Papers - THI THI ZIN
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An integrated framework for disaster event analysis in big data environments
Tin P., Zin T., Toriu T., Hama H.
Proceedings - 2013 9th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2013 255 - 258 2013
Language：Japanese Publishing type：Research paper (scientific journal) Publisher：Proceedings - 2013 9th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2013
Today world has witnessed the catastrophic consequences of natural and man-made disasters are demanding the urgent need for more research to advance fundamental knowledge and innovation for disaster prevention, mitigation and management. At the same time, the world is in the age of the Big Data revolution which holds the potential to mitigate the effects of disaster events by enabling access to critical real time information. Thus, in this paper an integrated framework for analyzing disaster events by using the Big Data analytics is proposed. The proposed framework shall address three key components to perform data organization, data integration and analysis, information presentation to users by utilizing Big Data with respect to disaster events. In doing so, the paper shall create a disaster domain-specific search engine using co-occurring theory and Markov chain concepts for preparing impacts of disaster attacks to make the society better aware of the situations. Specifically, stochastic clustering with constraints is used to automatically extract disaster events by defining the set of structural attributes. Some illustrative simulations are shown by using Big Data sources for the Great East Japan earthquake, tsunami and nuclear disaster events of 2011. © 2013 IEEE.
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A Markov chain model for image ranking system in social networks
Zin T., Tin P., Toriu T., Hama H.
Proceedings of SPIE - The International Society for Optical Engineering 9027 2013
Language：Japanese Publishing type：Research paper (scientific journal) Publisher：Proceedings of SPIE - The International Society for Optical Engineering
In today world, different kinds of networks such as social, technological, business and etc. exist. All of the networks are similar in terms of distributions, continuously growing and expanding in large scale. Among them, many social networks such as Facebook, Twitter, Flickr and many others provides a powerful abstraction of the structure and dynamics of diverse kinds of inter personal connection and interaction. Generally, the social network contents are created and consumed by the influences of all different social navigation paths that lead to the contents. Therefore, identifying important and user relevant refined structures such as visual information or communities become major factors in modern decision making world. Moreover, the traditional method of information ranking systems cannot be successful due to their lack of taking into account the properties of navigation paths driven by social connections. In this paper, we propose a novel image ranking system in social networks by using the social data relational graphs from social media platform jointly with visual data to improve the relevance between returned images and user intentions (i.e., social relevance). Specifically, we propose a Markov chain based Social-Visual Ranking algorithm by taking social relevance into account. By using some extensive experiments, we demonstrated the significant and effectiveness of the proposed social-visual ranking method.
DOI： 10.1117/12.2042621
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Knowledge based social network applications to disaster event analysis
Zin T., Tin P., Hama H., Toriu T.
Lecture Notes in Engineering and Computer Science 2202 279 - 284 2013
Language：Japanese Publishing type：Research paper (scientific journal) Publisher：Lecture Notes in Engineering and Computer Science
Today online social networking platforms such as Facebook, Flicker, Twitter, and YouTube often serve a breaking news role for natural disasters. The role of these social networks has significantly increased following the recent disasters around the world; the 2010 Philippine typhoon, the 2010 Haiti earthquake, the 2011 Brazil flood, and the 2011 Japan earthquake and tsunami. Moreover, these platforms are among the first ones to help communicate the news to a large mass of people since they are visited by millions of users regularly. In such emergency situations, detecting and analyzing hot spots or key events from the pool of information in the social networks are of major concerns in assessing the situation and in decision making. In this paper, a knowledge based event analysis framework for automatically analyzing key events is proposed by using various social network sources in case of disasters. In doing so, some mathematical modeling techniques of branching processes and Markov chain theory are explored and employed to investigate how news about these disasters spreads on the social networks and how to extract trust and reliable key information. Specifically the abnormal or suspicious topics and important events within various social network platforms are analyzed by using a set of selected messages and visual data. Finally some illustrative sample results are presented based on a limited datasets of YouTube and Twitter in the case of March 11, 2011 Japan Earthquake and Tsunami.
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Visual behavior analysis tool for consumer video surveillance
Zin T., Tin P., Toriu T., Hama H.
