A general technique for the recovery of signi cant
image features is presented. The technique is based on
the mean shift algorithm, a simple nonparametric pro-
cedure for estimating density gradients. Drawbacks of
the current methods (including robust clustering) are
avoided. Feature space of any nature can be processed,
and as an example, color image Segmentation is dis-
cussed. The Segmentation is completely autonomous,
only its class is chosen by the user. Thus, the same
program can produce a high quality edge image, or pro-
vide, by extracting all the signi cant colors, a prepro-
cessor for content-based query systems. A 512 512
color image is analyzed in less than 10 seconds on a
standard workstation. Gray level images are handled
as color images having only the lightness coordinate
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.
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
Summary: Simple face and eye detection
MATLAB Release: R13
Description: You can use this codes for face detection based on color Segmentation and eye region detection.
中心點漂移是一種非監(jiān)督聚類算法（與k-means算法相似，但應(yīng)用范圍更廣些),可用于圖像分割，基于Matlab實現(xiàn)的源碼。
MedoidShift is a unsupervised clustering algorithm(similar to k-means algorithm, but can be used in border application fields), can be used for image Segmentation. Included is the Matlab implementation source code.
This approach addresses two difficulties simultaneously: 1)
the range limitation of mobile robot sensors and 2) the difficulty of detecting buildings in
monocular aerial images. With the suggested method building outlines can be detected
faster than the mobile robot can explore the area by itself, giving the robot an ability to
“see” around corners. At the same time, the approach can compensate for the absence
of elevation data in Segmentation of aerial images. Our experiments demonstrate that
ground-level semantic information (wall estimates) allows to focus the Segmentation of
the aerial image to find buildings and produce a ground-level semantic map that covers
a larger area than can be built using the onboard sensors.
Semantic analysis of multimedia content is an on going research
area that has gained a lot of attention over the last few years.
Additionally, machine learning techniques are widely used for multimedia
analysis with great success. This work presents a combined approach
to semantic adaptation of neural network classifiers in multimedia framework.
It is based on a fuzzy reasoning engine which is able to evaluate
the outputs and the confidence levels of the neural network classifier, using
a knowledge base. Improved image Segmentation results are obtained,
which are used for adaptation of the network classifier, further increasing
its ability to provide accurate classification of the specific content.
15篇光流配準(zhǔn)經(jīng)典文獻，目錄如下：
1、A Local Approach for Robust Optical Flow Estimation under Varying
2、A New Method for Computing Optical Flow
3、Accuracy vs. Efficiency Trade-offs in Optical Flow Algorithms
4、all about direct methods
5、An Introduction to OpenCV and Optical Flow
6、Bayesian Real-time Optical Flow
7、Color Optical Flow
8、Computation of Smooth Optical Flow in a Feedback Connected Analog Network
9、Computing optical flow with physical models of brightness Variation
10、Dense estimation and object-based Segmentation of the optical flow with robust techniques
11、Example Goal Standard methods Our solution Optical flow under
12、Exploiting Discontinuities in Optical Flow
13、Optical flow for Validating Medical Image Registration
14、Tutorial Computing 2D and 3D Optical Flow.pdf
15、The computation of optical flow
