Download Excel 2013 Bible by John Walkenbach PDF

By John Walkenbach

Excel at Excel with assistance from this bestselling spreadsheet guide

John Walkenbach's identify is synonymous with excellence in desktop books that decipher the complexities of Microsoft Excel. referred to as "Mr. Spreadsheet," Walkenbach indicates you the way to maximise the ability of Excel 2013 whereas bringing you on top of things at the most up-to-date positive factors. This perennial bestseller is absolutely up to date to hide all of the new beneficial properties of Excel 2013, together with find out how to navigate the consumer interface, make the most of a number of dossier codecs, grasp formulation, study info with PivotTables, and more.

Whether you're an Excel newbie who's seeking to get extra savvy or a complicated consumer seeking to turn into an influence person, this most modern version offers you accomplished insurance in addition to valuable suggestions, methods, and strategies that you just won't locate anyplace else. stocks the helpful perception of Excel guru and bestselling writer "Mr. Spreadsheet" John Walkenbach as he publications you thru each point of Excel 2013 offers crucial assurance of all of the most up-to-date positive aspects of Excel 2013 offers fabric in a transparent, concise, logical structure that's perfect for all degrees of Excel event includes a web site that incorporates downloadable templates and worksheets from the book

Chart your route to exceptional formulation and stellar spreadsheets with "Excel 2013 Bible"!

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Example text

Edges, serving as basic features of an image, reveal the discontinuity of the image amplitude attribution or image texture properties. The location and strength of an edge provide important information of object boundaries and indicate the physical extent of objects in the image [21]. Edge detection refers to the process of identifying and locating sharp discontinuities in an image. It is the key and basic step toward image segmentation problems [22]. In the context of interactive segmentation, many algorithms have been proposed to segment objects of interest based on edge features, combined with user guidance and interaction.

4 shows pre-segmented superpixels , which are used as nodes for the min-cut formulation. After watershed’s pre-segmentation, an image is decomposed into small regions. Each small region corresponds a node in graph G = {V, E}. The location and the color of a node are given by the central position and the average color of the corresponding small region, respectively. , 1 or 0 if region i belongs to an object or background, respectively), E 1 (xi ) is the likelihood energy used to encode the color 24 3 Interactive Image Segmentation Techniques Fig.

Wang et al. [19] proposed a pre-segmentation scheme based on the mean-shift algorithm to reduce the number of graph nodes in the optimization process, which will be detailed in Sect. 2. They also extended the approach to video segmentation, and proposed an additional spatiotemporal alpha matting scheme as a postprocessing to refine the segmented boundary. To reduce the memory requirement for the processing of high resolution images, Lombaert et al. [20] proposed a scheme that conducts segmentation on a down-sampled input image, and then refines the segmentation result back to the original resolution level (Fig.

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