Difference between revisions of "Vision"
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| − | + | Computer vision involves developing algorithms and techniques to enable computers to interpret, analyze, and understand visual data from the world around them. It focuses on developing methods for computers to perceive, analyze, and interpret digital images or videos in a way that simulates human vision. Computer vision involves tasks such as object detection, image recognition, segmentation, tracking, and scene reconstruction. It is used in a wide range of applications, including robotics, autonomous vehicles, security and surveillance, medical imaging, and augmented reality. | |
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Revision as of 11:11, 26 March 2023
YouTube ... Quora ...Google search ...Google News ...Bing News
- Case Studies
- Capabilities
- Video ... Generated Image ... Vision ... Colorize ... Image/Video Transfer Learning
- End-to-End Speech ... Synthesize Speech ... Speech Recognition
- Predict image
- Screening; Passenger, Luggage, & Cargo
- DeepLens - deep learning enabled video camera
- Rekognition Video
- Deep Learning (DL) Amazon Machine Image (AMI) - DLAMI
- Image Classification
- Image-to-Image Translation
- Landing AI ... LandingLens™, an enterprise AIOps/MLOps platform that offers to build, iterate, and operationalize AI powered visual inspection solutions for manufacturers
- Object Detection Using Convolutional Neural Networks | The Straight Dope
- Object detection with neural networks — a simple tutorial using keras | Johannes Rieke - Towards Data Science
- Object Localization and Detection | leonardoaraujosantos
- Why Tesla Invented A New Neural Network | Ambika Choudhury - Analytics India Magazine
Computer vision involves developing algorithms and techniques to enable computers to interpret, analyze, and understand visual data from the world around them. It focuses on developing methods for computers to perceive, analyze, and interpret digital images or videos in a way that simulates human vision. Computer vision involves tasks such as object detection, image recognition, segmentation, tracking, and scene reconstruction. It is used in a wide range of applications, including robotics, autonomous vehicles, security and surveillance, medical imaging, and augmented reality.
Image Retrieval / Object Detection
- Feature:
- Character Recognition
- (Deep) Convolutional Neural Network (DCNN/CNN)
- ResNet-50
- Getting Started & Project: Object Detection
Faster Region-based Convolutional Neural Networks (R-CNN), You only Look Once (YOLO), Single Shot Detector(SSD)
LiDAR
