Computer Vision and Image Analysis for Industry 4.0
GBP 89.99
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| ISBN13: | 9781032164168 |
| ISBN10: | 1032164166 |
| Kötéstípus: | Keménykötés |
| Terjedelem: | 213 oldal |
| Méret: | 254x178 mm |
| Súly: | 720 g |
| Nyelv: | angol |
| Illusztrációk: | 110 Illustrations, black & white; 110 Line drawings, black & white |
| 619 |
Villamosmérnöki tudományok, híradástechnika, műszeripar
Energetika, energiaipar
A számítástudomány elmélete, a számítástechnika általában
Számítógép architektúrák, logikai tervezés
Szoftverfejlesztés
Mesterséges intelligencia
Digitális jel-, hang- és képfeldolgozás
Környezetmérnöki tudományok
Internetes szolgálatások (online vásárlás, bankolás)
Computer vision and image analysis play an essential role in 4.0 IR as machine vision and image analysis are indispensable components of every automated environment. Modern machine vision and image analysis techniques play key roles in automation and quality assurance.
Computer vision and image analysis are indispensable components of every automated environment. Modern machine vision and image analysis techniques play key roles in automation and quality assurance. Working environments can be improved significantly if we integrate computer vision and image analysis techniques. The more advancement in innovation and research in computer vision and image processing, the greater the efficiency of machines as well as humans. Computer Vision and Image Analysis for Industry 4.0 focuses on the roles of computer vision and image analysis for 4.0 IR-related technologies. The text proposes a variety of techniques for disease detection and prediction, text recognition and signature verification, image captioning, flood level assessment, crops classifications and fabrication of smart eye-controlled wheelchairs.
- A Benchmark Dataset for Document Level Offline Bangla Handwritten Text Recognition (HTR) and Line Segmentation. 2. A New Approach Using Convolutional Neural Network for Crops and Weeds Classification. 3. Lemon Fruits Detection and Instance Segmentation Under Orchard Environment Using Mask R-CNN and YOLOv5. 4. A Deep Learning Approach in Detailed Fingerprint Identification. 5. Probing Skin Lesions and Performing Classification of Skin Cancer Using Efficient Net while Resolving Class Imbalance Using SMOTE. 6. Advanced Grad CAM: Improved Visual Explanations of CNN?s decision in Diabetic Retinopathy. 7. Bangla Sign Language Recognition Using Concatenated BDSL Network. 8. Chest Xray Net: A Multi-class Deep Convolutional Neural Networks for Detecting Abnormalities in Chest X-Ray Images. 9. Achieving Human Level Performance on the Original Omniglot Challenge. 10. A Real-Time Classification Model for Bengali Character Recognition in Air-Writing. 11. A Deep Learning Approach for Covid-19 Detection in Chest X-Rays. 12. Automatic Image Captioning Using Deep Learning. 13. A Convolutional Neural Network Based Approach to Recognize Bangla Handwritten Characters. 14. Flood Region Detection Based on K-Means Algorithm and Color Probability. 15. Fabrication of Smart Eye Controlled Wheelchair for Disabled Person.
