ورود به حساب

نام کاربری گذرواژه

گذرواژه را فراموش کردید؟ کلیک کنید

حساب کاربری ندارید؟ ساخت حساب

ساخت حساب کاربری

نام نام کاربری ایمیل شماره موبایل گذرواژه

برای ارتباط با ما می توانید از طریق شماره موبایل زیر از طریق تماس و پیامک با ما در ارتباط باشید


09117307688
09117179751

در صورت عدم پاسخ گویی از طریق پیامک با پشتیبان در ارتباط باشید

دسترسی نامحدود

برای کاربرانی که ثبت نام کرده اند

ضمانت بازگشت وجه

درصورت عدم همخوانی توضیحات با کتاب

پشتیبانی

از ساعت 7 صبح تا 10 شب

دانلود کتاب Intelligent Systems and Applications in Computer Vision

دانلود کتاب سیستم های هوشمند و برنامه های کاربردی در بینایی کامپیوتر

Intelligent Systems and Applications in Computer Vision

مشخصات کتاب

Intelligent Systems and Applications in Computer Vision

ویرایش:  
نویسندگان: , , ,   
سری:  
ISBN (شابک) : 9781003453406 
ناشر: CRC Press 
سال نشر: 2024 
تعداد صفحات: 341 
زبان: English 
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) 
حجم فایل: 42 Mb 

قیمت کتاب (تومان) : 44,000

در صورت ایرانی بودن نویسنده امکان دانلود وجود ندارد و مبلغ عودت داده خواهد شد



ثبت امتیاز به این کتاب

میانگین امتیاز به این کتاب :
       تعداد امتیاز دهندگان : 4


در صورت تبدیل فایل کتاب Intelligent Systems and Applications in Computer Vision به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.

توجه داشته باشید کتاب سیستم های هوشمند و برنامه های کاربردی در بینایی کامپیوتر نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.


