排水量较小的护航航空母舰（Escort Aircraft Carriers，CVE），则另行收录于美国海军护航航空母舰列表中。

(1)  多光谱成像——光谱分辨率在 delta_lambda/lambda=0．1数量级，这样的传感器在可见光和近红外区域一般只有几个波段。

(2)  高光谱成像—— 光谱分辨率在 delta_lambda/lambda=0．01数量级，这样的传感器在可见光和近红外区域有几卜到数百个波段，光谱分辨率可达nm级。

(3)  超光谱成像—— 光谱分辨率在delta_lambda/lambda =O．001数量级，这样的传感器在可见光和近红外区域可达数千个波段。

众所周知，光谱分析是自然科学中一种重要的研究手段，光谱技术能检测到被测物体的物理结构、化学成分等指标。光谱评价是基于点测量，而图像测量是基于空间特性变化，两者各有其优缺点。因此，可以说光谱成像技术是光谱分析技术和图像分析技术发展的必然结果，是二者完美结合的产物。光谱成像技术不仅具有光谱分辨能力，还具有图像分辨能力，利用光谱成像技术不仅可以对待检测物体进行定性和定量分析，而且还能进对其进行定位分析。

高光谱成像系统的主要工作部件是成像光谱仪，它是一种新型传感器，2O世纪8O年代初正式开始研制，研制这类仪器的目的是为获取大量窄波段连续光谱图像数据，使每个像元具有几乎连续的光谱数据。它是一系列光波波长处的光学图像，通常包含数十到数百个波段，光谱分辨率一般为1～l0nm。由于高光谱成像所获得的高光谱图像能对图像中的每个像素提供一条几乎连续的光谱曲线，其在待测物上获得空间信息的同时又能获得比多光谱更为丰富光谱数据信息，这些数据信息可用来生成复杂模型，来进行判别、分类、识别图像中的材料。

通过高光谱成像获取待测物的高光谱图像包含了待测物的丰富的空间、光谱和辐射三重信息。这些信息不仅表现了

地物空间分布的影像特征，同时也可能以其中某一像元或像元组为目标获取它们的辐射强度以及光谱特征。影像、辐射与光谱是高光谱图像中的3个重要特征，这3个特征的有机结合就是高光谱图像。

高光谱图像数据为数据立方体(cube)。通常图像像素的横坐标和纵坐标分别用z和Y来表示，光谱的波长信息以(Z即轴)表示。该数据立方体由沿着光谱轴的以一定光谱分辨率间隔的连续二维图像组成。

Introduction

Filtering is perhaps the most fundamental operation of image processing and computer vision. In the broadest sense of the term "filtering", the value of the filtered image at a given location is a function of the values of the input image in a small neighborhood of the same location. For example, Gaussian low-pass filtering computes a weighted average of pixel values in the neighborhood, in which the weights decrease with distance from the neighborhood center. Although formal and quantitative explanations of this weight fall-off can be given, the intuition is that images typically vary slowly over space, so near pixels are likely to have similar values, and it is therefore appropriate to average them together. The noise values that corrupt these nearby pixels are mutually less correlated than the signal values, so noise is averaged away while signal is preserved.
The assumption of slow spatial variations fails at edges, which are consequently blurred by linear low-pass filtering. How can we prevent averaging across edges, while still averaging within smooth regions? Many efforts have been devoted to reducing this undesired effect. Bilateral filtering is a simple, non-iterative scheme for edge-preserving smoothing.

