COMP9517 Computer Vision

COMP9517: Computer Vision
2023 T3 Lab 1 Specification
Maximum Marks Achievable: 2.5
This lab is worth 2.5% of the total course mark .
Objectives: This lab revisits important concepts covered in the Week 1 and Week 2 lectures and aims to make you familiar with implementing specific algorithms.
Preliminaries: As mentioned in the first lecture, we assume you are familiar with programming in Python or are willing to learn it independently. You do not need to be an expert, as you will further develop your skills during the course, but you should at least know the basics. If you do not yet know Python, we assume you are familiar with at least one other programming language such as C, in which case it should be relatively easy to learn Python.
To learn or brush up your Python skills, see several free online resources listed at the end of this document. Especially if you already know C or similar languages, there is no need to go through all the linked resources in detail. Just quickly learn the syntax and the main features of the language. The rest will follow as you go.
For implementing and testing computer vision algorithms, we use OpenCV in this course.
OpenCV is a library of programming functions mainly for computer vision. The library is crossplatform and licensed as free and open-source software under Apache License 2. It also supports training and execution of machine/deep learning models. Originally written in C, with new algorithms developed in C++, it has wrappers for languages such as Python and Java. As stated above, in this course we will focus on programming in Python. See the links below for OpenCV tutorials and documentation.
Software: You are required to use OpenCV 3+ with Python 3+ and submit your code as a Jupyter notebook (see coding and submission requirements below). In the first tutor consultation session this week, your tutors will give a demo of the software to be used, and you can ask any questions you may have about this.
Materials: The sample images to be used in this lab are available via WebCMS3.
Submission: All code and requested results are assessable after the lab. Submit your source code as a Jupyter notebook (.ipynb) which includes all output and answers to all questions (see coding requirements at the end of this document) by the above deadline. The submission link will be announced in due time.
1. Contrast Stretching
Contrast is a measure of the range of intensity values in an image and is defined as the difference between the maximum pixel value and minimum pixel value. The maximum possible contrast of an 8-bit image is 255 (max) -- 0 (min) = 255. Any value less than that means the image has lower contrast than possible. Contrast stretching attempts to improve the contrast of the image by stretching the range of intensity values using linear scaling.

相关推荐
IMER SIMPLE9 分钟前
人工智能-python-深度学习-神经网络-GoogLeNet
人工智能·python·深度学习
钮钴禄·爱因斯晨12 分钟前
深入剖析LLM:从原理到应用与挑战
开发语言·人工智能
InternLM16 分钟前
专为“超大模型而生”,新一代训练引擎 XTuner V1 开源!
人工智能·开源·xtuner·书生大模型·大模型训练框架·大模型预训练·大模型后训练
JT85839634 分钟前
AI GEO 优化能否快速提升网站在搜索引擎的排名?
人工智能·搜索引擎
幂律智能36 分钟前
吾律——让普惠法律服务走进生活
人工智能·经验分享
IT_陈寒40 分钟前
Java性能优化:从这8个关键指标开始,让你的应用提速50%
前端·人工智能·后端
yzx99101344 分钟前
构建未来:深度学习、嵌入式与安卓开发的融合创新之路
android·人工智能·深度学习
非门由也1 小时前
《sklearn机器学习——特征提取》
人工智能·机器学习·sklearn
机器学习之心2 小时前
基于CNN的航空发动机剩余寿命预测 (MATLAB实现)
人工智能·matlab·cnn
钝挫力PROGRAMER2 小时前
AI中的“预训练”是什么意思
人工智能