Contains image processing applications. This repository serves as a valuable resource for learning and understanding image processing concepts and algorithms.

Fingerprint recognition system using Euclidean distance and OpenCV for biometric matching with dynamic thresholding

CVA highlights shifts in the process mean, making it particularly effective for detecting small changes.

Applying a color threshold in the HLS color space

Password Generator

Quick Tax Estimate for Australia +

I explored OpenCV's capabilities in working with video and webcam input, including real-time video capture. I also learned essential image processing techniques like filtering, blurring, edge detection, and thresholding. These foundational skills are crucial for building computer vision applications.

Detecting credit card fraud detection. Selecting an optimum threshold with analysis of confusion matrix and ROC curce

implementation of thresholding and changes in hue using OpenCV

Active metrics monitoring if the monitored metric exceed a predefined threshold

OCR from scratch using Chars74 Dataset: http://www.ee.surrey.ac.uk/CVSSP/demos/chars74k/ applied to the case of Spanish car license plates or any other with format NNNNAAA. The hit rate is lower than that achieved by pytesseract: in a test with 21 images, 12 hits are reached while with pytesseract the hits are 17.

Making Optical character recognition (OCR) app using Python and Kivy. This project is based on only image processing without any machine learning or deep learning methods.

This repository is about computer vision in detail about image loading image display cropping threshold resizing shape detection face and eyes detection making image like shape and last i create two mini projects face and eyes detection and car detection

Winner BrickHack3 **Most Creative Use of UPS API**

I explored OpenCV's capabilities in working with video and webcam input, including real-time video capture. I also learned essential image processing techniques like filtering, blurring, edge detection, and thresholding. These foundational skills are crucial for building computer vision applications.

Convert the Color & Grayscale Images to Black & White mode

This project implements an unsupervised machine learning approach to detect anomalous financial transactions using K-Means clustering algorithm. The system analyzes transaction patterns based on amount and timing to identify potentially fraudulent activities in financial data.

Made for Professor Hayes Lab (Cell Universe) at UCI

Configure sensor thresholds for automatic alarms without complex automations