Content-Based Image Retrieval (CBIR) using Faiss (Facebook) and many different feature extraction methods ( VGG16, ResNet50, Local Binary Pattern, RGBHistogram)

In this repository, we implement common image processing techniques in Python and fully describe their algorithms.

Pada project ini, akan dilakukan identifikasi nilai mata uang rupiah dengan menggabungkan metode ekstrasi ciri Local Binary Pattern dan metode klasifikasi Naïve Bayes. Serta untuk pengukuran akurasi identifikasi dilakukan dengan metode evaluasi K-Fold Cross Validation. Dataset yang digunakan berupa citra dengan rincian terdapat 120 citra yang terdiri dari 15 citra uang kertas Rp1.000, 15 citra uang kertas Rp2.000, 15 citra uang kertas Rp5.000, 15 citra uang kertas Rp10.000, 15 citra uang kertas Rp20.000, 15 citra uang kertas Rp50.000, 15 citra uang kertas Rp75.000, dan 15 citra uang kertas Rp100.000

A module that can extract LBP features (local binary pattern) from 3D images. Can be used for extracting features from medical images.

Fast Local Binary Patterns using TensorFlow

Texture Segmentation using: Gray-Level Co-occurence Matrix, Leung-Malik (LM) Filter Bank and Schmid (S) Filter Bank and Local Binary Pattern.

A powerful tool for texture recognition, the project presents a noise-resistant Local Binary Pattern (LBP) descriptor for robust texture recognition through image processing.

Local Binary Pattern: COVID-19 Recognition

Some tasks of computer vision using open-cv

Local Binary Patttern algorithm using OpenMP

Implement object detection and facial recognition techniques on input images and videos.

A Python project that estimates the calorie content of food from images. It enhances images using CLAHE, extracts color and texture features, and classifies food using machine learning models such as SVM and Random Forest. The predicted food class is then mapped to a reference calorie dataset to provide estimated calories for a given portion.

Real-time face liveness detection using Python, OpenCV, and MediaPipe. Detects blinks, smiles, head depth, and texture (LBP & Laplacian) to classify faces as real or spoof. Ideal for basic anti-spoofing using webcam input.

Structured implementations of classical computer vision primitives in MATLAB, covering filtering, frequency-domain analysis, wavelets, morphology, registration, and texture modeling with reproducible export-first design.

Brain tumor detection using machine learning algorithms

Local Binary Patttern algorithm implementation using GPU acceleration