Search results for "Landscape of Next Generation Sequencing Using Pattern Recognition"
Items we need to order for you
Sparse Representation, Modeling and Learning in Visual Recognition
This unique text/reference presents a comprehensive review of the state of the art in sparse representations, modeling and learning. The book examines both the theoretical foundations and details of algorithm implementation, highlighting the practical application of compressed sensing research in visual recognition and computer vision. Topics and features: describes sparse recovery approaches, robust and efficient sparse representation, and large-scale visual recognition; covers feature representation and learning, sparsity induced similarity, and sparse representation and learning-based classifiers; discusses low-rank matrix approximation, graphical models in compressed sensing, collaborative representation-based classification, and high-dimensional nonlinear learning; includes appendices outlining additional computer programming resources, and explaining the essential mathematics required to understand the book.
R 3,032.31
R 763.00
Feature Extraction Approaches for Optical Character Recognition
Since the 1950's character recognition has been an active field of research for computer scientists worldwide. The main reason is that character recognition is not only an interesting area of theoretical research with relevance to many pattern recognition sub-fields, but also a very needed and useful real life application. Making computers able to read would allow for substantial savings in terms of the costs for data entry, mail processing, form processing and many other similar situations. Every realistic character recognition system requires a feature extraction step in order to properly operate. This book is a large-scale review of the feature extraction approaches for character recognition based on literature review and experimental results. An original classification system is described, which groups feature extraction methods depending on their theoretical approach. The developed classification system aids in comparison and analysis of the feature extraction methods.
R 2,421.00
Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction
This book constitutes the thoroughly refereed post-workshop proceedings of the Third IAPR TC3 Workshop on Pattern Recognition of Social Signals in Human-Computer-Interaction, MPRSS 2014, held in Stockholm, Sweden, in August 2014, as a satellite event of the International Conference on Pattern Recognition, ICPR 2014. The 14 revised papers presented focus on pattern recognition, machine learning and information fusion methods with applications in social signal processing, including multimodal emotion recognition, user identification, and recognition of human activities.
R 1,768.57
Progress in Computer Recognition Systems
This book highlights recent research on computer recognition systems, one of the most promising directions in artificial intelligence. Offering the most comprehensive study on this field to date, it gathers 36 carefully selected articles contributed by experts on pattern recognition. Presenting recent research on methodology and applications, the book offers a valuable reference tool for scientists whose work involves designing computer pattern recognition systems. Its target audience also includes researchers and students in computer science, artificial intelligence, and robotics.
R 6,228.00
R 1,665.00
Artificial Intelligence in Recognition and Classification of Astrophysical and Medical Images
This book presents innovative techniques in recognition and classification of astrophysical and medical images. Coverage includes: image standardization and enhancement; region-based methods for pattern recognition in medical and astrophysical images; advanced information processing using statistical methods; and feature recognition and classification using spectral method.
R 5,499.03
Image Analysis and Recognition
The two-volume set LNCS 6753/6754 constitutes the refereed proceedings of the 8th International Conference on Image and Recognition, ICIAR 2011, held in Burnaby, Canada, in June 2011. The 84 revised full papers presented were carefully reviewed and selected from 147 submissions. The papers are organized in topical sections on image and video processing; feature extraction and pattern recognition; computer vision; color, texture, motion and shape; tracking; biomedical image analysis; biometrics; face recognition; image coding, compression and encryption; and applications.
