OpenCV高级应用探索

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"OpenCV使用方法——高级计算机视觉与CUDA并行计算" OpenCV（开源计算机视觉库）是一个广泛使用的开源库，专为C/C++程序员设计，支持Windows、Linux、MacOS和Android等多个平台。该库包含了一系列用于图像处理和计算机视觉的高级功能，旨在简化复杂的算法实现，促进计算机视觉在实际项目中的应用。本资源主要介绍了OpenCV中的高级计算机视觉主题，包括： 1. 主成分分析（PCA）：PCA是一种统计方法，用于将高维数据集转换成一组线性不相关的变量，即主成分。在OpenCV中，PCA可以用于图像降维、特征提取以及数据可视化等任务。 2. 匹配技术：OpenCV提供了多种图像匹配算法，如SIFT（尺度不变特征变换）、SURF（加速稳健特征）和ORB（快速ORB），这些方法在物体识别、图像拼接和三维重建等领域有广泛应用。 3. 机器学习技术：OpenCV支持各种机器学习算法，如支持向量机（SVM）、决策树、随机森林等，这些算法可用于分类、回归和物体检测等问题。 4. 跟踪与光流：跟踪是识别和追踪图像序列中特定对象的过程。OpenCV提供多种跟踪算法，如KCF（卡尔曼滤波器结合卡尔曼预测和卡尔曼更新的追踪）、CSRT（连续可分离卡尔曼滤波）。光流则是估计图像像素随时间的运动，有助于理解动态场景。 5. CUDA并行计算机视觉：CUDA是NVIDIA公司提供的并行计算平台，OpenCV利用CUDA可以实现GPU上的高性能计算，加速图像处理和计算机视觉任务，特别适用于大数据量的实时处理。除了理论知识，文档还提供了实践示例，以帮助读者理解如何在实际项目中应用这些概念。通过这些例子，开发者可以学习到如何有效地利用OpenCV解决复杂问题，提升计算机视觉算法的效率和精度。关键词：OpenCV；并行计算视觉；模式识别；计算机视觉总结来说，这篇文档是针对已经有一定基础的计算机视觉学习者，深入探讨OpenCV在高级计算机视觉领域的应用，旨在提升开发者的技能，使其能够利用OpenCV进行更复杂、高效的工作。对于想要在计算机视觉领域深化研究或应用的人士，这是一份极具价值的学习资料。

Fig. 1. An ideal contour around an object of interest.

after that uses a set of markers in a mask to group the small

regions into areas with similar properties, which are computed

from each marker. The watershed method is computed in

openCV using the function:

cvWatershed(src, markers);

Again, src is the input image, markers is an image with the

same size as src where the regions are marked, so the method

can group smaller regions and segment the image properly.

III. I

MAGE MATCHING

Pattern recognition is an area of computer vision where one

tries to ﬁnd if there is a known pattern in a given image.

The process that checks in the image for a pattern’s possible

location is called image matching. Matching techniques are

the simplest way to do pattern recognition. In this section it

will present two different approaches for matching: contour

matching and template matching.

A. Contour Matching

After segmentation an image is composed by a set of

groups of pixels where each group represents a region. This

segmented data can be transformed into a compact way that

facilitates the region’s description and help to compare and

match with a given pattern [8]. When thinking about a contour

one considers an object of interest and a line surrounding it as

an ideal contour (althought not necessarily a closed line), as

shown in Figure 1. A contour can be represented in different

ways, two of the most common ways to represent a contour

are polygons and Freeman chain codes [1].

A boundary can be represented by a connected sequence

of straight line segments, each with a speciﬁed length and

direction. This type of representation is called a Freeman chain

code, it uses a 4- or 8-connectivity and the segment’s direction

is coded following a numbering code. Figure 2 presents these

two methods, on top the Freeman chain code with the green

marker to set the starting point and the numbering code at the

right. On bottom the polygon given by the set of points at the

corners of the polygon.

In openCV contours are usually computed from a binary

image where it is easier to deﬁne contrast. The function used

to compute the contours is:

int cvFindContours(src,sto,first,header,mode,

Fig. 2. Contour representation: on top the Freeman chain code and on bottom

a set of points for a polygon.

method);

The input image, src, is a binary image, sto is a mem-

ory location where the contours will be written ﬁrst is the

ﬁrst contours in a sequence, header gives the size of the

object being retrieved and can be either sizeof(CvContour) or

sizeof(CvChain), depending on the method, mode deﬁnes the

data structure used to store the contours and can be:

• CV RETR EXTERNAL: retrieves only the most external

contour.

• CV RETR LIST: retrieves all contours as a list where

the ﬁrst element is most internal contour.

• CV RETR CCOMP: retrieves all contours and organize

them as a two level hierarchy, one level with external

contours and another with internal contours.

• CV RETR TREE: retrieves all contours and organize

them as a tree structure with the outer contour as a root.

The method parameter is related to the way openCV stores

the contour in memory. There are ﬁve ways to do this task:

• CV CHAIN CODE: uses the Freeman chain code.

• CV CHAIN APPROX NONE: uses the points deter-

mined by the Freeman chain code.

• CV CHAIN APPROX SIMPLE: compresses the Free-

man chain code and returns the ending points.

• CV CHAIN APPROX TC89 L1 or

CHAIN APPROX TC89 KCOS: uses a special

chain approximation algorithm.

• CV LINK RUNS: can only be used with

RETR LIST and represents contours as links

horizontal segments.

The returning value of the method is the total number of

contours found. One important step before calling cvFindCon-

tours is the input image binarization. This process requires

some ﬁltering afterwards to clean the image, otherwise too

many contours might be found. Figure 3 shows a simple and

clean binary image (on the left side) and the contours found

(on the right) there are a total of 7 contours on the right

image. Figure 4 shows the same process applied to a complex

(meaning real) image, the contour structure, in this case, has

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