CHMMBOX, version 1.2, Iead Rezek, Oxford University, Feb 2001
Matlab toolbox for max. aposteriori estimation of two chain
Coupled Hidden Markov Models.
The MatlabBGL library fills a hole in Matlab s suite of algorithms. Namely, it provides a rich set of algorithms to work with graphs, as in graph theory graphs. The MatlabBGL package uses Matlab s native sparse matrix type as a graph and provides algorithms that work
圖的深度優先遍歷和廣度優先遍歷，以鄰接矩陣方式輸入。（按照提示輸入）！ - The chart depth first spreads the calendar and the breadth first spreads the calendar, is next the matrix way input. (According
This paper introduces an affine invariant of trapezia, and the explicit constraint equation between the intrinsic matrix of a camera and the similarity invariants of a trapezium are established using the affine invariant. By this constraint, the inner parameters, motion parameters of the cameras and the similarity invariants of trapezia can be linearly determined using some prior knowledge on the cameras or the trapezia. The proposed algorithms have wide applicability since parallel lines are not rare in many scenes. Experimental results validate the proposed approaches. This work presents a unifying framework based on the parallelism constraint, and the previous methods based on the parallelograms or the parallelepipeds can be integrated into this framework.
Key words: invariant parallelism constraint camera calibration 3D reconstruction
In this talk we will consider two approaches in dealing with the risk of supplier bankruptcy. In the first model, we study the effects of supply disruption risk in a supply chain where one buyer deals with competing risky suppliers who may default during their production lead-times.
ICP fit points in data to the points in model. Fit with respect to minimize the sum of square errors with the closest model points and data points.
Ordinary usage:
[R, T] = icp(model,data)
INPUT:
model - matrix with model points,
data - matrix with data points,
OUTPUT:
R - rotation matrix and
T - translation vector accordingly
so
newdata = R*data + T .
newdata are transformed data points to fit model
see help icp for more information