R 1,583.84
Vein Pattern Recognition
As one of the most promising biometric technologies, vein pattern recognition (VPR) is quickly taking root around the world and may soon dominate applications where people focus is key. Among the reasons for VPR’s growing acceptance and use: it is more accurate than many other biometric methods, it offers greater resistance to spoofing, it focuses on people and their privacy, and has few negative cultural connotations. Vein Pattern Recognition: A Privacy-Enhancing Biometric provides a comprehensive and practical look at biometrics in general and at vein pattern recognition specifically. It discusses the emergence of this reliable but underutilized technology and evaluates its capabilities and benefits. The author, Chuck Wilson, an industry veteran with more than 25 years of experience in the biometric and electronic security fields, examines current and emerging VPR technology along with the myriad applications of this dynamic technology. Wilson explains the use of VPR and provides an objective comparison of the different biometric methods in use today—including fingerprint, eye, face, voice recognition, and dynamic signature verification. Highlighting current VPR implementations, including its widespread acceptance and use for identity verification in the Japanese banking industry, the text provides a complete examination of how VPR can be used to protect sensitive information and secure critical facilities. Complete with best-practice techniques, the book supplies invaluable guidance on selecting the right combination of biometric technologies for specific applications and on properly implementing VPR as part of an overall security system.
R 2,051.74
New Systems and Architectures for Automatic Speech Recognition and Synthesis
It is an established tradition that researchers from many countries get together on the average every three years for a two week Advanced Studies Institute on Automatic Speech Recognition and Synthesis. According to ASI policies the Institute is financed by NATO. This book contains the texts of lectures and papers contributed by the attendees of the ASI which was held July 2 - 14, 1984, at Bonas, Gers, France. Focussed on New Systems and Architectures for Automatic Speech Recognition and Synthesis, this book is divided into 4 parts: (a) Review of ba8ic algorithm8 (b) SY8tem architecture and VLSI for automatic Speech (c) Software 8Y8tem8 for automatic 8peech recognition, (d) Speech 8ynthe8i8 and phonetic8. Due to the international nature of the Institute, the readers will find in this book different styles, different points of view and applications to different languages. This reflects also some characteristics of the International Association for Pattern Recognition (!APR) whose technical committee on Speech Recognition has organized this ASI. Proposed contributions have been reviewed by an Editorial Committee composed of W. Ainsworth (Kent), R. Bisiani (Pittsburgh), J. P.Haton (Nancy), W. Hess (Munich), J. L. Houle (Montreal), P. Laface (Turin), R. Moore (Malvern), H. Niemann (Erlangen) and J. Ohala (Berkeley). Typesetting of the book was performed using SYMSET facilities developed entirely by the Department of Computer Science at Concordia University. Special thanks are due to L. Lam, H. Monkiewicz and L. Thiel.
R 3,316.14
Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction
This book constitutes the refereed post-workshop proceedings of the 5th IAPR TC9 Workshop on Pattern Recognition of Social Signals in Human-Computer-Interaction, MPRSS 2018, held in Beijing, China, in August 2018. The 10 revised papers presented in this book focus on pattern recognition, machine learning and information fusion methods with applications in social signal processing, including multimodal emotion recognition and pain intensity estimation, especially the question how to distinguish between human emotions from pain or stress induced by pain is discussed.
R 1,528.59
R 1,667.55
Pattern Recognition in Chemistry
Analytical chemistry of the recent years is strongly influenced by automation. Data acquisition from analytica~ instruments - and some times also controlling of instruments - by a computer are principally solved since many years. Availability of microcomputers made these tasks also feasible from the economic point of view. Besides these basic applications of computers in chemical measurements scientists developed computer programs for solving more sophisticated problems for which some kind of "intelligence" is usually supposed to be necessary. Harm less numerical experiments on this topic led to passionate discussions about the theme "which jobs cannot be done by a computer but only by human brain ?~. If this question is useful at all it should not be ans wered a priori. Application of computers in chemistry is a matter of utility, sometimes it is a social problem, but it is never a question of piety for the human brain. Automated instruments and the necessity to work on complex pro blems enhanced the development of automatic methods for the reduction and interpretation of large data sets. Numerous methods from mathematics, statistics, information theory, and computer science have been exten sively investigated for the elucidation of chemical information; a new discipline "chemometrics" has been established. Three different approaches have been used for computer-assisted interpretations of chemical data. 1. Heuristic methods try to formu late computer programs working in a similar way as a chemist would solve the problem. 2.
