LEADER 01322nam a2200337 i 4500 001 991001218359707536 005 20020507113025.0 008 940316s1978 de ||| | eng 020 $a3540086706 035 $ab10190053-39ule_inst 035 $aLE00644090$9ExL 040 $aDip.to Fisica$bita 084 $a53(06) 084 $a53.1.64 084 $a53.1.68 084 $a536'.401 084 $aQC20.7.R43C64 100 1 $aCollet, Pierre$045832 245 12$aA renormalization group analysis of the hierarchical model in statistical mechanics /$cPierre Collet, Jean-Pierre Eckmann 260 $aBerlin :$bSpringer-Verlag,$c1978 300 $aiv, 199 p. :$bill. ;$c25 cm. 490 0 $aLecture notes in physics / edited by J. Ehlers...[et al.] ;$v74 500 $aIncludes bibliographies and index. 650 4$aRenormalization group 700 1 $aEckmann, Jean-Pierre$eauthor$4http://id.loc.gov/vocabulary/relators/aut$045833 907 $a.b10190053$b17-02-17$c27-06-02 912 $a991001218359707536 945 $aLE006 53.1.64 COL$g1$i2006000044103$lle006$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i10234524$z27-06-02 996 $aRenormalization group analysis of the hierarchical model in statistical mechanics$91445047 997 $aUNISALENTO 998 $ale006$b01-01-94$cm$da $e-$feng$gde $h2$i1 LEADER 01500nam 2200433Ka 450 001 9910698093403321 005 20250409163008.0 035 $a(CKB)5470000002394065 035 $a(OCoLC)301229568 035 $a(EXLCZ)995470000002394065 100 $a20090202d1989 ua 0 101 0 $aeng 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDigital CODEC for real-time processing of broadcast quality video signals at 1.8 bits/pixel /$fMary Jo Shalkhauser and Wayne A. Whyte, Jr 210 1$a[Washington, D.C.] :$cNational Aeronautics and Space Administration,$d[1989] 215 $a8 pages $cdigital, PDF file 225 1 $aNASA technical memorandum ;$v102325 300 $aTitle from title screen (viewed Jan. 29, 2009) 606 $aBroadcasting$2nasat 606 $aData compression$2nasat 606 $aPixels$2nasat 606 $aReal time operation$2nasat 606 $aVideo signals$2nasat 615 7$aBroadcasting. 615 7$aData compression. 615 7$aPixels. 615 7$aReal time operation. 615 7$aVideo signals. 700 $aShalkhauser$b Mary Jo W$01396060 701 $aWhyte$b Wayne A$01418696 712 02$aUnited States.$bNational Aeronautics and Space Administration. 801 0$bGPO 801 1$bGPO 906 $aBOOK 912 $a9910698093403321 996 $aDigital CODEC for real-time processing of broadcast quality video signals at 1.8 bits$93530879 997 $aUNINA LEADER 10933nam 22005053 450 001 9911034577503321 005 20251201110045.0 010 $a1-394-27262-6 010 $a1-394-27261-8 035 $a(CKB)41524648000041 035 $a(MiAaPQ)EBC32326217 035 $a(Au-PeEL)EBL32326217 035 $a(OCoLC)1543150387 035 $a(CaSebORM)9781394272594 035 $a(EXLCZ)9941524648000041 100 $a20251004d2025 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApplied Computer Vision Through Artificial Intelligence 205 $a1st ed. 210 1$aNewark :$cJohn Wiley & Sons, Incorporated,$d2025. 210 4$d©2026. 