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Culture, Technology and the Image focuses on the technologies deployed when images are archived, accessed and presented. The chapters discuss the way that the habits and techniques used in learning and communicating knowledge about images are affected by technological developments.

Complex images such as graphs, charts, or diagrams, may contain too much information to be effectively described using alt text. Instead, detailed descriptions of these images should be provided elsewhere (e.g., on the same page or on a separate page).

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The National Center for Accessible Media (NCAM) has developed guidelines for describing complex images, plus a variety of examples. These are available in their Effective Practices for Describing STEM Images. Another excellent resource, with additional examples, is Describing Figures, a guide from the ACM Special Interest Group on Accessible Computing (SIGACCESS).

Decorative images do not need to be announced by screen readers. See below for techniques for hiding them. If decorative images are not hidden from screen readers, they may be announced with the full path and file name of the image, depending on how the screen reader is configured. This can be a very noisy and cumbersome experience for users.

Whenever possible, use text, not images, to create text content. Images of text lose fidelity when enlarged, which can make the text difficult to read, especially for users with visual impairments. Also, text downloads much faster than images. Instead of using images, try to attain all visual effects using cascading style sheets.

Computer vision needs lots of data. It runs analyses of data over and over until it discerns distinctions and ultimately recognize images. For example, to train a computer to recognize automobile tires, it needs to be fed vast quantities of tire images and tire-related items to learn the differences and recognize a tire, especially one with no defects.

Much like a human making out an image at a distance, a CNN first discerns hard edges and simple shapes, then fills in information as it runs iterations of its predictions. A CNN is used to understand single images. A recurrent neural network (RNN) is used in a similar way for video applications to help computers understand how pictures in a series of frames are related to one another.

Scientists and engineers have been trying to develop ways for machines to see and understand visual data for about 60 years. Experimentation began in 1959 when neurophysiologists showed a cat an array of images, attempting to correlate a response in its brain. They discovered that it responded first to hard edges or lines, and scientifically, this meant that image processing starts with simple shapes like straight edges.2

At about the same time, the first computer image scanning technology was developed, enabling computers to digitize and acquire images. Another milestone was reached in 1963 when computers were able to transform two-dimensional images into three-dimensional forms. In the 1960s, AI emerged as an academic field of study, and it also marked the beginning of the AI quest to solve the human vision problem.

1974 saw the introduction of optical character recognition (OCR) technology, which could recognize text printed in any font or typeface.3 Similarly, intelligent character recognition (ICR) could decipher hand-written text using neural networks.4 Since then, OCR and ICR have found their way into document and invoice processing, vehicle plate recognition, mobile payments, machine translation and other common applications.

By 2000, the focus of study was on object recognition, and by 2001, the first real-time face recognition applications appeared. Standardization of how visual data sets are tagged and annotated emerged through the 2000s. In 2010, the ImageNet data set became available. It contained millions of tagged images across a thousand object classes and provides a foundation for CNNs and deep learning models used today. In 2012, a team from the University of Toronto entered a CNN into an image recognition contest. The model, called AlexNet, significantly reduced the error rate for image recognition. After this breakthrough, error rates have fallen to just a few percent.5

When Europe's armies first marched to war in 1914, some were still carrying lances on horseback. By the end of the war, rapid-fire guns, aerial bombardment, armored vehicle attacks, and chemical weapon deployments were commonplace. Any romantic notion of warfare was bluntly shoved aside by the advent of chlorine gas, massive explosive shells that could have been fired from more than 20 miles away, and machine guns that spat out bullets like firehoses. Each side did its best to build on existing technology, or invent new methods, hoping to gain any advantage over the enemy. Massive listening devices gave them ears in the sky, armored vehicles made them impervious to small arms fire, tanks could (most of the time) cruise right over barbed wire and trenches, telephones and heliographs let them speak across vast distances, and airplanes gave them new platforms to rain death on each other from above. New scientific work resulted in more lethal explosives, new tactics made old offensive methods obsolete, and mass-produced killing machines made soldiers both more powerful and more vulnerable. I've gathered photographs of the Great War from dozens of collections, some digitized for the first time, to try to tell the story of the conflict, those caught up in it, and how much it affected the world. This entry is part 3 of a 10-part series on World War I.

Many of the images and audio/visual resources that are fundamental to research exist in silos, with access restricted to locally-built applications. IIIF gives you and your audience freedom to work across barriers.

Explore our academic departments and degree programs that prepare our students to become the next generation of engineers, computer scientists, and technology professionals. Graduates of our programs lead, serve, and transform the greater Los Angeles area and beyond, embodying our motto: Commit to excellence and engage with community.

As part of our Choose Accessible Learning Materials (CALM) campaign, we want you to join us and describe images. On the web, image descriptions are read aloud to the blind and visually impaired using assistive technology. They also appear as text when page images do not load or a user turns them off to reduce cognitive load. During presentations, describing images increases audience comprehension and engagement, as well as highlights visual details that may be hard to see in a large room. For images in digital documents or on the web, image descriptions are provided through the use of alt attributes. During presentations, both live and pre-recorded, verbal descriptions of images should highlight the key components of the image in a natural, non-obtrusive way. Examples of both are under why describe images.

Describing images is also required for meeting university policy and the law. Specifically, it addresses the Web Content Accessibility Guidelines 2.0 (WCAG) criterion 1.1.1. Make the move to communications that are more clear and effective by providing alternative text for images in digital/web documents and by verbally describing images when presenting!

Providing a text description of the image provides access to anyone using screen reading technology, most often the blind and visually impaired. As an added benefit, writing alternative text descriptions for images can assist with image selection, since the description is intended to convey in words the same meaning as the visual.

If this photo were part of a live demonstration, a natural way to describe the image aloud could be, "During fall apple harvest..." or "Consider an apple hanging in a tree..." or "Autumn looks more beautiful after the rain when the droplets glisten on ripe red apples." The level of detail, just like for web based images, depends on the context. Images should enhance all users' experience with content, whether it's on a web page, in a document, or during a live talk.

Once you start describing images, though, you will realize that not all images require a description. For example, decorative lines or shapes that are used to separate chunks of text should be marked as decorative so assistive technologies can pass over them. The rule of thumb is provide only what is visually communicated through an image in another format (alt attribute or verbal description) without redundancy. If the image has no meaning, then mark it decorative.

Image descriptions belong anywhere you like to use images. This might be in your favorite text editor or presentation tool (e.g., Microsoft suite, Google suite, etc.). It also means web pages in the Ensemble CMS and WordPress. It includes images in the rich content editor pages available throughout the Canvas LMS.

Image descriptions can be added in a number of ways depending on where the image lives. For photos on the web, use the "alt" atribute for all images. Guidance on how to describe images in MS Office Products is available in the Microsoft Office Accessibility Checker-Creating Accessible Documents knowledgebase article.

Our goal is to reduce the number of images without descriptions throughout the university. You can help us get there by sharing links to your described images @VT_TLOS #CALMImages or emailing links to assist@vt.edu. Every entry is eligible for some of our Keep C.A.L.M swag.

Images are used heavily across the modern web and are used for many purposes and in many contexts, however on their own they are of little use for those with a vision disability. For this reason, one of the most important aspects of web accessibility is to provide alternative text with images. This simply means that an "alt" attribute, a short description of the image (in its context), is added to the image, which allows assistive technology to read the description of the image aloud. 006ab0faaa