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 a new technique is proposed for detection of corners in images of polygonal objects. The corner detector works on the principle of ‘hypothesize and test’. Initial hypotheses about the...

A Hypothesize and Test Technique for Corner Detection

In this paper, a parametric texture based video coding scheme is proposed for multi-view videos. The proposed scheme exploits the perceptual redundancy of the textured region in a statistical manner which is ignored in the multi-view video coding scheme of H.264/MVC. This statistical nature of texture is independent of the 3D scene structure. The set of most similar texture macroblocks over different frames is termed as a motion thread. In the proposed scheme, each P slice/frame of multi-view video is categorized into non-texture blocks which are encoded by H.264/MVC and texture blocks which are encoded by our novel approach. These texture based motion threads are encoded with the help of Spatio-Temporal Autoregressive (STAR) model. These motion threads covers the temporal and interview movement of macroblocks maintaining consistency while STAR model exploits the temporal and inter-view perceptual redundancy in neighborhood. The proposed scheme achieves average gain as high as 4.9 dB PSNR over H.264/MVC for given multi-view videos.

Parametric coding of texture in multi-view videos for 3DTV

A method, apparatus and system for orienting a disoriented image, and a method, apparatus and system for training a plurality of Gaussian mixture models (GMMs) to orient the disoriented image are provided. The method of training the plurality of GMMs includes: obtaining a plurality of color and texture features from the disoriented image; selecting a plurality of discriminative features from the color and texture features; calculating probabilities of each of the GMMs orienting the disoriented image, where each of the GMMs represents one of a plurality of rotation classes, and each of the rotation classes represents a rotation angle that is a multiple of a right angle. Furthermore, the system includes an electronic device that includes an embedded platform including a processor which processes the disoriented image.

Method, apparatus and system for orienting a disoriented image

E-book refers to books in Electronic form. In this paper we provide a review of the technological issues involved in design and construction of e-books. We also address the issues involved in designing e-books in Indian languages. We have proposed a novel scheme for construction and delivery of legacy documents (in Indian and other languages) in the form of e-books with the facility of hyper-linking and indexing of various logical components in the document image.

A New Look at Books: E-books

In this paper, we have proposed a fuzzy rule based system for classification of video into semantic categories. The classification scheme uses an evolutionary learning methodology to evolve a fuzzy system for use in the classification process. This evolved fuzzy classifier has the inherent capability to tackle variations and ambiguities invariably present in the video data. A novel fuzzy theoretic sheme has been suggested for extraction of key frames from a given video after shot segmentation. Frame based temporal features and spatial features obtained from key frames have been used in the classification system. We have developed an experimental system for categorization of sports video. The experimental system has yielded reasonably correct recognition results for a large number of samples.

Santanu Chaudhury

Papers

A Hypothesize and Test Technique for Corner Detection

Parametric coding of texture in multi-view videos for 3DTV

Method, apparatus and system for orienting a disoriented image

A New Look at Books: E-books

Semantic Categorization of Video: An Evolutionary Learning based Approach