# 學生對 纽约州立大学布法罗分校 提供的 Image Processing, Features & Segmentation 的評價和反饋

## 課程概述

This course empowers learners to develop image processing programs and leverage MATLAB functionalities to implement sophisticated image applications. It provides a rich explanation of the fundamentals of computer vision’s lower- and mid-level tasks by examining several principle approaches and their historical roots. By the end of the course, learners are prepared to analyze images in frequency domain. Topics include image filters, image features and matching, and image segmentation. This course is ideal for anyone curious about or interested in exploring the concepts of computer vision. It is also useful for those who desire a refresher course in mathematical concepts of computer vision. Learners should have basic programming skills and experience (understanding of for loops, if/else statements), specifically in MATLAB (Mathworks provides the basics here: https://www.mathworks.com/learn/tutorials/matlab-onramp.html). Learners should also be familiar with the following: basic linear algebra (matrix vector operations and notation), 3D co-ordinate systems and transformations, basic calculus (derivatives and integration) and basic probability (random variables). Material includes online lectures, videos, demos, hands-on exercises, project work, readings and discussions. Learners gain experience writing computer vision programs through online labs using MATLAB* and supporting toolboxes. This is the second course in the Computer Vision specialization that lays the groundwork necessary for designing sophisticated vision applications. To learn more about the specialization, check out a video overview at https://youtu.be/OfxVUSCPXd0. * A free license to install MATLAB for the duration of the course is available from MathWorks....

## 1 - Image Processing, Features & Segmentation 的 12 個評論（共 12 個）

Jul 06, 2019

Overall is a very steep learning curve, mostly on discussion among students to figure out the solutions of the assignments. Not much example in the lecture videos.

Jul 03, 2019

The course is missing a lot of slides that are referenced in the lectures.

Aug 14, 2019

Aug 18, 2019

Some parts of the video lectures are missing and the coding exercises don't match what we learn so it's very hard to complete them.

Aug 21, 2019

I put 2 because the course exists and covers important foundations for anyone who wants to learn computer vision.

However I cannot put more because it looks like the class has been done in a hurry. For example, the videos are not complet so it is very hard to understand some concepts. Moreover the videos cover large subjects and the graduated exercises are on very specific functions so you will need to spend a lot of time on Google to figure them out. And finally don't count on the trainers to help you a lot in the forum (they go there maybe once a week...).

Jun 14, 2019

Lectures are incomplete, exercises are not well written. Staff is not helpful. Don't go with Computer Vision Specialization By University of Buffalo it is complete waste of time and money.

May 31, 2019

Jun 19, 2019

I have not completed the course yet but the lectures are incomplete. It's really difficult to understand the missing parts of the lectures. Please look in to the ambiguity. Thank you.

Jul 28, 2019

This course is a fraud ! Coursera should audit the quality of their content to maintain high standards !

Jul 31, 2019

The material is incomplete as are the lectures. Multiple topics are mentioned as "let us see in the following example about k-means...." from which the lesson just ends or the lecturer jumps to another aspect with no explanation of the material. From initial suspect I would hold the editing team at fault for not adding (what I hope) is material that the professors submitted. I had already paid for a month and as such did not withdraw from this course. Only aspect I learned is names of procedures used and my skills at googling them.

Aug 03, 2019

Poor content. Poor quality of video lectures. Poor study material.

Aug 17, 2019

This meant to be a great course.

However, it was delivered in an extremely poor quality. Slides are missing in each video, a number of links to the addition material for reading are either broken or refer to the papers one should pay to be able to download.

No connection between videos and quizzes.

Videos without slide are useless.

Coursera should require to verify the context of the course. I took a couple of courses on Coursera in the past. This one is the lowest quality I experienced.