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The Idea

The basic idea underlying bilateral filtering is to do in the range of an image what traditional filters do in its domain. Two pixels can be close to one another, that is, occupy nearby spatial location, or they can be similar to one another, that is, have nearby values, possibly in a perceptually meaningful fashion.
Consider a shift-invariant low-pass domain filter applied to an image:

The bold font for f and h emphasizes the fact that both input and output images may be multi-band. In order to preserve the DC component, it must be

Range filtering is similarly defined:

In this case, the kernel measures the photometric similarity between pixels. The normalization constant in this case is

The spatial distribution of image intensities plays no role in range filtering taken by itself. Combining intensities from the entire image, however, makes little sense, since the distribution of image values far away from x ought not to affect the final value at x. In addition, one can show that range filtering without domain filtering merely changes the color map of an image, and is therefore of little use. The appropriate solution is to combine domain and range filtering, thereby enforcing both geometric and photometric locality. Combined filtering can be described as follows:

with the normalization

Combined domain and range filtering will be denoted as bilateral filtering. It replaces the pixel value at x with an average of similar and nearby pixel values. In smooth regions, pixel values in a small neighborhood are similar to each other, and the bilateral filter acts essentially as a standard domain filter, averaging away the small, weakly correlated differences between pixel values caused by noise. Consider now a sharp boundary between a dark and a bright region, as in figure 1(a).

(a)	(b)	(c)
	Figure 1

When the bilateral filter is centered, say, on a pixel on the bright side of the boundary, the similarity function s assumes values close to one for pixels on the same side, and values close to zero for pixels on the dark side. The similarity function is shown in figure 1(b) for a 23x23 filter support centered two pixels to the right of the step in figure 1(a). The normalization term k(x) ensures that the weights for all the pixels add up to one. As a result, the filter replaces the bright pixel at the center by an average of the bright pixels in its vicinity, and essentially ignores the dark pixels. Conversely, when the filter is centered on a dark pixel, the bright pixels are ignored instead. Thus, as shown in figure 1(c), good filtering behavior is achieved at the boundaries, thanks to the domain component of the filter, and crisp edges are preserved at the same time, thanks to the range component.

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The Gaussian Case

A simple and important case of bilateral filtering is shift-invariant Gaussian filtering, in which both the closeness function c and the similarity function s are Gaussian functions of the Euclidean distance between their arguments. More specifically, c is radially symmetric:

where

is the Euclidean distance. The similarity function s is perfectly analogous to c :

where

is a suitable measure of distance in intensity space. In the scalar case, this may be simply the absolute difference of the pixel difference or, since noise increases with image intensity, an intensity-dependent version of it. Just as this form of domain filtering is shift-invariant, the Gaussian range filter introduced above is insensitive to overall additive changes of image intensity. Of course, the range filter is shift-invariant as well.

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Experiments with Black-and-White Images

Figure 2 (a) and (b) show the potential of bilateral filtering for the removal of texture. The picture "simplification" illustrated by figure 2 (b) can be useful for data reduction without loss of overall shape features in applications such as image transmission, picture editing and manipulation, image description for retrieval.

(a)		(b)
	Figure 2

Bilateral filtering with parameters sd =3 pixels and sr =50 intensity values is applied to the image in figure 3 (a) to yield the image in figure 3 (b). Notice that most of the fine texture has been filtered away, and yet all contours are as crisp as in the original image. Figure 3 (c) shows a detail of figure 3 (a), and figure 3 (d) shows the corresponding filtered version. The two onions have assumed a graphics-like appearance, and the fine texture has gone. However, the overall shading is preserved, because it is well within the band of the domain filter and is almost unaffected by the range filter. Also, the boundaries of the onions are preserved.

(a)		(b)
(c)		(d)
	Figure 3

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Experiments with Color Images

For black-and-white images, intensities between any two gray levels are still gray levels. As a consequence, when smoothing black-and-white images with a standard low-pass filter, intermediate levels of gray are produced across edges, thereby producing blurred images. With color images, an additional complication arises from the fact that between any two colors there are other, often rather different colors. For instance, between blue and red there are various shades of pink and purple. Thus, disturbing color bands may be produced when smoothing across color edges. The smoothed image does not just look blurred, it also exhibits odd-looking, colored auras around objects.