R 1,545.79
Motion-Based Recognition
Motion-based recognition deals with the recognition of an object and/or its motion, based on motion in a series of images. In this approach, a sequence containing a large number of frames is used to extract motion information. The advantage is that a longer sequence leads to recognition of higher level motions, like walking or running, which consist of a complex and coordinated series of events. Unlike much previous research in motion, this approach does not require explicit reconstruction of shape from the images prior to recognition. This book provides the state-of-the-art in this rapidly developing discipline. It consists of a collection of invited chapters by leading researchers in the world covering various aspects of motion-based recognition including lipreading, gesture recognition, facial expression recognition, gait analysis, cyclic motion detection, and activity recognition. Audience: This volume will be of interest to researchers and post- graduate students whose work involves computer vision, robotics and image processing.
R 1,569.73
Energy Minimization Methods in Computer Vision and Pattern Recognition
This volume constitutes the refereed proceedings of the 11th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2017, held in Venice, Italy, in October/November 2017.The 37 revised full papers were carefully reviewed and selected from 51 submissions. The papers are organized in topical sections on Clustering and Quantum Methods; Motion and Tracking; Image Processing and Segmentation; Color, Shading and Reflectance of Light; Propagation and Time-evolution; and Inference, Labeling, and Relaxation.
R 2,221.35
Challenges and Applications for Hand Gesture Recognition
Due to applications in recent electronic appliances and pervasive devices, Automated Hand Gesture Recognition (HGR) is of particular interest nowadays. HGR developments have come a long way from the traditional Sign Language Recognition (SLR) systems to innovations such as wearable sensor based electronic devices. A large amount of research on HGR is still on the way, both from the industry and academia, that is working towards introducing more practical gesture recognition systems and associated algorithms. This book highlights state-of-the-art practices in the direction of HGR research. It is organized into five coherent heads: HGR introduction, modalities, and challenges, practical hand segmentation schemes capable of working under cluttered backgrounds, gesture recognition schemes targeting different acquisition mechanisms, solutions sticking to different, practiced methodologies, and conclusions from the HGR works witnessed so far and future options. The book is ideal for undergraduates, researchers at all levels, and the developer community as it provides a basis of information about HGR, as well as new and in-depth research in the field.
R 8,311.42
Applied Graph Theory in Computer Vision and Pattern Recognition
This book presents novel graph-theoretic methods for complex computer vision and pattern recognition tasks. It presents the application of graph theory to low-level processing of digital images, presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, and provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks.
R 3,033.04
Energy Minimization Methods in Computer Vision and Pattern Recognition
Overthelastdecades,energyminimizationmethods havebecomeanestablished paradigm to resolve a variety of challenges in the ?elds of computer vision and pattern recognition. While traditional approaches to computer vision were often based on a heuristic sequence of processing steps and merely allowed very l- ited theoretical understanding of the respective methods, most state-of-the-art methods are nowadays based on the concept of computing solutions to a given problem by minimizing respective energies. This volume contains the papers presented at the 7th International Conf- ence on Energy Minimization Methods in Computer Vision and Pattern Rec- nition (EMMCVPR 2009), held at the University of Bonn, Germany, August 24-28, 2009. These papers demonstrate that energy minimization methods have become a mature ?eld of research spanning a broad range of areas from discrete graph theoretic approaches and Markov random ?elds to variational methods and partial di?erential equations. Application areas include image segmentation and tracking, shape optimization and registration, inpainting and image deno- ing, color and texture modeling, statistics and learning.Overall, we received 75 high-quality double-blind submissions. Based on the reviewer recommendations, 36paperswereselectedforpublication,18asoraland18asposterpresentations. Both oral and poster papers were attributed the same number of pages in the conference proceedings. Furthermore, we were delighted that three leading experts from the ?elds of computer vision and energy minimization, namely, Richard Hartley (C- berra, Australia), Joachim Weickert (Saarbruc .. ken, Germany) and Guillermo Sapiro(Minneapolis,USA)agreedtofurtherenrichtheconferencewithinspiring keynote lectures.