1st IEEE Global Conference on Consumer Electronics 2012, GCCE 2012 718 - 719 2012.12
Language：Japanese Publishing type：Research paper (scientific journal) Publisher：1st IEEE Global Conference on Consumer Electronics 2012, GCCE 2012
Consumers video surveillance systems are now being used not only for security reasons but also for better understanding consumer behaviors. In this paper, we propose a new visual behavior analysis tool for consumer video surveillance systems. This tool can be embedded in consumer videos to automatically detect and analyze unusual events. The proposed tool is developed by using a special type of Gamma Markov chain for background modeling and Petri Nets for object classification. We present some experimental results to show the effectiveness of the proposed system which will be leading to new visual behavior analysis tools for the consumers. © 2012 IEEE.
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Conceptual vision keys for consumer product images
Zin T., Tin P., Toriu T., Hama H.
1st IEEE Global Conference on Consumer Electronics 2012, GCCE 2012 435 - 436 2012.12
Language：Japanese Publishing type：Research paper (scientific journal) Publisher：1st IEEE Global Conference on Consumer Electronics 2012, GCCE 2012
In the consumer world, the ever growing image repositories in online shopping, consumer products images, consumer photos and video collections have resulted great demand of a system which can accurately retrieve similar images from image database. For this purpose, we propose a new concept of vision key for retrieving consumer product images. In our system, rather than considering an image as a whole, we consider it as a set of regions or sub-images with completely different semantic meanings. By using the properties of equivalence classes in the Markov chain, we first perform image segmentation and initial pixel grouping process. We then establish vision keys by using a Markov stationary feature. Finally, in the retrieval phase, users can interactively search candidate images which contain vision keys. In order to confirm the efficiency of our proposed method, we present the experimental results achieving on higher accuracy rates. © 2012 IEEE.
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A series of stochastic models for human behavior analysis
Zin T., Tin P., Toriu T., Hama H.
Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics 3251 - 3256 2012.12
Language：Japanese Publishing type：Research paper (scientific journal) Publisher：Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
In this paper, we propose a new approach to analyze human behaviors by using a series of stochastic models composed of a Bivariate Gamma Markov model, a two dimensional correlated Random Walk model and a finite state Markov Chain model. Specifically, the proposed method contains three modules namely: (i) image analysis module, (ii) probability analysis module and (iii) event analysis module. We model each module by a special type of stochastic processes forming a series of stochastic models for the complete behavior analysis system. This approach is more effective in utilizing modular stochastic models to describe complex behavior patterns. By assembling these modular models in a series we can design a robust model for the analysis of human behaviors. The feasibility and effectiveness of the proposed method are tested on two different datasets: a self-collected dataset and PETS 2006 dataset. © 2012 IEEE.
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A novel probabilistic video analysis for stationary object detection in video surveillance systems
Zin T., Tin P., Toriu T., Hama H.
IAENG International Journal of Computer Science 39 ( 3 ) 295 - 306 2012.9
Language：Japanese Publishing type：Research paper (scientific journal) Publisher：IAENG International Journal of Computer Science
In this paper, we propose a novel probabilistic approach for detecting and analyzing stationary objects driven visual events in video surveillance systems. This approach is based on a newly developed background modeling technique and an adaptive statistical sequential analysis method. For background modeling part, we use the concepts of periodic Markov chain theory producing a new background subtraction method in computer vision systems. We then develop an object classification algorithm which can not only classify the objects as stationary or dynamic but also eliminate the unnecessary examination tasks of the entire background regions. Finally, this paper introduces a sequential analysis model based on exponent running average measure to analyze object involved events such as whether it is either abandoned or very still person. In order to confirm our proposed method we present some experimental results tested on our own video sequences taken in international airports and some public areas in a big city. We have found that the results are very promising in terms of robustness and effectiveness.
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Motion-compensated inter-frame subtraction based on self-organized internal representation Reviewed
T. Toriu, Thi Thi Zin, H. Hama
ICIC Express Letters 6 ( 4 ) 905 - 910 2012.4
Language：English Publishing type：Research paper (scientific journal) Publisher：ICIC Express Letters
In a previous paper, we introduced a time evolution operator and based on it proposed an unsupervised learning algorithm for self-organizing an internal representation of ego-motion. In this paper, we propose a method to predict the image at the next instance using the time evolution operator generated on the basis of the internal representation of ego-motion. In addition, we propose a method of motion-compensated inter-frame subtraction. By subtracting the predicted image at the next instance from the true image, we can obtain an image that has high intensity in the region of the moving object. This method is effective even if the camera itself is in motion. We show the results of the experiments conducted using a randomly synthesized image and the real image. ICIC International © 2012.
