Digital images are everywhere. What can we do with those bunch of images, videos, online webcams? By analyzing and understanding the contents of those images, we can make all imaging devices, surveillance devices smarter.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
AHP validated literature review of forgery type dependent passive image forge...IJECEIAES
Nowadays, a lot of significance is given to what we read today: newspapers, magazines, news channels, and internet media, such as leading social networking sites like Facebook, Instagram, and Twitter. These are the primary wellsprings of phony news and are frequently utilized in malignant manners, for example, for horde incitement. In the recent decade, a tremendous increase in image information generation is happening due to the massive use of social networking services. Various image editing software like Skylum Luminar, Corel PaintShop Pro, Adobe Photoshop, and many others are used to create, modify the images and videos, are significant concerns. A lot of earlier work of forgery detection was focused on traditional methods to solve the forgery detection. Recently, Deep learning algorithms have accomplished high-performance accuracies in the image processing domain, such as image classification and face recognition. Experts have applied deep learning techniques to detect a forgery in the image too. However, there is a real need to explain why the image is categorized under forged to understand the algorithm’s validity; this explanation helps in mission-critical applications like forensic. Explainable AI (XAI) algorithms have been used to interpret a black box’s decision in various cases. This paper contributes a survey on image forgery detection with deep learning approaches. It also focuses on the survey of explainable AI for images.
An assistive model of obstacle detection based on deep learning: YOLOv3 for v...IJECEIAES
The World Health Organization (WHO) reported in 2019 that at least 2.2 billion people were visual-impairment or blindness. The main problem of living for visually impaired people have been facing difficulties in moving even indoor or outdoor situations. Therefore, their lives are not safe and harmful. In this paper, we proposed an assistive application model based on deep learning: YOLOv3 with a Darknet-53 base network for visually impaired people on a smartphone. The Pascal VOC2007 and Pascal VOC2012 were used for the training set and used Pascal VOC2007 test set for validation. The assistive model was installed on a smartphone with an eSpeak synthesizer which generates the audio output to the user. The experimental result showed a high speed and also high detection accuracy. The proposed application with the help of technology will be an effective way to assist visually impaired people to interact with the surrounding environment in their daily life.
REVIEW ON GENERIC OBJECT RECOGNITION TECHNIQUES: CHALLENGES AND OPPORTUNITIES IAEME Publication
Recognizing objects automatically from an image is a fundamental step for many real-world computer vision applications. It is the task of identifying an instance of object in an image or video sequence without or least human intervention and assistance. In-spite of very high complexity, human beings perform this task with very less effort and even in the state of least attention. Little effort is needed for the humans to recognize huge number of and various categories of objects in images, though ‘object’ in the image may be different with respect to size / scale, viewpoint, position or orientation. We are even able to recognize the objects from an image, when they are only partially visible or present against cluttered background. Not only this, the recognition can be for specific instance of object or object category/class. When the task is done for classes of the object it is known as Generic object recognition or object-class detection or category-level object recognition. It has been found that over the years many techniques have evolved for recognizing object classes from images, but any automated object recognition system till date has not gained this capability fully at par with human beings. This very fact makes recognition of objects from an image, the most basic and fundamental challenge in the field of computer vision research. The purpose of this study is to give an overview and categorization of the approaches used in the literature for the purpose of Generic Object Recognition and various technical advancements achieved in the field. Mostly the survey focusses on the leading work since year 2000.
