A Stream Algebra for Performance Optimization of Large Scale Computer Vision Pipelines

Area of Science:

• Computer Science
• Artificial Intelligence
• Image Processing

Background:

• Rapid growth in image and video data generation necessitates scalable computer vision systems.
• Existing systems face challenges in adaptively tuning algorithms due to varying data characteristics and speeds.
• Lack of formal frameworks hinders the optimization of large-scale visual processing pipelines.

Purpose of the Study:

• To present formal methods and algorithms for building and optimizing large-scale computer vision systems.
• To introduce a formal algebra for mathematically describing computer vision pipelines processing data streams.
• To enable adaptive tuning and optimization of computer vision algorithms in real-time.

Main Methods:

• Developed a formal algebra framework for describing computer vision pipelines.
• Integrated feedback control mechanisms within the stream algebra.
• Utilized a general optimizer with feedback control for online parameter optimization.

Main Results:

• The formal algebra provides a mathematical description of computer vision pipelines for image and video streams.
• The framework naturally incorporates feedback control for adaptive system behavior.
• A common online parameter optimization method is demonstrated for computer vision pipelines.

Conclusions:

• The proposed formal methods and algebra overcome challenges in building and optimizing large-scale computer vision systems.
• The framework facilitates adaptive algorithm tuning and enhances the efficiency of visual data stream processing.
• This work provides a foundation for developing more robust and scalable computer vision applications.