215 $a1 online resource (501 pages) 311 08$a1-394-27259-6 327 $aCover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 An Overview of Medical Diagnostics through Artificial Intelligence-Powered Histopathological Imaging and Video Analysis -- 1.1 Introduction -- 1.1.1 A Focus on Digital Image and Video Analysis -- 1.1.2 Overview of Research Article -- 1.1.2.1 Comparison Between Different Techniques/Comparative Analysis Among the Techniques Available -- 1.1.2.2 Overview of Data Preprocessing and Meta-Heuristic Algorithms -- 1.1.3 The Organizational of the Research Article -- 1.2 Background -- 1.2.1 Difficulties with Feature Selection -- 1.3 Preliminaries -- 1.3.1 Selection of Features (FS) -- 1.3.2 Classification -- 1.3.2.1 Support Vector Machine -- 1.3.2.2 Naïve Bayes -- 1.3.2.3 ANN -- 1.3.3 Meta-Heuristic Algorithms in FS -- 1.3.3.1 Genetic Algorithm -- 1.3.3.2 Cuckoo Search Optimization -- 1.3.3.3 BAT Algorithm -- 1.3.3.4 Grey Wolf Optimizer -- 1.3.3.5 Harris Hawk Optimization -- 1.3.3.6 Transition from Exploration to Exploitation -- 1.4 Experimental Results -- 1.4.1 Challenges in the Application of a Metaheuristic Algorithm for Classification and Prediction of Medical Disease -- 1.4.2 Summary of the Review -- 1.5 Conclusion -- References -- Chapter 2 Generative Adversarial Networks: Theory and Application in Synthesis -- 2.1 Introduction -- 2.2 Ideologies of GAN -- 2.3 Architecture of GAN -- 2.4 Applications of GAN -- 2.4.1 Image Processing and Computer Vision -- 2.4.2 Healthcare and Medical Imaging -- 2.4.3 Natural Language Processing (NLP) -- 2.4.4 Video and Animation -- 2.4.5 Gaming and Entertainment -- 2.4.6 Cybersecurity and Anomaly Detection -- 2.4.7 Fashion and Retail -- 2.4.8 Art and Creativity -- 2.5 Conclusion -- References -- Chapter 3 From Pixels to Predictions: Deep Learning for Glaucoma Detection -- 3.1 Introduction -- 3.1.1 Glaucoma. 327 $a3.1.2 Detection of Glaucoma -- 3.1.3 Deep Learning -- 3.1.4 Glaucoma Detection Using Deep Learning -- 3.2 Literature Review -- 3.2.1 Glaucoma Classification -- 3.2.2 Glaucoma Detecting -- 3.3 Problem Statement -- 3.4 Hybrid Approach for Glaucoma Detection -- 3.5 Result and Discussion -- 3.5.1 Confusion Matrix has been Obtained During Testing that is Shown Below for 4 Models -- 3.6 Conclusion -- 3.7 Future Scope -- References -- Chapter 4 Advancements in Computer Vision for Object Detection and Recognition using DenseNet Deep Learning Model -- 4.1 Introduction -- 4.2 Literature Survey -- 4.2.1 Application of Principles -- 4.3 Proposed System -- 4.4 Results and Discussion -- 4.5 Conclusion -- References -- Chapter 5 Deep Learning-Based Detection of Cyber Extortion -- 5.1 Introduction -- 5.2 Related Works -- 5.3 Existing System -- 5.4 Proposed System -- 5.5 System Architecture -- 5.6 Methodology -- 5.6.1 Data Collection and Preprocessing -- 5.6.2 Feature Extraction -- 5.6.3 Voice Processing -- 5.6.4 Model Architecture -- 5.6.4.1 Text Vectorization Layer -- 5.6.4.2 Embedding Layer -- 5.6.4.3 Bidirectional LSTM Layer -- 5.6.4.4 Dense Layers -- 5.6.4.5 Dropout Regularization -- 5.6.5 Evaluation -- 5.6.5.1 Precision -- 5.6.5.2 Recall -- 5.6.5.3 F1 Score -- 5.6.5.4 Accuracy -- 5.7 Results and Discussion -- 5.8 Conclusion -- 5.9 Future Work -- References -- Chapter 6 GANs Unleashed: From Theory to Synthetic Realities -- 6.1 Introduction -- 6.2 Related Works -- 6.2.1 Accurate Representation of the Density -- 6.2.2 Classification/Regression -- 6.2.3 Computer Algorithms for Image Synthesis -- 6.2.4 Computer Algorithms Synthesize Pictures -- 6.3 Limitations that are Enforced by GAN -- 6.4 Conclusion -- References -- Chapter 7 RFID and Computer Vision-Enhanced Automotive Authentication Verification System -- 7.1 Introduction -- 7.2 Literature Survey. 