Top Cited Article in Informatics Engineering Research: October 2020ieijjournal
Informatics is rapidly developing field. The study of informatics involves human-computer interaction and how an interface can be built to maximize user-efficiency. Due to the growth in IT, individuals and organizations increasingly process information digitally. This has led to the study of informatics to solve privacy, security, healthcare, education, poverty, and challenges in our environment. The Informatics Engineering, an International Journal (IEIJ) is a open access peer-reviewed journal that publishes articles which contribute new results in all areas of Informatics. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on the human use of computing fields such as communication, mathematics, multimedia, and human-computer interaction design and establishing new collaborations in these areas.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
AHP validated literature review of forgery type dependent passive image forge...IJECEIAES
Nowadays, a lot of significance is given to what we read today: newspapers, magazines, news channels, and internet media, such as leading social networking sites like Facebook, Instagram, and Twitter. These are the primary wellsprings of phony news and are frequently utilized in malignant manners, for example, for horde incitement. In the recent decade, a tremendous increase in image information generation is happening due to the massive use of social networking services. Various image editing software like Skylum Luminar, Corel PaintShop Pro, Adobe Photoshop, and many others are used to create, modify the images and videos, are significant concerns. A lot of earlier work of forgery detection was focused on traditional methods to solve the forgery detection. Recently, Deep learning algorithms have accomplished high-performance accuracies in the image processing domain, such as image classification and face recognition. Experts have applied deep learning techniques to detect a forgery in the image too. However, there is a real need to explain why the image is categorized under forged to understand the algorithm’s validity; this explanation helps in mission-critical applications like forensic. Explainable AI (XAI) algorithms have been used to interpret a black box’s decision in various cases. This paper contributes a survey on image forgery detection with deep learning approaches. It also focuses on the survey of explainable AI for images.
An assistive model of obstacle detection based on deep learning: YOLOv3 for v...IJECEIAES
The World Health Organization (WHO) reported in 2019 that at least 2.2 billion people were visual-impairment or blindness. The main problem of living for visually impaired people have been facing difficulties in moving even indoor or outdoor situations. Therefore, their lives are not safe and harmful. In this paper, we proposed an assistive application model based on deep learning: YOLOv3 with a Darknet-53 base network for visually impaired people on a smartphone. The Pascal VOC2007 and Pascal VOC2012 were used for the training set and used Pascal VOC2007 test set for validation. The assistive model was installed on a smartphone with an eSpeak synthesizer which generates the audio output to the user. The experimental result showed a high speed and also high detection accuracy. The proposed application with the help of technology will be an effective way to assist visually impaired people to interact with the surrounding environment in their daily life.
REVIEW ON GENERIC OBJECT RECOGNITION TECHNIQUES: CHALLENGES AND OPPORTUNITIES IAEME Publication
Recognizing objects automatically from an image is a fundamental step for many real-world computer vision applications. It is the task of identifying an instance of object in an image or video sequence without or least human intervention and assistance. In-spite of very high complexity, human beings perform this task with very less effort and even in the state of least attention. Little effort is needed for the humans to recognize huge number of and various categories of objects in images, though ‘object’ in the image may be different with respect to size / scale, viewpoint, position or orientation. We are even able to recognize the objects from an image, when they are only partially visible or present against cluttered background. Not only this, the recognition can be for specific instance of object or object category/class. When the task is done for classes of the object it is known as Generic object recognition or object-class detection or category-level object recognition. It has been found that over the years many techniques have evolved for recognizing object classes from images, but any automated object recognition system till date has not gained this capability fully at par with human beings. This very fact makes recognition of objects from an image, the most basic and fundamental challenge in the field of computer vision research. The purpose of this study is to give an overview and categorization of the approaches used in the literature for the purpose of Generic Object Recognition and various technical advancements achieved in the field. Mostly the survey focusses on the leading work since year 2000.
Top Cited Article in Informatics Engineering Research: October 2020ieijjournal
Informatics is rapidly developing field. The study of informatics involves human-computer interaction and how an interface can be built to maximize user-efficiency. Due to the growth in IT, individuals and organizations increasingly process information digitally. This has led to the study of informatics to solve privacy, security, healthcare, education, poverty, and challenges in our environment. The Informatics Engineering, an International Journal (IEIJ) is a open access peer-reviewed journal that publishes articles which contribute new results in all areas of Informatics. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on the human use of computing fields such as communication, mathematics, multimedia, and human-computer interaction design and establishing new collaborations in these areas.
