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Computer Vision - 计算机视觉

COMP90086 Computer Vision - Assignment 3: Fundamental Matrix Calculation

AustraliaCOMP90086Computer VisionFundamental Matrix CalculationPython

In this project you will implement the calculation of a Fundamental matrix using your own algorithms. You can use the keypoint detection and matching approach used in the week 8 Workshop, as well as code for drawing lines on images, but the implementation of the Fundamental Matrix calculation must be your own work.

CS 5330 - Pattern Recognition and Computer Vision - Project 3: Real-time Object 2-D Recognition

Northeastern UniversityReal-time Object 2-D RecognitionC++CS5330CS 5330Pattern Recognition and Computer Vision

This project is about 2D object recognition. The goal is to have the computer identify a specified set of objects placed on a white surface in a translation, scale, and rotation invariant manner from a camera looking straight down.

COMP27112 Introduction to Visual Computing Coursework Assignment 3 Image Processing Exercise 1

Computer VisionImage ProcessingCOMP27112Introduction to Visual ComputingUKUniversity Of Manchester

The aim of this exercise is to get you started with OpenCV and to do some very simple image processing.

COMP9517 Computer Vision Assignment: Otsu Thresholding, Isodata Thresholding and Triangle Thresholding

UNSWCOMP9517Computer VisionOtsu Thresholding Isodata ThresholdingTriangle Thresholding

The first technique is called Otsu thresholding (named after the inventor). It defines the optimal threshold as the one that minimises the intra -class variance or, equivalently, maximises the inter- class variance of the pixel values in the two classe

COMP9517 Computer Vision Lab 2: SIFT Scale-Invariant Feature Transform and RANSAC: Random Sample Consensus Algorithm

UNSWCOMP9517Computer VisionSIFT Scale-Invariant Feature TransformRANSAC: Random Sample Consensus AlgorithmPython

A well- known algorithm in computer vision to detect and describe local features in images is the scale -invariant feature transform (SIFT). Its applications include object recognition, mapping and navigation, image stitching, 3D modelling, object tracking, and others

COMP90086 Computer Vision - Project: Totally-Looks-Like Challenge

AustraliaCOMP90086Computer VisionPythonTotally-Looks-Like Challenge

Modern computer vision algorithms frequently meet or exceed human performance on constrained supervised tasks like object classification or face recognition. However, there are still many gaps between human and AI performance

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