# DennisLiu1993/Fastest_Image_Pattern_Matching

C++ implementation of a ScienceDirect paper "An accelerating cpu-based correlation-based image alignment for real-time automatic optical inspection"

Repository: https://github.com/DennisLiu1993/Fastest_Image_Pattern_Matching
Canonical: https://ross.abutalabs.com/products/fastest_image_pattern_matching
Language: C++
License: BSD-2-Clause
License Family: permissive
Topics: image-alignment, image-match, image-recognition, ncc, normalized-cross-correlation, opencv, pattern-finding, pattern-quick, pattern-matching, template-matching
Last push: 2026-01-20T23:55:39+00:00

## Health v2 (maintenance only)
Score: 61/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 63, release rhythm 35, longevity 100
- inputs: {"age_days": 1581, "days_push": 225, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1151, forks 269 (observed 2026-08-28T04:03:46.810622+00:00)

## What it is
A C++ library implementing an accelerated Normalized Cross Correlation (NCC)-based template matching and image alignment algorithm, based on a ScienceDirect paper for real-time automatic optical inspection. It uses image pyramids, rotation invariance, and SIMD (including ARM Neon) optimizations, with Python bindings via Pybind11.

## Use cases
- find a template image inside a larger image fast
- rotation-invariant template matching with OpenCV
- real-time automatic optical inspection pattern alignment
- locate objects in high-resolution images faster than cv2.matchTemplate
- match patterns at multiple angles and positions
- use fast NCC matching from Python
- industrial vision inspection alignment

## When to choose
- you need faster-than-OpenCV NCC template matching, especially on large images
- you need rotation-invariant matching with sub-degree precision
- you are building AOI (automatic optical inspection) or machine vision alignment pipelines
- you want Python bindings for a fast C++ matching core

## When to avoid
- you need deep-learning-based object detection robust to appearance changes
- you need matching under scale changes or perspective distortion
- you need a pure-Python or easily portable solution without C++ compilation
- your templates are tiny and OpenCV's matchTemplate is already fast enough

## Facets
- artifact type: library
- maturity: active
- function: image-processing, computer-vision, search-engine
- domain: computer-vision, image-processing, machine-learning
- platform: cpp, python, windows, cross-platform
- tags: template-matching, ncc, normalized-cross-correlation, opencv, image-alignment, simd, neon, pybind11, automatic-optical-inspection, pattern-matching, linux, macos

## Member repositories
- DennisLiu1993/Fastest_Image_Pattern_Matching (main) score 61

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:46.810622+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:33:34.855568+00:00, confidence not recorded.
  - readme: https://github.com/DennisLiu1993/Fastest_Image_Pattern_Matching (fetched 2026-08-28T04:03:46.810622+00:00, sha e74f00a2932b)
- Data as of 2026-08-30T08:39:29.467469+00:00.