Mining and Clustering the Feature Similarities of Images on Smart PhoneIIRindia
With the fame of visual sensor on smart phone devices, (i.e. camera) it becomes a habit for many people to capture photos everyday and everywhere. This led to the rapid developing of more personal images and becomes a nuisance to the users in storing and organizing them, which had not been used before. Luckily, cloud storage provided a comprehensive solution at the right moment, and it facilitates the synchronization and sharing of images acquired. However, organizing this bulk number of personal images is still a tedious and difficult task. Common needs in photo organization may involve tagging, destroying replicated or same images, and collecting photos into albums. In our proposed system, we target to provide a features similarity images, face detection and recognition, avoid redundancy on smart mobile application which makes use of existing sensors and related technologies to help users to manage replicate or same images more effectively. By sharpening the power of cloud computing for SSIM algorithm, our system significantly reduce the time spent on managing photos in a neat and simple way which reduce user stress and increase user experience.
Explore the comprehensive ML Campaign by GDSC MMCOE, spanning 4 days of immersive learning.
From foundational ML concepts to cutting-edge topics like Deep Learning, Gen AI, and Computer Vision, this PPT contains all the content that was used to teach various topics of AIML in 4 days of ML Campaign
Educate to elevate the students and make them prepare for the industrial requirements of IT Industry and all their future endeavors. And also interested both in academic and research sector for self-development.
Attendance management system using face recognitionIAESIJAI
Traditional attendance systems consist of registers marked by teachers, leading to human error and a lot of maintenance. Time consumption is a key point in this system. We wanted to revolutionize the digital tools available in today's time i.e., facial recognition. This project has revolutionized to overcome the problems of the traditional system. Face recognition and marking the present is our project. A database of all students in the class is kept in single folder, and attendance is marked if each student's face matches with one of the stored faces. Otherwise, the face is ignored and not marked for attendance. In our project, face detection (machine learning) is used.
Cross Pose Facial Recognition Method for Tracking any Person's Location an Ap...ijtsrd
In todays world, there are number of existing methods for facial recognition. These methods are based on frontal view face data. There are few methods which are based on non-frontal view face recognition method. In most of the face recognition algorithm, œFeature space approach is used. In this approach, different feature vectors are extracted from face. These distances are compared to determine matches. In this paper, it is proposed that how any person can be located in a campus or in a city using a cross pose face recognition method. This paper is focusing on three parts 1) generation of multi-view images 2) comparison of images 3) showing the actual location of a person. Sanjay D. Sawaitul"Cross Pose Facial Recognition Method for Tracking any Persons Location an Approach" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7186.pdf http://www.ijtsrd.com/computer-science/data-processing/7186/cross-pose-facial-recognition-method-for--tracking-any-persons-location-an-approach/sanjay-d-sawaitul
Top Cited Articles in Computer Graphics and Animationijcga
Computer graphics and animation has become a key technology in determining future research and development activities in many academic and industrial branches. The aim of this journal is to be an international peer-reviewed open access forum for scientific and technical presentations and discus the latest advances in Computer graphics and animation.
Performance Comparison of Face Recognition Using DCT Against Face Recognition...CSCJournals
In this paper, a face recognition system using simple Vector quantization (VQ) technique is proposed. Four different VQ algorithms namely LBG, KPE, KMCG and KFCG are used to generate codebooks of desired size. Euclidean distance is used as similarity measure to compare the feature vector of test image with that of trainee images. Proposed algorithms are tested on two different databases. One is Georgia Tech Face Database which contains color JPEG images, all are of different size. Another database used for experimental purpose is Indian Face Database. It contains color bitmap images. Using above VQ techniques, codebooks of different size are generated and recognition rate is calculated for each codebook size. This recognition rate is compared with the one obtained by applying DCT on image and LBG-VQ algorithm which is used as benchmark in vector quantization. Results show that KFCG outperforms other three VQ techniques and gives better recognition rate up to 85.4% for Georgia Tech Face Database and 90.66% for Indian Face Database. As no Euclidean distance computations are involved in KMCG and KFCG, they require less time to generate the codebook as compared to LBG and KPE
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Explore the comprehensive ML Campaign by GDSC MMCOE, spanning 4 days of immersive learning.
