Object detection in video frames http://www.robots.ox.ac.uk/~vgg/publications/papers/sivic03.pdf

✏️ My homeworks of NTU CSIE 7694 Digital Visual Effects [2019 spring] (by Prof. CYY)

Scene Stitching and Object Recognition

Using SIFT features, BOW, model: SVM

Contains OpenCV projects which include image augmentation, border detection, and panorama-stitching using feature matching with the help of SIFT features.

applied neural graph learning on CNN architectures to improve model accuracy and robustness

[Book Course] - Course: Book-OpenCV with Python By Example_ Build real-world computer vision applications and develop cool demos using OpenCV for Python

Detect and localize trees inside images (OpenCV project).

Image classification using SVM, KNN, Bayes, Adaboost, Random Forest and CNN.Extracting features and reducting feature dimension using T-SNE, PCA, LDA.

Classification of scenes using Bag of words and SVM.

Reconstruction of a scene given two non stereo images and the intrinsic parameters matrix.

PURPOSE to Understand SIFT through video subject matching Present code require video device to be connected to computer eg-WebCam Capture Test Image to match with other images Good Matches will be represented through images graphs and its numeric count in console

Classification of Images using Support Vector Machines and Feature Extraction using SIFT.

Code for beer label classification using SIFT and ORB

Implement SIFT descriptor by C++

Lab Experiments under Lab component of CSE3018 - Content-based Image and Video Retrieval course at Vellore Institute of Technology, Chennai

Stitching multiple images from surrounding area to generate its unbroken view called as "panorama"

Demonstration of sift algorithm to track objects and observing the effect of each parameter on performance.

Content-Based Image Retrieval System using multiple images deciphers for feature extraction

Coin identification and recognition systems may drammatically enhance the extended operation of vending machines, pay phone systems and coin counting machines. The primary purpose of this project is to develop a detector capable of finding and classifying Euro coins in images purely relying on Computer Vision based frameworks.