Intro

Around 2010, when Lian and I was working on our gesture recognition demo, out of the frustration to abstract redundant image preprocessing operations into a set of clean and concise functions, I started to consider moving away from the stack. Why? Well, after two years, ccv is the very answer.

Cached Image Preprocessing

Many computer vision tasks nowadays consist of quite a few preprocessing layers: image pyramid generation, color space conversion etc. These potentially redundant operations cannot be easily eliminated within a mature API. ccv provides a built-in cache mechanism that, while maintains a clean function interface, effectively does transparent cache for you.

Easy to Embed

While it depends on quite a few libraries for the best performance and complete feature, ccv's majority functionalities will still work without these libraries. You can even drop the ccv source code into your project, and it will work!

Modern Computer Vision Algorithms

One core concept of ccv development is "application driven". As a result, ccv end up implementing a handful state-of-art algorithms. It includes a very fast detection algorithm for rigid object (face etc.), an accurate object detection algorithm for somewhat difficult object (pedestrian, cat etc.), a state-of-art text detection algorithm, a long term object tracking algorithm, and the long-standing feature point detection algorithm.

For computer vision community, there is no shortage of good algorithms, good implementation is what it lacks of. After years, we stuck in between either the high-performance, battle-tested but old algorithm implementations, or the new, shining but Matlab algorithms. ccv is my take on this problem, hope you enjoy it.