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Attendance management system using face recognitionIAESIJAI
Traditional attendance systems consist of registers marked by teachers, leading to human error and a lot of maintenance. Time consumption is a key point in this system. We wanted to revolutionize the digital tools available in today's time i.e., facial recognition. This project has revolutionized to overcome the problems of the traditional system. Face recognition and marking the present is our project. A database of all students in the class is kept in single folder, and attendance is marked if each student's face matches with one of the stored faces. Otherwise, the face is ignored and not marked for attendance. In our project, face detection (machine learning) is used.
Cross Pose Facial Recognition Method for Tracking any Person's Location an Ap...ijtsrd
In todays world, there are number of existing methods for facial recognition. These methods are based on frontal view face data. There are few methods which are based on non-frontal view face recognition method. In most of the face recognition algorithm, œFeature space approach is used. In this approach, different feature vectors are extracted from face. These distances are compared to determine matches. In this paper, it is proposed that how any person can be located in a campus or in a city using a cross pose face recognition method. This paper is focusing on three parts 1) generation of multi-view images 2) comparison of images 3) showing the actual location of a person. Sanjay D. Sawaitul"Cross Pose Facial Recognition Method for Tracking any Persons Location an Approach" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7186.pdf http://www.ijtsrd.com/computer-science/data-processing/7186/cross-pose-facial-recognition-method-for--tracking-any-persons-location-an-approach/sanjay-d-sawaitul
Top Cited Articles in Computer Graphics and Animationijcga
Computer graphics and animation has become a key technology in determining future research and development activities in many academic and industrial branches. The aim of this journal is to be an international peer-reviewed open access forum for scientific and technical presentations and discus the latest advances in Computer graphics and animation.
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This work is part of the SAME 2013 result, in the collaboration of Computer Vision Laboratory University of Padjadjaran INDONESIA and Cognition and Interaction Laboratory Informatics Research Center University of Skövde SWEDEN.
http://blogs.unpad.ac.id/setiawanhadi/?cat=8
http://informatika.unpad.ac.id/visilab/
http://www.his.se/en/Research/informatics/Interaction-Lab/Cognition--Interaction-Lab/
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Session Overview
-------------------------------------------
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https://alandix.com/academic/papers/synergy2024-epistemic/
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19. What research we conduct
related with image?
IP
• Image Processing
• Image IN – Image OUT
IA
• Image Analysis
• Image IN – Measurement OUT
IU
• Image Understanding
• Image IN – High level description OUT
20. Our Research Theme
Visual Recognition and
Scene Understanding
How computer can describe
information content of a digital image
• Objects Detection and Recognition
• Activity Detection and Recognition
• Scene Detection and recognition
36. Current Research Project (2014)
• Digital imaging system in visual detection of
cancer and its implementation on biomedical
image. Funded by Excellent Research
University (PUPT) Grant. 2nd Year.
37. Current Work (2014)
• Biomedical Imaging (ongoing)
– Early detection of skin cancer using artificial neural
network. PIC: Ebby Syabilal Rasyad. Started: 18
February 2014.
• Face Processing and Analysis (ongoing)
– 3D Face recognition using Kinect. PIC: Riyo Maulana.
Started: 18 February 2014
• Human Activity Detection and Recognition
(ongoing)
– 3D Detection and tracking of hand beating on static
object. PIC: Giga Ahmad Rizki Jatmiko. Started: 5
February 2014
38. Current Publication (2014)
• Biomedical Imaging
– Prostate cancer classification by analyzing digital biomedical
image. PIC: Gyan Aryadi/Dian Nursantika. Presented on
Mathematics National Seminar (SNM) 2014, University of
Indonesia, February 2014
• Face Processing and Analysis
– Facegen modeler parameter estimation for 3D Indonesian face
generating. PIC: Setiawan Hadi/Dian Nursantika. Presented on
Mathematics National Seminar (SNM) 2014, University of
Indonesia, February 2014
• Human Activity Detection and Recognition (ongoing)
– Mathematical Model for Vision-Based Recognition of Human
Gestures. PIC: Setiawan Hadi. Submited to International
Congress of Mathematician (ICM) 2014, Korea, August 2014
51. Previous Research Project (<=2013)
2013
Digital Imaging System In Visual Detection of Cancer and Its Implementation On
Biomedical Image, funded by Excellent Research University Grant.
