Statistical Region Merging Benchmark

The following reposiroty contains a benchmark which compares different implementations of the Statistical Region Merging algorithm. <

Given that many image processing and computer vision algorithms are frequently utilized in their 'Pythonic' form, which introduces a notable performance overhead, implementing the algorithm in a highly efficient and fast programming language like C++ was an obvious choice. However, considering the well-known challenges associated with memory management in C++, implementing the algorithm in the Rust programming language presents a compelling alternative, as it offers both high performance and strong memory safety guarantees.

The implementations are benchmarked against the Berkely Image Segmentation Dataset 500 (BSDS500), a widely used dataset for image segmentation (even if for Deep Learning and Computer Vision models), and then I measure the resource consumption of each implementation.

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Requirements

Component	Version
OpenCV	4.5.1
C++	10.2.1
Rust	1.81.0
Python	3.12.4
pip	24.2
CMake	3.18.4
Make	4.3

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Disclaimer

This repo is meant to contain a benchmark, each directory however contains a standalone implementation.

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Project Structure

SRM-Benchmark
├── assets
├── BSDS500-Benchmark
├── libsrm_cpp
├── libsrm_py
└── libsrm_rust

Every directory aside from the assets one, contains a specific implementation wich detailed README.md regarding the benchmark, and the BSDS500-Benchmark contains the benchmark on the BSDS500 dataset.

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System Specifications

The hardware specs of the machine on which the benchmark was run are:

Model name:                           Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz
CPU MHz:                              3998.428

The software specs are:

Operating System Information:
Description:    Debian GNU/Linux 11 (bullseye)
Release:        11

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