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Seok-Woo Jang
Department of Software, Anyang University, Beongil, Samdeok-Ro, Manan-Gu, Anyang, Korea

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Journal article
Published: 01 January 2021 in Computer Science and Information Systems
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When uploading multimedia data such as photos or videos on social network services, websites, and so on, certain parts of the human body or personal information are often exposed. Therefore, it is frequent that the face of a person is blurred out to protect the portrait right of a particular person, and that repulsive objects are covered with mosaic blocks to prevent others from feeling disgusted. In this paper, an algorithm that detects mosaic regions blurring out certain blocks based on the edge projection is proposed. The proposed algorithm initially detects the edge and uses the horizontal and vertical line edge projections to detect the mosaic candidate blocks. Subsequently, geometrical features such as size, aspect ratio and compactness are used to filter the candidate mosaic blocks, and the actual mosaic blocks are finally detected. The experiment results showed that the proposed algorithm detected mosaic blocks more accurately than other methods.

ACS Style

Seok-Woo Jang. Extraction of mosaic regions through projection and filtering of features from image big data. Computer Science and Information Systems 2021, 18, 553 -574.

AMA Style

Seok-Woo Jang. Extraction of mosaic regions through projection and filtering of features from image big data. Computer Science and Information Systems. 2021; 18 (2):553-574.

Chicago/Turabian Style

Seok-Woo Jang. 2021. "Extraction of mosaic regions through projection and filtering of features from image big data." Computer Science and Information Systems 18, no. 2: 553-574.

Article
Published: 19 November 2020 in The Journal of Supercomputing
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Due to the rapid development of the high-speed wired and wireless Internet, image contents including objects with exposed personal information are being distributed freely, which is a social problem. In this paper, we introduce a method of robustly detecting a target object with facial region exposed from an image that is quickly entered using skin color and a deep learning algorithm and effectively covering the detected target object through prediction. The proposed method in this paper accurately detects the target object containing facial region exposed from the image entered by applying an image adaptive skin color model and a CNN-based deep learning algorithm. Subsequently, the location prediction algorithm is used to quickly track the detected object. A mosaic is overlapped over the target object area to effectively protect the object area where the facial region is exposed. The experimental results show that the proposed approach accurately detects the target object including the facial region exposed from the continuously entered video, and efficiently covers the detected object through mosaic processing while quickly tracking it using a prediction-based tracking algorithm. The tracking-based target covering method proposed in this study is expected to be useful in various practical applications related to pattern recognition and image security, such as content-based image retrieval, real-time surveillance, human–computer interaction, and face detection.

ACS Style

Seok-Woo Jang. Efficient covering of target areas using a location prediction-based algorithm. The Journal of Supercomputing 2020, 77, 6105 -6122.

AMA Style

Seok-Woo Jang. Efficient covering of target areas using a location prediction-based algorithm. The Journal of Supercomputing. 2020; 77 (6):6105-6122.

Chicago/Turabian Style

Seok-Woo Jang. 2020. "Efficient covering of target areas using a location prediction-based algorithm." The Journal of Supercomputing 77, no. 6: 6105-6122.

Article
Published: 06 November 2020 in Multimedia Tools and Applications
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With the development of many devices that support high-speed wired and wireless Internet, it has been universalized to upload and download various kinds of social multimedia information. However, image data including sensitive personal information are easily exposed to Internet users. In this study, the algorithm is introduced to make it possible to exclude background regions from various types of input color images, to robustly detect the target regions including personal information, and then effectively protect the detected target regions through context-adaptative blocking. In this study, the background regions are first excluded from input color images, and then only the target regions including personal information are robustly segmented on the basis of human skin color. Subsequently, the detected target regions are effectively blocked in the way of selecting regional blurring adaptively in line with surrounding situations, and therefore it is possible to prevent personal information from being exposed. The experimental result of this study reveals that the proposed method robustly blocks the target object regions including personal information of input images in line with surrounding situations. The algorithm proposed in this study is expected to be applied practically to many fields associated with image processing, such as video surveillance, video monitoring, and target object covering.

