By John Levine
If you must parse or method textual content info in Linux or Unix, this helpful publication explains the best way to use flex and bison to unravel your difficulties fast. flex & bison is the long-awaited sequel to the vintage O'Reilly booklet, lex & yacc. within the approximately twenty years because the unique ebook was once released, the flex and bison utilities have confirmed to be extra trustworthy and extra robust than the unique Unix tools.
flex & bison covers an identical middle performance very important to Linux and Unix software improvement, in addition to numerous very important new subject matters. You'll locate revised tutorials for rookies and references for complicated clients, in addition to a proof of every utility's easy utilization and easy, standalone purposes you could create with them. With flex & bison, you'll detect the big variety of makes use of those versatile instruments offer.
* tackle syntax crunching that common expressions instruments can't deal with
* construct compilers and interpreters, and deal with a variety of textual content processing capabilities
* Interpret code, configuration records, or the other established structure
* examine key programming thoughts, together with summary syntax bushes and image tables
* enforce a whole SQL grammar-with whole pattern code
* Use new beneficial properties similar to natural (reentrant) lexers and parsers, robust GLR parsers, and interfaces to C++
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Extra info for Flex & Bison: Text Processing Tools
Evaluation of Interest Point Detectors”. g. circles) of different sizes around a point • Regions of corresponding sizes will look the same in both images Scale Invariant Detection • The problem: how do we choose corresponding circles independently in each image? Scale Invariant Detection • Solution: – Design a function on the region (circle), which is “scale invariant” (the same for corresponding regions, even if they are at different scales) Example: average intensity. For corresponding regions (even of different sizes) it will be the same.
Distinguished Regions for Wide-baseline Stereo”. Research Report of CMP, 2001. Affine Invariant Detection : Summary • Under affine transformation, we do not know in advance shapes of the corresponding regions • Ellipse given by geometric covariance matrix of a region robustly approximates this region • For corresponding regions ellipses also correspond Methods: 1. Search for extremum along rays [Tuytelaars, Van Gool]: 2. ] Contents • Harris Corner Detector – Description – Analysis • Detectors – Rotation invariant – Scale invariant – Affine invariant • Descriptors – Rotation invariant – Scale invariant – Affine invariant Point Descriptors • We know how to detect points • Next question: How to match them?
Indexing Based on Scale Invariant Interest Points”. Lowe. “Distinctive Image Features from Scale-Invariant Keypoints”. t. Schmid. “Indexing Based on Scale Invariant Interest Points”. ICCV 2001 Scale Invariant Detection: Summary • Given: two images of the same scene with a large scale difference between them • Goal: find the same interest points independently in each image • Solution: search for maxima of suitable functions in scale and in space (over the image) Methods: 1. Harris-Laplacian [Mikolajczyk, Schmid]: maximize Laplacian over scale, Harris’ measure of corner response over the image 2.
Flex & Bison: Text Processing Tools by John Levine