Ruben Ray

Bio: Ruben Ray is an academic researcher from Post Graduate Institute of Medical Education and Research. The author has contributed to research in topics: Digital watermarking & Transfer RNA. The author has an hindex of 8, co-authored 14 publications receiving 211 citations. Previous affiliations of Ruben Ray include University of Washington & University of Calcutta.

Adaptive Thresholding: A comparative study

Abstract: — With the growth of image processing applications, image segmentation has become an important part of image processing. The simplest method to segment an image is thresholding. Using the thresholding method, segmentation of an image is done by fixing all pixels whose intensity values are more than the threshold to a foreground value. The remaining pixels are set to a background value. Such technique can be used to obtain binary images from grayscale images. The conventional thresholding techniques use As previously noted, recently a number of worksa global threshold for all pixels, whereas done adaptive thresholding changes the threshold value dynamically over the image. This paper offers a comparative study on adaptive thresholding techniques to choose the accurate method for binarizing an image based on the contrast, texture, resolution etc. of an image. Keywords —Threshold, Otsu’s Method, Kapur’s threshold, Rosin’s threshold, Entropy based thresholding,

Video Shot Boundary Detection: A Review

Abstract: Video image processing is a technique to handle the video data in an effective and efficient way. It is one of the most popular aspects in the video and image based technologies such as surveillance. Shot change boundary detection is also one of the major research areas in video signal processing. Previous works have developed various algorithms in this domain. In this paper, a brief literature survey is presented that establishes an overview of the works that has been done previously. In this paper we have discussed few algorithms that were proposed previously which also includes histogram based, DCT based and motion vector based algorithms as well as their advantages and their limitations.

Cellular Automata based Encrypted ECG-hash Code Generation: An Application in Inter-human Biometric Authentication System

Abstract: —In this modern era, biometrics incorporate various mechanisms to recognize inimitable features of human beings by utilizing their biological and evident features. This paper proposes a novel technique for constructing a resilient and secure biometric recognition system. In this paper, an ECG-hash code of two distinct individuals has been formed by taking dot product of electrocardiogram (ECG) feature matrices of two persons located at two different sites at respective databases. The validity of the system increases as samples from both persons, between whom the transmission takes place, are essential. Besides, electrocardiogram is such a unique feature of an individual that could not be compromised at any circumstance as contradictory to other features like fingerprints, face recognition etc. Moreover, the ECG-hash code is encrypted using rule vector of cellular automata that gives better security in terms of randomness of generated cipher text.

Watermarking in motion vector for security enhancement of medical videos

Abstract: With the advancement in technology, it becomes easy for some individuals to use digital data without the permission of the owner. To increase authentication and security of copyrights, digital watermarking was introduced. Medical videos contain very significant information about the condition of the patient. A good watermarking scheme should always contain very less distortion. Videos generally contain huge temporal redundancy, which demands the use of motion vector estimation technique in order to remove the temporal redundancy. In this work, an image was watermarked inside the motion vector of two consecutive frames of an echo-cardiograph video. Quality analysis of the recovered video with the original video proves the robustness of the proposed scheme.

High Payload Watermarking using Residue Number System

Abstract: Information hiding or data hiding, also known as watermarking, has become a part and parcel of covert communication and copyright protection. Maximizing watermark payload is a major challenge for watermark researchers. To overcome this issue, we have proposed a new color image watermarking technique, using residue number system (RNS). RNS refers to a large integer using a set of smaller integers which relies on the Chinese remainder theorem of modular arithmetic for its operation. The proposed method takes pixel values from three watermark images and embeds them into the main cover image. Experimental results presented in this paper shows that the watermark can be successfully embedded and extracted from an image, without distorting the original image using the proposed technique. The high peak signal to noise ratio (PSNR) and payload values claims the robustness of the proposed method.

The cytogenetics of Neurospora.

