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Beginning Pixlr Editor

Learn digital image editing without the expense of using subscription-based editors. This book will pave the way for you to leverage Pixlr Editor, a free, web-based image editing solution that works on virtually any computer platform. You'll learn image editing first hand by using the practice images with the corresponding tutorials—everything from creating simple graphics to enhancing and retouching photos. Explore all of the features you'd expect in a high end photo editing application; layers, history (multiple undos), and an array of powerful tools for handling almost any editing task. Powerful image editing used to require purchasing and installing expensive, complicated image editing software on your computer. Beginning Pixlr Editor takes an easy-to-access and convenient look at the alternative from the Pixlr family of tools and utilities and offers you advanced editing techniques so you can enhance, retouch, and edit your digital images like a pro. What You'll Learn: Easily access Pixlr Editor from any computer with a high speed Internet connection Create an new image, or open an image from your computer, a URL, or the Pixlr library Save your images on to your computer or the Pixlr library Navigate the the Pixlr interface Use the Tools, Layers, image Adjustments, History, and much more Who This Book Is For: Beginner and those with some image editing experience (anyone accustomed to Adobe Photoshop will instantly feel at home with Pixlr Editor).

Remote Sensing Based Building Extraction

Building extraction from remote sensing data plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Even though significant research has been carried out for more than two decades, the success of automatic building extraction and modeling is still largely impeded by scene complexity, incomplete cue extraction, and sensor dependency of data. Most recently, deep neural networks (DNN) have been widely applied for high classification accuracy in various areas including land-cover and land-use classification. Therefore, intelligent and innovative algorithms are needed for the success of automatic building extraction and modeling. This Special Issue focuses on newly developed methods for classification and feature extraction from remote sensing data for automatic building extraction and 3D

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