توضیحاتی درمورد کتاب به خارجی



فهرست مطالب

Cover
Half Title
Title Page
Copyright Page
Table of Contents
About the Editors
List of Contributors
Chapter 1 A Review Approach On Deep Learning Algorithms in Computer Vision
	1.1 Introduction
	1.2 Deep Learning Algorithms
		1.2.1 Convolutional Neural Networks
		1.2.2 Restricted Boltzmann Machines
		1.2.3 Deep Boltzmann Machines
		1.2.4 Deep Belief Networks
		1.2.5 Stacked (de-Noising) Auto-Encoders
			1.2.5.1 Auto-Encoders
			1.2.5.2 Denoising Auto Encoders
	1.3 Comparison of the Deep Learning Algorithms
	1.4 Challenges in Deep Learning Algorithms
	1.5 Conclusion and Future Scope
	References
Chapter 2 Object Extraction From Real Time Color Images Using Edge Based Approach
	2.1 Introduction
	2.2 Applications of Object Extraction
	2.3 Edge Detection Techniques
		2.3.1 Roberts Edge Detection
		2.3.2 Sobel Edge Detection
		2.3.3 Prewitt’s Operator
		2.3.4 Laplacian Edge Detection
	2.4 Related Work
	2.5 Proposed Model
	2.6 Results and Discussion
	2.7 Conclusion
	References
Chapter 3 Deep Learning Techniques for Image Captioning
	3.1 Introduction to Image Captioning
		3.1.1 How Does Image Recognition Work?
	3.2 Introduction to Deep Learning
		3.2.1 Pros of the Deep Learning Algorithm
		3.2.2 Customary / Traditional CV Methodology
		3.2.3 Limitations/challenges of Traditional CV Methodology
		3.2.4 Overcome the Limitations of Deep Learning
	3.3 Deep Learning Algorithms for Object Detection
		3.3.1 Types of Deep Models for Object Detection
	3.4 How Image Captioning Works
		3.4.1 Transformer Based Image Captioning
		3.4.2 Visual Scene Graph Based Image Captioning
	3.4.3 Challenges in Image Captioning
	3.5 Conclusion
	References
Chapter 4 Deep Learning-Based Object Detection for Computer Vision Tasks: A Survey of Methods and Applications
	4.1 Introduction
	4.2 Object Detection
	4.3 Two-Stage Object Detectors
		4.3.1 R-CNN
		4.3.2 SPPNet
		4.3.3 Fast RCNN
		4.3.4 Faster RCNN
		4.3.5 R-FCN
		4.3.6 FPN
		4.3.7 Mask RCNN
		4.3.8 G-RCNN
	4.4 One-Stage Object Detectors
		4.4.1 YOLO
		4.4.2 CenterNet
		4.4.3 SSD
		4.4.4 RetinaNet
		4.4.5 EfficientDet
		4.4.6 YOLOR
	4.5 Discussion On Model Performance
		4.5.1 Future Trends
	4.6 Conclusion
	References
Chapter 5 Deep Learning Algorithms for Computer Vision: A Deep Insight Into Principles and Applications
	5.1 Introduction
	5.2 Preliminary Concepts of Deep Learning
		5.2.1 Artificial Neural Network
		5.2.2 Convolution Neural Network (CNNs)
	5.3 Recurrent Neural Network (RNNs)
	5.4 Overview of Applied Deep Learning in Computer Vision
	5.6 Industrial Applications of Computer Vision
	5.7 Future Scope in Computer Vision
	5.8 Conclusion
	References
Chapter 6 Handwritten Equation Solver Using Convolutional Neural Network
	6.1 Introduction
	6.2 State-Of-The-Art
	6.3 Convolutional Neural Network
		6.3.1 Convolution Layer
		6.3.2 Pooling Layer
		6.3.3 Fully Connected Layer
		6.3.4 Activation Function
	6.4 Handwritten Equation Recognition
		6.4.1 Dataset Preparation
		6.4.2 Proposed Methodology
			6.4.2.1 Dataset Acquisition
			6.4.2.2 Preprocessing
			6.4.2.3 Recognition Through CNN Model
			6.4.2.4 Processing Inside CNN Model
		6.4.3 Solution Approach
	6.5 Results and Discussion
	6.6 Conclusion and Future Scope
	References
Chapter 7 Agriware: Crop Suggester System By Estimating the Soil Nutrient Indicators
	7.1 Introduction
	7.2 Related Work
	7.3 Proposed Methodology
	7.4 Experimental Results and Discussion
	7.5 Conclusion and Future Work
	References
Chapter 8 A Machine Learning Based Expeditious Covid-19 Prediction Model Through Clinical Blood Investigations
	8.1 Introduction
	8.2 Literature Survey
	8.3 Methodology
		8.3.1 Dataset and Its Preparation
		8.3.2 Classification Set Up
		8.3.3 Performance Evaluation
	8.4 Results and Discussion
	8.5 Conclusion
	References
Chapter 9 Comparison of Image Based and Audio Based Techniques for Bird Species Identification
	9.1 Introduction
	9.2 Literature Survey
	9.3 Methodology
	9.4 System Design
		9.4.1 Dataset Used
		9.4.2 Image Based Techniques
		9.4.3 Audio Based Techniques
	9.5 Results and Analysis
	9.6 Conclusion
	References
Chapter 10 Detection of Ichthyosis Vulgaris Using SVM
	10.1 Introduction
	10.2 Literature Survey
	10.3 Types of Ichthyosis
		10.3.1 Ichthyosis Vulgaris
		10.3.2 Hyperkeratosis
	10.4 Sex-Connected Ichthyosis
	10.5 Symptoms
	10.6 Complications
	10.7 Diagnosis
	10.8 Methodology
	10.9 Results
	10.10 Future Work
	10.11 Conclusion
	References
Chapter 11 Chest X-Ray Diagnosis and Report Generation: Deep Learning Approach
	11.1 Introduction
	11.2 Literature Review
	11.3 Proposed Methodology
		11.3.1 Overview of Deep Learning Algorithms
		11.3.2 Data
		11.3.3 Feature Extraction
		11.3.4 Report Generation
		11.3.5 Evaluation Metrics
	11.4 Results and Discussions
		11.4.1 Feature Extraction
		11.4.2 Report Generation
	11.5 Conclusion
	References
Chapter 12 Deep Learning Based Automatic Image Caption Generation for Visually Impaired People
	12.1 Introduction
	12.2 Related Work
	12.3 Methods and Materials
		12.3.1 Data Set