(a)		(b)
(c)		(d)
	Figure 4

Figure 4 (a) shows a detail from a picture with a red jacket against a blue sky. Even in this unblurred picture, a thin pink-purple line is visible, and is caused by a combination of lens blurring and pixel averaging. In fact, pixels along the boundary, when projected back into the scene, intersect both red jacket and blue sky, and the resulting color is the pink average of red and blue. When smoothing, this effect is emphasized, as the broad, blurred pink-purple area in figure 4 (b) shows.
To address this difficulty, edge-preserving smoothing could be applied to the red, green, and blue components of the image separately. However, the intensity profiles across the edge in the three color bands are in general different. Smoothing the three color bands separately results in an even more pronounced pink and purple band than in the original, as shown in figure 4 (c). The pink-purple band, however, is not widened as in the standard-blurred version of figure 4 (b).
A much better result can be obtained with bilateral filtering. In fact, a bilateral filter allows combining the three color bands appropriately, and measuring photometric distances between pixels in the combined space. Moreover, this combined distance can be made to correspond closely to perceived dissimilarity by using Euclidean distance in the CIE-Lab color space. This color space is based on a large body of psychophysical data concerning color-matching experiments performed by human observers. In this space, small Euclidean distances are designed to correlate strongly with the perception of color discrepancy as experienced by an "average" color-normal human observer. Thus, in a sense, bilateral filtering performed in the CIE-Lab color space is the most natural type of filtering for color images: only perceptually similar colors are averaged together, and only perceptually important edges are preserved. Figure 4 (d) shows the image resulting from bilateral smoothing of the image in figure 4 (a). The pink band has shrunk considerably, and no extraneous colors appear.

(a)	(b)	(c)
	Figure 5

Figure 5 (c) shows the result of five iterations of bilateral filtering of the image in figure 5 (a). While a single iteration produces a much cleaner image (figure 5 (b)) than the original, and is probably sufficient for most image processing needs, multiple iterations have the effect of flattening the colors in an image considerably, but without blurring edges. The resulting image has a much smaller color map, and the effects of bilateral filtering are easier to see when displayed on a printed page. Notice the cartoon-like appearance of figure 5 (c). All shadows and edges are preserved, but most of the shading is gone, and no "new" colors are introduced by filtering.

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References

[1] C. Tomasi and R. Manduchi, "Bilateral Filtering for Gray and Color Images", Proceedings of the 1998 IEEE International Conference on Computer Vision, Bombay, India.
[2] T. Boult, R.A. Melter, F. Skorina, and I. Stojmenovic,"G-neighbors", Proceedings of the SPIE Conference on Vision Geometry II, pages 96-109, 1993.
[3] R.T. Chin and C.L. Yeh, "Quantitative evaluation of some edge-preserving noise-smoothing techniques", Computer Vision, Graphics, and Image Processing, 23:67-91, 1983.
[4] L.S. Davis and A. Rosenfeld, "Noise cleaning by iterated local averaging", IEEE Transactions on Systems, Man, and Cybernetics, 8:705-710, 1978.
[5] R.E. Graham, "Snow-removal - a noise-stripping process for picture signals", IRE Transactions on Information Theory, 8:129-144, 1961.
[6] N. Himayat and S.A. Kassam, "Approximate performance analysis of edge preserving filters", IEEE Transactions on Signal Processing, 41(9):2764-77, 1993.
[7] T.S. Huang, G.J. Yang, and G.Y. Tang, "A fast two-dimensional median filtering algorithm", IEEE Transactions on Acoustics, Speech, and Signal Processing, 27(1):13-18, 1979.
[8] J.S. Lee, "Digital image enhancement and noise filtering by use of local statistics", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2(2):165-168, 1980.
[9] M. Nagao and T. Matsuyama, "Edge preserving smoothing", Computer Graphics and Image Processing, 9:394-407, 1979.
[10] P.M. Narendra, "A separable median filter for image noise smoothing", IEEE Transactions on Pattern Analysis and Machine Intelligence, 3(1):20-29, 1981.
[11] K.J. Overton and T.E. Weymouth, "A noise reducing preprocessing algorithm",Proceedings of the IEEE Computer Science Conference on Pattern Recognition and Image Processing, pages 498-507, Chicago, IL, 1979.
[12] P. Perona and J. Malik, "Scale-space and edge detection using anisotropic diffusion", IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7):629-639, 1990.
[13] G. Ramponi, "A rational edge-preserving smoother", Proceedings of the International Conference on Image Processing, volume 1, pages 151-154, Washington, DC, 1995.
[14] G. Sapiro and D.L. Ringach, "Anisotropic diffusion of color images", Proceedings of the SPIE, volume 2657, pages 471-382, 1996.
[15] D.C.C. Wang, A.H. Vagnucci, and C.C. Li, "A gradient inverse weighted smoothing scheme and the evaluation of its performance", Computer Vision, Graphics, and Image Processing, 15:167-181, 1981.
[16] G. Wyszecki and W. S. Styles, Color Science: Concepts and Methods, Quantitative Data and Formulae, John Wiley and Sons, New York, NY, 1982.
[17] L. Yin, R. Yang, M. Gabbouj, and Y. Neuvo, "Weighted median filters: a tutorial",IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, 43(3):155-192, 1996.

