The Autonomous Image:Cinematic Narration and Humanism Armando J. Prats
Image Dependent Relative Formation Navigation for Autonomous Aerial Refueling: James Michael Howard
In this work I have merged most of the the behaviors which happen in real road conditions like obstacle avoidance, Target Alignment, Wall following from the previous research works and newly introduced behaviors like Tilting Avoidance taking into account the effect of centrifugal force and centripetal force during Turning, Overtaking low speed vehicle, Passing over speeded vehicle from backside and opposite side vehicle collision avoidance. We have taken into consideration the effect of speed breakers, Traffic signals and Traffic rules during design. We implemented the Newton´s Equations of Motion so that we could limit the acceleration and retardation in the bearing capacity of suspension range of the vehicle. We have used RF Sensors to find absolute target distance and target angle together with Laser sensors to find the distance of obstacles. In this work we have used the camera sensors utilizing the vast area of image processing for finding the distance of road sides from vehicle whereby it is usable in navigating through the path of varying width, single Lane or congested roads, ground or no road area. This work introduces the new technique of Multistage ANFIS (MS-ANFIS).
Processing Imagery for Low-Cost Autonomous Remote Sensing Platforms:A Geospatial Real-Time Aerial Image Display for a Low-Cost Autonomous Multispectral Remote Sensing Platform (AggieAir) Austin Jensen
This book is a collection of all the experimental results and analysis carried out on medical images of diabetic related causes. The experimental investigations have been carried out on images starting from very basic image processing techniques such as image enhancement to sophisticated image segmentation methods. This book is intended to create an awareness on diabetes and its related causes and image processing methods used to detect and forecast in a very simple way. This book is useful to researchers, Engineers, Medical Doctors and Bioinformatics researchers. Dr. Amit Kumar is the CEO and Chief Scientific Officer of BioAxis DNA Research Centre (BDRC) Pvt Ltd. His research interests are in the field of Reverse Vaccinology, Forensic Bioinformatics, DNA Forensics, Rational drug discovery, Forensic Criminology and Police Administration etc. He also serves as the Chairman of IEEE Computational Intelligence Society, IEEE Hyderabad Section and the Treasurer for IEEE Hyderabad Section. Mr. Fahimuddin Shaik is with Annamacharya Institute of Technology & Sciences (an Autonomous Institute), Rajampet, A.P., India working as an Assistant Professor in Dept. of ECE. He is BOS Member of the Department and also held a position as the Academic Council Member of the Institute. His research interests include Signal Processing, Time Series Analysis and Biomedical Image Processing. He chaired a Session at IEEE International Conference (ICMET-2010) held in Singapore on Sep11th 2010.He has authored a book called Medical Imaging in Diabetes in 2011.
This book highlights recent findings on and analyses conducted on signals and images in the area of medicine. The experimental investigations involve a variety of signals and images and their methodologies range from very basic to sophisticated methods. The book explains how signal and image processing methods can be used to detect and forecast abnormalities in an easy-to-follow manner, offering a valuable resource for researchers, engineers, physicians and bioinformatics researchers alike. Dr. Amit Kumar is the CEO and Chief Scientific Officer of BioAxis DNA Research Centre (BDRC) Pvt Ltd. His research interests are in the field of Reverse Vaccinology, Forensic Bioinformatics, DNA Forensics, Rational drug discovery, Forensic Criminology and Police Administration etc. He also serves as the Chairman of IEEE Computational Intelligence Society, IEEE Hyderabad Section and the Treasurer for IEEE Hyderabad Section. Mr. Fahimuddin Shaik is with Annamacharya Institute of Technology & Sciences (an Autonomous Institute), Rajampet, A.P., India working as an Assistant Professor in Dept. of ECE. He is BOS Member of the Department and also held a position as the Academic Council Member of the Institute. His research interests include Signal Processing, Time Series Analysis and Biomedical Image Processing. He chaired a Session at IEEE International Conference (ICMET-2010) held in Singapore on 11 September 2010. He also authored a book called Medical Imaging in Diabetes in 2011. Mr. B Abdul Rahim is with Annamacharya Institute of Technology & Sciences (an Autonomous Institute), Rajampet, A.P., India working as Professor and Head, Dept. of ECE. He received the B.E in Electronics & Communication Engineering from Gulbarga University in 1990. M.Tech (Digital Systems &Computer Electronics) from Jawaharlal Nehru Technological University in 2004 and PGDVLSI from Annamalai University, Chennai. He is BOS chairman of the department and Ex-officio Member of Academic Council of the Institute. He is a member of professional bodies like IEEE, EIE, ISTE, IACSIT, IAENG etc. His research interests include Fault Tolerant Systems, Embedded Systems and parallel processing. He achieved Best Teacher Award for his services by Lions Club, Rajampet. Sravan Kumar D is with Cognizant Technology Solutions , New Jersy, USA. He is having 8+ years of technical expertise in providing solution designs and developing Enterprise and web applications using Java/J2EE technologies and 3+ years of lead experience. He has a rack record of delivering quality Java/JEE based solutions/portals in the Travel, Education, Automobiles and Healthcare.
