Sesion Computer Algebra for Digital Image Processing and Computer Vision, ACA 2024

 Organizers:

Damian Valdés Santiago, PhD. Faculty of Mathematics and Computer Science, University of Havana, Cuba, dvs89cs@matcom.uh.cu

Ángela M. León Mecías, PhD. Faculty of Mathematics and Computer Science, University of Havana, Cuba, angela@matcom.uh.cu

Marta Lourdes Baguer Díaz-Romañach, PhD. Faculty of Mathematics and Computer Science, University of Havana, Cuba, mbaguer@matcom.uh.cu

José Alejandro Mesejo Chiong, PhD. Faculty of Mathematics and Computer Science, University of Havana, Cuba, mesejo@matcom.uh.cu

 

Overview:

Computer algebra plays a crucial role in advancing digital image processing and computer vision by providing powerful mathematical tools for analyzing and manipulating images. This scientific section focuses on the application of algebraic techniques to enhance image processing algorithms and improve the accuracy of computer vision systems.

In image processing, computer algebra systems enable symbolic computation, allowing for precise mathematical operations on image data. Algebraic methods help in developing complex algorithms for image enhancement, segmentation, feature extraction, and pattern recognition. By leveraging algebraic structures, image processing tasks can be optimized for efficiency and speed.

There are applications in Computer Vision to Geometric Transformations such as rotation, scaling, and translation on images; Camera Calibration for correcting distortions, and improving the accuracy of depth perception in computer vision systems; and Object Recognition by modeling shapes, textures, and patterns within images.

We are interested in the following Research Areas (but not exclude other related areas):

• Algebraic Image Analysis: Studying the algebraic properties of images to develop new analysis techniques.
• Sparse Representation: Using algebraic structures for sparse representation of images to reduce data redundancy.
• Machine and Deep Learning Integration: Exploring the integration of computer algebra with deep learning for improved image processing and feature extraction.
• Hybrid Approaches: Combining computer algebra with machine learning techniques for more robust image analysis.
• Real-time Processing: Developing algebraic algorithms optimized for real-time image processing applications.
• Interdisciplinary Research: Collaborating with experts from mathematics, computer science, physicians and engineering to push the boundaries of image processing and computer vision using algebraic methods.

In conclusion, the integration of computer algebra into digital image processing and computer vision opens up new avenues for research and innovation, leading to more accurate, efficient, and advanced imaging systems with diverse applications across various industries.

If you are interested in proposing a talk, please send an abstract to Damian Valdés Santiago (dvs89cs@matcom.uh.cu). Please use this LaTeX template for your abstract and send both the LaTeX source and a compiled PDF version. We suggest that abstracts be at least half a page including references.