ACS Style

ByeongTae Ahn; Seok-Woo Jang. Context-adaptive blocking for protecting personal information exposed to social multimedia content. Multimedia Tools and Applications 2020, 1 -19.

AMA Style

ByeongTae Ahn, Seok-Woo Jang. Context-adaptive blocking for protecting personal information exposed to social multimedia content. Multimedia Tools and Applications. 2020; ():1-19.

Chicago/Turabian Style

ByeongTae Ahn; Seok-Woo Jang. 2020. "Context-adaptive blocking for protecting personal information exposed to social multimedia content." Multimedia Tools and Applications , no. : 1-19.

Article
Published: 16 June 2020 in Multimedia Tools and Applications
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Minutiae used in most fingerprint recognition devices is robust to presentation attack, but generates a high false match rate. Thus, it is applied along with orientation map or skeleton images. There has been plenty of research on security vulnerability of minutiae, whereas few research has been conducted on orientation map or skeleton images. This study analyzes vulnerability of presentation attack for skeleton images. For this purpose, it proposes a new algorithm of recovering fingerprints with the use of machine learning and skeleton image features of fingerprints. In the proposed method, we suggest the new machine learning Pix2Pix model to generate more natural images. The suggested model is developed in the way of adding a latent vector to the conventional image-to-image translation model Pix2Pix. In the experiment, fingerprints were recovered with the use of the proposed Pix2Pix model, and it was found that a fingerprint recognition device which recognized the recovered fingerprints had a high success rate of recognition. Therefore, it was proved that a fingerprint recognition device using skeleton images as well was vulnerable to presentation attack. It is expected that the algorithm proposed in this study will be very useful to many different application areas related to image processing, including biometrics, fingerprint recognition and recovery, and image surveillance.

ACS Style

Samuel Lee; Seok-Woo Jang; Dongho Kim; Hernsoo Hahn; Gye-Young Kim. A Novel Fingerprint Recovery Scheme using Deep Neural Network-based Learning. Multimedia Tools and Applications 2020, 1 -15.

AMA Style

Samuel Lee, Seok-Woo Jang, Dongho Kim, Hernsoo Hahn, Gye-Young Kim. A Novel Fingerprint Recovery Scheme using Deep Neural Network-based Learning. Multimedia Tools and Applications. 2020; ():1-15.

Chicago/Turabian Style

Samuel Lee; Seok-Woo Jang; Dongho Kim; Hernsoo Hahn; Gye-Young Kim. 2020. "A Novel Fingerprint Recovery Scheme using Deep Neural Network-based Learning." Multimedia Tools and Applications , no. : 1-15.

Journal article
Published: 18 March 2020 in Sustainability
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High-speed wired and wireless Internet are one of the useful ways to acquire various types of media data easily. In this circumstance, people also can easily get media data including objects with exposed personal information through the Internet. Exposure of personal information emerges as a social issue. This paper proposes an effective blocking technique that makes it possible to robustly detect target objects with exposed personal information from various types of input images with the use of deep neural computing and to effectively block the detected objects’ regions. The proposed technique first utilizes the neural computing-based learning algorithm to robustly detect the target object including personal information from an image. It next generates a grid-type mosaic and lets the mosaic overlap the target object region detected in the previous step so as to effectively block the object region that includes personal information. Experimental results reveal that the proposed algorithm robustly detects the target object region with exposed personal information from a variety of input images and effectively blocks the detected region through grid-type mosaic processing. The object blocking technique proposed in this paper is expected to be applied to various application fields such as image security, sustainable anticipatory computing, object tracking, and target blocking.

ACS Style

Seok-Woo Jang; Sang-Hong Lee. Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing. Sustainability 2020, 12, 2373 .

AMA Style

Seok-Woo Jang, Sang-Hong Lee. Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing. Sustainability. 2020; 12 (6):2373.

