Take free online classes from 80+ top universities and organizations. Coursera is a social entrepreneurship company partnering with Stanford University, Yale University, Princeton University and others around the world to offer courses online for anyone to take, for free. We believe in connecting people to a great education so that anyone around the world can learn without limits.
I joined the EVASION team in september 2006 in order to work on real time rendering of natural landscapes as a whole. I'm interested in the animation and realistic rendering of terrain, atmosphere, ocean, vegetation, rivers, clouds, etc. I'm looking for real-time and scalable algorithms allowing users to navigate freely anywhere in very large landscapes (up to whole planets), from ground to space, without visible transitions.
Mat estimateRigidTransform(const Mat& srcpt, const Mat& dstpt, bool fullAffine)¶ Computes optimal affine transformation between two 2D point sets Parameters: * srcpt – The first input 2D point set * dst – The second input 2D point set of the same size and the same type as A * fullAffine – If true, the function finds the optimal affine transformation with no any additional resrictions (i.e. there are 6 degrees of freedom); otherwise, the class of transformations to choose from is limited to combinations of translation, rotation and uniform scaling (i.e. there are 5 degrees of freedom) The function finds the optimal affine transform [A|b] (a 2 \times 3 floating-point matrix) that approximates best the transformation from \texttt{srcpt}_i to \texttt{dstpt}_i : [A^*|b^*] = arg \min _{[A|b]} \sum _i \| \texttt{dstpt} _i - A { \texttt{srcpt} _i}^T - b \| ^2 where [A|b] can be either arbitrary (when fullAffine=true ) or have form
Synopsis: Homography transform in Fourier spectrum with application to object recognition. Ideally, recognition of objects should be projection, scale, translation and rotation invariant, just as they are in human vision. This, however, is a very complex problem, since numerous times an object is occluded and many objects rarely appear the same twice, due to different camera/observer positions, variable lighting or object motion. Our goal in this regard is to investigate autonomous object recognition in unconstrained environments by means of outlines of the objects, which we will refer to as the contours. One of the reasons for the popularity of contour-based analysis techniques is that edge detection constitutes an important aspect of shape recognition by the human visual system. The main motivation behind this work is that 2-D homography may overcome the problem of noise sensitivity and boundary variations.
N. Rattehalli, и I. Jain. A UTILIZATION OF CONVOLUTIONAL MATRIX METHODS ON SLICED HIPPOCAMPAL NEURON REGION IMAGES FOR CELL SEGMENTATION, 9 (1/2/3):
01-09(2020)