Multicore processing was a paradigm shift in computer science. The move was such big that today its really hard to find single-core CPUs even on low power SBCs. Computer vision algorithms, from simple pixel manipulations to the more complex tasks like classification with deep neural networks, have the potential to run parallel on multi cores. […]

Single Instruction Multiple Data (SIMD), also known as vectorization, is a powerful technique for accelerating computer vision algorithms. In this post, I will explain the concept and then introduce an easy way to use it inside your codes. We will see how we can benefit from SIMD to further reduce the runtime of the Gaussian-blur […]

The future of computer vision and machine learning is toward edge devices. This is why C++ matters. Following my previous post on simple and general tips to optimize C++ codes, I decided to explain further tips that are specified for implementing computer vision algorithms.

QR codes can store different types of data. Recently, I was involved in a project that a robot had to detect and decode pre-installed QR codes to refine its position. This post explains how I got 10 FPS performance on a Raspberry Pi Zero.

When I was coding in Matlab, there were some techniques to boost the execution time. Predefining the matrix size, using vectorization and importing C codes via Matlab Executable (.MEX) files were some of the well-known solutions. Similar solutions exist for Python codes. But the story is somewhat different with C++. This comes from the fact […]

When it comes to embedded computer vision, fractions of code acceleration are regarded as a huge success for programmers. Today, I’ll explain how to build a customized OpenCV for Raspberry Pi as one of the most famous single board computers. By following these simple tips, you’ll experience a 2-3x faster OpenCV on your board.