High Performance Computer Vision

This repository is an ongoing and layered exploration of image processing and computer vision, primarily using OpenCV in combination with modern C++. It is not a single unified project, but a collection of pedagogical examples and project-based investigations, designed to balance conceptual clarity with hands-on experimentation.

Purpose and Orientation

This repository serves two complementary purposes:

1. Pedagogical Clarity: Many of the implementations are didactic in nature, focusing on core techniques and algorithms from image processing and computer vision. These examples aim to be both instructive and cleanly implemented, emphasizing correctness and readability over premature optimization.

2. Project-Based Inquiry: Alongside the foundational examples, the repository includes a variety of projects — some of which are theoretically informed and others more experimental. These projects fall into two broad categories:

   • Exploratory Projects (Core curiosity. Familiar Territory): Projects that align with ongoing areas of interest or that build upon concepts I've previously studied or engaged with. These tend to focus on refining ideas, testing variations, or developing deeper intuitions through implementation.

   • Exploratory Projects (Risky or Outside Expertise): Projects in this category push beyond my current expertise. They often begin from conceptual uncertainty and evolve through iterative understanding. These are built with the goal of learning-by-building — attempting to internalize unfamiliar ideas by working with them directly.

The broader intent is to use this space as a flexible environment for learning, prototyping, and thinking critically about the structure of visual algorithms, while also extending toward scientific computing with modern C++ as a foundational methodology.

Theoretical and Practical Considerations

The content in this repository draws from a range of academic and practical resources, including:

• Computer Vision: Algorithms and Applications by Richard Szeliski
• Select MOOCs, academic courses, and online materials in the fields of computer vision and scientific programming
• Ongoing engagement with relevant literature and research topics

As the repository matures, additional references will be integrated and annotated where appropriate.

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_{Note: Some of the more “risky” or conceptually ambitious projects are hosted elsewhere on my GitHub profile. I plan to cross-reference them within this repository once I define a suitable indexing scheme or internal structure.}