Introduction to Computational Biology
Overview of computational biology as a multidisciplinary field.
The role of mathematics, statistics, and computer science in understanding biological systems.
Bioinformatics and Data Analysis
Tools and techniques used in computational biology to analyze biological data.
Sequence analysis (DNA, RNA, protein sequences).
Gene expression data, and genomic data analysis.
Structural bioinformatics and protein modeling.
Algorithms in Computational Biology
The design and application of algorithms to solve biological problems.
Common algorithms used in sequence alignment, genome assembly, and evolutionary biology.
Applications in Genomics and Proteomics
Genomic sequencing, variant detection, and data interpretation.
Proteomics: Protein structure, folding, and function prediction.
Evolutionary genomics: Phylogenetics, comparative genomics.
Computational Tools and Software
Software and programming languages used in computational biology (e.g., Python, R, bioinformatics tools).
Databases like GenBank, PDB, and others in the field.

Computer Vision and Image Processing: Fundamentals and Applications explores the core concepts and techniques used to analyze and interpret visual information from the world using computational methods. The book serves as a comprehensive guide for understanding the fundamentals of image processing and its application in computer vision, with a strong focus on how these technologies are used to solve real-world problems.
The book begins with a detailed introduction to the fundamentals of image processing, including essential techniques such as image filtering, noise reduction, and edge detection. It then moves on to discuss core computer vision topics, such as object detection, image segmentation, and feature extraction, providing the foundation for understanding how machines can "see" and interpret visual data.
As the field of computer vision is deeply intertwined with machine learning, the authors delve into how deep learning (specifically Convolutional Neural Networks, or CNNs) is revolutionizing image recognition and analysis. The book also addresses practical applications of computer vision in areas such as medical image analysis, autonomous vehicles, facial recognition, and augmented reality.

Advanced topics such as 3D image processing, image stitching, and the integration of computer vision with language processing (e.g., image captioning) are covered in the latter sections, showcasing the diverse potential of these technologies. Additionally, the book discusses current challenges in the field—such as achieving real-time processing and improving accuracy in dynamic, real-world environments—and provides insights into future trends.