Chicago/Turabian Style

Seok-Woo Jang; Sang-Hong Lee. 2020. "Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing." Sustainability 12, no. 6: 2373.

Article
Published: 24 January 2020 in Multimedia Tools and Applications
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This paper suggests a method for tracking gaze of a person at a distance around 2 m, using a single pan-tilt-zoom (PTZ) camera. In the suggested method, images are acquired for gaze tracking by turning the camera to the wide angle mode, or the narrow angle mode, depending on the location of the person. The face that is present in the field of view (FOV) of a camera, is detected in the wide angle mode. Once the location of the face is calculated, the camera turns to the narrow angle mode. The images, which have been acquired in the narrow angle mode, contain information on the direction of gaze of the person, who is at a distance. The method for calculating the direction of gaze is comprised of the head pose estimation and gaze direction calculation steps. The head pose estimation is performed using the location information on the eyes and nose in the face. The direction of gaze is generated using the process of partitioning the pupil through a deformable template, and extracting the center of an eye using the end points of both eyes and head pose information. This paper shows that the proposed gaze tracking algorithm can effectively track the direction of a person’s gaze, at varying distances.

ACS Style

Gyung-Ju Lee; Seok-Woo Jang; Gye-Young Kim. Pupil detection and gaze tracking using a deformable template. Multimedia Tools and Applications 2020, 79, 12939 -12958.

AMA Style

Gyung-Ju Lee, Seok-Woo Jang, Gye-Young Kim. Pupil detection and gaze tracking using a deformable template. Multimedia Tools and Applications. 2020; 79 (19-20):12939-12958.

Chicago/Turabian Style

Gyung-Ju Lee; Seok-Woo Jang; Gye-Young Kim. 2020. "Pupil detection and gaze tracking using a deformable template." Multimedia Tools and Applications 79, no. 19-20: 12939-12958.

Article
Published: 19 July 2019 in Multimedia Tools and Applications
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With the development of high-performance visual sensors, it has been very easy to obtain a variety of image data. Of these image data, human face regions contain personal information to distinguish one from the others. Therefore, it is important to accurately detect unhidden face regions from an input image. This paper proposes a method of robustly detecting human face regions from an input color image with the use of a deep learning algorithm, one of the machine learning algorithms. The proposed method first transforms the RGB color model of an input image to the YCbCr color model, and then removes other regions than face regions to segment skin regions with the use of the pre-learned elliptical skin color distribution model. Subsequently, a CNN model-based deep learning algorithm was applied to robustly detect human face regions from the detected skin regions in the previous step. As a result, the proposed method segments face regions more efficiently than an existing method. The face region detection method proposed in this paper is expected to be usefully applied to practical areas related to multimedia data processing, such as video surveillance, target blocking, image security, visual data analysis, and object recognition and tracking.

ACS Style

Seok-Woo Jang; ByeongTae Ahn. Effective detection of exposed target regions based on deep learning from multimedia data. Multimedia Tools and Applications 2019, 79, 16609 -16625.

AMA Style

Seok-Woo Jang, ByeongTae Ahn. Effective detection of exposed target regions based on deep learning from multimedia data. Multimedia Tools and Applications. 2019; 79 (23-24):16609-16625.

Chicago/Turabian Style

Seok-Woo Jang; ByeongTae Ahn. 2019. "Effective detection of exposed target regions based on deep learning from multimedia data." Multimedia Tools and Applications 79, no. 23-24: 16609-16625.

Article
Published: 03 April 2019 in Multimedia Tools and Applications
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Due to the development of IT technology, harmful multimedia contents are spreading out. In addition, obscene and violent contents have a negative impact on children. Therefore, in this paper, we propose a multimodal approach for blocking obscene and violent video contents. Within this approach, there are two modules each detects obsceneness and violence. In the obsceneness module, there is a model that detects obsceneness based on adult and racy score. In the violence module, there are two models for detecting violence: one is the blood detection model using RGB region and the other is motion extraction model for observation that violent actions have larger magnitude and direction change. Through result of these three models, this approach judges whether or not the content is harmful. This can contribute to the blocking obscene and violent contents that are distributed indiscriminately.

