Mathematical Optimization in Computer Graphics and Vision,
To count the blood cells in a clinical laboratory different two methods and techniques are used. One is the old conventional method of cell counting under the microscope and the other is to produce cell counting report by latest but very expensive haematology analyser machine. But both these methods have their own different drawbacks. The main problem with the method of counting manually under the microscope is accuracy, this method needs a real experienced laboratory technician who is trained enough to produce an accurate cell counting report, and even if the laboratory technician if well trained and experienced still we one cant neglect the chance of error in the report due to error caused by apparatus, personal errors, statistical errors etc. While on the other hand latest haematology analyser somehow error free and fast but it is widely unavailable and very expensive machine and the countries like Pakistan are resource less to provide it in every hospital laboratory in country. So as a result of the problem this research based project proposed a new method of cell counting which is easy to use, don't need fully experienced men to handle, accurate and economical.
Vision is perhaps the most important sense for humans. It consists of processing images of scenes so as to make explicit what needs to be known about them. Among the different complex tasks accomplished by the Human Visual System, the tasks of representing and understanding the content of an observed scene are fundamental; these tasks, indeed, allow to humans the interpretation of their surroundings. Computer vision aims to build robust and reusable vision systems that act taking into account the visual content of images and videos. Just as learning is an essential component of biological visual systems, the design of machine vision systems that learn and adapt represent an important challenge in modern computer vision research. This book focuses on some key ingredients useful to represent images for scene recognition, image retrieval and content based learning.
The shape analysis performed by our brain is very complex, but it is performed so quickly and in such a natural way that we usually don't think about this intrinsic complexity. Hence, although “to see and understand” seems to be natural and straightforward, the design of versatile and robust computer vision systems is a tough task. Among all different aspects underlying visual information, the shape of objects plays a very important role, which will be emphasized in this book, by showing how such a fundamental feature can be efficiently used to solve a wide range of relevant problems in Computer Vision, Pattern Recognition and Computer Graphics, in both the 2-D and 3-D realms.
Computer vision-based gender detection from facial image is a challenging and important task for computer vision-based researchers. The automatic gender detection from face image has potential applications in visual surveillance and human-computer interaction sys- tems (HCI). Human faces provide important visual information for gender perception. The system described in this book can automatically detect face from input images and the detected facial area is taken as region of interest (ROI). Some techniques and algorithm of Image Processing is applied on that ROI which identifies the gender of the face image.The experimental reseult described on chapter 4 in this book finds the accuracy of the system is more than 80%.
The second edition of this accepted reference work has been updated to reflect the rapid developments in the field and now covers both 2D and 3D imaging. Written by expert practitioners from leading companies operating in machine vision, this one-stop handbook guides readers through all aspects of image acquisition and image processing, including optics, electronics and software. The authors approach the subject in terms of industrial applications, elucidating such topics as illumination and camera calibration. Initial chapters concentrate on the latest hardware aspects, ranging from lenses and camera systems to camera-computer interfaces, with the software necessary discussed to an equal depth in later sections. These include digital image basics as well as image analysis and image processing. The book concludes with extended coverage of industrial applications in optics and electronics, backed by case studies and design strategies for the conception of complete machine vision systems. As a result, readers are not only able to understand the latest systems, but also to plan and evaluate this technology. With more than 500 images and tables to illustrate relevant principles and steps.
Over the last decades, numerous face recognition methods have been proposed to overcome the problem limited by the current technology associated with face variations. Among them, the PCA/LDA method has known to be one of the best face recognition methods. In this thesis, we implement a face recognition method, using PCA&LDA Algorithm and compare these both algorithms with respect to time, memory and accuracy. Face recognition has received substantial attention from researches in biometrics, pattern recognition field and computer vision communities. Face recognition can be applied in Security measure at Air ports, Passport verification, Criminals list verification in police department, Visa processing , Verification of Electoral identification and Card Security measure at ATM’s
The present volume is to acquaint the readers with the mathematical models extensively used not only in natural and engineering sciences, but also in a variety of disciplines of social sciences. The mathematical models find tremendous applications through their use in facilitating economic evaluation and in a number of decision-making contexts including risk assessment, service planning and capacity modeling. In the present era, the applications of computational techniques to the disciplines of linear algebra, probability and statistics are of great importance. These three areas of continuous mathematics are critical in many parts of computer science, including machine learning, scientific computing, computer vision, computational biology, computational finance, natural language processing, and computer graphics. The specialized language for mathematical computation is learned by MATLAB, and is known for the fact that how the language can be used for computation and for graphical output.
Knowledge discovery in data is called data mining. Many data mining techniques require classification and clustering. Large data sets are available nowadays in the world and fast approaches of classification or clustering becomes a tedious work with large data sets. For example, computer vision, text mining, semantic web mining, natural language processing etc., require non-parametric pattern recognition methods. This book describes fast approaches to discover knowledge from large data sets.This book deals with condensing of large data and also preserving essential information in the data. This book describes many efficient fast classifiers and clustering methods which are based on density information in the large data sets. It describes to resolve vagueness and uncertainty that is present in large data sets using combination principles of rough sets and fuzzy sets. Approaches in this book are adaptive and they can be applied in many machine learning methods in many domains.
In view of the immense and rapidly increasing quantity of user-created 3D content and real-world scene data publicly available on the internet, as well as the widespread popularity of data acquisition devices such as low-cost depth cameras, it has become convenient to acquire or access data that can potentially be utilized for modeling. In this book, we explore how data-driven optimization can be adapted to the essential tasks of functionality modeling and reasoning. We first discuss the conceptual innovations inherent to model synthesis through data-driven optimization, along with the advantages of and considerations in its application. We then tackle various challenging functionality modeling and reasoning problems within our novel framework. In the context of computer graphics, we devise data-driven optimization methods for virtual world modeling and virtual character modeling. In the context of computer vision, we devise data-driven optimization methods for 3D surface reconstruction from images.
The year is 2015, MEMS (Microelectromechanical Systems) technology is a growing field that requires more automative tools to lower the cost of production. Current industry standards of tele-operated 3D manipulated MEMS parts to create new devices are labor intensive and expensive process. Using computer vision as a main feedback tool to recognize parts on a chip, it is possible to program a close loop system to instruct a computer to pick and assemble parts on the chip without the aid of a user. To make this process a viable means, new chip designs, robotic systems and computer vision algorithms working along side with motion controllers were developed. This work shows in detail the hardware, software and processes in place to make it possible.
Advances in Computer Methods for Systematic Biolog y
A wide selection of stereo matching algorithms have been evaluated for the purpose of creating a collision avoidance module. Varying greatly in the accuracy, a few of the algorithms were fast enough for further use. Two computer vision libraries, OpenCV and MRF, were evaluated for their implementations of various stereo matching algorithms. In addition OpenCV provides a wide variety of functions for creating sophisticated computer vision programs and were evaluated on this basis as well. Two low-power platforms, The Pandaboard and the Beaglebone Black, were evaluated as viable platforms for developing a computer vision module on top. In addition they were compared to an Intel platform as a reference. Based on the results gathered, a fast, but simple, collision detector could be made using the simple block matching algorithm found in OpenCV. A more advanced detector could be built using semi-global stereo matching. These were the only implementations that were fast enough. The other energy minimization algorithms (Graph cuts and belief propagation) did produce good disparity maps, but were too slow for any realistic collision detector.