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Computer Vision - 计算机视觉

COMP 4102A Computer Vision: Assignment 1: SVD, Edge Detection and Sticks filter

COMP 4102AComputer VisionEdge detectionSticks filterC++Python

Write a function that finds edge intensity and orientation in an image. Display the output of your function for one of the given images in the handout

Introduction to Computer Vision (ECSE 415) Assignment 4: Neural Networks

EECS415Introduction to Computer VisionNeural NetworksCIFAR-10YOLOPython

For this assignment, you are going to train models on the subset derived from the publicly available CIFAR-10 dataset source. The CIFAR-10 dataset consists of 60000 32x32 color images in 10 classes, with 6000 images per class.

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 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

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

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.

[2022] Fundamentals of Computer Vision - Project 3 Tracking Objects in Videos

CMSC 828DFundamentals of Computer VisionMatlabTracking Objects in Videos

You will first implement the Lucas-Kanade tracker, and then a more computationally efficient version called the Matthew-Baker (or inverse compositional) method

[2022] UNSW - COMP9517 Computer Vision - Lab4 Image Segmentation

COMP9517Computer VisionPythonJupyter NotebookOpenCVImage Segmentation

The goal of image segmentation is to assign a label to each pixel in an image, indicating whether it belongs to an object (and which object) or the background. In this lab the MeanShift clustering algorithm and the Watershed algorithm will be used to solve unsupervised image segmentation.

[2022] COMPSCI 773 Intelligent Vision Systems - Assignment2 Camera Calibration

COMPSCI 773Intelligent Vision SystemsAssignmentProgramming HelpCamera Calibration

This assignment will look at the optical distortion free camera calibration components (step 1 and 2) of the full stereo vision pipeline with other components of the pipeline explored in subsequent assignments.

[2022] UNSW - COMP9517 Computer Vision - Lab2 SIFT: Scale-Invariant Feature Transform

COMP9517Computer VisionSIFTScale-Invariant Feature TransformOpenCVPython

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.

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