327 $a7.3 Proposed System -- 7.4 Working -- 7.5 Block Diagram -- 7.6 Hardware Components -- 7.7 Result -- 7.8 Conclusion -- Bibliography -- Chapter 8 Synergizing Ensemble Learning Techniques for Robust Emotion Detection using EEG Signals -- 8.1 Introduction -- 8.1.1 Overview of EEG-Based Emotion Detection -- 8.1.2 Motivation for Using Ensemble Learning -- 8.2 Ensemble Learning Techniques -- 8.2.1 Random Forest Classifier -- 8.2.2 AdaBoost Classifier -- 8.2.3 Gradient Boosting Classifier -- 8.2.4 CatBoost Classifier -- 8.2.5 XGBoost Classifier -- 8.2.6 Extra Trees Classifier -- 8.3 Methodology -- 8.3.1 Data Collection and Preprocessing -- 8.3.2 Implementation Details -- 8.4 Experimental Results -- 8.4.1 Impact of Different Ensemble Techniques on Emotion Detection Accuracy -- 8.4.2 Robustness and Reliability -- 8.5 Discussion -- 8.5.1 Advantages of Ensemble Methods in EEG Emotion Detection -- 8.5.2 Future Directions -- 8.6 Conclusion -- Chapter 9 Understanding the Unseen: Explainability in Deep Learning for Computer Vision -- 9.1 Introduction -- 9.1.1 An Overview of the Success of Deep Learning in Computer Vision -- 9.1.2 The Importance of Interpretability and Explainability -- 9.2 The Need for Interpretation in Computer Vision -- 9.3 Understanding Interpretability in Deep Learning -- 9.4 Visualization Techniques -- 9.5 Maps of the Headland -- 9.6 Model Simplification -- 9.7 Meaning of Function -- 9.8 Feature Importance -- 9.9 Methods Based on Prototypes -- 9.10 Challenges and Future Directions -- 9.11 Conclusion -- 9.12 Future Vision -- References -- Chapter 10 Prefatory Study on Landslide Susceptibility Modeling Based on Binary Random Forest Classifier -- 10.1 Introduction -- 10.2 Materials and Methodology -- 10.2.1 Region of Study -- 10.2.2 Preparation of Dataset -- 10.2.3 Random Forest -- 10.2.4 Evaluation of Landslide Susceptibility Model. 327 $a10.3 Result Analysis -- 10.3.1 10-Fold Cross-Validation -- 10.3.2 Feature Selection -- 10.3.3 LSM by Binary RF Model -- 10.4 Conclusion -- References -- Chapter 11 Improving Digital Interactions using Augmented Reality and Computer Vision -- 11.1 Introduction -- 11.2 Literature Survey -- 11.3 Methodology -- 11.4 Results -- 11.5 Conclusion and Future Scope -- References -- Chapter 12 The Evolutionary Dynamics of Machine Learning and Deep Learning Architectures in Computer Vision -- 12.1 Introduction to Computer Vision and Its Evolution -- 12.2 Foundations of Machine Learning in Computer Vision -- 12.3 Rise of Deep Learning in Computer Vision -- 12.4 Key Architectures and Techniques in Deep Learning for Computer Vision -- 12.5 CNN Architectures -- 12.5.1 Inception -- 12.5.2 ResNet (Residual Network) -- 12.5.3 DenseNet -- 12.6 Transfer Learning and Fine-Tuning -- 12.7 Object Detection, Image Segmentation, and Image Classification -- 12.7.1 Visual Geometry Group (VGG) -- 12.7.2 MobileNet -- 12.7.3 Transfer Learning and Fine-Tuning -- 12.7.4 Mask R-CNN -- 12.7.5 DeepLab -- 12.7.6 EfficientNet -- 12.8 Evolution of Image Processing