前提： 我自己已经安装好VS2010  + opencv2.4
1.下载python 2.6
2.下载numpy，此处需要下载对应python版本的，如numpy-1.6.1-win32-superpack-python2.6.exe

3.然后将此目录下的cv2.pyd文件（即D:\opencv2.4\build\python\2.6\cv2.pyd文件）复制到你的python的site-packages目录下面，比如我的是C:\Python26\Lib\site-packages这个目录下面.

好了，测试一下：

进入D:\opencv2.4\samples\python双击drawing.py或者cd到目录下面然后python drawing.py

     共生矩阵用两个位置的象素的联合概率密度来定义，它不仅反映亮度的分布特性，也反映具有同样亮度或接近亮度的象素之间的位置分布特性，是有关图象亮度变化的二阶统计特征。它是定义一组纹理特征的基础。

     一幅图象的灰度共生矩阵能反映出图象灰度关于方向、相邻间隔、变化幅度的综合信息，它是分析图象的局部模式和它们排列规则的基础。

　　设f(x,y)为一幅二维数字图象，其大小为M×N，灰度级别为Ng,则满足一定空间关系的灰度共生矩阵为

P(i,j)=#｛(x1,y1),(x2,y2)∈M×N｜f(x1,y1)=i,f(x2,y2)=j｝

　　其中#(x)表示集合x中的元素个数，显然P为Ng×Ng的矩阵，若(x1,y1)与(x2,y2)间距离为d,两者与坐标横轴的夹角为θ，则可以得到各种间距及角度的灰度共生矩阵P(i,j,d,θ)。

      纹理特征提取的一种有效方法是以灰度级的空间相关矩阵即共生矩阵为基础的［7］，因为图像中相距(Δx，Δy)的两个灰度像素同时出现的联合频率分布可以用灰度共生矩阵来表示。若将图像的灰度级定为N级，那么共生矩阵为N×N矩阵，可表示为M(Δx，Δy)(h,k)，其中位于(h,k)的元素mhk的值表示一个灰度为h而另一个灰度为k的两个相距为(Δx，Δy)的像素对出现的次数。
　　对粗纹理的区域，其灰度共生矩阵的mhk值较集中于主对角线附近。因为对于粗纹理，像素对趋于具有相同的灰度。而对于细纹理的区域，其灰度共生矩阵中的mhk值则散布在各处。

    为了能更直观地以共生矩阵描述纹理状况，从共生矩阵导出一些反映矩阵状况的参数，典型的有以下几种：

（1）能量： 是灰度共生矩阵元素值的平方和，所以也称能量，反映了图像灰度分布均匀程度和纹理粗细度。如果共生矩阵的所有值均相等，则ASM值小；相反，如果其中一些值大而其它值小，则ASM值大。当共生矩阵中元素集中分布时，此时ASM值大。ASM值大表明一种较均一和规则变化的纹理模式。

（2）对比度： ，其中 。反映了图像的清晰度和纹理沟纹深浅的程度。纹理沟纹越深，其对比度越大，视觉效果越清晰；反之，对比度小，则沟纹浅，效果模糊。灰度差即对比度大的象素对越多，这个值越大。灰度公生矩阵中远离对角线的元素值越大，CON越大。

