#image extraction

[smbhax]

Amazon / Kindle / Comixology comic page image extraction tip!

For some reason, Amazon / Kindle / Comixology have two separate web-based comic readers.

Googling "Kindle Cloud Reader," clicking the green "Read Now" button on a comic's Amazon store page, or using the "Kindle Library" button in the upper left corner of the "slice reader" (see below) gets you to a reader with URLs in the format "https://read.amazon.com/manga/[content ID]"; this reader has a zoom function, a panel-by-panel Guided View mode, and fullscreening your browser (F11) actually scales the images up slightly. In addition to turning pages by clicking on the arrows on the sides of the screen, you can also can turn pages with your mouse wheel or the keyboard keys on your keyboard.

On the other hand, going through your "Content Library" from amazon.com and opening a comic by clicking its "More Actions" button and then "Read Now" will serve you your comics in a reader with URLs in the format "https://read.amazon.com/?asin=[content ID]"; this reader has no zoom function, no Guided View mode, does not scale up when you fullscreen the browser, seems to have more little delays in loading, doesn't let you turn comic pages with the mouse wheel. I call this one the "slice reader," which will make more sense in a bit.

In both readers, double-clicking a panel zooms it up to near full-screen.

The Cloud Reader is more user-friendly--but not for getting images of your comics. Sure you can take a screenshot, but it's using a "blob" renderer that is always scaling the graphics some way or other, and doesn't seem to provide the actual underlying images themselves in any easily-accessible format.

The "slice reader" loads pages a lot slower and in general feels like an older, crustier implementation that maybe they meant to replace completely with the Cloud Reader but had to keep around for some reason (or have they just kind of forgotten it comes up when accessing comics via some of their many reading buttons?), but I guess I'm glad they did because if you poke through the "slice reader's" page source code you'll find two "sliced" "jpeg" URLs per comic page, one for the top half of the comic page and the other for the bottom half; they're in what appears to be a streamed data format, but you can copy and paste them into a browser window and it'll give you an actual image; join them together in an image editing program, and you've got the base comic image. The image resolution is a lot less than you get from, say, Marvel Unlimited's web reader--DC pages 1249 pixels wide vs Marvel pages 1821 pixels wide--but at least it isn't extra blurry like it would be if you just screenshotted it from either reader.

All of this only really matters if you're trying to grab one of the smaller comic panels, really--and then you still have to be kind of picky to notice a difference--but it's the kind of thing that drives me up the wall. ; )

This was about the best I could do for a couple smallish early Batman panels from the Cloud Reader, for instance (DC's image quality in the Kindle versions of their Golden Age comic collections is rather poor 'p'):

Whereas with the images I could dig out of the "slice reader," I was able to avoid having to use Sharpen on the drawing, so after some color indexing, got cleaner, non-haloed edges:

So uh yeah this made me happy. ^ _^

[imageextraction]

Extract and Download Images from Any Website Instantly

Image Extraction is a fast, free online tool that instantly pulls images from any public website. Built for creators, designers, marketers, and students, it automatically gathers every image from blogs, e-commerce sites, portfolios, and galleries within seconds—no accounts, no limits, and no complicated steps. Simply paste a link, click “Extract,” and instantly access HD images. Secure, efficient, and easy to use, helps you save time, streamline your workflow, and extract images effortlessly whenever you need them.

[the-cps]

Image extraction is the process of separating foreground objects from the background. You know, sometimes it happens that the image for your commercial, or business use is against a solid or transparent background.

But where can you get the best service for it when you need it the most, or how much will it cost? To get a grasp of everything that you are willing to know? just hit the images and read!

[ijtsrd]

GRS “ Gesture based Recognition System for Indian Sign Language Recognition System for Deaf and Dumb People”

By D. Anbarasan | R. Aravind | K. Alice"GRS '“ Gesture based Recognition System for Indian Sign Language Recognition System for Deaf and Dumb People"

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018,

URL: http://www.ijtsrd.com/papers/ijtsrd9638.pdf

Direct URL: http://www.ijtsrd.com/engineering/computer-engineering/9638/grs-'“-gesture-based-recognition-system-for-indian-sign-language-recognition-system-for-deaf-and-dumb-people/d-anbarasan

call for paper in ugc approved journals, open access journal of management, medical science journal

Recognition languages are developed for the better communication of the challenged people. The recognition signs include the combination of various with hand gestures, movement, arms and facial expressions to convey the words thought. The languages used in sign are rich and complex as equal as to languages that are spoken. As the technological world is growing rapidly, the sign languages for human are made to recognised by systems in order to improve the accuracy and the multiply the various sign languages with newer forms. In order to improve the accuracy in detecting the input sign, a model has been proposed. The proposed model consists of three phases a training phase, a testing phase and a storage output phase. A gesture is extracted from the given input picture. The extracted image is processed to remove the background noise data with the help of threshold pixel image value. After the removal of noise from the image and the filtered image to trained model is tested with a user input and then the detection accuracy is measured. A total of 50 sign gestures were loaded into the training model. The trained model accuracy is measured and then the output is extracted in the form of the mentioned language symbol. The detection mechanism of the proposed model is compared with the other detection methods such as Hidden Markov Model(HMM), Convolutional Neural Networks(CNN) and Support Vector Machine(SVM). The classification is done by means of a Support Vector Machine(SVM) which classifies at a higher accuracy. The accuracy obtained was 99 percent in comparison with the other detection methods.

[infrrd-ai]

Classifying property images and extracting metadata from pictures to enhance listings and search becomes easy with our #ML based #ImageRecognition model. Download this case study to see how it worked for a #RealEstate company.

[easytweaks]

Kurt Cobain