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An innovative background model based on multiple queuing framework Reviewed
Thi Thi Zin,Pyke Tin, T. Toriu, H. Hama
ICIC Express Letters 6 ( 4 ) 1039 - 1044 2012.4
Language：English Publishing type：Research paper (scientific journal) Publisher：ICIC Express Letters
Many computer vision applications, especially video surveillance systems, highly depend on background model and foreground segmentation. In this paper, we propose an innovative background subtraction method based on a multiple queuing framework. Under this framework, both the static and dynamic background pixels are investigated by using a novel hypothesis method to establish an active real time background modeling in presence of moving foreground objects in the complex scene and adaptation of background model to gradual and sudden "once-off" background changes. Experiments were conducted by using public datasets PETS2006 and our own video sequences taken at an international airport and a university campus.
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A general framework for knowledge based human behavior understanding Reviewed
Pyke Tin, Thi Thi Zin, T. Toriu, H. Hama
ICIC Express Letters 6 ( 4 ) 899 - 904 2012.4
Language：English Publishing type：Research paper (scientific journal) Publisher：ICIC Express Letters
Human behavior understanding systems today are mainly knowledge based. And the diffusion of human actions and interactions knowledge is imperative for proper and prompt decision making processes in human behavior related crime prevention, care support systems, intelligent surveillance systems, etc. In this paper, we propose a general framework for knowledge based human behavior understanding system comprising three components: (i) knowledge acquisition, (ii) knowledge representation and modeling, and (in) knowledge base and use of knowledge. Specifically, human behavior is modeled as a Stochastic sequence of low level actions. The results of the Low Level Activities (LLA) analysis will subsequently be fed into a Knowledge Base System (KBS) that is used as High Level Activities (HLA) model. As an application, we will focus on the detection of theft and robbery related events. Unlike the traditional approach to just detecting stationary and moving objects in monitored scenes, our KBS approach detects the events based on accumulated knowledge about human and non-human objects from continuous object classification. ICIC International © 2012.
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A probability-based model for detecting abandoned objects in video surveillance systems
Zin T.
Lecture Notes in Engineering and Computer Science 2198 1246 - 1251 2012
Language：English Publishing type：Research paper (scientific journal) Publisher：Lecture Notes in Engineering and Computer Science
© 2012 Newswood Limited. All rights reserved. Detection of suspicious packages or abandoned objects is one of the most important tasks in video surveillance systems. Some recent terrorist attacks involving explosive packages left behind in many contexts such as airports, rail stations and etc. illustrate the importance of this problem. In this paper, we propose a probability-based model for robustly and efficiently detecting abandoned objects in complex environments. Specifically, we develop a new probability-based background subtraction algorithm based on combination of multiple background models for motion detection. In addition, several improvements are implemented to the background subtraction method for shadow removal and quick lighting change adaptation. We then analyze the extracted objects to classify as static or dynamic objects. After the analysis, we employ the statistical running average of the static foreground masks for event type decision making either abandoned or very still person. Finally, the robustness and efficiency of the method are tested on our video sequences and PETS2006 datasets.
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Simultaneous visual ranking and clustering using weighted multiple features
Geng T., Zin T., Tin P., Toriu T., Hama H.
ICIC Express Letters 5 ( 10 ) 3773 - 3778 2011.10
Language：Japanese Publishing type：Research paper (scientific journal) Publisher：ICIC Express Letters
In this paper, we present a new approach to visual ranking and clustering system by jointly exploring the multiple visual features information using adaptive visual similarity. We investigate how to effectively incorporate both intra-image and inter-image spatial structure information into Markov Stationary Features derived from the normalized co-occurrence matrix. The convex quadratic programming algorithm is developed to learn the weights for fusing the rankings from multiple features using color and texture. As a result, the multiple visual features based re-ranking can take more reliable information from each other. Experimental results on a real-world datasets collected from various image search engines show that our method outperforms several existing approaches which do not or weakly consider multi-feature interactions. © 2011 ISSN 1881-803X.
