A Biologically Motivated and Computationally Tractable Model of Low and Mid-Level Vision Tasks 1st edition by Iasonas Kokkinos, Rachid Deriche, Petros Maragos, Olivier Faugeras ISBN 3540219835 9783540219835

A Biologically Motivated and Computationally Tractable Model of Low and Mid-Level Vision Tasks 1st edition by Iasonas Kokkinos, Rachid Deriche, Petros Maragos, Olivier Faugeras – Ebook PDF Instant Download/Delivery. 3540219835, 978-3540219835
Full download A Biologically Motivated and Computationally Tractable Model of Low and Mid-Level Vision Tasks 1st Edition after payment

Product details:

ISBN 10:       3540219835
ISBN 13: 978-3540219835
Author:        Iasonas Kokkinos, Rachid Deriche, Petros Maragos, Olivier Faugeras 
 

A Biologically Motivated and Computationally Tractable Model of Low and Mid-Level Vision Tasks 1st Table of contents:

1. Introduction

   • 1.1 Background and Motivation
   • 1.2 Overview of Biological Vision Systems
   • 1.3 Challenges in Modeling Low and Mid-Level Vision Tasks
   • 1.4 Objectives and Contributions of the Paper
   • 1.5 Structure of the Paper

2. Related Work

   • 2.1 Biological Inspirations for Vision Models
   • 2.2 Computational Models for Low-Level Vision
   • 2.3 Mid-Level Vision Tasks and Scene Understanding
   • 2.4 Advances in Computational Tractability for Vision Systems
   • 2.5 Limitations and Gaps in Existing Approaches

3. Biological Motivation for the Vision Model

   • 3.1 Overview of the Human Visual System
   • 3.2 Low-Level Visual Processing in the Retina and Early Cortical Areas
   • 3.3 Mid-Level Visual Processing in Higher Cortical Regions
   • 3.4 How Biological Constraints Shape Vision Models
   • 3.5 Transfer of Biological Insights to Computational Models

4. Low-Level Vision Tasks

   • 4.1 Edge Detection and Contour Formation
   • 4.2 Color and Texture Perception
   • 4.3 Motion Detection and Optical Flow
   • 4.4 Depth Perception and Stereo Vision
   • 4.5 Computational Models for Low-Level Vision Tasks
   • 4.6 Biological Mechanisms and Corresponding Computational Approaches

5. Mid-Level Vision Tasks

   • 5.1 Object Recognition and Categorization
   • 5.2 Scene Segmentation and Interpretation
   • 5.3 Depth and 3D Structure Recovery
   • 5.4 Integration of Information from Multiple Modalities (e.g., Motion, Depth)
   • 5.5 Computational Models for Mid-Level Vision Tasks
   • 5.6 Bridging the Gap Between Low-Level Features and High-Level Understanding

6. Computational Tractability

   • 6.1 Design Principles for Computationally Efficient Models
   • 6.2 Trade-Offs Between Biological Plausibility and Computational Efficiency
   • 6.3 Real-Time Processing and Efficiency Considerations
   • 6.4 Optimizing Vision Algorithms for Performance
   • 6.5 Scalability and Generalization of the Model

7. Proposed Vision Model

   • 7.1 Model Overview and Architecture
   • 7.2 Integration of Low and Mid-Level Vision Tasks
   • 7.3 Biological Constraints in Model Design
   • 7.4 Use of Hierarchical Processing for Efficient Computation
   • 7.5 Feedback and Top-Down Processing in Vision Systems
   • 7.6 Implementation Details and Algorithms

8. Experimental Setup and Evaluation

   • 8.1 Datasets and Test Images
   • 8.2 Evaluation Metrics: Accuracy, Speed, and Biological Plausibility
   • 8.3 Benchmarking the Model Against State-of-the-Art Methods
   • 8.4 Results from Low-Level Vision Tasks
   • 8.5 Results from Mid-Level Vision Tasks
   • 8.6 Comparative Analysis of Biological Motivation vs. Traditional Approaches

9. Results and Discussion

   • 9.1 Visual and Quantitative Results from Experiments
   • 9.2 Biological Plausibility and Model Performance
   • 9.3 Impact of Computational Tractability on Task Performance
   • 9.4 Generalization to Different Vision Tasks and Environments
   • 9.5 Discussion on the Strengths and Weaknesses of the Model

10. Applications

• 10.1 Robotic Vision: Object Detection and Grasping
• 10.2 Autonomous Systems: Navigation and Scene Understanding
• 10.3 Augmented Reality and Computer Vision Interfaces
• 10.4 Neuroscience and Cognitive Modeling
• 10.5 Vision-Based Medical Imaging and Diagnostics

11. Challenges and Future Directions

    • 11.1 Improving Biological Plausibility without Sacrificing Efficiency
    • 11.2 Extending the Model to Higher-Level Vision Tasks (e.g., Decision Making)
    • 11.3 Real-Time Implementation and Scaling for Complex Environments
    • 11.4 Incorporating Learning and Adaptation into the Model
    • 11.5 Future Directions in Biologically-Inspired Computational Vision

12. Conclusion

    • 12.1 Summary of Contributions and Key Findings
    • 12.2 Practical Implications of the Model for Vision Systems
    • 12.3 Closing Remarks and Future Work

People also search for A Biologically Motivated and Computationally Tractable Model of Low and Mid-Level Vision Tasks 1st:

a biological motive would be
biologically based motivation
causality biology
examples of biological motivations
facts about biological approach