R 1,590.03
Siegfried Matthies; Galina W. Vinel; Kurt Helming: Standard Distributions in Texture Analysis. Volume 2
No detailed description available for "STANDARD DISTRIBUTIONS IN TEXTURE ANALYSIS V. 2 SDTA".
R 6,071.00
Computer Applications for Web, Human Computer Interaction, Signal and Image Processing, and Pattern Recognition
This book comprises the refereed proceedings of the International Conferences, SIP, WSE, and ICHCI 2012, held in conjunction with GST 2012 on Jeju Island, Korea, in November/December 2012. The papers presented were carefully reviewed and selected from numerous submissions and focus on the various aspects of signal processing, image processing, and pattern recognition, and Web science and engineering, and human computer interaction.
R 2,189.03
Data Complexity in Pattern Recognition
Automatic pattern recognition has uses in science and engineering, social sciences and finance. This book examines data complexity and its role in shaping theory and techniques across many disciplines, probing strengths and deficiencies of current classification techniques, and the algorithms that drive them. The book offers guidance on choosing pattern recognition classification techniques, and helps the reader set expectations for classification performance.
R 5,439.08
Gesture Recognition
This book presents a selection of chapters, written by leading international researchers, related to the automatic analysis of gestures from still images and multi-modal RGB-Depth image sequences. It offers a comprehensive review of vision-based approaches for supervised gesture recognition methods that have been validated by various challenges. Several aspects of gesture recognition are reviewed, including data acquisition from different sources, feature extraction, learning, and recognition of gestures.
R 6,228.00
Generative AI with LangChain
Go beyond foundational LangChain documentation with detailed coverage of LangGraph interfaces, design patterns for building AI agents, and scalable architectures used in production—ideal for Python developers building GenAI applicationsKey FeaturesBridge the gap between prototype and production with robust LangGraph agent architecturesApply enterprise-grade practices for testing, observability, and monitoringBuild specialized agents for software development and data analysisPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThis second edition tackles the biggest challenge facing companies in AI today: moving from prototypes to production. Fully updated to reflect the latest developments in the LangChain ecosystem, it captures how modern AI systems are developed, deployed, and scaled in enterprise environments. This edition places a strong focus on multi-agent architectures, robust LangGraph workflows, and advanced retrieval-augmented generation (RAG) pipelines.You'll explore design patterns for building agentic systems, with practical implementations of multi-agent setups for complex tasks. The book guides you through reasoning techniques such as Tree-of -Thoughts, structured generation, and agent handoffs—complete with error handling examples. Expanded chapters on testing, evaluation, and deployment address the demands of modern LLM applications, showing you how to design secure, compliant AI systems with built-in safeguards and responsible development principles. This edition also expands RAG coverage with guidance on hybrid search, re-ranking, and fact-checking pipelines to enhance output accuracy.Whether you're extending existing workflows or architecting multi-agent systems from scratch, this book provides the technical depth and practical instruction needed to design LLM applications ready for success in production environments.What you will learnDesign and implement multi-agent systems using LangGraphImplement testing strategies that identify issues before deploymentDeploy observability and monitoring solutions for production environmentsBuild agentic RAG systems with re-ranking capabilitiesArchitect scalable, production-ready AI agents using LangGraph and MCPWork with the latest LLMs and providers like Google Gemini, Anthropic, Mistral, DeepSeek, and OpenAI's o3-miniDesign secure, compliant AI systems aligned with modern ethical practicesWho this book is forThis book is for developers, researchers, and anyone looking to learn more about LangChain and LangGraph. With a strong emphasis on enterprise deployment patterns, it’s especially valuable for teams implementing LLM solutions at scale. While the first edition focused on individual developers, this updated edition expands its reach to support engineering teams and decision-makers working on enterprise-scale LLM strategies. A basic understanding of Python is required, and familiarity with machine learning will help you get the most out of this book.
