New PDF release: Advances in Embedded Computer Vision

By Branislav Kisačanin, Margrit Gelautz

ISBN-10: 331909386X

ISBN-13: 9783319093864

ISBN-10: 3319093878

ISBN-13: 9783319093871

This illuminating assortment deals a clean examine the very most up-to-date advances within the box of embedded machine imaginative and prescient. rising components coated by means of this entire text/reference comprise the embedded recognition of 3D imaginative and prescient applied sciences for numerous purposes, comparable to stereo cameras on cellular units. fresh developments in the direction of the improvement of small unmanned aerial automobiles (UAVs) with embedded photo and video processing algorithms also are tested. subject matters and contours: discusses intimately 3 significant luck tales – the improvement of the optical mouse, imaginative and prescient for patron robotics, and imaginative and prescient for car safeguard; stories state of the art examine on embedded 3D imaginative and prescient, UAVs, car imaginative and prescient, cellular imaginative and prescient apps, and augmented fact; examines the opportunity of embedded computing device imaginative and prescient in such state of the art parts because the web of items, the mining of enormous information streams, and in computational sensing; describes historic successes, present implementations, and destiny challenges.

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Additional info for Advances in Embedded Computer Vision

Example text

1. Features in the query image are looked up in the global appearance model database. 2. The results of the database lookup are used to rank potential views by visual similarity, and the m top-ranked views are chosen as candidates (we use m = 3 throughout). 3. For each candidate view, correspondences are established between query features and the features in the view. 4. Geometric constraints are applied to these correspondences, using reprojection constraints and the estimated view structure to reject outliers.

The results of the database lookup are used to rank potential views by visual similarity, and the m top-ranked views are chosen as candidates (we use m = 3 throughout). 3. For each candidate view, correspondences are established between query features and the features in the view. 4. Geometric constraints are applied to these correspondences, using reprojection constraints and the estimated view structure to reject outliers. This yields a rough relative pose estimate. 5. The relative pose and structure estimates are refined using by optimizing over the inlier correspondences and internal correspondences of the view, yielding maximum-likelihood relative pose with covariance.

Munich et al. the following sections. The work of Cummins et al. [2] takes a more sophisticated approach to recognition, building a visual vocabulary offline, and approximating the joint probability distribution of visual words with a Chow-Liu tree. Each view’s appearance model is updated upon recognition. Our view recognition front end bears many similarities to the view-based maps of Konolige et al. [12]. That system constructs views from stereo images and performs two-step recognition using first a vocabulary tree and then a geometric matching stage.

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Advances in Embedded Computer Vision by Branislav Kisačanin, Margrit Gelautz


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