ACS Style

ByeongTae Ahn; Seok-Woo Jang. Multimodal approach for multimedia injurious contents blocking. Multimedia Tools and Applications 2019, 79, 16459 -16472.

AMA Style

ByeongTae Ahn, Seok-Woo Jang. Multimodal approach for multimedia injurious contents blocking. Multimedia Tools and Applications. 2019; 79 (23-24):16459-16472.

Chicago/Turabian Style

ByeongTae Ahn; Seok-Woo Jang. 2019. "Multimodal approach for multimedia injurious contents blocking." Multimedia Tools and Applications 79, no. 23-24: 16459-16472.

Journal article
Published: 28 September 2016 in Indian Journal of Science and Technology
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Objectives: This paper proposes a technique of segmenting a target object more robustly by combining and clustering 2-dimensional and 3-dimensional features of 3D stereoscopic images coming sequentially. Methods/Statistical analysis: The proposed object detection technique first applies a disparity computation algorithm to the left and right stereo input images that are captured to extract the depth values representing the distance between a camera and a target object by each image pixel. The technique then effectively clusters color and depth features using feature similarity. Subsequently, the method excludes the background area from the image and finally detects an object of interest. Findings: The experimental results of this paper show that the proposed 3-dimensional feature-based target detection technique extracted objects more robustly than other existing object detection methods. To evaluate the performance of the proposed algorithm of detecting an object of interest, this paper uses various types of indoor and outdoor input images without any particular constraints. To compare the performance of the suggested object segmentation method, this paper defined root mean square error measure. The measure which is the scale to deal with general and particular viewpoints of image quality is often used to measure the difference between an actual value and a measured value. It is known as a good scale tool for accuracy measurement. The two existing methods use 2D features only to segment an object of interest, and thereby include many false positive errors, whereas the proposed method effectively clusters the 3-dimensional distance feature as well as 2-dimensional feature so that it segments an object of interest more accurately than the other two methods in terms of quantitative aspect. Improvements/Applications: It is expected that the proposed technique of detecting an object of interest will be used in various types of actual application areas related to multimedia contents.

ACS Style

Seok-Woo Jang; Myunghee Jung. Extracting Objects of Interest based on Three Dimensional Feature. Indian Journal of Science and Technology 2016, 9, 1 .

AMA Style

Seok-Woo Jang, Myunghee Jung. Extracting Objects of Interest based on Three Dimensional Feature. Indian Journal of Science and Technology. 2016; 9 (35):1.

Chicago/Turabian Style

Seok-Woo Jang; Myunghee Jung. 2016. "Extracting Objects of Interest based on Three Dimensional Feature." Indian Journal of Science and Technology 9, no. 35: 1.

Journal article
Published: 27 January 2016 in Multimedia Tools and Applications
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This paper proposes a method of effectively segmenting text areas that exist in images by using the texture features of various types of input images obtained in social multimedia networks with an artificial neural network. The proposed text segmentation method consists of four main steps: a step for extracting candidate text areas, a step for localizing the text areas, a step for separating the text from the background, and a step for verifying the candidate text areas. In the candidate text area extraction step, candidate blocks that have any text areas are segmented in an input image on the basis of the texture features of the candidate blocks. In the text area localization step, only strings are extracted from the candidate text blocks. In the text and background separation step, the text areas are separated from the background area in the localized text blocks. In the candidate text area verification step, an artificial neural network is used to verify whether the extracted text blocks include actual text areas and exclude non-text areas. In the experimental results, the proposed method was applied to various types of news and non-news images, and it was found that the proposed method extracted text regions more accurately than existing methods.