2012
Lips Print Biometrics Identification, funded by Short Term Research from Informatics
Department
Virtual Kendang: An Interactive Tool for Sundanesse Cultural Learning and
Preservation, funded by University Self-sufficient Special Grant
Exploration of New Computer Vision Methods for Semantic Analysis and
Understanding Digital Image Contents, funded thru Fundamental Research Project
Directorate of Higher Education Ministry of Education and Culture
2011
Exploration of New Computer Vision Methods for Semantic Analysis and
Understanding Digital Image Contents, funded thru Fundamental Research Project
Directorate of Higher Education Ministry of Education and Culture
2006-2007
Intelligent Framework for Image Understanding, funded by Postgraduate Research
Project (Hibah Pasca) from Institute of Technology Bandung
2003-2008
DeWa : A Multiaspect Approach for Multiple Face Detection in Complex Scene Digital
Image, funded by PhD Scholarship Scheme Indonesian Government
52. Previous Publications (<=2013)
2013
1.
2.
3.
Detection and Classification of Breast Cancer Using Connected
Component Labeling Method, Accepted in SKIM 2013
Performance Evaluation of Curvilinear Structure Removal Methods in
Mammogram Image Analysis, In Proceedings of ICTS 2013
Comparison Operator Performance of Edge Detection for Support
Identification Process of Lips Print Biometric System, in Proceedings of
SNATi 2013
2012
1.
2.
3.
4.
Vehicle Detection using Computer Vision Method, Published in Computer
Science Journal (Jurnal Ilmu Komputer), University of Pelita Harapan
(UPH), Vol 8 No 2 March 2012, pp. 215-223, ISSN 1412-9523.
Integrating computer vision technique to support Tsunami Early Warning
System, In Proceedings of International Conference on Systems and
Informatics (ICSAI), Yantai China, 19-20 May 2012
Using RoboRealms for Computer Vision Implementation, In Proceedings
of Mathematics National Conference, July 2012.
Segmentation using Watershed Algorithms, In Proceedings of
Mathematics National Conference, July 2012
53. Previous Publications (<=2013)
2011
1. Teknik Aljabar Citra Untuk Meningkatkan Kualitas Citra Dijital,
Seminar Nasional Aljabar 2011, 30 April 2011, UNPAD Bandung
2. Teknologi Visi Komputer Untuk Sistem Peringatan Dini Bencana
Tsunami, Dalam Prosiding Seminar Nasional Ilmu Komputer
SEMINASIK GAMA 2011
3. Deteksi Objek Kendaraan Pada Citra Dijital Jalan Raya
Menggunakan Metode Visi Komputer, 30 Nov 2011, Jurnal
Ilmiah Ilmu Komputer, Untuk edisi Maret 2012, UPH
4. Identifikasi Kualitas Buah Strawberry Menggunakan Metode
Image Moments dan Bounding Box, Dipresentasikan pada
Seminar Nasional Unpad-UI, Jatinangor 2011, diterbitkan pada
Jurnal Matematika Integratif edisi 2011-2012
54. Previous Publications (<=2013)
2010
1. Deteksi Tekstur Convex Hull Pada Benda Menggunakan
Algoritma Graham Scan, Konferensi Nasional Matematika XV,
Universitas Negeri Manado, Juli 2010
2. Pendeteksian Kontur Berdasarkan Relasi Angular Antara Vektor
Normal Dan Vektor Ke Pusat Gravitasi, Seminar Nasional UIUNPAD, Januari 2010
2008
1. Quantitative Measurement Of Face Detection Algorithm
Performance, Int. Conf. on Information & Communication
Technology and Systems (ICTS), Surabaya 4-5 August 2008 ISSN
1858-1633
2. Non-rigid Digital Imaging Enhancement using Mathematical
Morphology, presented on Konferensi Nasional Matematika 2427 Juli 2008 UNSRI Palembang
55. Previous Publications (<=2013)
2007
1.
2.
3.
4.
5.
6.
