圖像處理的關(guān)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(xué)習(xí)圖像處理的人必看的幾篇文章
This paper examines the asymptotic (large sample) performance
of a family of non-data aided feedforward (NDA FF) nonlinear
least-squares (NLS) type carrier frequency estimators for burst-mode
phase shift keying (PSK) modulations transmitted through AWGN and
flat Ricean-fading channels. The asymptotic performance of these estimators
is established in closed-form expression and compared with the
modified Cram`er-Rao bound (MCRB). A best linear unbiased estimator
(BLUE), which exhibits the lowest asymptotic variance within the family
of NDA FF NLS-type estimators, is also proposed.
本程序?qū)嵶鯩LP(Multi-layer perceptron)算法，使用者可以自行設(shè)定訓(xùn)練數(shù)據(jù)集與測(cè)試數(shù)據(jù)集，將訓(xùn)練數(shù)據(jù)集加載，在2、3維下可以顯示其分布狀態(tài)，并分別設(shè)定鍵節(jié)值、學(xué)習(xí)率、迭代次數(shù)來訓(xùn)練其類神經(jīng)網(wǎng)絡(luò)，最后可觀看辨識(shí)率與RMSE（Root Mean squared error）來判別訓(xùn)練是否可以停止。
for the TI dm6446 platformBackground subtraction moduleMaintain a background model, which can distinguish background, foreground and theft/left pixelsShadow removal moduleDetect shadow regions in monochrome videosCamera shift detection and suppression moduleDetect of small camera shift, and suppress the false alarms due to themBlob extraction moduleExtract connected foreground and theft/left pixels as blobsTheft/left baggage distinguish moduleDistinguish theft blobs and left baggage blobs
The Fuzzy Clustering and Data Analysis Toolbox is a collection of Matlab
functions. Its propose is to divide a given data set into subsets (called
clusters), hard and fuzzy partitioning mean, that these transitions between
the subsets are crisp or gradual.
