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

There are monocular depth cues present in images or videos that aid in depth perception in two-dimensional images or videos. Our objective is to preserve the defocus depth cue present in the videos along with the salient regions during compression application. A method is provided for opportunistic bit allocation during the video compression using visual saliency information comprising both the image features, such as color and contrast, and the defocus-based depth cue. The method is divided into two steps: saliency computation followed by compression. A nonlinear method is used to combine pure and defocus saliency maps to form the final saliency map. Then quantization values are assigned on the basis of these saliency values over a frame. The experimental results show that the proposed scheme yields good results over standard H.264 compression as well as pure and defocus saliency methods.

Defocus cue and saliency preserving video compression

The ever-increasing demand of smart sensors for Internet of things applications, drove the change in outlook towards smart sensor system design. This paper focuses on smart sensing approach using single pristine tin-oxide (SnO 2 ) sensing film based sensor. It is a novel attempt to not only identify but also quantify the bio-marker gases like ammonia (NH 3 ), carbon monoxide (CO) and hydrogen sulphide (H 2 S) using single sensor with intelligent pattern recognition algorithms and its readout circuit. The presented work uses temperature modulated gas sensor response obtained at different concentrations for mentioned gases with in-house developed sensor for gas discrimination and quantification. This work uses wavelet features with Independent Component Analysis (ICA) based feature extraction technique and proposed Adaptive Gradient Boosting classifier for gas sensor response pattern recognition. The proposed approach provides gas detection accuracy 93.28%. Further, our approach uses maximum likelihood fit to quantify concentration of the predicted gas and reported the average MSE for all three gases is 0.11, which makes the proposed work smart to mitigate the cross-selectivity of MOS based smart sensor design in presence of drift and noise.

Smart Gas Sensing using Single MOS Gas Sensor with Adaptive Gradient Boosting

Reconstruction based recognition of scenes with multiple repeated components

In this paper we present a technique that estimates the Structure from Motion (SFM) in a recursive fashion. Traditionally successful SFM algorithms take the set of images and estimate the scene geometry and camera positions either using incremental algorithms or the global algorithms and do the refinement process [2] to reduce the reprojection error. In this work it is assumed that we don’t have complete image set at the start of the reconstruction process, unlike most of the traditional approaches present in the literature. It is assumed that the set of images come in at the regular intervals and we recursively perform the SFM on the incoming set of images and update the previously reconstructed structure with the structure estimated from the current set of images. The proposed system has been tested on two datasets which consist of 12 images and 60 images respectively and reconstructions obtained show the validity of our proposed technique.

Santanu Chaudhury

Papers

Defocus cue and saliency preserving video compression

Smart Gas Sensing using Single MOS Gas Sensor with Adaptive Gradient Boosting

Reconstruction based recognition of scenes with multiple repeated components

Recursive Structure from Motion