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image_processing's Introduction

Introduction

This repo contains works of CENG391 course,Introduction to Image Understanding, and my studies about computer vision area.

Laboratory Works

Both C++ and Python's OpenCv library is used during laboratory studies of this course.The content of homeworks listed below.

  • OpenCV Installation and Setup
  • Basic data types and UI with OpenCV
  • Smoothing & Sharpening operations
  • Image compositing with Laplace pyramids
  • Harris corner detection
  • Image stitching for panorama generation
  • Measuring the keypoint recognition rate, FAST & BRIEF
  • Homography estimation, DLT
  • Homography estimation with RANSAC

Homeworks

  • Manuel image correspondence
  • Bilateral filter algorithm
  • Bilinear interpolation
  • Object detection with RANSAC
  • Epipolar Line

Internship Projects

  • project_rollor_deformation_detection

    • This project is about finding deformation on rollers during production process in a factory. Normally, there are four different text on a roller. However, there may be some missing text due to production errors so the aim of this project is to find rollers that has missing text on it.

    Figure1: A Roller sample

  • project_balata_deformation_detection

    • This project is about finding deformation on rollers during production process in a factory. Normally, a product should has specific dimensions. However, dimensions of a product can change due to some production errors. This project aims to find products which don't have desired dimensions.

      Figure2: A sample product

  • project_ffmpegCapture

    • This project implements capturing a camera stream using ffmpeg library of C++ language. OpenCv library has a videoCapture library for capturing videos. However, I tried to implement a C++ class to capture frames more efficiently.
  • project_count_people

    • This project aims to count number of people entered a shop during the day using camera stream.
  • project_create_heatmap

    • This project aims to create heatmap of customers in a market. It creates heatmap for every hour in a day so it can be used to determine the most crowded hours of a day and the most used places in a market.

      Figure3: Heatmap of a market using fisheye camera

image_processing's People

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