R 1,954.00
Energy Minimization Methods in Computer Vision and Pattern Recognition
This book constitutes the refereed proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR'97, held in Venice, Italy, in May 1997.The book presents 29 revised full papers selected from a total of 62 submissions. Also included are four full invited papers and a keynote paper by leading researchers. The volume is organized in sections on contours and deformable models, Markov random fields, deterministic methods, object recognition, evolutionary search, structural models, and applications. The volume is the first comprehensive documentation of the application of energy minimization techniques in the areas of compiler vision and pattern recognition.
R 1,599.70
R 1,793.39
Fundamentals of Speaker Recognition
An emerging technology, Speaker Recognition is becoming well-known for providing voice authentication over the telephone for helpdesks, call centres and other enterprise businesses for business process automation. "Fundamentals of Speaker Recognition" introduces Speaker Identification, Speaker Verification, Speaker (Audio Event) Classification, Speaker Detection, Speaker Tracking and more. The technical problems are rigorously defined, and a complete picture is made of the relevance of the discussed algorithms and their usage in building a comprehensive Speaker Recognition System. Designed as a textbook with examples and exercises at the end of each chapter, "Fundamentals of Speaker Recognition" is suitable for advanced-level students in computer science and engineering, concentrating on biometrics, speech recognition, pattern recognition, signal processing and, specifically, speaker recognition. It is also a valuable reference for developers of commercial technology and for speech scientists. Please click on the link under "Additional Information" to view supplemental information including the Table of Contents and Index.
R 2,882.53
Integrating Graphics and Vision for Object Recognition
Integrating Graphics and Vision for Object Recognition serves as a reference for electrical engineers and computer scientists researching computer vision or computer graphics. Computer graphics and computer vision can be viewed as different sides of the same coin. In graphics, algorithms are given knowledge about the world in the form of models, cameras, lighting, etc., and infer (or render) an image of a scene. In vision, the process is the exact opposite: algorithms are presented with an image, and infer (or interpret) the configuration of the world. This work focuses on using computer graphics to interpret camera images: using iterative rendering to predict what should be visible by the camera and then testing and refining that hypothesis. Features of the book include: Many illustrations to supplement the text; A novel approach to the integration of graphics and vision; Genetic algorithms for vision; Innovations in closed loop object recognition. Integrating Graphics and Vision for Object Recognition will be of interest to research scientists and practitioners working in fields related to the topic. It may also be used as an advanced-level graduate text.
R 4,638.14
R 410.00
Technical Analysis for Algorithmic Pattern Recognition
The main purpose of this book is to resolve deficiencies and limitations that currently exist when using Technical Analysis (TA). Particularly, TA is being used either by academics as an “economic test” of the weak-form Efficient Market Hypothesis (EMH) or by practitioners as a main or supplementary tool for deriving trading signals. This book approaches TA in a systematic way utilizing all the available estimation theory and tests. This is achieved through the developing of novel rule-based pattern recognizers, and the implementation of statistical tests for assessing the importance of realized returns. More emphasis is given to technical patterns where subjectivity in their identification process is apparent. Our proposed methodology is based on the algorithmic and thus unbiased pattern recognition. The unified methodological framework presented in this book can serve as a benchmark for both future academic studies that test the null hypothesis of the weak-form EMH and for practitioners that want to embed TA within their trading/investment decision making processes.
R 4,762.00
R 1,630.94
Pattern-Based Compression of Multi-Band Image Data for Landscape Analysis
This book describes an integrated approach to using remotely sensed data in conjunction with geographic information systems for landscape analysis. Remotely sensed data are compressed into an analytical image-map that is compatible with the most popular geographic information systems as well as freeware viewers. The approach is most effective for landscapes that exhibit a pronounced mosaic pattern of land cover.
