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

There is a large growth in hardware and software systems capable of producing vast amounts of image and video data. These systems are rich sources of continuous image and video streams. This motivates researchers to build scalable computer vision systems that utilize data-streaming concepts for processing of visual data streams. However, several challenges exist in building large-scale computer vision systems. For example, computer vision algorithms have different accuracy and speed profiles depending on the content, type, and speed of incoming data. Also, it is not clear how to adaptively tune these algorithms in large-scale systems. These challenges exist because we lack formal frameworks for building and optimizing large-scale visual processing. This paper presents formal methods and algorithms that aim to overcome these challenges and improve building and optimizing large-scale computer vision systems. We describe a formal algebra framework for the mathematical description of computer vision pipelines for processing image and video streams. The algebra naturally describes feedback control and provides a formal and abstract method for optimizing computer vision pipelines. We then show that a general optimizer can be used with the feedback-control mechanisms of our stream algebra to provide a common online parameter optimization method for computer vision pipelines.

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; Vol.44 No.2 February 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 905-923

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

PARAMETER TUNING
PERFORMANCE OPTIMIZATION
STREAM ALGEBRA
ONLINE VISION ALGORITHMS
STREAM PROCESSING
LARGE SCALE COMPUTER VISION SYSTEMS

Info Detail Spesifik

DOI: 10.1109/TPAMI.2020.3015867

Pernyataan Tanggungjawab

Mohamed A. Helala, Faisal Z. Qureshi, Ken Q. Pu

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