There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

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Computer Vision: Algorithms and Applications

In recent years, a variety of nonlinear dimensionality reduction techniques have been proposed that aim to address the limitations of traditional techniques such as PCA and classical scaling. The paper presents a review and systematic comparison of these techniques. The performances of the nonlinear techniques are investigated on artificial and natural tasks. The results of the experiments reveal that nonlinear techniques perform well on selected artificial tasks, but that this strong performance does not necessarily extend to real-world tasks. The paper explains these results by identifying weaknesses of current nonlinear techniques, and suggests how the performance of nonlinear dimensionality reduction techniques may be improved.

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Dimensionality Reduction: A Comparative Review

http://www.eecs.qmul.ac.uk/~sgg/papers/ShanGongMcOwan_IVC09.pdf

Facial expression recognition based on Local Binary Patterns: A comprehensive study

Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. More than 300 research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. We finish the survey by identifying promising directions for future research.

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Deep Learning for Generic Object Detection: A Survey

Face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. The paper presents a methodology for face recognition based on information theory approach of coding and decoding the face image. Proposed methodology is connection of two stages - Feature extraction using Principle Component Analysis and recognition using the feed forward back propagation Neural Network. The goal is to implement the system (model) for a particular face and distinguish it from a large number of stored faces with some real-time variations as well. The Eigenface approach uses Principal Component Analysis (PCA) algorithm for the recognition of the images. It gives us efficient way to find the lower dimensional space.

Face recognition using Eigenfaces

Summary In this paper robustness of image watermarking algorithm in transform domain using combined DWT-DCT is evaluated. Host image is decomposed up to 3-levels using discrete wavelet transform. DCT transform of selected DWT sub band coefficients blocks are computed. Binary Watermark is embedded in the DCT transform of each selected DWT sub-band. For embedding watermark bits corresponding random number sequence is added in the middle frequency coefficients of the DCT block. To extract the watermark same procedures as the embedding process is used. Correlation between mid-band coefficients and random-sequences is calculated to determine watermarked bits. The implementation of algorithm shows good imperceptibility and higher robustness against common signal processing attacks.

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Robust Watermarking Scheme Based on Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) for Copyright Protection of Digital Images

The extraction of hidden predictive information from large databases is possible with data mining. Anemia is the most common disorder of the blood. Anemia can be classified in a variety of ways, based on the morphology of RBCs, etiology, etc . In this paper we present an analysis of the prediction and classification of anemia in patients using data mining techniques. The dataset constructed from complete blood count test data from various hospitals. We have worked out with classification method C4.5 decision tree algorithm and Support vector machine which are implemented as J48 and SMO(sequential minimal optimization) in Weka. Several experiments are conducted using these algorithms. The decision ree for classification of anemia is generated which gives best possible classification of anemia based on CBC reports along with severity of anemia. We have observed that C4.5 algorithm has best performance with highest accuracy.

Classification of anemia using data mining techniques

One of the likeliest problems faced by the banks is the way the nature of money transaction takes place is not always known to the bank. One such problem consisting large amounts of money transferring through various accounts by the same person or entity is Money Laundering. Money laundering system is quite a convoluted process. It takes some understanding of the fund transferring activities at various stages. The system that works against Money laundering is Anti-Money Laundering (AML) system. The existing system for Anti-Money Laundering accepts bulk of data and converts it to large volumes reports that are tedious and topsy-turvy for a person to read without any help of software. Hence, the purpose of making decision within specified time span is de-facilitated. The basic motive behind designing a system is that it should either notify or generate alarm for Money Laundering process within time when the illicit actions are been carried out so that, some action can be taken or an decision could be made before the actual laundering task is completed. The Transaction flow analysis (TFA) is a system that facilitates the basic ideology of Anti-Money Laundering system. TFA consists of various segments those results in clusters of various sizes. However, the existing TFA system allows the data to be taken from a single entity may be bank or a financial institution. Also the cluster generated are large in volume hence the analyst sometime may find it difficult to analyze such huge amount of data. This project uses combination of TFA system with the system designed to extract customer behavior in order to make decision whether the customer is suspicious customer or not. Also this project will allow taking data as input from various sources, integrating and then performing operations on the data. General Terms Laundering

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Data Mining Model for Money Laundering Detection in Financial Domain

One of the challenges in biomedical research and clinical practice is that we need to consolidate tremendous efforts in order to use all kinds of medical data for improving work processes, to increase capacity while lessening costs and enhancing efficiencies. Very few medical centers in India have digitized their patient records. Because of less interoperability among themselves, they end up having scattered and incomplete data. Health data is proprietary and being a personal asset of the patient, its distribution or use should be accomplished only with the patient’s consent and for a specific duration. This research proposes multichain as a secure, decentralized network for storing Electronic Health Records. The architecture provides users with a holistic, transparent view of their medical history by disintermediation of trust while insuring data integrity among medical facilities. This will open up new horizons of vital trends and insights for research, innovation, and development through robust analysis. The platform focuses on an interactive dashboard containing year, month, and season wise statistics of various diseases which are used to notify the users and the medical authorities on a timely basis. Prediction of epidemics using machine learning techniques will facilitate users by providing personalized care and the medical institutions for managing inventory and procuring medicines. Vital insights like patient to doctor ratio, infant mortality rates, and prior knowledge of the forthcoming epidemics will help government institutions to analyze and plan infrastructural requirements and services to be provided.

Vivek Kshirsagar

Papers

Face recognition using Eigenfaces

Robust Watermarking Scheme Based on Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) for Copyright Protection of Digital Images

Classification of anemia using data mining techniques

Data Mining Model for Money Laundering Detection in Financial Domain

Mutichain Enabled EHR Management System and Predictive Analytics