• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.8
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• pp.1843-1859
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• 2013

The continuous increase of the spatial resolution of remote sensing images brings great challenge to image analysis and processing. Traditional prior knowledge-based region detection and target recognition algorithms for processing high resolution remote sensing images generally employ a global searching solution, which results in prohibitive computational complexity. In this paper, a more efficient region of interest (ROI) detection algorithm based on visual attention and threshold segmentation (VA-TS) is proposed, wherein a visual attention mechanism is used to eliminate image segmentation and feature detection to the entire image. The input image is subsampled to decrease the amount of data and the discrete moment transform (DMT) feature is extracted to provide a finer description of the edges. The feature maps are combined with weights according to the amount of the "strong points" and the "salient points". A threshold segmentation strategy is employed to obtain more accurate region of interest shape information with the very low computational complexity. Experimental statistics have shown that the proposed algorithm is computational efficient and provide more visually accurate detection results. The calculation time is only about 0.7% of the traditional Itti's model.

• Journal of Korea Multimedia Society
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• v.20 no.1
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• pp.43-50
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• 2017

According to living environment, the number of deaths due to thyroid diseases increased. In this paper, we proposed an algorithm for recognizing a thyroid detection using texture analysis based on shape, gray level co-occurrence matrix and gray level run length matrix. First of all, we segmented the region of interest (ROI) using active contour model algorithm. Then, we applied a total of 18 features (5 first order descriptors, 10 Gray level co-occurrence matrix features(GLCM), 2 Gray level run length matrix features and shape feature) to each thyroid region of interest. The extracted features are used as statistical analysis. Our results show that first order statistics (Skewness, Entropy, Energy, Smoothness), GLCM (Correlation, Contrast, Energy, Entropy, Difference variance, Difference Entropy, Homogeneity, Maximum Probability, Sum average, Sum entropy), GLRLM features and shape feature helped to distinguish thyroid benign and malignant. This algorithm will be helpful to diagnose of thyroid nodule on ultrasound images.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.12
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• pp.100-108
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• 2004

In reverse engineering, data acquisition methodology can generally be categorized into contacting and non-contacting types. Recently, researches on hybrid or sensor fusion of the two types have been increasing. In addition, efficient construction of a geometric model from the measurement data is required, where considerable amount of user interaction to classify and localize regions of interest is inevitable. Our research focuses on the classification of each bounded region into a pre-defined feature shape fer a hybrid measuring scheme, where the overall procedures are described as fellows. Firstly, the physical model is digitized by a non-contacting laser scanner which rapidly provides cloud-of-points data. Secondly, the overall digitized data are approximated to a z-map model. Each bounding curve of a region of interest (featured area) can be 1.aced out based on our previous research. Then each confined area is systematically classified into one of the pre-defined feature types such as floor, wall, strip or volume, followed by a more accurate measuring step via a contacting probe. Assigned to each feature is a specific digitizing path topology which may reflect its own geometric character. The research can play an important role in minimizing user interaction at the stage of digitizing path planning.

• Journal of Korea Multimedia Society
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• v.18 no.1
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• pp.1-8
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• 2015

In recent years, ear has emerged as a new biometric trait, because it has advantage of higher user acceptance than fingerprint and can be captured at remote distance in an indoor or outdoor environment. This paper proposes an individual identification method using ear region based on SIFT(shift invariant feature transform). Unlike most of the previous studies using rectangle shape for extracting a region of interest(ROI), this study sets an ROI as a flexible expanded region including ear. It also presents an effective extraction and matching method for SIFT keypoints. Experiments for evaluating the performance of the proposed method were performed on IITD public database. It showed correct identification rate of 98.89%, and it showed 98.44% with a deformed dataset of 20% occlusion. These results show that the proposed method is effective in ear recognition and robust to occlusion.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.471-480
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• 2002

In this paper, we implemented a real-time system improving the performance of the paper currency discrimination by integrating a weighted region of interest matching algorithm with a weighted shape feature matching algorithm of the blocked image. The system classifies the paper currency by comparing a query image with compared images based on the database that contain images of paper currency. Especially, the system has good efficiency at the contaminated, rotated, and translated paper currency. The system hardware consists of three parts as follows : the paper currency image acquired by CIS(contact image sensor) is applied to the pre-processing part with A/D converter and PLD. Finally the pre-processed image data are classified by the main image processing part with a high-speed DSP based on the proposed algorithm.