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Zin T., Tin P., Hama H., Nakajima S., Toriu T.
ICIC Express Letters 5 ( 10 ) 3767 - 3772 2011.10
Language：Japanese Publishing type：Research paper (scientific journal) Publisher：ICIC Express Letters
In this paper, we present a robust and effective multiple layers stochastic background modeling and novel stationary object detection method, which comprise of probability based filtering operations to detect stationary objects in a monitoring scene. Conventionally, a statistical background model is extracted by using a training sequence without foreground objects. Our method does not require starting with a period of empty scenes to facilitate the original background. In our proposed algorithm, three layers background model is constructed by using periodic Markov chain concepts. We then apply background subtractions to the current frame for objects detection and classification. Extensive experimental work has been done, results of which show that the present approach provides a better solution compared with the conventional approach, including the problem of re-active objects in real world complex environments. © 2011 ISSN 1881-803X.
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Challenges and promises in human behavior understanding research
Tin P., Zin T., Hama H., Toriu T.
ICIC Express Letters 5 ( 10 ) 3761 - 3766 2011.10
Language：Japanese Publishing type：Research paper (scientific journal) Publisher：ICIC Express Letters
Visual surveillance for human behavior understanding is an active research topic in image processing. Behavior analysis in dynamic scenes is a complex task, especially when it concerns human populated environments. Study to emulate the astonishing performances of such a perfect system as the natural and computer vision system represents, without any doubt, a real challenge. It has a wide spectrum of promising applications, including access control in special areas, human identification at a distance, crowd flux statistics and congestion analysis, detection of anomalous behaviors, and interactive surveillance using multiple cameras, etc. In this paper, the current state-of-the-art image processing methods for automatic behavior recognition techniques are described, with a focus on the surveillance of human activities in the context of transit applications. The main purpose of this paper is to provide researchers in the field with a summary of progress achieved to date and to help identify areas where further research is needed. This paper presents a comprehensive study of the research on relevant human behavior understanding methods for public safety and security surveillance. A classification table of research papers on relevant behavior analysis is presented, including behaviors, datasets, implementation details, and results. © 2011 ISSN 1881-803X.
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The method of area segmentation of cancer using multispectral image
Takahashi H., Yamada K., Tareda M., Yoshida S., Zin T., Aida T.
ICIC Express Letters 5 ( 10 ) 3859 - 3864 2011.10
Language：Japanese Publishing type：Research paper (scientific journal) Publisher：ICIC Express Letters
Transnasal endoscopy has recently become a widely accepted screening method in practical application areas of medical endoscopy. Pain tolerance and safety seem to be the greatest advantage of transnasal endoscopy when compared with those of a conventional peroral endoscopy. Although there are some disadvantages such as poor endoscopic images and less illumination caused by downsizing of scopes, as a whole, slim endoscopy is a safe and less invasive tool for screening purposes. On the other hand, flexible spectral imaging color enhancement (FICE) is one of the diagnostic methods using specific light spectra based on spectral image processing technology. FICE provides comparison of spectral images of diseased and surrounding normal areas for enhancement of the contrast by combining wavelengths with greater differences in signals. It is thus in this paper, that we propose a novel method of segmenting tumor region for medical endoscope surgery and investigation by using the narrowband images on several wavelengths. © 2011 ISSN 1881-803X.
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Small sample learning for motion estimation based on self-organized internal representation
Toriu T., Zin T., Hama H.
ICIC Express Letters 5 ( 10 ) 3921 - 3926 2011.10
Language：Japanese Publishing type：Research paper (scientific journal) Publisher：ICIC Express Letters
In the previous paper, we proposed an unsupervised learning algorithm for self-organizing an internal representation of ego-motion. We showed that motion param-eters could be topographically mapped onto a robot's internal parameter space sponta-neously. In this paper, first, we provide the theoretical rationale why the previous method can self-organize the internal representation of ego-motion. Then, on the basis of this theoretical foundation, we propose a novel learning method to estimate real motion parameters such as translation and rotation parameters. Only small samples of input and output data are needed to complete this learning. We show that this method works well by experiments using randomly synthesized image. © 2011 ISSN 1881-803X.
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Pedestrian detection based on hybrid features using near infrared images
Zin T., Tin P., Hama H.
