圖像處理的關于Snakes : Active Contour Models算法和水平集以及GVF的幾篇文章，文章列表為：
[1]Snakes Active Contour Models.pdf
[2]Multiscale Active Contours.pdf
[3]Snakes, shapes, and gradient vector flow.pdf
[4]Motion of level sets by mean curvature I.pdf
[5]Spectral Stability of Local Deformations Spectral Stability of Local Deformations.pdf
[6]An active contour model for object tracking using the previous contour.pdf
[7]Volumetric Segmentation of Brain Images Using Parallel Genetic AlgorithmsI.pdf
[8]Segmentation in echocardiographic sequences using shape-based snake model.pdf
[9]Active Contours Without Edges.pdf
學習圖像處理的人必看的幾篇文章
這是一個模擬第3類模式地震波的matlab腳本。
This a collection of Matlab scripts that solve the antiplane
(mode III) earthquake dynamic problem with slip-weakening friction,
on a 1D fault embedded in a 2D homogeneous elastic unbounded medium.
The problem is formulated as a boundary integral equation
and the elastodynamic kernels are analytically derived in
the Spectral domain (spatial wavenumber).
The method is explained e.g. by Morrysey and Geubelle (1997),
and has been improved and extensively used by Nadia Lapusta,
Alain Cochard, etc.
JLAB is a set of Matlab functions I have written or co-written over the past fifteen years for the purpose of analyzing data. It consists of four hundred m-files spanning thirty thousand lines of code. JLAB includes functions ranging in complexity from one-line aliases to high-level algorithms for certain specialized tasks. These have been collected together and made publicly available for you to use, modify, and --- subject to certain very reasonable constraints --- to redistribute.
Some of the highlights are: a suite of functions for the rapid manipulation of multi-component, potentially multi-dimensional datasets a systematic way of dealing with datasets having components of non-uniform length tools for fine-tuning figures using compact, straightforward statements and specialized functions for Spectral and time / frequency analysis, including advanced wavelet algorithms developed by myself and collaborators.
Multirate filters provide a practical approach to designing and implementing finite response (FIR) filters with narrow Spectral constraints. By changing the input data rate at one or more intermediate points the filter lengths and computational rates can be greatly reduced when compared to a standard single-rate filter implementation.
97 law to enhance the classic procedure
Ridge wavelet extraction
Modulus maximum for the wavelet edge detection
Small Spectral analysis method mallat classic procedure
Fast Fourier Transform power point
The rectangular window introduces broadening of any frequency components [`smearing鈥? and sidelobesthat may overlap with other frequency components [`leakage鈥?.
鈥he effect improves as Nincreases
鈥owever, the rectangle window has poor properties and better choices of wncan lead to better Spectral properties [less leakage, in particular] 鈥搃.e. instead of just truncating the summation, we can pre-multiply by a suitable window function wnthat has better frequency domain properties.
鈥ore on window design in the filter design section of the course
Capabilities of the latest version of MultiSpec include the following.
Import data
Display multiSpectral images
Histogram
Reformat
Create new channels
Cluster data
Define classes via designating rectangular
Determine the best Spectral features
Classify a designated area in the data file
List classification results
The 4.0 kbit/s speech codec described in this paper is based on a
Frequency Domain Interpolative (FDI) coding technique, which
belongs to the class of prototype waveform Interpolation (PWI)
coding techniques. The codec also has an integrated voice
activity detector (VAD) and a noise reduction capability. The
input signal is subjected to LPC analysis and the prediction
residual is separated into a slowly evolving waveform (SEW) and
a rapidly evolving waveform (REW) components. The SEW
magnitude component is quantized using a hierarchical
predictive vector quantization approach. The REW magnitude is
quantized using a gain and a sub-band based shape. SEW and
REW phases are derived at the decoder using a phase model,
based on a transmitted measure of voice periodicity. The Spectral
(LSP) parameters are quantized using a combination of scalar
and vector quantizers. The 4.0 kbits/s coder has an algorithmic
delay of 60 ms and an estimated floating point complexity of
21.5 MIPS. The performance of this coder has been evaluated
using in-house MOS tests under various conditions such as
background noise. channel errors, self-tandem. and DTX mode
of operation, and has been shown to be statistically equivalent to
ITU-T (3.729 8 kbps codec across all conditions tested.