New Approach of Face Detection Technique for Improving Quality of
Biometric Identification, Simposium Kebudayaan Indonesia Malaysia
SKIM, 29-31 Mei 2007
Small Device Implementation of DeWa Algorithm, ICEEI Int. Conf. on
Electrical Engineering and Informatics 2007 in Bandung, 16-18 June
2007
A generic multiaspect framework for multiple face detection in
complex image, in Proc. of Intl. Conf. on Soft Computing, Intelligent
Systems and Information Technology (ICSIIT) 2007, Bali, 26-27 July
2007 ISBN 978-979-756-250-2
Mathematical Morphology as Preprocessing Step for Face
Identification, presented on SEAMS International Conference on
Mathematics and Its Applications 2007 in UGM Yogyakarta 24-27 July
2007
Pemodelan Distribusi Warna Kulit Untuk Klasifikasi Piksel Pada Ruang
Warna Krominan, Jurnal GEMATEK March 2007
DeWa : A multiaspect approach for multiple face detection in
complex scene digital image, Journal ITB in Information and
Communication Technology, April 2007
56. Previous Publications (<=2013)
2006
1.
2.
3.
4.
5.
6.
7.
Clustering Techniques Implementation for Detecting Faces in Digital
Image, Int. Conf. of Information and Communication Technology
(ICTS) 2006, ITS Surabaya Indonesia, 29 August 2006
Pendeteksian Wajah pada Citra Dijital Menggunakan Teknik
Partitioning dengan Fungsi Validitas Otomatis, Mathematics National
Conference XIII 2006, Unnes Semarang 24-27 Juli 2006
Parametric Skin Distribution Modeling using Elliptical Boundary
Model, in proceedings of Int. Conf. on Statistics and Mathematics
(ICOMS) 2006, Bandung, 19-21 June 2006
Generating Skin Distribution Map of Face Images, Seminar Nasional
Aplikasi Teknologi Informasi SNATI 2006, Yogyakarta UII, 17 Juni 2006
Human Face Cropping in Digital Image, Seminar on Intelligence and
Technology Application SITIA 2006, Surabaya ITS 2-3 Mei 2006
Interpolation Methods in Image Processing, West Java Mathematics
Regional Conference KONFERDA 2006, Bandung 29 April 2006
Implementasi Learning Vector Quantization, Perceptron dan Self
Organizing Maps Untuk Pendeteksian Wajah pada Citra Dua Dimensi,
Metode Retinex dan Implementasinya pada Dunia Citra Dijital, pada
Prosiding Semnas Matematika UNPAR, 9 Sep 2006 ISSN 1907-3909
57. Previous Publications (<=2013)
<=2005
1.
2.
3.
4.
5.
6.
Intelligence Skin Model Selection for Face Detection, in proceedings
International Conference on Intelligence System (ICIS) 2005, Kuala
Lumpur 2005, 1-3 December 2005
Mathematical Model of Skin Color for Face Detection, in proceedings
International Conference on Applied Mathematics ICAM 2005, ITB,
Bandung, Indonesia, 22-26 August 2005
Face Sketch Recognition to Support Security Investigation, in
Proceedings of Indonesian Cryptographics and Information Security
Conference INA-CISC 2005, Jakarta Indonesia, 30-31 March 2005
Evaluasi Kinerja Konversi Citra Ke Citra Titik Acak. Seminar Nasional
Komputer dan Sistem Intelijen (KOMMIT) 2002, Universitas
Gunadarma.
Moire Patterns Implementation in Measuring Similarity of Images.
Presented in Indonesia German International Conference, Bandung,
July 11-13 2001
Model Komputasi Matematika untuk Estimasi Pusat dan Radius Busur
Lingkaran. Presented in Seminar Nasional Matematika Institut
Teknologi Surabaya, 2 November 2000
58. References
1. Anil Jain, 50 Years Biometrics Research:
Almost Solved, The Unsolved, and The
Unexplored, 2013
2. Tian Lan, From Flat to Hierarchical: Modeling
Structures in Visual Recognition, 2013
3. Antonio Torralba, Object Recognition and
Scene Understanding, 2008
4. TIIMNet Project, 2014