A compendium of sheet music

Cover Browser has thousands of these in all sorts of different art styles.

The man "alert while locked in" to a vegetative state appears to be a farce

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Many news sites have been carrying a story today about a man who allegedly for 23 years has been "locked in" to a paralyzed body while being "fully alert" but misdiagnosed as being in a vegetative state.

The James Randi Educational Foundation has published a rebuttal -

The "facilitated communication" process consists of the "facilitator" actually holding the hand of the subject over the keyboard, moving the hand to the key, then drawing the hand back from the keyboard! This very intimate participatory action lends itself very easily to transferring the intended information to the computer screen. In the video you have just viewed, it is very evident that (a) the "facilitator" is looking directly at the keyboard and the screen, and (b) is moving the subject's hand. The video editing is also biased, giving angles that line up the head of the subject with the screen, as if the subject were watching the screen...

Watch the first 30 seconds or so of the video embedded above to see what James Randi is talking about. He explains the "clever Hans" effect more at the link.

Via Reddit, where commenters are equally outraged at this travesty.

Yes, we know it's a "computer glitch"...

...but is there no human at Toys 'R Us who can spot these things before they are posted?

Found at Consumerist.

Free Online OCR

I have been a fan of optical character recognition since the early days of the technology. I remember about 15 years ago using a hand-held scanner to try to digitize my files of journal articles and magazine clipppings. The process required a lot of "cleanup" and ultimately proved to be not worth the time.

So it was with some interest that I saw a notice for "free online OCR" - Whether you have a scanned document or a photo, NewOCR.com can analyze the text in any image file that you upload, and then convert the text from the image into text that you can easily edit on your computer. I bookmarked the site and used it several days later when I encountered the story above in a pdf file. I took a screen shot of the page and uploaded the image to the free online OCR site. This was how the image was rendered...

Click to enlarge both for comparison, but the rendering needs work, shall we say. I suppose you get what you pay for.

In all fairness there are certain fonts that are intrinsically hard for OCR to interpret, and the test image had poor black/white contrast. It likely will do better with other challenges.

An Effective Journalistic Technique?

(The Onion Magazine is a parody of the New York Times Magazine)

Explore Images with Google Image Swirl

Posted by Yushi Jing and Henry Rowley, Google Research

Earlier this week, we announced the Labs launch of Google Image Swirl, an experimental search tool that organizes image-search results. We hope to take this opportunity to explain some of the research underlying this feature, and why it is an important area of focus for computer vision research at Google.

As the Web becomes more "visual," it is important for Google to go beyond traditional text and hyperlink analysis to unlock the information stored in the image pixels. If our search algorithms can understand the content of images and organize search results accordingly, we can provide users with a more engaging and useful image-search experience.

Google Image Swirl represents a concrete step towards reaching that goal. It looks at the pixel values of the top search results and organizes and presents them in visually distinctive groups. For example, in ambiguous queries such as "jaguar," Image Swirl separates the top search results into categories such as jaguar the animal and jaguar the brand of car. The top-level groups are further divided into collections of subgroups, allowing users to explore a broad set of visual concepts associated with the query, such as the front view of a Jaguar car or Eiffel Tower at night or from a distance. This is a distinct departure from the way images are ranked by the Google Similar Images, which excels at finding images very visually similar to the query image.

No matter how much work goes into engineering image and text features to represent the content of images, there will always be errors and inconsistencies. Sometimes two images share many visual or text features, but have little real-world connection. In other cases, objects that look similar to the human eye may appear drastically different to computer vision algorithms. Most difficult of all, the system has to work at Web Scale -- it must cover a large fraction of query traffic, and handle ambiguities and inconsistencies in the quality of information extracted from Web images.

In Google Image Swirl, we address this set of challenges by organizing all available information about an image set into a pairwise similarity graph, and applying novel graph-analysis algorithms to discover higher-order similarity and category information from this graph. Given the high dimensionality of image features and the noise in the data, it can be difficult to train a monolithic categorization engine that can generalize across all queries. In contrast, image similarities need only be defined for similar enough objects and trained with limited sets of data. Also, invariance to certain transformations or typical intra-class variation can be built into the perceptual similarity function. Different features or similarity functions may be selected, or learned, for different types of queries or image contents. Given a robust set of similarity functions, one can generate a graph (nodes are images and edges are similarity values) and apply graph analysis algorithms to infer similarities and categorical relationships that are not immediately obvious. In this work, we combined multiple sources of similarity such as those used in Google Similar Images, landmark recognition, Picasa's face recognition, anchor text similarity, and category-instance relationships between keywords similar to that in WordNet. It is a continuation of our prior effort [paper] to rank images based on visual similarity.

As with any practical application of computer vision techniques, there are a number of ad hoc details which are critical to the success of the system but are scientifically less interesting. One important direction of our future work will be to generalize some of the heuristics present in the system to make them more robust, while at the same time making the algorithm easier to analyze and evaluate against existing state-of-the-art methods. We hope that this work will lead to further research in the area of content-based image organization and look forward to your feedback.

UPDATE:  Due to the shutdown of Google Labs, this service is longer active.

The Disaster of 2012

As seen by the writers of SNL...

Update: The video was unofficial and low-rez (i.e. someone taped off his/her TV screen) and thus has been removed. I'll repost when SNL makes their licensed version available.