ACS Style

Sul-Ho Kim; Kwon-Jae An; Seok-Woo Jang; Gye-Young Kim. Texture feature-based text region segmentation in social multimedia data. Multimedia Tools and Applications 2016, 75, 12815 -12829.

AMA Style

Sul-Ho Kim, Kwon-Jae An, Seok-Woo Jang, Gye-Young Kim. Texture feature-based text region segmentation in social multimedia data. Multimedia Tools and Applications. 2016; 75 (20):12815-12829.

Chicago/Turabian Style

Sul-Ho Kim; Kwon-Jae An; Seok-Woo Jang; Gye-Young Kim. 2016. "Texture feature-based text region segmentation in social multimedia data." Multimedia Tools and Applications 75, no. 20: 12815-12829.

Journal article
Published: 13 August 2015 in Wireless Personal Communications
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Various types of harmful content such as adult images and video clips have been increasingly distributed through wired or wireless visual sensor-based networks. In this paper, we propose a new algorithm for extracting human nipple regions, representing the harmfulness of the images, by using a multilevel verification technique in visual sensor-based image data. The proposed algorithm first detects human face regions including eyes and lips from input images. The method then generates a nipple map utilizing representative features that female nipples have and detects candidate nipple regions by applying the generated nipple map to segmented skin regions followed by morphological operations. Subsequently, the proposed method selects real nipple areas after eliminating non-nipple regions at multiple levels by applying geometrical information and an average color filter to the detected candidate nipple regions. Experimental results show that the proposed method can robustly extract female nipple regions in various types of input images captured in environments where certain constraints are not imposed on.

ACS Style

Seok-Woo Jang; Myunghee Jung. Detection of Harmful Content Using Multilevel Verification in Visual Sensor Data. Wireless Personal Communications 2015, 86, 109 -124.

AMA Style

Seok-Woo Jang, Myunghee Jung. Detection of Harmful Content Using Multilevel Verification in Visual Sensor Data. Wireless Personal Communications. 2015; 86 (1):109-124.

Chicago/Turabian Style

Seok-Woo Jang; Myunghee Jung. 2015. "Detection of Harmful Content Using Multilevel Verification in Visual Sensor Data." Wireless Personal Communications 86, no. 1: 109-124.

Journal article
Published: 01 July 2015 in International Journal of Distributed Sensor Networks
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Generally, one fixed camera is used to take still or dynamic images and extract proper information from the captured images. However, the process of analyzing images through the use of one camera is very sensitive to neighboring environmental factors, such as illumination, background, and noise; thus, it is hard to guarantee precision. To extract proper information from images more precisely in visual sensor networks, this paper proposes an image-switching strategy where, among different types of installed cameras, the one camera best suited to neighboring circumstances is chosen. The proposed strategy is to first receive initial images as input data and then extract multiple features representing neighboring circumstances from the input images. Subsequently, it is to define the neighboring circumstances metric, which is the weighted sum of the extracted features, and to dynamically switch cameras to obtain images in accordance with the neighboring circumstances. The results of the experiment show that the proposed dynamic switching strategy reliably chooses, from among different cameras, the one camera that is best suited to the neighboring circumstances.

ACS Style

Seok-Woo Jang; Gye-Young Kim. A Multiple Feature-Based Image-Switching Strategy in Visual Sensor Networks. International Journal of Distributed Sensor Networks 2015, 11, 1 .

AMA Style

Seok-Woo Jang, Gye-Young Kim. A Multiple Feature-Based Image-Switching Strategy in Visual Sensor Networks. International Journal of Distributed Sensor Networks. 2015; 11 (7):1.

Chicago/Turabian Style

Seok-Woo Jang; Gye-Young Kim. 2015. "A Multiple Feature-Based Image-Switching Strategy in Visual Sensor Networks." International Journal of Distributed Sensor Networks 11, no. 7: 1.