• 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.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.3
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• pp.145-150
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• 2014

This paper presents an effective vision based method for LED traffic light detection at the daytime. First, the proposed method calculates horizontal coordinates to set region of interest (ROI) on input sequence images. Second, the proposed uses color segmentation method to extract region of green and red traffic light. Next, to classify traffic light and another noise, shape filter and haar-like feature value are used. Finally, temporal delay filter with weight is applied to remove blinking effect of LED traffic light, and state and weight of traffic light detection are used to classify types of traffic light. For simulations, the proposed method is implemented through Intel Core CPU with 2.80 GHz and 4 GB RAM, and tested on the urban and rural road video. Average detection rate of traffic light is 94.50 % and average recognition rate of traffic type is 90.24 %. Average computing time of the proposed method is 11 ms.

• Journal of computational fluids engineering
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• v.12 no.4
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• pp.68-73
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• 2007

Computational Fluid Dynamics (CFD) approach is now playing an important role in the engineering process in these days. Generating proper grid system in time for the region of interest is prerequisite for the efficient numerical calculation of flow physics using CFD approach. Grid generation is, however, usually considered as a major obstacle for a routine and successful application of numerical approaches in the engineering process. CFD approach based on the unstructured grid system is gaining popularity due to its simplicity and efficiency for generating grid system compared to the structured grid approaches, especially for complex geometries. In this paper an automated triangular surface grid generation using CAD(Computer Aided Design) surface data is proposed. According to the present method, the CAD surface data imported in the STL(Stereo-lithography) format is processed to identify feature edges defining the topology and geometry of the surface shape first. When the feature edges are identified, node points along the edges are distributed. The initial fronts which connect those feature edge nodes are constructed and then they are advanced along the CAD surface data inward until the surface is fully covered by triangular surface grid cells using Advancing Front Method. It is found that this approach can be implemented in an automated way successfully saving man-hours and reducing human-errors in generating triangular surface grid system.

• 한국전산유체공학회:학술대회논문집
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• 2007.04a
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• pp.103-107
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• 2007

Computational Fluid Dynamics (CFD in short) approach is now playing an important role in the engineering process recently. Generating proper grid system for the region of interest in time is prerequisite for the efficient numerical calculation of flow physics using CFD approach. Grid generation is, however, usually considered as a major obstacle for a routine and successful application of numerical approaches in the engineering process. CFD approach based on the unstructured grid system is gaining popularity due to its simplicity and efficiency for generating grid system compared to the structured grid approaches. In this paper an automated triangular surface grid generation using CAD surface data is proposed According to the present method, the CAD surface data imported in the STL format is processed to identify feature edges defining the topology and geometry of the surface shape first. When the feature edges are identified, node points along the edges are distributed. The initial fronts which connect those feature edge nodes are constructed and then they are advanced along the CAD surface data inward until the surface is fully covered by triangular surface grid cells using Advancing Front Method. It is found that this approach can be implemented in an automated way successfully saving man-hours and reducing human-errors in generating triangular surface grid system.

• Journal of KIISE:Information Networking
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• v.32 no.3
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• pp.318-326
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• 2005

This paper presents the image feature extraction method and the image classification technique to select the harmful image flowed from the Internet by grade of image contents such as harmlessness, sex-appealing, harmfulness (nude), serious harmfulness (adult) by the characteristic of the image. In this paper, we suggest skin area detection technique to recognize whether an input image is harmful or not. We also propose the ROI detection algorithm that establishes region of interest to reduce some noise and extracts harmful degree effectively and defines the characteristics in the ROI area inside. And this paper suggests the multiple-SVM training method that creates the image classification model to select as 4 types of class defined above. This paper presents the multiple-SVM classification algorithm that categorizes harmful grade of input data with suggested classification model. We suggest the skin likelihood image made of the shape information of the skin area image and the color information of the skin ratio image specially. And we propose the image feature vector to use in the characteristic category at a course of traininB resizing the skin likelihood image. Finally, this paper presents the performance evaluation of experiment result, and proves the suitability of grading image using image feature classification algorithm.