Bir Bhanu

  • Video Bioinformatics


    • Springer
    • 16 Décembre 2015

    The advances of live cell video imaging and high-throughput technologies for functional and chemical genomics provide unprecedented opportunities to understand how biological processes work in subcellularand multicellular systems. The interdisciplinary research field of Video Bioinformatics is defined by BirBhanu as the automated processing, analysis, understanding, data mining, visualization, query-basedretrieval/storage of biological spatiotemporal events/data and knowledge extracted from dynamic imagesand microscopic videos. Video bioinformatics attempts to provide a deeper understanding of continuousand dynamic life processes.Genome sequences alone lack spatial and temporal information, and video imaging of specific moleculesand their spatiotemporal interactions, using a range of imaging methods, are essential to understandhow genomes create cells, how cells constitute organisms, and how errant cells cause disease. The bookexamines interdisciplinary research issues and challenges with examples that deal with organismal dynamics,intercellular and tissue dynamics, intracellular dynamics, protein movement, cell signaling and softwareand databases for video bioinformatics.Topics and Featureso Covers a set of biological problems, their significance, live-imaging experiments, theory andcomputational methods, quantifiable experimental results and discussion of results.o Provides automated methods for analyzing mild traumatic brain injury over time, identifying injurydynamics after neonatal hypoxia-ischemia and visualizing cortical tissue changes during seizureactivity as examples of organismal dynamicso Describes techniques for quantifying the dynamics of human embryonic stem cells with examplesof cell detection/segmentation, spreading and other dynamic behaviors which are important forcharacterizing stem cell healtho Examines and quantifies dynamic processes in plant and fungal systems such as cell trafficking,growth of pollen tubes in model systems such as Neurospora Crassa and Arabidopsiso Discusses the dynamics of intracellular molecules for DNA repair and the regulation of cofilintransport using video analysiso Discusses software, system and database aspects of video bioinformatics by providing examples of5D cell tracking by FARSIGHT open source toolkit, a survey on available databases and software,biological processes for non-verbal communications and identification and retrieval of moth imagesThis unique text will be of great interest to researchers and graduate students of Electrical Engineering,Computer Science, Bioengineering, Cell Biology, Toxicology, Genetics, Genomics, Bioinformatics, ComputerVision and Pattern Recognition, Medical Image Analysis, and Cell Molecular and Developmental Biology.The large number of example applications will also appeal to application scientists and engineers.Dr. Bir Bhanu is Distinguished Professor of Electrical & C omputer Engineering, Interim Chair of theDepartment of Bioengineering, Cooperative Professor of Computer Science & Engineering, and MechanicalEngineering and the Director of the Center for Research in Intelligent Systems, at the University of California,Riverside, California, USA.Dr. Prue Talbot is Professor of Cell Biology & Neuroscience and Director of the Stem Cell Center and Core atthe University of California Riverside, California, USA.

  • Most biometric systems employed for human recognition require physical contact with, or close proximity to, a cooperative subject. Far more challenging is the ability to reliably recognize individuals at a distance, when viewed from an arbitrary angle under real-world environmental conditions. Gait and face data are the two biometrics that can be most easily captured from a distance using a video camera.
    This comprehensive and logically organized text/reference addresses the fundamental problems associated with gait and face-based human recognition, from color and infrared video data that are acquired from a distance. It examines both model-free and model-based approaches to gait-based human recognition, including newly developed techniques where the both the model and the data (obtained from multiple cameras) are in 3D. In addition, the work considers new video-based techniques for face profile recognition, and for the super-resolution of facial imagery obtained at different angles. Finally, the book investigates integrated systems that detect and fuse both gait and face biometrics from video data.
    Topics and features:

    Discusses a framework for human gait analysis based on Gait Energy Image, a spatio-temporal gait representation
    Evaluates the discriminating power of model-based gait features using Bayesian statistical analysis
    Examines methods for human recognition using 3D gait biometrics, and for moving-human detection using both color and thermal image sequences
    Describes approaches for the integration face profile and gait biometrics, and for super-resolution of frontal and side-view face images
    Introduces an objective non-reference quality evaluation algorithm for super-resolved images
    Presents performance comparisons between different biometrics and different fusion methods for integrating gait and super-resolved face from video

    This unique and authoritative text is an invaluable resource for researchers and graduate students of computer vision, pattern recognition and biometrics. The book will also be of great interest to professional engineers of biometric systems.

  • This timely text/reference presents a broad overview of advanced deep learning architectures for learning effective feature representation for perceptual and biometrics-related tasks. The text offers a showcase of cutting-edge research on the use of convolutional neural networks (CNN) in face, iris, fingerprint, and vascular biometric systems, in addition to surveillance systems that use soft biometrics. Issues of biometrics security are also examined.Topics and features: addresses the application of deep learning to enhance the performance of biometrics identification across a wide range of different biometrics modalities; revisits  deep learning for face biometrics, offering insights from neuroimaging, and provides comparison with popular CNN-based architectures for face recognition; examines deep learning for state-of-the-art latent fingerprint and finger-vein recognition, as well as iris recognition; discusses deep learning for soft biometrics, including approaches for gesture-based identification, gender classification, and tattoo recognition; investigates deep learning for biometrics security, covering biometrics template protection methods, and liveness detection to protect against fake biometrics samples; presents contributions from a global selection of pre-eminent experts in the field representing academia, industry and government laboratories.Providing both an accessible introduction to the practical applications of deep learning in biometrics, and a comprehensive coverage of the entire spectrum of biometric modalities, this authoritative volume will be of great interest to all researchers, practitioners and students involved in related areas of computer vision, pattern recognition and machine learning.