The resources for the important references, Opencv algorithm study contribute to the development of computer vision

OpenCV参考文献
..............\1 CenSurE Center Surround Extremas for Realtime Feature Detection and Matching.pdf
..............\10 Theory and practice of projective rectification.pdf
..............\11 Stereo Processing by Semiglobal Matching and Mutual Information.pdf
..............\12 Determining Optical Flow.pdf
..............\13 Visual Correspondence Using Energy Minimization and Mutual Information.pdf
..............\14 An iterative image registration technique with an application to stereo vision.pdf
..............\15 Robust Detection of Lines Using the Progressive Probabilistic Hough Transform.pdf
..............\16 Color image segmentation.pdf
..............\17 Fast approximate nearest neighbors with automatic algorithm configuration.pdf
..............\18 Finding the convex hull of a simple polygon.pdf
..............\19 Topological structural analysis of digitized binary images by border following.pdf
..............\2 k-means++ The Advantages of Careful Seeding.pdf
..............\20 On the Detection of Dominant Points on Digital Curves.pdf
..............\21 An image inpainting technique based on the fast marching method.pdf
..............\22 Depth Discontinuities by Pixel-to-Pixel Stereo.pdf
..............\23 Good Features to Track.pdf
..............\24 An Introduction to the Kalman Filter.pdf
..............\25 Comparative study of Hough transform methods for circle finding.pdf
..............\3 SURF Speeded Up Robust Features.pdf
..............\4 Distance transformations in digital images
..............\5 Pyramidal implementation of the lucas-kanade feature tracker Description of the algorithm.pdf
..............\6 Computer Vision Face Tracking For Use in a Perceptual User Interface.pdf
..............\7 The Representation and Recognition of Action Using Temporal Templates.pdf
..............\8 Motion segmentation and pose recognition with motion history gradients.pdf
..............\9 Distance Transforms of Sampled Functions.pdf

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