User:Hoafy/Evaluate an Article

Which article are you evaluating?

Machine vision

Why you have chosen this article to evaluate?

I chose this article because it is an ever growing field in socially cognizant robotics in social way finding and other perception based modules of robots, connecting it with the class 'Robots & Society' while also leaning into my interests of machine learning. My initial thoughts were that it was a well written article on a surface level providing important detail when needed.

Evaluate the article

Lead Section:

• The starting sentence does a good job of conveying the overall purpose of machine vision while also giving examples of what it can be used for in a real world setting, setting up the basic premise of the article well. This flows into the rest of the lead where it dives into more detail while not diving too deep which would overwhelm readers not familiar with the topic. This makes it in my opinion a well written and concise lead.
• The lead loosely gives a brief description of the major sections. While not referring to them directly, they sprinkle in parts of the main sections throughout the lead.
• The lead does not include any information not present in the article.
• The lead is concise not going too far into detail, but giving enough so the readers have a broad understanding of machine vision and the applications it may be used for.

Content:

• Yes, the articles content is relevant to the topic. It discuses machine vision throughout the article not steering away from it at any point, except for when they compare the field in different use cases (which is the furthest it strays, despite still being related)
• The content is fairly up to date with the latest citations going back to 1996 (building on the foundation of machine vision) and with the newer sources being as recent as 2022, although the majority are around the late 2010s. Machine vision is currently gaining popularity, which would make good edits including a more recent take on the field.
• The content seems to look like it all belong.
• The article does a good job covering the basics across many years, starting from it's initial creation to more modern times.

Tone and Balance:

• Yes, the article remains neutral and does not pick a said in saying that machine vision is good or bad. It mainly states the facts in its creation and what it is used for along with some innovations in the field like deep learning.
• No, there are no claims of bias.
• More modern viewpoints are underrepresented. Although there is a deep learning section and robot guidance section, two emerging fields in machine vision, it does not cover it more detail like it did for its creation and early usage.
• Minority or fringe viewpoints are not applicable to a topic like this and are not included because of this.
• The article is purely factual and does not try to persuade anyone into thinking it is good or bad, leaving it up to the reader.

Sources and References:

• Yes, the articles are backed up by reliable sources like classic papers, academic papers, and scholarly reviewed papers.
• The sources do reflect the content of the page, all of which are about the main topic or use the main topic of machine vision in one way or another.
• The sources vary. Some are current like 2022 (but could be newer) and some are from the late 1990s. I would say the mean year is around 2010s, making it relatively current.
• There seems to be a fair diversity in authors. Some like Demant, Wilson, and Hornberg appear often, but not enough to garner a majority of the sources. There are historic machine vision papers included for the founding of the field.
• The 5 links I used did seem to all work.

Organization and Writing Quality:

• The article from my standpoint, of someone who knows the field well and is not that great with understanding grammar, seemed clear and concise. I understood the words and didn't have to look anything up and got a good background of machine vision.
• I could not spot any grammatical or spelling errors, but this is also not my best area of expertise.
• The article presents material in a structurally sound way, starting broad and getting more specific as it goes down, eventually ending on growing fields. I personally liked this organizational method.

Images and Media:

• The article uses one image, but it provides zero context or enhances to my understanding of the article. It simply shows one of the first machine vision computers. A better image would have been machine vision in action to give readers a better idea of what it does and looks like.
• The image is well captioned including links and a good background to the image itself.
• Yes, the image is both cited and is safe to use in the article.
• The image is laid out in a fine way, but could have been done better by indued it in the text where it is relevant in the background of machine vision.

Talk Page Discussion:

• The talk page only has one discussion and has not been touched since 2019 with some responses taking years. It goes over making the English better using terms appropriate to the time period of the article.
• It relates to the article by discussing the important terms used when writing it, making it easier for readers to understand. It is not a part of any WikiProjects that I was able to find.
• Since the discusses are very limited, it does not differ from the way I've talked about it in class.

Overall Impression:

• It seems like the article is no longer active due to the lack of modern citations and discussions. The last edit according to the history was back in December 2024, making it inactive for almost a year now.
• The article does a good job in giving a broad overview of machine vision, using basic wording.
• The article can be improved by going further into detail about modern machine vision, using more modern techniques. Mentioning python libraries and ways to use it would be helpful too.
• The article in my opinion is adequately developed. It is good for what it is, but can certainly be improved.