• Journal of Korea Multimedia Society
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• v.18 no.10
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• pp.1189-1196
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• 2015

In this paper, we proposed automatic extraction of brain tumor using morphological operation and statistical tumors size in MR images. Neurosurgery have used gamma-knife therapy by MR images. However, the gamma-knife plan systems needs the brain tumor regions, because gamma-ray should intensively radiate to the brain tumor except for normal cells. Therefore, gamma-knife plan systems spend too much time on designating the tumor regions. In order to reduce the time of designation of tumors, we progress the automatical extraction of tumors using proposed method. The proposed method consist of two steps. First, the information of skull at MRI slices remove using statistical tumors size. Second, the ROI is extracted by tumor feature and average of tumors size. The detection of tumor is progressed using proposed and threshold method. Moreover, in order to compare the effeminacy of proposed method, we compared snap-shot and results of proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.9
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• pp.2121-2128
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• 2015

In this paper presents a method of extracting reliable and regions of interest (ROI) in barcode for the purpose of factory automation. backgrounds are separated based on directional components and the characteristics of detected patterns. post-processing is performed on candidate images with analysis of problems caused by blur, rotation and areas of high similarity. In addition, the resizing factor is used to achieve faster calculations through image resizing. The input images contained multiple product or barcode for application to diverse automation environments; a high extraction success rate is accomplished despite the maximum shooting distance of 80 cm. Simulations involving images with various shooting distances gave an ROI detection rate of 100% and a post-processing success rate of 99.3%.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.246-253
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• 2016

Infrared (IR) imaging has been researched for various applications such as surveillance. IR radiation has the capability to detect thermal characteristics of objects under low-light conditions. However, automatic segmentation for finding the object of interest would be challenging since the IR detector often provides the low spatial and contrast resolution image without color and texture information. Another hindrance is that the image can be degraded by noise and clutters. This paper proposes multi-level segmentation for extracting regions of interest (ROIs) and objects of interest (OOIs) in the IR scene. Each level of the multi-level segmentation is composed of a k-means clustering algorithm, an expectation-maximization (EM) algorithm, and a decision process. The k-means clustering initializes the parameters of the Gaussian mixture model (GMM), and the EM algorithm estimates those parameters iteratively. During the multi-level segmentation, the area extracted at one level becomes the input to the next level segmentation. Thus, the segmentation is consecutively performed narrowing the area to be processed. The foreground objects are individually extracted from the final ROI windows. In the experiments, the effectiveness of the proposed method is demonstrated using several IR images, in which human subjects are captured at a long distance. The average probability of error is shown to be lower than that obtained from other conventional methods such as Gonzalez, Otsu, k-means, and EM methods.

• The KIPS Transactions:PartB
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• v.14B no.5
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• pp.329-336
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• 2007

In this paper we propose both the construction of image DB including information on the shooting location and direction of the captured outdoor images and the efficient retrieval method from the DB. Furthermore, for the automatic extraction of the location and direction information, we suggest to have the Digital Camera equipped with an expandable GPS modulo which has a function to calculate the location and direction and also to utilize GPS IFD tags in the EXIF. Then that will make it possible for us to retrieve quickly and precisely the target image with its geography and other objects on the ground included. In the previous retrieval method based only on the location, we eel some extra useless images due to the fact that all the images in the ROI(Region Of Interest) are searched on one condition, radius. However, with the proposed method in this paper, we can not only retrieve all the images selectively within the ROI but also achieve nearly 100% of precision when we search for the target images within DOI(Direction Of Interest) with another condition, direction, added. Applying this method to an image retrieval system, we can classify or retrieve natural images based on the location and direction information, which, in turn, will be vitally useful to diverse industrial fields such as disaster alarm system, fire and disaster prevention system, traffic information system, and so forth.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.4
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• pp.374-380
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• 2019

Containers are fitted with various placards on the surface to indicate the risk of cargo. If the containers are loaded with dangerous goods, care should be taken in handling the containers. Therefore, as part of the port automation system, there is a demand for automatic placard recognition. In this paper, proposed is a method to extract placard areas from a container image, which is the first part of the placard recognition system. The fact that placards are of various types but all have a diamond shape can be an advantage in recognition. However, it is a disadvantage in recognition that the placards can be distorted in various ways because the container surface is not flat. When the proposed method was applied to actual images, type I error did not occur. In addition, since the shape feature of the object and basic image operations are used to extract regions of interest, it can be applied to various shape-based region extraction problems.

• Proceedings of the Korean Society of Computer Information Conference
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• 2009.01a
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• pp.215-218
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• 2009

본 논문에서는 웨이블릿 변환 된 고주파 서브밴드들의 에지 정보를 이용하여 관심 객체 영역을 고속으로 자동 검출해주는 새로운 알고리즘을 제안하였다. 제안된 방법에서는 에지정보를 이용하여 블록단위의 4-방향 객체 윤곽 탐색 알고리즘(4-DOBS)을 수행하여 관심객체를 검출한다. 전체 이미지는 $64{\times}64$ 또는 $32{\times}32$ 크기의 코드 블록으로 먼저 나누어지고, 각 코드 블록 내에 에지들이 있는지 없는지에 따라 관심 코드블록 또는 배경이 된다. 4-방향은 바깥쪽에서 이미지의 중앙으로 탐색하여 접근하며, 피사계 심도가 낮은 이미지는 중앙으로 갈수록 에지가 발견된다는 특징을 이용한다. 기존 방법들의 문제점 이였던 복잡한 필터링 과정과 영역병합 문제로 인한 높은 계산도를 상당히 개선시킬 수 있었다. 또한 블록 단위의 처리로 인하여 실시간 처리를 요하는 응용에서도 적용 가능 하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5436-5458
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• 2017

Automatic Video Surveillance System (AVSS) has become important to computer vision researchers as crime has increased in the twenty-first century. As a new branch of AVSS, baggage detection has a wide area of security applications. Some of them are, detecting baggage in baggage restricted super shop, detecting unclaimed baggage in public space etc. However, in this paper, a detection & classification framework of baggage is proposed. Initially, background subtraction is performed instead of sliding window approach to speed up the system and HSI model is used to deal with different illumination conditions. Then, a model is introduced to overcome shadow effect. Then, occlusion of objects is detected using proposed mirroring algorithm to track individual objects. Extraction of rotational signal descriptor (SP-RSD-HOG) with support plane from Region of Interest (ROI) add rotation invariance nature in HOG. Finally, dynamic human body parameter setting approach enables the system to detect & classify single or multiple pieces of carried baggage even if some portions of human are absent. In baggage detection, a strong classifier is generated by boosting similarity measure based multi layer Support Vector Machine (SVM)s into HOG based SVM. This boosting technique has been used to deal with various texture patterns of baggage. Experimental results have discovered the system satisfactorily accurate and faster comparative to other alternatives.