There are 1 repository under image-preprocessing topic.
😎 Finding duplicate images made easy!
:book: This guide is to help you understand the basics of the computerized image and develop computer vision projects with OpenCV. Includes Python, Java, JavaScript, C# and C++ examples.
A simpler way of reading and augmenting image segmentation data into TensorFlow
Designing and Training of A Dual CNN for Image Denoising (Knowledge-based Systems, 2021)
Face Recognition/Detection (image/video) using skin tone threshold algorithm, haar cascade for face detection and LBPH for face recognition. It also implements the concept of multithreaded server with multiple clients.
Algorithm to segment pectoral muscles in breast mammograms
image filtering techniques in python with examples
A GUI for Image Processing
The binary classification problem focused on first IEEE Image forensics challenge-phase 1, to predict the given image is pristine or manipulated/edited/fake. Comparing CNN & Transfer Learning models for the problem and boosting the performance by feature extraction
Edge-preserving image smoothing algorithm
This repository contains all my exercises and projects for computer vision.
Generic Image Sorter Interface for Streamlit
Preprocessing the images and saving them in a specific directory with python
This repository contains all the files of the project OCR for Telugu.
This program enables real-time face recognition for attendance marking. It uses a webcam or video input to detect and recognize faces in real-time. When a recognized face is identified with sufficient accuracy, the corresponding name is recorded in an attendance file named Attendance.csv, along with the timestamp of the recognition.
⛏️ Contains 4 python modules. Basic OCR using Google's Tesseract on single image and pdf. Auto orientation correction for scanned docs. Auto noise type detection and reduction. Watermark and stain removal on scanned docs. These modules act as preprocessing tools for the best OCR results.
India Academia Connect AI Hackathon October 2021
Python Program to convert images to .npy files along with their labels and store them for future use in Machine Learning.
This DR detection methodology has six steps: preprocessing, segmentation of blood vessels, segmentation of OD, detection of MAs and hemorrhages, feature extraction and classification. For segmentation of blood vessels BCDU-Net is used. For OD segmentation, U-Net model is used. MAs and hemorrhages are extracted using Otsu thresholding technique. Both clinical and non-clinical features are extracted and fed to SVM classifier.
A collection of Jupyter Notebooks containing some important functions regarding image preprocessing and machine learning.
Extract text from handwritten infomation on bank checks images
Implementation of fast Data Augmentation for Image Classification / Detection tasks.
House price estimation from visual and textual features using both machine learning and deep learning models
Hyperspectral Image Denoising using Attention and Adjacent Features Extraction Hybrid Dense Network
Fast median filter based on color histogram.
Image Rating using CNN-based model.
ITK external module to control topology of binary mask regions
Whole Slide Image mask preprocessing modules.
Classification, Image Preprocessing and Similarity Retrieval on FoodX-251 datset