Kooperativer Bibliotheksverbund

UID:

Format: 1 online resource (225 pages)

Edition: First edition.

ISBN: 9780443289606

Note: Front Cover -- Gesture Recognition -- Copyright Page -- Contents -- 1 Basic concepts and development of gesture recognition -- 1.1 Principles of gesture recognition -- 1.1.1 Gesture in human society -- 1.1.2 Gesture and human-computer interaction -- 1.2 Development of gesture recognition algorithms -- 1.2.1 Methods based on handicraft features -- 1.2.2 Methods based on probabilistic graphical model -- 1.2.3 Methods based on bag of visual words -- 1.2.4 Methods based on neural network -- 1.3 Current challenges in the field of gesture recognition -- 1.4 Summary -- References -- 2 Common datasets in the field of gesture recognition -- 2.1 Image-based gesture dataset -- 2.1.1 American Sign Language fingerspelling dataset -- 2.1.2 MU HandImages ASL gesture dataset -- 2.1.3 LaRED gesture dataset -- 2.1.4 Marcel gesture dataset -- 2.1.5 Senz3D gesture dataset -- 2.2 Video-based gesture dataset -- 2.2.1 20BN-jester dataset -- 2.2.2 RWTH-PHOENIX-weather dataset -- 2.2.3 CSL series datasets -- 2.2.4 DEVISIGN sign language dataset -- 2.2.5 CGD series datasets -- 2.2.5.1 CGD 2011 -- 2.2.5.2 CGD 2013 -- 2.2.5.3 CGD 2016 -- 2.2.6 Traffic police gesture dataset -- 2.2.7 SKIG dataset -- 2.2.8 EgoGesture dataset -- 2.2.9 MSRC-12 dataset -- 2.2.10 NvGesture dataset -- 2.3 Dataset summary -- 2.4 Summary -- References -- 3 Gesture recognition method based on handicraft features -- 3.1 Hand region segmentation -- 3.1.1 Edge information-based segmentation methods -- 3.1.1.1 Edge operator-based segmentation approach -- 3.1.1.2 Active contour model-based segmentation -- 3.1.2 Motion analysis-based segmentation methods -- 3.1.2.1 Background subtraction-based segmentation -- 3.1.2.2 Interframe difference threshold-based method -- 3.1.2.3 Optical flow field-driven segmentation -- 3.1.3 Skin color feature-based segmentation methods -- 3.1.4 Summary. , 3.2 Gesture feature extraction -- 3.2.1 Haar-like features -- 3.2.2 Local binary pattern features -- 3.2.3 SIFT features -- 3.2.3.1 Build Gaussian difference pyramid -- 3.2.3.2 Determine location of keypoints -- 3.2.3.2.1 Primary selection of keypoints -- 3.2.3.2.2 Adjustment of keypoints -- 3.2.3.2.3 Removal of edge effects -- 3.2.3.3 Direction of keypoints -- 3.2.3.4 Construction of keypoint descriptors -- 3.2.4 SURF features -- 3.2.4.1 Construction of scale space -- 3.2.4.2 Location of keypoints -- 3.2.4.3 Determination of the direction of feature points -- 3.2.4.4 Generation of feature descriptors -- 3.2.5 Features of histogram of oriented gradient -- 3.2.6 Features of histogram of oriented optical flow -- 3.2.7 Summary -- 3.3 Gesture recognition -- 3.3.1 Template matching -- 3.3.2 Finite-state machine -- 3.3.3 Dynamic time warping -- 3.4 Summary -- References -- 4 Gesture recognition method based on convolutional neural network -- 4.1 Basic operations of deep convolutional neural network -- 4.1.1 Characteristics of convolutional neural network -- 4.1.1.1 Local connection -- 4.1.1.2 Weight sharing -- 4.1.1.3 Downsampling -- 4.1.2 Basic structure of convolutional network -- 4.1.2.1 Input layer -- 4.1.2.2 Convolutional layer -- 4.1.2.3 Activation function of convolutional neural network -- 4.1.2.3.1 Sigmoid function -- 4.1.2.3.2 Tanh function -- 4.1.2.3.3 Rectified linear unit function -- 4.1.2.4 Pooling layer -- 4.1.3 Training process of convolutional neural network -- 4.2 Application of 2D convolutional neural network in gesture recognition -- 4.2.1 Two-stream network -- 4.2.2 Temporal segment network -- 4.3 Basic operations of 3D convolutional neural network -- 4.3.1 3D convolution -- 4.3.2 3D pooling -- 4.4 Application of 3D convolutional neural network in gesture recognition -- 4.4.1 C3D network -- 4.4.2 ResC3D network. , 4.4.3 Two-stream inflated 3D ConvNet (I3D) network -- 4.5 Summary -- References -- 5 Enhancing gesture recognition with advanced recurrent neural networks and memory networks -- 5.1 Overview of development of recurrent neural networks -- 5.2 Recurrent neural networks and their variants -- 5.2.1 Basic structure of recurrent neural networks -- 5.2.2 Bidirectional recurrent neural networks -- 5.2.3 Long short term memory -- 5.2.4 Gate recurrent unit -- 5.3 Memory network combined with external storage units -- 5.3.1 Entropic associative memory and memory networks proposed by facebook AI research institute -- 5.3.2 Memory network framework -- 5.3.3 Neural turing machine -- 5.3.3.1 Reading operation -- 5.3.3.2 Writing operation -- 5.3.3.2.1 Content-based addressing mechanism -- 5.3.3.2.2 Location-based addressing mechanism -- 5.3.3.2.2.1 Interpolation -- 5.3.3.2.2.2 Shift -- 5.3.3.2.2.3 Sharpen -- 5.4 Application of recurrent neural network in gesture recognition -- 5.4.1 Application of recurrent neural networks in gesture recognition -- 5.4.2 Application of long short term memory in gesture recognition -- 5.4.3 Application of combining memory network and long short term memory in gesture recognition -- 5.5 Summary -- References -- 6 Gesture recognition method based on multimodal data fusion -- 6.1 Techniques for acquiring multimodal data -- 6.1.1 Depth data -- 6.1.1.1 Passive sensors -- 6.1.1.2 Active sensors -- 6.1.2 Infrared data -- 6.1.3 Skeleton data -- 6.1.3.1 Using wearable sensor systems -- 6.1.3.2 Estimating through RGB-D camera -- 6.1.3.3 Approximating only with RGB images -- 6.1.4 Optical flow data -- 6.1.4.1 Brightness constancy assumption -- 6.1.4.2 Gradient constancy assumption -- 6.1.4.3 Spatiotemporal smoothness assumption -- 6.1.5 Saliency data -- 6.2 Fusion algorithm of different modality data -- 6.2.1 Data-level fusion. , 6.2.2 Feature-level fusion -- 6.2.2.1 Feature-level fusion method -- 6.2.2.1.1 Pointwise addition strategy and feature-concatenation strategy -- 6.2.2.1.2 Fusion method based on statistical analysis features -- 6.2.2.2 Application of feature-level fusion in gesture recognition -- 6.2.3 Decision-level fusion -- 6.2.3.1 Strategies of decision-level fusion -- 6.2.3.2 Application of decision-level fusion method in gesture recognition -- 6.2.4 Other fusion methods -- 6.2.4.1 Method of application -- 6.3 Summary -- References -- 7 Gesture recognition and attention mechanisms -- 7.1 Concept of attention mechanism -- 7.1.1 Progress in the study of attention mechanism -- 7.1.2 Human visual attention -- 7.1.3 Using attention mechanisms in computer vision -- 7.2 Attention mechanism as preprocess for gesture recognition -- 7.2.1 Light balance -- 7.2.2 Prehand detection -- 7.3 Attention mechanism based on complementarity of different modal data -- 7.3.1 Attention mechanism based on skeletal data -- 7.3.1.1 Attention mechanism based on salient features -- 7.3.1.2 Attention mechanism based on optical flow characteristics -- 7.4 Summary -- References -- 8 Gesture recognition-based human-computer interaction cases -- 8.1 Application areas of gesture recognition -- 8.1.1 Intelligent driving -- 8.1.2 Smart home control -- 8.1.3 Unmanned aerial vehicle control -- 8.1.4 Robot control -- 8.2 Gesture recognition case studies -- 8.2.1 Gesture recognition case 1: drone control -- 8.2.1.1 Hand detection -- 8.2.1.2 Gesture recognition -- 8.2.2 Gesture recognition case 2: smart home control -- 8.2.2.1 Hardware platform -- 8.2.2.2 Input device -- 8.2.2.3 Construction of dataset -- 8.2.2.4 Video streaming data acquisition module -- 8.2.2.5 Gesture segmentation module -- 8.2.2.6 Feature extraction and classification module -- 8.3 Summary -- References. , 9 Exploring development of gesture recognition for future human-computer interaction applications -- 9.1 New gesture recognition technology for human-computer interaction -- 9.1.1 Current issues in gesture recognition technology -- 9.1.1.1 Treatment of irrelevant factors in open environment -- 9.1.1.2 Dynamic gesture tracking and matching -- 9.1.1.3 Algorithmic cost issues -- 9.1.2 Directions for future research -- 9.1.2.1 3D gesture reconstruction based on multisensor devices -- 9.1.2.2 Gesture feature learning based on multiscale representation -- 9.1.2.3 Model compression based on network structure search -- 9.1.2.4 Semisupervised modeling for massive open data -- 9.2 New applications of gesture recognition in human-computer interaction -- 9.2.1 Intelligent driving -- 9.2.2 Smart home -- 9.2.3 Drone control -- 9.2.4 Robot control -- 9.3 Summary -- References -- Index -- Back Cover.