This edited volume contains technical contributions in the field of computer vision and image processing presented at the First International Conference on Computer Vision and Image Processing (CVIP 2016). The contributions are thematically divided based on their relation to operations at the lower, middle and higher levels of vision systems, and their applications. The technical contributions in the areas of sensors, acquisition, visualization and enhancement are classified as related to low-level operations. They discuss various modern topics - reconfigurable image system architecture, Scheimpflug camera calibration, real-time autofocusing, climate visualization, tone mapping, super-resolution and image resizing. The technical contributions in the areas of segmentation and retrieval are classified as related to mid-level operations. They discuss some state-of-the-art techniques - non-rigid image registration, iterative image partitioning, egocentric object detection and video shot boundary detection. The technical contributions in the areas of classification and retrieval are categorized as related to high-level operations. They discuss some state-of-the-art approaches - extreme learning machines, and target, gesture and action recognition. A non-regularized state preserving extreme learning machine is presented for natural scene classification. An algorithm for human action recognition through dynamic frame warping based on depth cues is given. Target recognition in night vision through convolutional neural network is also presented. Use of convolutional neural network in detecting static hand gesture is also discussed. Finally, the technical contributions in the areas of surveillance, coding and data security, and biometrics and document processing are considered as applications of computer vision and image processing. They discuss some contemporary applications. A few of them are a system for tackling blind curves, a quick reaction target acquisition and tracking system, an algorithm to detect for copy-move forgery based on circle block, a novel visual secret sharing scheme using affine cipher and image interleaving, a finger knuckle print recognition system based on wavelet and Gabor filtering, and a palmprint recognition based on minutiae quadruplets. Dr. R. Balasubramanian is currently an associate professor in the department of Computer Science and Engineering at IIT Roorkee, India. He completed his Ph.D. (Mathematics and Computer Science) from Indian Institute of Technology Madras, Chennai in 2001. His areas of interest include Computer Vision - Optical Flow Problems, Fractional Transform Theory, Wavelet Analysis, Image and Video Processing, Multimedia Security: Digital Image Watermarking and Encryption, Biometrics, Content Based Image and Video Retrieval, Hyperspectral and Microwave Imaging and Visualization, Volume Graphics. He has published over 80 papers in refereed journal and contributed 7 chapters in books. Dr. Sanjeev Kumar is an assistant professor of Mathematics at IIT Roorkee, India. His areas of interest are Computer Vision & Mathematical Imaging, Inverse Problems, and Machine Learning, He completed his Ph.D. in mathematics from IIT Roorkee in 2008. He is a member of IEEE Computer Society and International Association of Pattern Recognition and life member of ACEEE and IACSIT. He has got over 40 papers published in journals as well as conference proceedings. Dr. Partha Pratim Roy is an assistant professor of Computer Science and Engineering at IIT Roorkee, India. Before joining IIT Roorkee, he has worked with Samsung Research Institute Noida as chief engineer. He did his Ph.D. in Computer Science and Engineering (2010) from Autonomous University of Barcelona, Spain. He has got seve ral papers published in journals and international conference proceedings. His interest areas include Pattern Recognition, Multilingual Text Recognition, Biometrics, Identification and Verification of Signature, Fingerprint, etc., Computer Vision, Image Segmentation, Machine Learning, and Hidden Markov Model. Dr. Debashis Sen is Assistant Professor, Electronics & Electrical Communication Engineering at IIT Kharagpur, India. He did his Ph.D in Engineering (Image Processing) from Jadavpur University, Kolkata in 2011. He has got 12 papers published in journals, 13 in conference proceedings and 1 book chapter. His research areas are Vision Image and Video Processing, Uncertainty Handling, Bio-inspired Computation, Eye
