Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.

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Data clustering: a review

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the online case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered.

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Online and off-line handwriting recognition: a comprehensive survey

IEEE Transactions on Pattern Analysis and Machine Intelligence

In this paper, we are presenting a framework for “User's Personalized Workspace” by augmenting the physical paper and digital document. The paper based interactions are seamlessly integrated with digital document based interactions for reading as a activity. For instance when user is involved in reading activity, writing becomes complimentary. In a academic system, paper based presentation mode has facilitated such exercises. Despite rendering the annotation on digital document and store it onto the database, the content of the paper encircled or underlined is used to hyperlink the document. Synchronizing a physical paper and those of digital version in seamless fashion from a user's perspective is the main objective of this work. We have also compared the existing systems which focus on one activity or the other in our proposed system.

/pdf/augmented-paper-system-a-framework-for-user-s-personalized-2oltcj16pv.pdf

Augmented paper system: A framework for User's Personalized Workspace

We are proposing a self deploying mobile sensor network which is empowered with event based relocation of redundant sensors for enhancing the quality of event sensing. Energy efficient Cell quorum based protocol is used for communication between event location and redundant sensors. Computationally light cascaded reorganization of sensor is suggested for relocation of sensors. The proposed method provides good coverage as it dynamically relocates the sensors either for avoiding coverage hole or for improving the quality of event sensing.

Self Organizing Sensor Network to Enhance Event Coverage

In this paper, we have proposed an ontology-based context-aware framework for providing intelligent services such as smart surveillance, which employ IoT technologies to ensure better quality of life in a smart city. An IoT network such as a smart surveillance system combines the working of Closed-circuit television (CCTV) cameras and various sensors to perform real-time computation for identifying threats and critical situations with the help of valuable context information. This information is perceptual in nature and needs to be converted into higher-level abstractions that can further be used for reasoning to recognize situations. Semantic abstractions for perceptual inputs are possible with the use of a multimedia ontology encoded using Multimedia Web Ontology Language (MOWL) that helps to define concepts, properties and structure of a possible environment. MOWL also allows for a dynamic modeling of real-time situations by employing Dynamic Bayesian networks (DBN), which suits the requirements of a intelligent IoT system. In this paper, we show the application of this framework in a smart surveillance system. Surveillance is enhanced by not only helping to analyze past events, but by predicting dangerous situations for which preventive actions can be taken. In our proposed approach, continuous video stream of data captured by CCTV cameras can be processed on the fly to give real-time alerts to concerned authorities. These alerts can be disseminated using e-mail, text messaging, on-screen alerts and alarms.

An Ontology-based Context-aware IoT Framework for Smart Surveillance

Cultural heritage is encoded in a variety of forms. The task of preserving heritage involves preserving the tangible and intangible resources that broadly define that heritage. A significant aspect of intangible heritage resources are performing arts which include classical dance and music. Digital heritage resources include heritage artefacts in digitized form as well as the background knowledge that puts them in perspective. We present an ontology based approach to capture and preserve the knowledge with digital heritage artefacts. Since the artefacts are generally preserved in multimedia format, we propose the use of Multimedia Web Ontology (MOWL) that supports probabilistic reasoning with media properties of domain concepts, to encode the domain knowledge. We propose an architectural framework that includes a method to construct the ontology with a labelled set of training data and use of the ontology to automatically annotate new instances of digital heritage artefacts. The annotations enable creation of a semantic navigation environment in a cultural heritage repository. We have realized a proof of concept in the domain of Indian Classical Dance and present some results.

Preservation of intangible heritage: a case-study of indian classical dance

Video skimming is one of the recently, getting popular technique for preparing preview for long watching video sequences. Most of the video skimming techniques developed in the literature uses manual intervention of users to prepare the review. Mostly the literature reported video skimming for sports and movie industries. In sports the portion of video where audience claps are used and in movie important contents are manually selected for preparing the preview. However in literature rarely any work reported for skimming of lecture video sequences. Lecture videos are generally, recorded indoor, low illuminated, noisy environment condition and contents of the scene rarely changes much. Hence designing an automatic skimming scheme is quite difficult task. In this article, we put forward an intelligent expert video skimming technique for lecture video sequences, where human intervention is not required. In the proposed scheme, initially the lecture video is segmented into a number of shots. We proposed the use of radiometric correlation technique for lecture video segmentation or finding the shot transitions. After getting the shot transitions in a video, the shots are recognized. The fuzzy K-nearest neighborhood technique is proposed to recognize the shots in a video. The shots are recognized into three categories: title slides, written texts/displayed slides and talking heads/writing hands. Three contrast based features: one existing i.e., average sharpness (AS) and two newly proposed: relative height (RH) and edge potential (EP) are used to find the contents of a frame. The frames with different contrast values are categorized to prepare the video skimming or the capsule. The media recreation is achieved by selecting a set of frames around these selected content frames. The effectiveness of the proposed scheme is demonstrated in this paper using five test sequences, including three NPTEL and two non NPTEL. It is also observed that the capsule prepared by the proposed scheme, provides a better preview of the actual sequence. The performance of the proposed scheme is tested by comparing it against three state-of-the-art techniques. The evaluation of the proposed scheme is carried out by using three evaluation measures. It is also observed that the proposed scheme is found to be better than that of the existing schemes.

Santanu Chaudhury

Papers

Augmented paper system: A framework for User's Personalized Workspace

Self Organizing Sensor Network to Enhance Event Coverage

An Ontology-based Context-aware IoT Framework for Smart Surveillance

Preservation of intangible heritage: a case-study of indian classical dance

Automatic lecture video skimming using shot categorization and contrast based features