International Journal of Innovative Computing, Information and Control 7 ( 8 ) 5015 - 5025 2011.8
Language：Japanese Publishing type：Research paper (scientific journal) Publisher：International Journal of Innovative Computing, Information and Control
This paper explores a hybrid-based method to fuse multi-slit features and Histograms of Oriented Gradients (HOG) features for pedestrian detection from Near Infrared (NIR) images. The fused feature set utilizes both the multi-slit method's capability of accurately capturing the local spatial layout of body parts (head, torso and legs) in individual frames and the HOG's capability in region information relevant to higher frequency components. The hybrid feature vector describing various types of poses is then constructed and used for detecting the pedestrians. The part based pattern matching analysis indicates that the fused features have much higher feature space separation than the pure features. Experiments with a database of NIR images show that the proposed method achieves a substantial improvement in tackling some difficult cases such as side view, back view which the conventional HOG method cannot handle. Detection and recognition performance is less computationally expensive than existing approaches. © 2011 ICIC International.
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Background modeling using special type of Markov Chain
Zin T., Tin P., Toriu T., Hama H.
IEICE Electronics Express 8 ( 13 ) 1082 - 1088 2011.8
Language：Japanese Publishing type：Research paper (scientific journal) Publisher：IEICE Electronics Express
Background modeling is important in video surveillance for extracting foreground regions from a complex environment. In this paper, we present a novel background modeling technique based on a special type of Markov Chain. The method is a substantial extension to the existing background subtraction techniques. First, a background pixel is statistically modeled by a linear regressive Gamma Markov distribution. Then, these statistical estimates are used as important parameters in background update schemes. The experimental results show that the proposed model is less sensitive to movements of the texture background and more robust for real time segmenting the foreground object accurately. © IEICE 2011.
DOI： 10.1587/elex.8.1082
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A novel concept of morphology pivotal elements for object image retrieval
Hama H., Zin T., Tin P.
International Journal of Innovative Computing, Information and Control 7 ( 7 A ) 3891 - 3901 2011.7
Language：Japanese Publishing type：Research paper (scientific journal) Publisher：International Journal of Innovative Computing, Information and Control
In this paper, we introduce a novel and simple Pivotal Element (PE) concept in mathematical morphological operations for object image retrieval schemes based on combinations of empirical and statistical analyses. Mathematical morphology is very attractive for this purpose because it efficiently deals with geometrical features like size, shape, contrast or connectivity that can be considered as image retrieval oriented features. With an optimized structure, morphological dilation is more effective to detect object spot target in image sequences. Based on the real convex figure, morphological operation with circular structure is designed in this paper. The PE is introduced to optimize the noisy background elements. The empirical threshold is decided approximately based on the statistical characters. In this aspect, two approaches for solving morphological applications to image data distributed on the unit circle are presented. In the first approach, a framework for analyzing images, called pivotal role, has been developed based on a set of concentric circles with adjustable radii, with exactly one circle centered at each pivotal image pixel. The second approach is based on Markov decision processes which operate only on grouped data. The retrieval quality is improved by dynamically changing the combinatorial coefficients that are used in equations of optimality principles. by using it as a priori knowledge of the morphology operation, it does favor to improve the algorithm's accuracy and adaptability. The experiment shows that the new concept of PE has made the morphological operations to achieve a higher retrieval efficiency and accuracy. © 2011.
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Unsupervised learning algorithm for self-organizing internal representation of ego-motion Reviewed
T. Toriu, Thi Thi Zin, H. Hama
ICIC Express Letters, Part B: Applications 2 ( 3 ) 559 - 564 2011.6
Language：English Publishing type：Research paper (scientific journal) Publisher：ICIC Express Letters, Part B: Applications
In this paper, we propose an unsupervised learning algorithm for self-organi-zing an internal representation of ego-motion. In this method, the system self-organizes the internal space for representing ego-motion without employing a supervisor. We es-tablish in our experiments that objective ego-motion parameters can be topographically mapped onto a robot's internal parameter space spontaneously using this learning. Once the learning is complete, the system can recall the representation of ego-motion from the pair of input sensation and its time derivative. One important aspect of this method is that the system does not use any knowledge of geometrical nature during image gen-eration; therefore, it is not affected by any image distortion such as that induced in omnidirectional or fish eye cameras.