Abstract: Publisher Summary This chapter concerns genetically significant aspects of Neurospora cytology, and the relation between genes and chromosomes, with special emphasis on chromosome rearrangements. In addition to reviewing the published literature, numerous results have been presented. Many of these are cytological, appearing under various headings. The chapter is also concerned with the morphology and identification of individual chromosomes. New genetic results concerning chromosome rearrangements and general characteristics of aberrations are given and a brief description of each known rearrangement has been provided. The chapter also deals briefly with other topics such as accessory, the genetic control of recombination, and the cytoplasmic genome. Neurospora possesses several favorable features compared to the more conventional organisms that are used in cytogenetic research, and these, in part, compensate for the small size of its chromosomes. All four products of individual meioses survive. Progeny can be obtained either as random meiotic products, as unordered tetrads, or as ordered tetrads whose linear spore arrangement reflects the events of meiosis. The spores show high viability and germination. The vegetative (somatic) part of the life cycle is haploid. Duplications are more readily identified as partial diploids against a haploid background than as partial triploids against a diploid background. Somatic variants can be obtained in pure culture. Any somatic cell can serve as a germ cell. For Aspergillus, only a few rearrangements have been recognized in other eukaryotic microorganisms, where usually they are more difficult to detect than in Neurospora, with its pigmented spores. Failure to recognize an existing rearrangement can lead to spurious conclusions regarding linkage, recombination, interference, preferential segregation, or the presence of synthetic lethal genes.

Object-Level Video Advertising: An Optimization Framework

Abstract: In this paper, we present new models and algorithms for object-level video advertising. A framework that aims to embed content-relevant ads within a video stream is investigated in this context. First, a comprehensive optimization model is designed to minimize intrusiveness to viewers when ads are inserted in a video. For human clothing advertising, we design a deep convolutional neural network using face features to recognize human genders in a video stream. Human parts alignment is then implemented to extract human part features that are used for clothing retrieval. Second, we develop a heuristic algorithm to solve the proposed optimization problem. For comparison, we also employ the genetic algorithm to find solutions approaching the global optimum. Our novel framework is examined in various types of videos. Experimental results demonstrate the effectiveness of the proposed method for object-level video advertising.

Evolution, Current Challenges, and Future Possibilities in ECG Biometrics

Abstract: Face and fingerprint are, currently, the most thoroughly explored biometric traits, promising reliable recognition in diverse applications. Commercial products using these traits for biometric identification or authentication are increasingly widespread, from smartphones to border control. However, increasingly smart techniques to counterfeit such traits raise the need for traits that are less vulnerable to stealthy trait measurement or spoofing attacks. This has sparked interest on the electrocardiogram (ECG), most commonly associated with medical diagnosis, whose hidden nature and inherent liveness information make it highly resistant to attacks. In the last years, the topic of ECG-based biometrics has quickly evolved toward the commercial applications, mainly by addressing the reduced acceptability and comfort by proposing new off-the-person, wearable, and seamless acquisition settings. Furthermore, researchers have recently started to address the issues of spoofing prevention and data security in ECG biometrics, as well as the potential of deep learning methodologies to enhance the recognition accuracy and robustness. In this paper, we conduct a deep review and discussion of 93 state-of-the-art publications on their proposed methods, signal datasets, and publicly available ECG collections. The extracted knowledge is used to present the fundamentals and the evolution of ECG biometrics, describe the current state of the art, and draw conclusions on prior art approaches and current challenges. With this paper, we aim to delve into the current opportunities as well as inspire and guide future research in ECG biometrics.

Big Data Knowledge System in Healthcare

Abstract: The health care systems are rapidly adopting large amounts of data, driven by record keeping, compliance and regulatory requirements, and patient care. The advances in healthcare system will rapidly enlarge the size of the health records that are accessible electronically. Concurrently, fast progress has been made in clinical analytics. For example, new techniques for analyzing large size of data and gleaning new business insights from that analysis is part of what is known as big data. Big data also hold the promise of supporting a wide range of medical and healthcare functions, including among others disease surveillance, clinical decision support and population health management. Hence, effective big data based knowledge management system is needed for monitoring of patients and identify the clinical decisions to the doctor. The chapter proposes a big data based knowledge management system to develop the clinical decisions. The proposed knowledge system is developed based on variety of databases such as Electronic Health Record (EHR), Medical Imaging Data, Unstructured Clinical Notes and Genetic Data. The proposed methodology asynchronously communicates with different data sources and produces many alternative decisions to the doctor.