3D reconstruction, medical image processing from colons, using intel image processing for based class. This source code. Some code missing but I think you can understand it. Development version. This source code is very interesting for learning segmentation and registration from dataset. This code also has some technique about GPU image processing for ray tracing. Also learn many filter apply for transform from spatial domain to frequency domain.
The Joint Video Team (JVT) of ISO/IEC MPEG and ITU-T VCEG are finalising a new standard for
the coding (compression) of natural video images. The new standard [1] will be known as H.264 and
also MPEG-4 Part 10, “Advanced Video Coding”. This document describes the transform and
quantization processes defined, or implied, by the standard.
When I first studied Kalman filtering, I saw many advanced signal processing submissions here at the MATLAB Central File exchange, but I didn t see a heavily commented, basic Kalman filter present to allow someone new to Kalman filters to learn about creating them. So, a year later, I ve written a very simple, heavily commented discrete filter.
In this article, we present an overview of methods for sequential simulation from posterior distributions.
These methods are of particular interest in Bayesian filtering for discrete time dynamic models
that are typically nonlinear and non-Gaussian. A general importance sampling framework is developed
that unifies many of the methods which have been proposed over the last few decades in several
different scientific disciplines. Novel extensions to the existing methods are also proposed.We showin
particular how to incorporate local linearisation methods similar to those which have previously been
employed in the deterministic filtering literature these lead to very effective importance distributions.
Furthermore we describe a method which uses Rao-Blackwellisation in order to take advantage of
the analytic structure present in some important classes of state-space models. In a final section we
develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.
Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on discrete and continuous attributes (words and real values). See http://en.wikipedia.org/wiki/AdaBoost and the papers by Y. Freund and R. Schapire for more details [1]. This approach is one of most efficient and simple to combine continuous and nominal values. Our implementation is aimed at allowing training from millions of examples by hundreds of features in a reasonable time/memory.
A new cable fault location method based on
wavelet reconstruction is proposed. In this method the
difference between the currents of faulty phase and sound
phase under the high voltage pulse excitation is used as the
measured signal and is decomposed in multi-scale by wavelet
transform, then reconstructed in single scale. Comparing with
traditional fault location method by travelling wave, the
presented method will not be interfered by the reflected wave
from the branch joint of cables or from other positions where
the impedances are not matched and not be influenced by fault
types, otherwise, the reflected waves can be recognized even
the faulty position is near to the measuring terminal, at the
same time, the influence of the wave speed uncertainty can be
reduced. The correctness of the proposed method is proved by
simulation results.
A Numerical Photonics library written in C++. The library includes beam propagation method, coupled mode method, Bragg Gating Analysis, transfer matrix method, and vectorial Fourier Decomposition method. Very useful in simulating integrated Photonic devices
the attached utility is a work I ve submitted to the university
It shows what a jpeg compression is all about.
the function implements the DCT transform, using a matrix operator.
note that matlab has a function for the DCT and iDCT transforms
that might be more efficient.