify risk signs through lightweight image processing to promptly respond only with currently available resources. In this study, it was intended to propose the system concept that identified potential risk factors of falling accidents by histogram equalization, which was known as the fastest image processing method presently, used visual words, which could enhance model classification by wording image records, to determine the risk factors and notified them to the work manager.

• Journal of Korea Multimedia Society
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• v.19 no.3
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• pp.633-641
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• 2016

Automatic defect inspection system is composed of the step in the pre-processing, defect candidate detection, and classification. Polarizing films containing various defects should be minimized over-detection for classifying defect blobs. In this paper, we propose a defect detection algorithm using a skewness of histogram for minimizing over-detection. In order to detect up defects with similar to background pixel, we are used the characteristics of the local region. And the real defect pixels are distinguished from the noise using the probability density function. Experimental results demonstrated the minimized over-detection by utilizing the artificial images and real polarizing film images.

• Journal of KIISE
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• v.41 no.9
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• pp.666-673
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• 2014

This paper proposes a scalable face recognition method for unconstrained face databases, and shows a simple experimental result. Existing face recognition research usually has focused on improving the recognition rate in a constrained environment where illumination, face alignment, facial expression, and background is controlled. Therefore, it cannot be applied in unconstrained face databases. The proposed system is face feature extraction algorithm for unconstrained face recognition. First of all, we extract the area that represent the important features(landmarks) in the face, like the eyes, nose, and mouth. Each landmark is represented by a high-dimensional LBP(Local Binary Pattern) histogram feature vector. The multi-scale LBP histogram vector corresponding to a single landmark, becomes a low-dimensional face feature vector through the feature reduction process, PCA(Principal Component Analysis) and LDA(Linear Discriminant Analysis). We use the Rank acquisition method and Precision at k(p@k) performance verification method for verifying the face recognition performance of the low-dimensional face feature by the proposed algorithm. To generate the experimental results of face recognition we used the FERET, LFW and PubFig83 database. The face recognition system using the proposed algorithm showed a better classification performance over the existing methods.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1315-1318
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• 2003

In this paper, a neural network based approach using a self-organizing feature map is proposed for the segmentation of X ray images. A number of algorithms based on such approaches as histogram analysis, region growing, edge detection and pixel classification have been proposed for segmentation of general images. However, few approaches have been applied to X ray image segmentation because of blur of the X ray image and vagueness of its edge, which are inherent properties of X ray images. To this end, we develop a new model based on the neural network to detect objects in a given X ray image. The new model utilizes Mumford-Shah functional incorporating with a modified adaptive SOFM. Although Mumford-Shah model is an active contour model not based on the gradient of the image for finding edges in image, it has some limitation to accurately represent object images. To avoid this criticism, we utilize an adaptive self organizing feature map developed earlier by the authors.[1] It's learning rule is derived from Mumford-Shah energy function and the boundary of blurred and vague X ray image. The evolution of the neural network is shown to well segment and represent. To demonstrate the performance of the proposed method, segmentation of an industrial part is solved and the experimental results are discussed in detail.