Additional Edition: ISBN 9780443289590

Language: English

UID:

Format: 1 Online-Ressource (144 p.)

ISBN: 9789535112068 , 9789535157144

Content: Image Fusion is an important branch of information fusion, and it is also an important technology for image understanding and computer vision. The fusion process is to merging different images into one to get more accurate description for the scene. The original images for image fusion are always obtained by several different image sensors, or the same sensor in different operating modes. The fused image can provide more effective information for further image processing, such as image segmentation, object detection and recognition. Image fusion is a new study field which combined with many different disciplines, such as sensors, signal processing, image processing, computer and artificial intelligence. In the past two decades, a large number of research literatures appear. This book is edited based on these research results, and many research scholars give a great help to this book

Note: English

Language: English

URL: kostenfrei

UID:

Format: 1 Online-Ressource (XX, 590 Seiten, 219 illus)

ISBN: 9789811329227

Series Statement: Communications in computer and information science 945

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-132-921-0

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-132-923-4

Language: English

Keywords: Konferenzschrift

DOI: 10.1007/978-981-13-2922-7

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (xii, 161 Seiten) , 48 Illustrationen, 42 in Farbe

ISBN: 9783030567255

Series Statement: Lecture notes in computer science 12285

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-56724-8

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-56726-2

Language: English

Subjects: Computer Science

Keywords: Information Retrieval ; Datenbankverwaltung ; Electronic Commerce ; Konferenzschrift

DOI: 10.1007/978-3-030-56725-5

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (332 p.)

ISBN: 9783036570389 , 9783036570396

Content: Computational Intelligence (CI) is the theory, design, application, and development of biologically and linguistically motivated computational paradigms. Traditionally, the three main pillars of CI have been neural networks, fuzzy systems, and evolutionary computation. However, in time, many nature-inspired computing paradigms have evolved. Thus, CI is an evolving field, and, at present, in addition to the three main constituents, it encompasses computing paradigms such as ambient intelligence, artificial life, cultural learning, artificial endocrine networks, social reasoning, and artificial hormone networks. CI plays a major role in developing successful intelligent systems, including games and cognitive developmental systems. Over the last few years, there has been an explosion of research on deep learning, specifically deep convolutional neural networks, and deep learning has become the core method for artificial intelligence. In fact, some of the most successful AI systems today are based on CI. Therefore, this reprint focuses on the theoretical study of computational intelligence and its applications

Note: English

Language: Undetermined

URL: kostenfrei

UID:

Format: XII, 161 p. 48 illus., 42 illus. in color. , online resource.

Edition: 1st ed. 2020.

ISBN: 9783030567255

Series Statement: Theoretical Computer Science and General Issues ; 12285

Content: This book constitutes the refereed proceedings of the 26th China Conference on Information Retrieval, CCIR 2020, held in Xi'an, China, in August 2020.* The 12 full papers presented were carefully reviewed and selected from 102 submissions. The papers are organized in topical sections: search and recommendation, NLP for IR, and IR in finance. * Due to the COVID-19 pandemic the conference was held online supplemented with local on-site events.

Note: Search and Recommendation -- Improving Search Snippets in Context-aware Web Search Scenarios -- Investigating Fine-grained Usefulness Perception Process in Mobile Search -- ResFusion: A Residual Learning based Fusion Framework for CTR Prediction -- NLP for IR -- A Framework for Identifying Event's Relevance Comments in Twitter -- Enriching Pre-trained Language Model with Dependency Syntactic Information for Chemical-Protein Interaction Extraction -- Leveraging Label Semantics and Correlations for Judgment Prediction -- Position-aware hybrid attention network for Aspect-level Sentiment Analysis -- IR in Finance -- An Integrated Machine Learning Framework for Stock Price Prediction -- Empirical Research on Futures Trading Strategy Based on Time Series Algorithm -- Hierarchical Attention Network in Stock Prediction -- Online Topic Detection and Tracking System and its Application on Stock Market in China -- Semi-Supervised Sentiment Analysis for Chinese Stock Texts in Scarce Labeled Data Scenario and Price Prediction.