Discover the potential applications, challenges, and opportunities of deep learning from a business perspective with technical examples. These applications include image recognition, segmentation and annotation, video processing and annotation, voice recognition, intelligent personal assistants, automated translation, and autonomous vehicles. An Introduction to Deep Learning Business Applications for Developers covers some common DL algorithms such as content-based recommendation algorithms and natural language processing. You´ll explore examples, such as video prediction with fully convolutional neural networks (FCNN) and residual neural networks (ResNets). You will also see applications of DL for controlling robotics, exploring the DeepQ learning algorithm with Monte Carlo Tree search (used to beat humans in the game of Go), and modeling for financial risk assessment. There will also be mention of the powerful set of algorithms called Generative Adversarial Neural networks (GANs) that can be applied for image colorization, image completion, and style transfer. After reading this book you will have an overview of the exciting field of deep neural networks and an understanding of most of the major applications of deep learning. The book contains some coding examples, tricks, and insights on how to train deep learning models using the Keras framework. What You Will Learn Find out about deep learning and why it is so powerful Work with the major algorithms available to train deep learning models See the major breakthroughs in terms of applications of deep learning Run simple examples with a selection of deep learning libraries Discover the areas of impact of deep learning in business Who This Book Is For Data scientists, entrepreneurs, and business developers.
This book focuses on light invariant bare hand gesture recognition while there is no restriction on the types of gestures. Observations and results have confirmed that this research work can be used to remotely control a robotic hand using hand gestures. The system developed here is also able to recognize hand gestures in different lighting conditions. The pre-processing is performed by developing an image-cropping algorithm that ensures only the area of interest is included in the segmented image. The segmented image is compared with a predefined gesture set which must be installed in the recognition system. These images are stored and feature vectors are extracted from them. These feature vectors are subsequently presented using an orientation histogram, which provides a view of the edges in the form of frequency. Thereby, if the same gesture is shown twice in different lighting intensities, both repetitions will map to the same gesture in the stored data. The mapping of the segmented images orientation histogram is firstly done using the Euclidian distance method. Secondly, the supervised neural network is trained for the same, producing better recognition results. An approach to controlling electro-mechanical robotic hands using dynamic hand gestures is also presented using a robot simulator. Such robotic hands have applications in commercial, military or emergency operations where human life cannot be risked. For such applications, an artificial robotic hand is required to perform real-time operations. This robotic hand should be able to move its fingers in the same manner as a human hand. For this purpose, hand geometry parameters are obtained using a webcam and also using KINECT. The parameter detection is direction invariant in both methods. Once the hand parameters are obtained, the fingers angle information is obtained by performing a geometrical analysis. An artificial neural network is also implemented to calculate the angles. These two methods can be used with only one hand, either right or left. A separate method that is applicable to both hands simultaneously is also developed and fingers angles are calculated. The contents of this book will be useful for researchers and professional engineers working on robotic arm/hand systems. Dr. Ankit Chaudhary received his Master of Engineering degree in Computer Science from the Birla Institute of Technology and Science, Pilani and his Ph.D. from the Central Electronics Engineering Research Institute, Council of Scientific and Industrial Research (CSIR). His research interests include vision-based applications, intelligent systems, and Robotics. Having authored sixty research publications and edited one book, Dr. Chaudhary is an Associate Editor for Computers and Electrical Engineering and serves on the Editorial Boards of several international journals. He is also a reviewer for numerous journals, including IEEE Transactions on Image Processing, IET Image Processing, Machine Vision and Applications, and Robotics and Autonomous Systems. In the past, Dr. Chaudhary was associated with the University of Iowas Department of Electrical and Computer Engineering and the Department of Computer Science BITS Pilani, also working as a Visiting Faculty/researcher at many research laboratories.