1 / 7

Exploring the Intersection: Computer Vision Evolution and Challenges

Delve into the history, progress, and current challenges of computer vision from its inception in the 1960s to the present day. Explore key milestones such as foundational work in image formation, geometric analysis, probabilistic learning approaches, and successful applications in various fields. The central questions of understanding scenes from images, the interplay of bottom-up and top-down information, segmentation and recognition interaction, and dynamic scene understanding are examined. Uncover the evolution of computer vision, from early vision to static and dynamic scene understanding, and the transition from images to object recognition.

aubreym
Download Presentation

Exploring the Intersection: Computer Vision Evolution and Challenges

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The Hilbert Problems of Computer Vision Jitendra Malik

  2. Forty years of computer vision 1963-2003 • 1960s: Beginnings in artificial intelligence, image processing and pattern recognition • 1970s: Foundational work on image formation: Horn, Koenderink, Longuet-Higgins … • 1980s: Vision as applied mathematics: geometry, multi-scale analysis, control theory, optimization … • 1990s: • Geometric analysis largely completed • Probabilistic/Learning approaches in full swing • Successful applications in graphics, biometrics, HCI …

  3. And now … • Back to basics: the classic problem of understanding the scene from its image/s • Central question: Interplay of bottom-up and top-down information

  4. Early Vision • What can we learn from image statistics that we didn't know already? • How far can bottom-up image segmentation go? • How do we make inferences from shading and texture patterns in natural images?

  5. Static Scene Understanding • What is the interaction between segmentation and recognition? • What is the interaction between scenes, objects, and parts? • What is the role of design vs. learning in recognition systems?

  6. Dynamic Scene Understanding • What is the role of high-level knowledge in long range motion correspondence? • How do we find and track articulated structures? • How do we represent "movemes" and actions?

  7. From Images to Objects "I stand at the window and see a house, trees, sky. Theoretically I might say there were 327 brightnesses and nuances of colour. Do I have "327"? No. I have sky, house, and trees." --Max Wertheimer

More Related