（3）相关：它度量空间灰度共生矩阵元素在行或列方向上的相似程度，因此，相关值大小反映了图像中局部灰度相关性。当矩阵元素值均匀相等时，相关值就大;相反，如果矩阵像元值相差很大则相关值小。如果图像中有水平方向纹理，则水平方向矩阵的COR大于其余矩阵的COR值。

（4）熵： 是图像所具有的信息量的度量，纹理信息也属于图像的信息，是一个随机性的度量，当共生矩阵中所有元素有最大的随机性、空间共生矩阵中所有值几乎相等时，共生矩阵中元素分散分布时，熵较大。它表示了图像中纹理的非均匀程度或复杂程度。

（5）逆差距： 反映图像纹理的同质性，度量图像纹理局部变化的多少。其值大则说明图像纹理的不同区域间缺少变化，局部非常均匀。

      其它参数:

中值<Mean>

协方差<Variance>

同质性/逆差距<Homogeneity>

反差<Contrast>

差异性<Dissimilarity>

熵<Entropy>

二阶距<Angular Second Moment>

自相关<Correlation>

       PCA是主成分分析，主要用于数据降维，对于一系列sample的feature组成的多维向量，多维向量里的某些元素本身没有区分性，比如某个元素在所有的sample中都为1，或者与1差距不大，那么这个元素本身就没有区分性，用它做特征来区分，贡献会非常小。所以我们的目的是找那些变化大的元素，即方差大的那些维，而去除掉那些变化不大的维，从而使feature留下的都是“精品”，而且计算量也变小了。

       对于一个k维的feature来说，相当于它的每一维feature与其他维都是正交的（相当于在多维坐标系中，坐标轴都是垂直的），那么我们可以变化这些维的坐标系，从而使这个feature在某些维上方差大，而在某些维上方差很小。例如，一个45度倾斜的椭圆，在第一坐标系，如果按照x,y坐标来投影，这些点的x和y的属性很难用于区分他们，因为他们在x,y轴上坐标变化的方差都差不多，我们无法根据这个点的某个x属性来判断这个点是哪个，而如果将坐标轴旋转，以椭圆长轴为x轴，则椭圆在长轴上的分布比较长，方差大，而在短轴上的分布短，方差小，所以可以考虑只保留这些点的长轴属性，来区分椭圆上的点，这样，区分性比x,y轴的方法要好！

       所以我们的做法就是求得一个k维特征的投影矩阵，这个投影矩阵可以将feature从高维降到低维。投影矩阵也可以叫做变换矩阵。新的低维特征必须每个维都正交，特征向量都是正交的。通过求样本矩阵的协方差矩阵，然后求出协方差矩阵的特征向量，这些特征向量就可以构成这个投影矩阵了。特征向量的选择取决于协方差矩阵的特征值的大小。

        举一个例子：

         对于一个训练集，100个sample，特征是10维，那么它可以建立一个100*10的矩阵，作为样本。求这个样本的协方差矩阵，得到一个10*10的协方差矩阵，然后求出这个协方差矩阵的特征值和特征向量，应该有10个特征值和特征向量，我们根据特征值的大小，取前四个特征值所对应的特征向量，构成一个10*4的矩阵，这个矩阵就是我们要求的特征矩阵，100*10的样本矩阵乘以这个10*4的特征矩阵，就得到了一个100*4的新的降维之后的样本矩阵，每个sample的维数下降了。

当给定一个测试的特征集之后，比如1*10维的特征，乘以上面得到的10*4的特征矩阵，便可以得到一个1*4的特征，用这个特征去分类。

       所以做PCA实际上是求得这个投影矩阵，用高维的特征乘以这个投影矩阵，便可以将高维特征的维数下降到指定的维数。

        在opencv里面有专门的函数，可以得到这个这个投影矩阵（特征矩阵）。

void cvCalcPCA( const CvArr* data, CvArr* avg, CvArr* eigenvalues, CvArr* eigenvectors, int flags );