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Three-Dimensional Face Point Cloud Smoothing Based on Modified Anisotropic Diffusion Method
Suryo Adhi Wibowo,Sungshin Kim 한국지능시스템학회 2014 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.14 No.2
This paper presents the results of three-dimensional face point cloud smoothing based on a modified anisotropic diffusion method. The focus of this research was to obtain a 3D face point cloud with a smooth texture and number of vertices equal to the number of vertices input during the smoothing process. Different from other methods, such as using a template D face model, modified anisotropic diffusion only uses basic concepts of convolution and filtering which do not require a complex process. In this research, we used 6D point cloud face data where the first 3D point cloud contained data pertaining to noisy x-, y-, and z-coordinate information, and the other 3D point cloud contained data regarding the red, green, and blue pixel layers as an input system. We used vertex selection to modify the original anisotropic diffusion. The results show that our method has improved performance relative to the original anisotropic diffusion method.
A Comparative Study of Phishing Websites Classification Based on Classifier Ensembles
Bayu Adhi Tama,이경현 한국멀티미디어학회 2018 멀티미디어학회논문지 Vol.21 No.5
Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions, and to name but a few. One way to build anti-phishing detection mechanism is to construct classification algorithm based on machine learning techniques. The objective of this paper is to compare different classifier ensemble approaches, i.e. random forest, rotation forest, gradient boosted machine, and extreme gradient boosting against single classifiers, i.e. decision tree, classification and regression tree, and credal decision tree in the case of website phishing. Area under ROC curve (AUC) is employed as a performance metric, whilst statistical tests are used as baseline indicator of significance evaluation among classifiers. The paper contributes the existing literature on making a benchmark of classifier ensembles for web phishing detection.
Discrete Wavelet Transform for Watermarking Three-Dimensional Triangular Meshes from a Kinect Sensor
Suryo Adhi Wibowo,Eun Kyeong Kim,Sungshin Kim 한국지능시스템학회 2014 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.14 No.4
We present a simple method to watermark three-dimensional (3D) triangular meshes that have been generated from the depth data of the Kinect sensor. In contrast to previous methods, which maintain the shape of 3D triangular meshes and decide the embedding place, requiring calculations of vertices and their neighbors, our method is based on selecting one of the coordinate axes. To maintain shape, we use discrete wavelet transform and constant regularization. We know that the watermarking system needs the information to be embedded; we used a text to provide that information. We used geometry attacks such as rotation, scales, and translation, to test the performance of this watermarking system. Performance parameters in this paper include the vertices error rate (VER) and bit error rate (BER). The results from the VER and BER indicate that using a correction term before the extraction process makes our system robust to geometry attacks.
Learning to Prevent Inactive Student of Indonesia Open University
( Bayu Adhi Tama ) 한국정보처리학회 2015 Journal of information processing systems Vol.11 No.2
The inactive student rate is becoming a major problem in most open universities worldwide. In Indonesia, roughly 36% of students were found to be inactive, in 2005. Data mining had been successfully employed to solve problems in many domains, such as for educational purposes. We are proposing a method for preventing inactive students by mining knowledge from student record systems with several state of the art ensemble methods, such as Bagging, AdaBoost, Random Subspace, Random Forest, and Rotation Forest. The most influential attributes, as well as demographic attributes (marital status and employment), were successfully obtained which were affecting student of being inactive. The complexity and accuracy of classification techniques were also compared and the experimental results show that Rotation Forest, with decision tree as the base-classifier, denotes the best performance compared to other classifiers.
Implicit Surface Representation of Three-Dimensional Face from Kinect Sensor
Suryo Adhi Wibowo(수료 아드히 워보워),Eun-Kyeong Kim(김은경),Sungshin Kim(김성신) 한국지능시스템학회 2015 한국지능시스템학회논문지 Vol.25 No.4
Kinect sensor has two output data which are produced from red green blue (RGB) sensor and depth sensor, it is called color image and depth map, respectively. Although this device’s prices are cheapest than the other devices for three-dimensional (3D) reconstruction, we need extra work for reconstruct a smooth 3D data and also have semantic meaning. It happened because the depth map, which has been produced from depth sensor usually have a coarse and empty value. Consequently, it can be make artifact and holes on the surface, when we reconstruct it to 3D directly. In this paper, we present a method for solving this problem by using implicit surface representation. The key idea for represent implicit surface is by using radial basis function (RBF) and to avoid the trivial solution that the implicit function is zero everywhere, we need to defined on-surface point and off-surface point. Based on our simulation results using captured face as an input, we can produce smooth 3D face and fill the holes on the 3D face surface, since RBF is good for interpolation and holes filling. Modified anisotropic diffusion is used to produced smoothed surface.
Collaborative Learning based on Convolutional Features and Correlation Filter for Visual Tracking
Suryo Adhi Wibowo,이한수,김은경,김성신 제어·로봇·시스템학회 2018 International Journal of Control, Automation, and Vol.16 No.1
One of the most important and challenging research topics in the area of computer vision is visual object tracking, which is relevant to many real-world applications. Recently, discriminative correlation filters (DCF) have been demonstrated to overcome the problems in visual object tracking efficiently. So far, only single-resolution feature maps have been utilized in DCF. Owing to this limitation, the potential of DCF has not been exploited. Moreover, convolutional features have demonstrated a better performance for visual tracking than histogram of oriented gradients (HOG) features and color features. Based on these facts, in this paper, we propose collaborative learning based on multi-resolution feature maps for DCF, employing convolutional features. Further, the confidence score, which represents the location of the target object, is selected from various candidates based on certain rules. In addition, the continuous filters are trained to handle the variations of appearance of the target. The extensive experimental results obtained using VOT2015 and OTB-100 benchmark datasets show that the proposed algorithm performs favorably against state-of-the-art tracking algorithms.
Bayu Adhi Tama,김도현,김규원,김수환,이승철 대한이비인후과학회 2020 Clinical and Experimental Otorhinolaryngology Vol.13 No.4
This study presents an up-to-date survey of the use of artificial intelligence (AI) in the field of otorhinolaryngology, considering opportunities, research challenges, and research directions. We searched PubMed, the Cochrane Central Register of Controlled Trials, Embase, and the Web of Science. We initially retrieved 458 articles. The exclusion of non-English publications and duplicates yielded a total of 90 remaining studies. These 90 studies were divided into those analyzing medical images, voice, medical devices, and clinical diagnoses and treatments. Most studies (42.2%, 38/90) used AI for image-based analysis, followed by clinical diagnoses and treatments (24 studies). Each of the remaining two subcategories included 14 studies. Machine learning and deep learning have been extensively applied in the field of otorhinolaryngology. However, the performance of AI models varies and research challenges remain.