Models -- 12.8.1 Progression of Deep Learning (DL) Architectures -- 12.8.2 Recent Advancements in Computer Vision Research -- 12.8.3 Integration of Multimodal Learning -- 12.8.4 Continual Learning and Lifelong Adaptation -- 12.8.5 Ethical Considerations and Responsible AI -- 12.8.6 Robustness and Adversarial Defense -- 12.8.7 Interpretability and Explainability -- 12.8.8 Domain-Specific Adaptation and Transfer Learning -- 12.8.9 Human-Centric Vision Systems -- 12.9 Challenges and Future Directions -- 12.9.1 Challenges -- 12.9.1.1 Interpretability -- 12.9.1.2 Robustness -- 12.9.1.3 Scalability -- 12.9.1.4 Interpretability -- 12.9.1.5 Robustness -- 12.9.1.6 Scalability -- 12.9.1.7 Interpretability -- 12.9.1.8 Robustness. 327 $a12.9.1.9 Scalability -- 12.9.2 Future Directions -- 12.9.2.1 Multimodal Learning -- 12.9.2.2 Self-Supervised Learning -- 12.9.2.3 Incorporating Domain Knowledge -- 12.9.2.4 Multimodal Learning -- 12.9.2.5 Self-Supervised Learning -- 12.9.2.6 Incorporating Domain Knowledge -- 12.9.2.7 Multimodal Learning -- 12.9.2.8 Self-Supervised Learning -- 12.9.2.9 Incorporating Domain Knowledge -- 12.10 Applications and Impacts -- 12.10.1 Autonomous Driving -- 12.10.2 Medical Imaging -- 12.10.3 Surveillance and Security -- 12.10.4 Societal Impacts -- 12.10.5 Retail and E-Commerce -- 12.10.6 Agriculture -- 12.10.7 Art and Creative Industries -- 12.10.8 Accessibility -- 12.10.9 Environmental Monitoring -- 12.10.10 Industrial Quality Control -- 12.10.11 Augmented Reality (AR) and Virtual Reality (VR) -- 12.10.12 Smart Cities -- 12.10.13 Education -- 12.10.14 Humanitarian Aid and Disaster Response -- 12.11 Conclusion -- References -- Chapter 13 Real-World Applications: Transforming Industries with Computer Vision -- 13.1 Introduction -- 13.1.1 Definition and Brief History of Computer Vision -- 13.1.2 Importance of Computer Vision in Modern Industries -- 13.1.3 Purpose and Structure of the Paper -- 13.2 Healthcare -- 13.2.1 Medical Imaging Analysis -- 13.2.1.1 Use in Early Disease Detection (e.g., Cancer, Diabetic Retinopathy) -- 13.2.1.2 Case Studies and Statistics on Improved Diagnostic Accuracy -- 13.2.2 Robotic Surgery -- 13.2.2.1 Enhancements in Precision and Patient Outcomes -- 13.2.3 Patient Monitoring -- 13.2.3.1 Continuous Monitoring Systems and their Benefits -- 13.3 Manufacturing -- 13.3.1 Quality Control and Defect Detection -- 13.3.1.1 Automated Visual Inspection Systems -- 13.3.1.2 Case Studies on Efficiency and Waste Reduction -- 13.3.2 Predictive Maintenance -- 13.3.2.1 Early Detection of Machinery Issues. 327 $a13.3.2.2 Impact on Reducing Downtime and Extending Machinery Lifespan. 330 $aMaster the cutting-edge field of computer vision and artificial intelligence with this accessible guide to the applications of machine learning and deep learning for real-world solutions in robotics, healthcare, and autonomous systems. 700 $aSandhu$b Jasminder Kaur$01852378 701 $aKumar$b Abhishek$0977677 701 $aSahu$b Rakesh$01852379 701 $aAhuja$b Sachin$01837067 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911034577503321 996 $aApplied Computer Vision Through Artificial Intelligence$94447445 997 $aUNINA