Journal article
Published: 19 February 2015 in Cluster Computing
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Various types of three-dimensional (3D) cameras have been used to analyze real-world objects or environments effectively. However, because most existing 3D cameras capture scenes by statically using one type of camera, there may be a limit to the quality of the captured images. Therefore, in this paper, we build a hybrid camera system that combines passive triangulation (PT)- and active triangulation (AT)-based cameras and suggest a new mechanism of estimating accurate 3D depth by adaptively switching between the two types of cameras depending on the complexity of the environment. The suggested method initially uses initial input images to extract brightness and texture, which are major features representing the current state of the surrounding environment. The method subsequently generates a set of rules that dynamically select the PT- or AT-based camera, whichever can operate more suitably in the current environment, by analyzing the two extracted features. In experimental results, we demonstrate that the proposed adaptive camera-selection approach can be applied to extract 3D depth reliably with reasonable performance in terms of accuracy and time.

ACS Style

Seok-Woo Jang; Myunghee Jung. An adaptive camera-selection algorithm to acquire higher-quality images. Cluster Computing 2015, 18, 647 -657.

AMA Style

Seok-Woo Jang, Myunghee Jung. An adaptive camera-selection algorithm to acquire higher-quality images. Cluster Computing. 2015; 18 (2):647-657.

Chicago/Turabian Style

Seok-Woo Jang; Myunghee Jung. 2015. "An adaptive camera-selection algorithm to acquire higher-quality images." Cluster Computing 18, no. 2: 647-657.

Journal article
Published: 01 April 2014 in International Journal of Distributed Sensor Networks
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A ubiquitous city (u-City) is a next-generation information-based city that combines state-of-the-art IT (information technology) infrastructures and ubiquitous information services. In general, the urban facilities of a u-City can be categorized into ground and underground facilities. This paper proposes a guardrail context-awareness system based on the analysis of acceleration sensors to manage guardrails systematically. Guardrails are one of the major ground facilities in a u-City. The suggested system generates alarms when acceleration sensors on guardrails recognize a certain level of physical shock. General information on the size and direction from the acceleration sensors is transmitted to the context-awareness system. The context-awareness system analyzes the scalar magnitude of the impacted shock, acceleration values to the x, y, and z directions, gravity direction of the sensors, and threshold values. It then determines if there is pronounced physical activity on the guardrail. Experimental results show that the suggested guardrail context-awareness approach accurately recognizes guardrail shock events that occur in various environments.

ACS Style

Seok-Woo Jang; Sung-Youn Cho; Gi-Sung Lee. An Intelligent Guardrail Context-Awareness System Based on Acceleration Sensors in Ubiquitous Sensor Networks. International Journal of Distributed Sensor Networks 2014, 10, 1 .

AMA Style

Seok-Woo Jang, Sung-Youn Cho, Gi-Sung Lee. An Intelligent Guardrail Context-Awareness System Based on Acceleration Sensors in Ubiquitous Sensor Networks. International Journal of Distributed Sensor Networks. 2014; 10 (4):1.

Chicago/Turabian Style

Seok-Woo Jang; Sung-Youn Cho; Gi-Sung Lee. 2014. "An Intelligent Guardrail Context-Awareness System Based on Acceleration Sensors in Ubiquitous Sensor Networks." International Journal of Distributed Sensor Networks 10, no. 4: 1.

Journal article
Published: 01 April 2014 in International Journal of Distributed Sensor Networks
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We suggest a method of effectively detecting and classifying network traffic attacks by visualizing their IP (Internet protocol) addresses and ports and clustering the visualized ports based on their variance. The proposed approach first visualizes the IP addresses and ports of the senders and receivers into two-dimensional images. The method then analyzes the visualized images and extracts linear and/or high brightness patterns, which represent traffic attacks. Subsequently, it groups the ports using an improved clustering algorithm, allowing an artificial neural network to learn the extracted features and to automatically detect and classify normal traffic data, DDoS attacks, DoS attacks, or Internet Worms. The experiments conducted in this work prove that our suggested clustering-based algorithm effectively detects various traffic attacks.

