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.

/pdf/online-and-off-line-handwriting-recognition-a-comprehensive-4qmt9ty596.pdf

Online and off-line handwriting recognition: a comprehensive survey

IEEE Transactions on Pattern Analysis and Machine Intelligence

Smart cameras are important components in any Human Computer Interaction. In any remote surveillance scenario, smart cameras have to take intelligent decisions to select frames of interest to minimize communication and processing overhead. A clustering based change detection algorithm has been implemented in our smart camera system for filtering frames with significant changes. In this paper we propose a platform based framework for implementing clustering based change detection algorithm using HW-SW co-design based methodology. The complete system is implemented on Xilinx XUP Virtex-II Pro FPGA board. The overall algorithm is running on PowerPC405 and some of the blocks which are computationally intensive and more frequently called are implemented as custom IP using VHDL. Total gate count of the design is 2699K.

Embedded Implementation of Change Detection Algorithm for Smart Camera Applications

In this paper, we propose an archiving scheme for heritage mural paintings. The mural paintings typically depict stories from folk-lore, mythology and history. These narratives provide content-based correlations between different pieces of art. Our e-heritage scheme for archiving the mural paintings is based on an ontology which captures the background knowledge of these narratives. Media features and patterns derived from the mural content are used to enrich the ontology with multimedia data. We have used the multimedia web ontology language as our ontology representation scheme, as it allows perceptual modelling of domain concepts in terms of their media properties, as well as reasoning with uncertainties. Besides the mural content and its knowledge, the ontology also helps encode other aspects of the mural paintings like their painting style, color, physical location, time-period, etc., which are important parameters of their preservation. We propose a framework to provide cross-modal semantic linkage between semantically annotated content of a repository of Indian mural paintings, and a collection of labelled text documents of their narratives. This framework, based on a multimedia ontology of the domain, helps preserve the cultural heritage encoded in these artefacts.

Archiving mural paintings using an ontology based approach

Novel relative relevance score for estimating brain connectivity from fMRI data using an explainable neural network approach.

Cyber physical space is potentially hosting innumerable spatio-temporal data streams due to increasing use of social networking platforms as real-time information dissemination system and world-wide deployment of sensors for continuous monitoring of physical phenomena. In this paper we address the problem of how cyber physical space can be used for sensing and responding to global calamities such as earthquake. The paper proposes a novel approach of generating online adaptive response in assisting search-and-rescue operations using situation awareness built from real-time heterogeneous spatio-temporal data streams. Online adaptive response is achieved by using agent-based cooperative task sharing and modeling agent decision making as self-organized emergent behavior based on concepts of complex adaptive system. An implemented simulation platform use concepts of situation modeling, domain task network, contract net protocol based negotiation and complex adaptive system to generate adaptive plans. Preliminary simulation results are promising as we have been able to demonstrate a repertoire of self-organized emergent behaviors.

Towards Adaptive Multi-agent Planning in Cyber Physical Space

Industrial vision systems should be capable of recognising noisy objects, partially occluded objects and randomly located and/or oriented objects. This paper considers the problem of recognition of partially occluded planar shapes using contour segment-based features. None of the techniques suggested in the literature for solving the above problem guarantee reliable results for problem instances which require memory in excess of what is available. In this paper, a heuristic search-based recognition algorithm is presented, which guarantees reliable recognition results even when memory is limited. This algorithm identifies an object, the maximum portion of whose contour is visible in a conglomerate of objects. For increasing efficiency of the method, a two-stage recognition scheme has been designed. In the first phase, a relevant subset of the known model shapes is chosen and in the second stage, matching between the unknown shape and elements of the relevant subset is attempted using the above approach. The technique is general in the sense that it can be used with any kind of contour features. To evaluate the efficiency of the method, experimentation was carried out using polygonal approximations of the object contours. Results are cited for establishing the effectiveness of the approach.

Santanu Chaudhury

Papers

Embedded Implementation of Change Detection Algorithm for Smart Camera Applications

Archiving mural paintings using an ontology based approach

Novel relative relevance score for estimating brain connectivity from fMRI data using an explainable neural network approach.

Towards Adaptive Multi-agent Planning in Cyber Physical Space

Recognition of partial planar shapes in limited memory environments