		12.3.2 Deep Neural Network Architectures
			12.3.2.1 Convolution Neural Networks (CNNs)
			12.3.2.2 Long Short-Term Memory (LSTM)
		12.3.3 Proposed Model
			12.3.3.1 Feature Extraction Models
			12.3.3.2 Workflow for Image Caption Generation
	12.4 Results and Discussion
		12.4.1 Evaluation Metrics
		12.4.2 Analysis of Results
		12.4.3 Examples
	12.5 Discussion and Future Work
	12.6 Conclusions
	References
Chapter 13 Empirical Analysis of Machine Learning Techniques Under Class Imbalance and Incomplete Datasets
	13.1 Introduction
	13.2 Related Work
		13.2.1 Class Imbalance
		13.2.2 Missing Values
		13.2.3 Missing Value in Class Imbalance Datasets
	13.3 Methodology
	13.4 Results
		13.4.1 Overall Performance
		13.4.2 Effect of Class Imbalance and Missing Values
	13.5 Conclusion
	References
Chapter 14 Gabor Filter as Feature Extractor in Anomaly Detection From Radiology Images
	14.1 Introduction
	14.2 Literature Review
	14.3 Research Methodology
		14.3.1 Data Set
		14.3.2 Gabor Filter
	14.4 Results
	14.5 Discussion
	14.6 Conclusion
	References
Chapter 15 Discriminative Features Selection From Zernike Moments for Shape Based Image Retrieval System
	15.1 Introduction
	15.2 Zernike Moments Descriptor (ZMD)
		15.2.1 Zernike Moments (ZMs)
		15.2.2 Orthogonality
		15.2.3 Rotation Invariance
		15.2.4 Features Selection
	15.3 Discriminative Features Selection
	15.4 Similarity Measure
	15.5 Experimental Study
		15.5.1 Experiment Setup
		15.5.2 Performance Measurement
		15.5.3 Experiment Results
	15.6 Discussions and Conclusions
	References
Chapter 16 Corrected Components of Zernike Moments for Improved Content Based Image Retrieval: A Comprehensive Study
	16.1 Introduction
	16.2 Proposed Descriptors
		16.2.1 Invariant Region Based Descriptor Using Corrected ZMs Features
		16.2.2 Selection of Appropriate Features
		16.2.3 Invariant Contour Based Descriptor Using HT
	16.3 Similarity Metrics
	16.4 Experimental Study and Performance Evaluation
		16.4.1 Measurement of Retrieval Accuracy
		16.4.2 Performance Comparison and Experiment Results
	16.5 Discussion and Conclusion
	References
Chapter 17 Translate and Recreate Text in an Image
	17.1 Introduction
	17.2 Literature Survey
	17.3 Existing System
	17.4 Proposed System
		17.4.1 Flow Chart
		17.4.2 Experimental Setup
		17.4.3 Dataset
		17.4.4 Text Detection and Extraction
		17.4.5 Auto Spelling Correction
		17.4.6 Machine Translation and Inpainting
	17.5 Implementation
		17.5.1 Text Detection and Extraction
		17.5.2 Auto Spelling Correction
			17.5.2.1 Simple RNN
			17.5.2.2 Embed RNN
			17.5.2.3 Bidirectional LSTM
			17.5.2.4 Encoder Decoder With LSTM
			17.5.2.5 Encoder Decoder With Bidirectional LSTM + Levenshtein Distance
		17.5.3 Machine Translation
		17.5.4 Inpainting
	17.6 Result Analysis
		17.6.1 Simple RNN
		17.6.2 Embed RNN
		17.6.3 Bidirectional LSTM
		17.6.4 Encoder Decoder With LSTM
		17.6.5 Encoder Decoder With Bidirectional LSTM + Levenshtein Distance
		17.6.6 BLEU (Bilingual Evaluation Understudy)
	17.7 Conclusion
	Acknowledgments
	References
Chapter 18 Multi-Label Indian Scene Text Language Identification: Benchmark Dataset and Deep Ensemble Baseline
	18.1 Introduction
	18.2 Related Works
	18.3 IIITG-MLRIT2022
	18.4 Proposed Methodology
		18.4.1 Transfer Learning
			18.4.1.1 ResNet50 [37]
			18.4.1.2 XceptionNet [39]
			18.4.1.3 DenseNet [38]
			18.4.1.4 MobileNetV2 [36]
		18.4.2 Convolutional Neural Network
		18.4.3 Multi-Label Deep Ensemble Via Majority Voting
		18.4.4 Weighted Binary Cross Entropy
	18.5 Training and Experiment
	18.6 Results and Discussion
	18.7 Conclusion
	References
Chapter 19 AI Based Wearables for Healthcare Applications: A Survey of Smart Watches
	19.1 Introduction
	19.2 Systematic Review
		19.2.1 Criterion to Select Research
		19.2.2 Source of Information
			19.2.2.1 Search Plan
			19.2.2.2 Data Abstraction
		19.2.3 Outcomes
		19.2.4 Healthcare Applications
			19.2.4.1 Activity and Human Motion
			19.2.4.2 Healthcare Education
		19.2.5 Ideal Smart watch Characteristics
			19.2.5.1 Operating System
			19.2.5.2 Sensors
	19.3 Discussion
	19.4 Concluding Remarks
	References
Chapter 20 Nature Inspired Computing for Optimization
	20.1 Introduction
	20.2 Components of Nature-Inspired Computing
		20.2.1 Fuzzy Logic Based Computing
		20.2.2 Artificial Neural Networks
		20.2.3 Search and Optimization Approaches
			20.2.3.1 Evolutionary Computing
	20.3 Swarm Intelligence
		20.3.1 Particle Swarm Optimization (PSO)
		20.3.2 Ant Colony Optimization (ACO)
		20.3.3 Artificial Bee Colony (ABC)
	20.4 Physics Or Chemistry-Based Search and Optimization Approaches
		20.4.1 Intelligent Water Drops Algorithm (IWD)
		20.4.2 EM (Electromagnetism-Like Mechanism) Algorithm
		20.4.3 Gravitational Search Algorithm (GSA)
	20.5 Conclusion
	References
Chapter 21 Automated Smart Billing Cart for Fruits
	21.1 Introduction
	21.2 Literature Survey
	21.3 Proposed Method
		21.3.1 System Design
	21.4 Implementation
	21.5 Results and Discussions
	21.6 Results
	21.7 Conclusion
	References
Index




نظرات کاربران