abel Tool Sample
Requires: Visual Basic 6 and MapObjects 2.x
Data: redlands.shp (Redlands sample data set from MO 2.x)
Interactive Labeling Tool
If the check box is checked, then the mouse down location will search for the Closest line, and label it with the street name. If the check box is not checked, then the mouse down will turn into a pan/zoom tool.
There is a slider bar to control the search tolerance in screen pixels for the labeling.
The Hopfield model is a distributed model of an associative memory. Neurons are pixels and can take the values of -1 (off) or +1 (on). The network has stored a certain number of pixel patterns. During a retrieval phase, the network is started with some initial configuration and the network dynamics evolves towards the stored pattern which is Closest to the initial configuration.
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
We propose a novel approach for head tracking, which combines particle filters with Isomap. The particle filter works on the low-dimensional embedding of training images. It indexes into the Isomap with its state variables to find the Closest template for each particle. The most weighted particle approximates the location of head. We develop a synthetic video sequence to test our technique. The results we get show that the tracker tracks the head which changes position, poses and lighting conditions.
How the K-mean Cluster work
Step 1. Begin with a decision the value of k = number of clusters
Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following:
Take the first k training sample as single-element clusters
Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster.
Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the Closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample.
Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.
This directory includes matlab interface of the curvelet transform
using usfft.
Basic functions
fdct_usfft.m -- forward curvelet transform
afdct_usfft.m -- adjoint curvelet transform
ifdct_usfft.m -- inverse curvelet transform
fdct_usfft_param.m -- returns the location of each curvelet in phase-space
Useful tools
fdct_usfft_dispcoef.m -- returns a matrix contains all curvelet coefficients
fdct_usfft_pos2idx.m -- for fixed scale and fixed direction, returns
the curvelet which is Closest to a certain point on the image
Demos
fdct_usfft_demo_basic.m -- display the shape of a curvelet
fdct_usfft_demo_recon.m -- partial reconstruction using curvelet
fdct_usfft_demo_disp.m -- display all the curvelet coefficients of an image
fdct_usfft_demo_denoise.m -- image denoising using curvelet