R 3,022.33
On Hierarchical Models for Visual Recognition and Learning of Objects, Scenes, and Activities
In many computer vision applications, objects have to be learned and recognized in images or image sequences. This book presents new probabilistic hierarchical models that allow an efficient representation of multiple objects of different categories, scales, rotations, and views. The idea is to exploit similarities between objects and object parts in order to share calculations and avoid redundant information. Furthermore inference approaches for fast and robust detection are presented. These new approaches combine the idea of compositional and similarity hierarchies and overcome limitations of previous methods. Besides classical object recognition the book shows the use for detection of human poses in a project for gait analysis. The use of activity detection is presented for the design of environments for ageing, to identify activities and behavior patterns in smart homes. In a presented project for parking spot detection using an intelligent vehicle, the proposed approaches are used to hierarchically model the environment of the vehicle for an efficient and robust interpretation of the scene in real-time.
R 3,727.46
Plant Pattern Recognition Receptors
This volume covers protocols on techniques ranging from MAMP isolations from diverse microorganisms, PRR identifications from different plant species, MAMP-PRR binding, and a series of signaling responses and events revealed by various biochemical, cellular, genetic and bioinformatic tools. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.Authoritative and practical, Plant Pattern Recognition Receptors: Methods and Protocols aims to ensure successful results in the further study of this vital field.
R 6,228.00
R 310.00
Energy Minimization Methods in Computer Vision and Pattern Recognition
C 2 - E C - (EC '99) , $ % & , E , % 26 29, 1999. + . + 1997. + % % - , , , , % . A % % , - , , ? % % % % . 35 % . E % % . B % - 17 % . % . + , - , , , , , . % % . F , ? % . F, % % . % % % : . C , . D ,;. D, F. F , . . A . ? % A% % @ - >; C @ . A & % % % ?- @ - F . % B . 1999 E Program Co Chairs E . UniversityofYork,UK UniversityofVenice,Italy Program Committee B INRIA,France B UniversityofBonn,Germany F InstitutoSuperiorTecnico,Portugal D < NewYorkUniversity,USA A . MichiganStateUniversity,USA % UniversityofSurrey,UK @ . > NanyangTechnologicalUniversity,Singapore UniversityofSurrey,UK A YaleUniversity,USA @ McGillUniversity,Canada @ $ TheWeizmannInstituteofScience,Israel > UniversityofNewMexico,USA A >. & Smith KettlewellEyeResearchInstitute,USA INRIA,France @ . YaleUniversity,USA
R 1,564.16
Hierarchical Perceptual Grouping for Object Recognition
This unique text/reference presents a unified approach to the formulation of Gestalt laws for perceptual grouping, and the construction of nested hierarchies by aggregation utilizing these laws. The book also describes the extraction of such constructions from noisy images showing man-made objects and clutter. Each Gestalt operation is introduced in a separate, self-contained chapter, together with application examples and a brief literature review. These are then brought together in an algebraic closure chapter, followed by chapters that connect the method to the data – i.e., the extraction of primitives from images, cooperation with machine-readable knowledge, and cooperation with machine learning.Topics and features: offers the first unified approach to nested hierarchical perceptual grouping; presents a review of all relevant Gestalt laws in a single source; covers reflection symmetry, frieze symmetry, rotational symmetry, parallelism and rectangular settings, contour prolongation, and lattices; describes the problem from all theoretical viewpoints, including syntactic, probabilistic, and algebraic perspectives; discusses issues important to practical application, such as primitive extraction and any-time search; provides an appendix detailing a general adjustment model with constraints.This work offers new insights and proposes novel methods to advance the field of machine vision, which will be of great benefit to students, researchers, and engineers active in this area.