In: Springer Nature eBook

Additional Edition: Printed edition: ISBN 9783030567248

Additional Edition: Printed edition: ISBN 9783030567262

Language: English

DOI: 10.1007/978-3-030-56725-5

URL: https://doi.org/10.1007/978-3-030-56725-5

URL: lizenzpflichtig

URL: Cover

UID:

Format: 1 online resource (XVII, 354 p.)

ISBN: 9783110692327

Series Statement: De Gruyter Textbook

Content: This book covers C-Programming focussing on its practical side. Volume 1 deals mainly with basic data structures, algorithms and program statements. An extensive use of figures and examples help to give a clear description of concepts help the reader to gain a systematic understanding of the language.

Note: Frontmatter -- , Preface -- , Introduction -- , Structure of content -- , Division of work -- , Notes -- , Acknowledgments -- , Contents -- , 1 Introduction to programs -- , 2 Algorithms -- , 3 Basic data types -- , 4 Input/output -- , 5 Program statements -- , 6 Preprocessing: work before compilation -- , 7 Execution of programs -- , Appendix A: Precedence and associativity of operators -- , Appendix B: ASCII table -- , Appendix C: Common library functions of C -- , Appendix D: Common escape characters -- , Appendix E: Bitwise operations -- , Index , In English.

Additional Edition: ISBN 9783110692495

Additional Edition: ISBN 9783110691177

Language: English

Subjects: Computer Science

DOI: 10.1515/9783110692327

URL: https://doi.org/10.1515/9783110692327

URL: https://www.degruyter.com/isbn/9783110692327

URL: Volltext  (URL des Erstveröffentlichers)

URL: https://doi.org/10.1515/9783110692327

URL: https://www.degruyter.com/isbn/9783110692327

UID:

Format: 1 Online-Ressource (xii, 161 Seiten) : , 48 Illustrationen, 42 in Farbe.

ISBN: 978-3-030-56725-5

Series Statement: Lecture notes in computer science 12285

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-56724-8

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-56726-2

Language: English

Subjects: Computer Science

Keywords: Information Retrieval ; Datenbankverwaltung ; Electronic Commerce ; Konferenzschrift

DOI: 10.1007/978-3-030-56725-5

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 online resource (X, 288 p.)

ISBN: 9783110692303

Series Statement: De Gruyter Textbook

Content: This book covers C-Programming focussing on its practical side. Volume 2 deals mainly with composite data structures and their composition. An extensive use of figures and examples help to give a clear description of concepts and help the reader to gain a systematic understanding of the programming language.

Note: Frontmatter -- , Contents -- , 1. Arrays -- , 2. Pointers -- , 3. Composite data -- , 4. Functions -- , 5. Files: operations on external data -- , Appendix A. Adding multiple files to a project -- , Appendix B. Programming paradigms -- , Appendix C. void type -- , Index , In English.

Additional Edition: ISBN 9783110692501

Additional Edition: ISBN 9783110692297

Language: English

Subjects: Computer Science

DOI: 10.1515/9783110692303

URL: https://doi.org/10.1515/9783110692303

URL: https://www.degruyter.com/isbn/9783110692303

URL: Volltext  (URL des Erstveröffentlichers)

URL: https://doi.org/10.1515/9783110692303

URL: https://www.degruyter.com/isbn/9783110692303

UID:

Format: 1 online resource (144 pages)

ISBN: 953-51-5714-0 , 953-51-1206-6

Content: Image Fusion is an important branch of information fusion, and it is also an important technology for image understanding and computer vision. The fusion process is to merging different images into one to get more accurate description for the scene. The original images for image fusion are always obtained by several different image sensors, or the same sensor in different operating modes. The fused image can provide more effective information for further image processing, such as image segmentation, object detection and recognition. Image fusion is a new study field which combined with many different disciplines, such as sensors, signal processing, image processing, computer and artificial intelligence. In the past two decades, a large number of research literatures appear. This book is edited based on these research results, and many research scholars give a great help to this book.

Note: English

Language: English