/SIFT-Feature-Extraction-Texture-Analysis-and-Image-Matching

Implement texture classification and segmentation based on the 5x5 Laws Filters. Implement Scale Invariant Feature Transform (SIFT) which is an image feature extractor useful for representing the image information in a low dimensional form based on paper Lowe, David G. "Object recognition from local scale-invariant features." iccv. Ieee, 1999.. Used SIFT Features for Image Matching

Primary LanguageJupyter Notebook

SIFT-Feature-Extraction-Texture-Analysis-and-Image-Matching

Implement texture classification and segmentation based on the 5x5 Laws Filters. Implement Scale Invariant Feature Transform (SIFT) which is an image feature extractor useful for representing the image information in a low dimensional form based on paper Lowe, David G. "Object recognition from local scale-invariant features." iccv. Ieee, 1999.. Used SIFT Features for Image Matching:

  1. Brute Force Matching
  2. FLANN Based Matching

Requirements

  1. Ubuntu/Windows
  2. Python 3.6 + (preferably with anaconda)
  3. OpenCV, Numpy, Pandas, Matplotlib, Scikit-learn

General Usage Information

Use below command:

python filename.py