R 4,257.07
Energy Minimization Methods in Computer Vision and Pattern Recognition
This volume consists of the 33 papers presented at the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2003)which was held at Instituto Superior T' ecnico (IST), the - gineeringSchooloftheTechnicalUniversityofLisbon,PortugalduringJuly7-9, 2003.Thisworkshopwasthefourthinthe serieswhichstartedwithEMMCVPR 1997 held in Venice, Italy in May 1997 and continued with EMMCVPR 1999 held in York, UK in July 1999 and EMMCVPR 2001 held in Sophia-Antipolis, France in September 2001. Many problems in computer vision and pattern recognition (CVPR) are couchedintheframeworkofoptimization.Theminimizationofaglobalquantity, often referred to as the energy, forms the bulwark of most approachesin CVPR. Disparate approaches,such as discrete and probabilistic formulations on the one hand and continuous, deterministic strategies on the other, often have optimi- tion or energy minimization as a common theme.Instances of energy minimi- tion arise in Gibbs/Markov modeling, Bayesian decision theory, geometric and variational approaches and in areas in CVPR such as object recognition and - trieval, image segmentation, registration, reconstruction, classi?cation and data mining. The aim of the EMMCVPR workshops is to bring together researchers with interests in these disparate areas of CVPR but with an underlying commitment to some form of energy minimization. Although the subject is traditionally well representedinmajorinternationalconferencesonCVPR,thisworkshopprovides a forum wherein researchers can report their recent work and engage in more informal discussions.
R 4,030.00
Human Face Recognition Using Third-Order Synthetic Neural Networks
Human Face Recognition Using Third-Order Synthetic Neural Networks explores the viability of the application of High-order synthetic neural network technology to transformation-invariant recognition of complex visual patterns. High-order networks require little training data (hence, short training times) and have been used to perform transformation-invariant recognition of relatively simple visual patterns, achieving very high recognition rates. The successful results of these methods provided inspiration to address more practical problems which have grayscale as opposed to binary patterns (e.g., alphanumeric characters, aircraft silhouettes) and are also more complex in nature as opposed to purely edge-extracted images - human face recognition is such a problem. Human Face Recognition Using Third-Order Synthetic Neural Networks serves as an excellent reference for researchers and professionals working on applying neural network technology to the recognition of complex visual patterns.
R 4,629.17
Machine Learning and Data Mining in Pattern Recognition
This book constitutes the refereed proceedings of the 8th International Conference, MLDM 2012, held in Berlin, Germany in July 2012. The 51 revised full papers presented were carefully reviewed and selected from 212 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and web mining.
R 1,618.53
Laboratory Guide to Genomic Sequencing
A Safety Considerations Genomic sequencing involves a number of hazardous steps, such as high current, high voltage, radioactive and highly toxic chemicals. It is, therefore, absolutelyessen- tial that the instructions of equipment manufacturers be followed and that particular attention is paid to the local and federal safety regulations. INTRODUCTION 9 B Introduction During the cloning of genomic DNA many of its characteristics are perma- nently lost. It was therefore necessary to develop a new technique that would give us a closer look at a gene in its normal environment. The powerful technique of genomic sequencing, first described by Church and Gilbert (1984) now makes it possible to have a precise view of a given DNA sequence in a chromosome. This method combines the chemical DNA-sequencing procedure of Maxam and Gilbert (1980) with the detection of DNA sequences by electroblotting and indirect end-labeling by hybridization. Besides studies on the methylation state of single bases in a given gene (Nick et al. , 1986; Saluz and Jost, 1986; Saluz et al. , 1986), genomic sequencing can also be used to study specific DNA-protein interactions in vivo (Church et al., 1985; Giniger et al. , 1985; Becker et al. , 1986; Ephrussi et al. , 1985; Martin et al. , 1986; Nick et al. , 1986; Zinn and Maniatis, 1986).
R 1,539.93
