Affine optic flow

버전 (1.03 MB) 작성자: David Young
Estimates the parameters of an affine (first-order) optic flow model from two images.
다운로드 수: 6.8K
업데이트 날짜: 2016/3/4

라이선스 보기

An affine (or first-order) optic flow model has 6 parameters, describing image translation, dilation, rotation and shear. The class affine_flow provides methods to estimates these parameters for two frames of an image sequence.
The class implements a least-squares fit of the parameters to estimates of the spatial and temporal grey-level gradients. This is an extension of the well-known Lucas-Kanade method. The images are either sampled conventionally, on a rectilinear grid, or on a log-polar grid. In the latter case, the class may iteratively refine its estimates by moving the sampling grid to track the motion. Options to specify a region of interest and smoothing and sampling parameters are provided.
The file includes a demonstration of the class, and test images for this. The functions for smoothing images and estimating gradients may be useful independently, and log-polar sampling functions are included (and are available separately in submission 27023).

인용 양식

David Young (2024). Affine optic flow (, MATLAB Central File Exchange. 검색됨 .

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개발 환경: R2016a
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버전 게시됨 릴리스 정보

Fixed bug which caused error when sample step was greater than 1.

Minor corrections to comments.

Changed order of arguments in gsmooth2 and gradients_xyt.
MathWorks update: Added Live Script.

Changed from a function to a class, for more efficient application to image sequences. (Argument checking etc. can be moved outside a loop, and some intermediate computations can be saved for later use.)

Fixed bug in use of ROI, allowed ROI in conjunction with log-polar sampling, simplified gradient computation by using exindex, minor changes to demo.