Computer vision - Applications and Trends
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This document summarizes computer vision applications and trends. It discusses how computer vision works by turning light into digital formats and bridging pixels and meaning. Early applications included structure from motion, 3D stereo, object tracking and face detection. Current applications include object detection, recognition, tracking and camera image signal processing. Challenges include achieving deterministic time/output through deep learning, handling heterogeneous processing and improving robustness through multiple sensors. The future involves deeper integration of computer vision and robotics/autonomous vehicles using deep learning across perception, world modeling, planning and control.
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Computer vision - Applications and Trends
- 1. Computer Vision Applications and Trends - Kshitij Agrawal https://www.linkedin.com/in/agrawalkshitij/
- 2. About Me • Computer Vision Engineer at Intel Past • BTech (2013), MS (2015) – IIIT Hyderabad • Multiple publications in IEEE conferences • DRDO, Tonbo Imaging Exp with • camera ISP, hardware acceleration • Scene segmentation, object detection, tracking • Computer vision in the cloud
- 3. What Is Computer Vision? Slide Source: CS131 –Fei Fei Li- Stanford
- 4. TURNING LIGHT INTO A DIGITAL FORMAT Img. Source: www.digitalcameraworld.com HERE WE ARE
- 5. What is Computer Vision Related To? Img. Source: Brian Thorne, University of Cantenbury Bridging the gap between pixels and meaning
- 6. Computer Vision – Early 90-2000s a) Structure from motion b) 3D from stereo c) Object tracking d) Face detection
- 7. Img. Source: James Bareham / The Verge Source : INRIA Pose Esimation Computer Vision Applications In the WILD Img. Source : Skydio youtube channel CAMERAS ARE IN ALL ELECTRONIC DEVICES Mobiles Movie production, VR Smart Devices Drones Now
- 8. Object Detection Object Recognition Object Tracking Camera Image Signal Processing (ISP) Infra-Red (IR) Sensor Hardware Acceleration Urban IR Image during day– White areas are hot water-tanks Real time processing Custom Hardware Multiple “intelligent” Application areas Needs to be highly optimized For power and computation Noise removal Image enhancements Compression Storage/Transmission Img. Source: Tonbo Imaging. Images are property of respective copyright holders.
- 9. Image Processing in the Cloud Image Input Client Image processing micro service Image processing micro service Image processing micro service Central DB Image Queue . . . Worker Pool • Automatic placement of images in games • “lazy load” of modified assets in client • Prediction of displaying best ad unit for a game
- 10. Identifying Visual Defects • What are the defects – corrosion, holes etc • Classical approaches – GLCM, HSV
- 11. Design Considerations • Batch processing • Server based + maybe accelerated • Transfer speeds matter • Higher data >TBs • Training (Deep Learning) Cloud Processing Edge Processing • Real time • Embedded + Accelerator • Less data <GBs • Inference (Deep Learning)
- 12. Autonomous Driving breaks all - 4TB of data per hour Img Source: For Self-Driving Cars, There’s Big Meaning Behind One Big Number: 4 Terabytes- Intel Editorial by Kathy Winters
- 13. Towards Deep Learning Human accuracy Alexnet ResNet, Inception v1
- 14. What Can DL Do ? • Imagine a 3 year old child • He has seen a Trillion images by age of 3 • Give data and DL architectures will amaze
- 16. Rust Identification for visual inspection
- 17. Semantic Segmentation for Autonomous Driving • Able to identify objects and their type • Can use this for the next step Img. Source: Cityscapes dataset
- 19. Breaking AI Minds Img Source: Adversarial Patch – TB Brown et al
- 20. Future Pixels KeyPoints SIFT Features Labels Deformable Part Model Sensors Perception World Model Action Planning & Control 2012: Computer Vision Future: Robotics and Autonomous Vehicles DEEP LEARNING DEEP LEARNING
- 21. Challenges that remain • Deterministic time and output through DL • Heterogeneous processing Problems to Solve • Robustness – increased by multiple sensors • Accuracy – solved by Deep Learning • Data explosion – Even on the edge a car generates 4 TB /hour
- 23. Resources • Richard Szeliski – Computer Vision Algorithms & Applciations • Rafael C. Gonzalez – Digital Image Processing • Gary Bradski and Adrian Kaehler – Learning Opencv • Standford and Univ Toronto Lectures
- 24. State of Industry • Total computer vision market expected at $33.3 billion in 2019 Key Areas of Applications • Implementing application endpoints with idea of cv • Hardware and software
- 25. Companies
Editor's Notes
- What it takes to go from a project to a product – Computer Vision in the wild
- Deep learning evolved from a need to replicated the function of the human brain – each “layer” picks up higher level features and gathers semantic meaning.
- Software 2.0 is not going to replace 1.0 (indeed, a large amount of 1.0 infrastructure is needed for training and inference to “compile” 2.0 code), but it is going to take over increasingly large portions of what Software 1.0 is responsible for today.