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
G皮T3 小时前
【人工智能】ChatGPT、DeepSeek-R1、DeepSeek-V3 辨析
人工智能·chatgpt·llm·大语言模型·deepseek·deepseek-v3·deepseek-r1
九年义务漏网鲨鱼3 小时前
【大模型学习 | MINIGPT-4原理】
人工智能·深度学习·学习·语言模型·多模态
元宇宙时间3 小时前
Playfun即将开启大型Web3线上活动,打造沉浸式GameFi体验生态
人工智能·去中心化·区块链
开发者工具分享3 小时前
文本音频违规识别工具排行榜(12选)
人工智能·音视频
产品经理独孤虾4 小时前
人工智能大模型如何助力电商产品经理打造高效的商品工业属性画像
人工智能·机器学习·ai·大模型·产品经理·商品画像·商品工业属性
老任与码4 小时前
Spring AI Alibaba(1)——基本使用
java·人工智能·后端·springaialibaba
蹦蹦跳跳真可爱5894 小时前
Python----OpenCV(图像増强——高通滤波(索贝尔算子、沙尔算子、拉普拉斯算子),图像浮雕与特效处理)
人工智能·python·opencv·计算机视觉
雷羿 LexChien4 小时前
从 Prompt 管理到人格稳定:探索 Cursor AI 编辑器如何赋能 Prompt 工程与人格风格设计(上)
人工智能·python·llm·编辑器·prompt
两棵雪松5 小时前
如何通过向量化技术比较两段文本是否相似?
人工智能
heart000_15 小时前
128K 长文本处理实战:腾讯混元 + 云函数 SCF 构建 PDF 摘要生成器
人工智能·自然语言处理·pdf