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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.

Computer vision 2022 Assignment 3: Deep Learning for Perception Tasks

For this exercise, we will provide a demo code showing how to train a network on a small dataset called FashionMinst. Please go through the following tutorials first. You will get a basic understanding about how to train an image classification network in pytorch. You can change the training scheme and the network structure. Please answer the following questions then. You can orginaze your own text and code cell to show the answer of each questions.

[2022] UNSW - COMP9517 Computer Vision - Lab1 Image Processing

Computer Visionniversity of New South WalesUNSW

This lab revisits important concepts covered in the Week 1 and Week 2 lectures and aims to make you familiar with implementing specific algorithms.

[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.

[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 - 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] 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

COMP9517: Computer Vision 2022 Term 2 Group Project Specification - Tracking pedestrians and analysing their motion in real-world video recordings

COMP9517Computer VisionObject TrackingPython

The goal of this group project is to develop and evaluate a method for tracking pedestrians and analysing their motion in real-world video recordings.

COMP9517: Computer Vision 2022 Term 3 - Assignment: Image Background Substraction

University of New South WalesUNSWCOMP9517Computer VisionBackground SubstractionOpenCV

This assignment is to familiarise you with basic image processing methods. It also introduces you to common image processing and analysis tasks using OpenCV.

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