ACS Style

Seok-Woo Jang; Gye-Young Kim; Siwoo Byun. Clustering-Based Pattern Abnormality Detection in Distributed Sensor Networks. International Journal of Distributed Sensor Networks 2014, 10, 1 .

AMA Style

Seok-Woo Jang, Gye-Young Kim, Siwoo Byun. Clustering-Based Pattern Abnormality Detection in Distributed Sensor Networks. International Journal of Distributed Sensor Networks. 2014; 10 (4):1.

Chicago/Turabian Style

Seok-Woo Jang; Gye-Young Kim; Siwoo Byun. 2014. "Clustering-Based Pattern Abnormality Detection in Distributed Sensor Networks." International Journal of Distributed Sensor Networks 10, no. 4: 1.

Journal article
Published: 01 April 2014 in International Journal of Distributed Sensor Networks
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This paper suggests a new method to extract the initial movement of moving objects in digital image data obtained in visual sensor networks. First, consecutive images are received as input. Then, the frames are partitioned into nonoverlapping square blocks of pixels, and finally, the block-based motion vectors, which represent the movement information between two adjacent frames, are extracted from the received images using a block-matching algorithm. The extracted motion vectors are subsequently applied to an outlier-elimination algorithm called robust estimation to discriminate between the background motion vectors and those of noise or moving objects. The motion vectors corresponding to the noise or objects are clustered with an unsupervised clustering algorithm to segment the individual moving objects. Experimental results prove that the proposed method can effectively detect the initial movement of objects in various indoor and outdoor environments.

ACS Style

Seok-Woo Jang; Si-Ho Cha. An Approach to Segmenting Initial Object Movement in Visual Sensor Networks. International Journal of Distributed Sensor Networks 2014, 10, 1 .

AMA Style

Seok-Woo Jang, Si-Ho Cha. An Approach to Segmenting Initial Object Movement in Visual Sensor Networks. International Journal of Distributed Sensor Networks. 2014; 10 (4):1.

Chicago/Turabian Style

Seok-Woo Jang; Si-Ho Cha. 2014. "An Approach to Segmenting Initial Object Movement in Visual Sensor Networks." International Journal of Distributed Sensor Networks 10, no. 4: 1.

Journal article
Published: 01 March 2014 in International Journal of Distributed Sensor Networks
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This paper presents a method for detecting harmful images using an active shape model (ASM) in social network services (SNS). For this purpose, our method first learns the shape of a woman's breast lines through principal component analysis and alignment, as well as the distribution of the intensity values of the corresponding control points. This method then finds actual breast lines with a learned shape and the pixel distribution. In this paper, to accurately select the initial positions of the ASM, we attempt to extract its parameter values for the scale, rotation, and translation. To obtain this information, we search for the location of the nipple areas and extract the location of the candidate breast lines by radiating in all directions from each nipple position. We then locate the mean shape of the ASM by finding the scale and rotation values with the extracted breast lines. Subsequently, we repeat the matching process of the ASM until saturation is reached. Finally, we determine objectionable images by calculating the average distance between each control point in a converged shape and a candidate breast line.

ACS Style

Sung-Il Joo; Seok-Woo Jang; Seung-Wan Han; Gye-Young Kim. ASM-Based Objectionable Image Detection in Social Network Services. International Journal of Distributed Sensor Networks 2014, 10, 1 .

AMA Style

Sung-Il Joo, Seok-Woo Jang, Seung-Wan Han, Gye-Young Kim. ASM-Based Objectionable Image Detection in Social Network Services. International Journal of Distributed Sensor Networks. 2014; 10 (3):1.

Chicago/Turabian Style

Sung-Il Joo; Seok-Woo Jang; Seung-Wan Han; Gye-Young Kim. 2014. "ASM-Based Objectionable Image Detection in Social Network Services." International Journal of Distributed Sensor Networks 10, no. 3: 1.