Don’t use OpenCV’s findHomography as it estimates a general homography Note that a general homography has 8 degrees of freedeom while a plane is determined by only 3 degrees of freedom (= use additional constraints) Reference: R. Zisserman, 'Multiple View Geometry in Computer Vision'. ChessVision: Chess Board and Piece Recognition Jialin Ding Stanford University jding09@stanford.edu Abstract This paper details a method to take an image of a chess board and output a reconstructed computer representation of the board through board and piece recognition. Though techniques for board recognition have been thoroughly ex.

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Key novelties Simulated annealing:User has two options - focal length is known or random at the beginning (control variables are set in Reconstructor::init). In the second case, user has to specify range of focal length - there is no need to specify exact value of the focal length - algorithm is parameter-less in this case (quality is a little bit worse, but it's still parameter-less). In both cases there was implemented additional optimisation step of focal length using heuristic method - Simulated Annealing. From now on focal length is adjusted using randomly-picked values in adjustable manner, not just changed arbitrarly according to displacement of points during reprojection as it was before. Focal adjustment:I presented novelty in adapted 3D reconstruction algorithm (EPFL). There are algorithms that optimise extrinsic camera parameters, but - in the knowledge of authors - noone tried to estimate the intrinsic parameters.

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In unconstrained reconstruction step I track displacement of reprojected points in each iteration. If the displacement in two successive iterations shows that the point is closer to the middle of the image - I assume that focal length is a little bit too long (somehow the distance from camera is expressed as a function of focal length). For better reprojection in next iteration I adjust the focal length and make it smaller. Otherwise, this parameter is enlarged. Simulated local deformation:Descriptor robust to out-of-plane rotations by performing similar approach as in ASIFT article - simulated local deformation. I used OpenCV implementation of SIFT.

How does it work:. find keypoints and descriptors on reference frame. simulated local deformation - add a stack of descriptors for each keypoint using perspective transformations (that is why we need chessboard files under variety of rotations).

detect and describe keypoints on test frame. perform matching descriptors from test frame with stac of descriptors from reference image - improved matching process. profitUsed ideas Robust to in-plane rotation algorithm (ASIFT):Adapted 3D reconstruction algorithm:HOWTO Build:. build using standard procedure (mkdir build - cd build - cmake. make).

in build create chessboard.txt file where are listed all images needed for simulated local deformation.