图像识别算法的学习步骤
我决定在办公时间后花我的个人时间来学习如何解析图像 jpeg 类型并在屏幕上表示的构建块。我的兴趣是图像中的对象识别。所以我想知道从哪里开始，我知道这涉及到数学。所以我需要逐步了解互联网上的哪些资源专门需要查看。
I have decided to spend my personal time after office hours to learn the building blocks of how images jpeg type are parsed and represented in screen. My interest is on object recognition in an image.so I want to know where to start , I know there are math involved in this.so I needed step by step on what resources in Internet specifically to look at.
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需要更多有关您想要的信息，但请查看 OpenCV
http://sourceforge.net/projects/ opencvlibrary/
查看好的示例。
Need a lot more information on what you want, but take a look at OpenCV
http://sourceforge.net/projects/opencvlibrary/
To see good examples.
我会得到 Ritter 的书（警告：昂贵！）并认真学习。如果您只是想获取现有代码并继续使用，那么也许您应该查看像 OpenCV 这样的库（请参阅 Lou 的回答）。
I'd get Ritter's book (warning: costly!) and give it serious studying. If you just want to grab existing code and go play then perhaps you should look at libraries like OpenCV (see Lou's answer).
大多数图像处理的最终目标是从低级（像素）形式的图像中提取有关某些高级且依赖于应用程序的对象的信息。 物体可能是人们每天都感兴趣的，例如机器人技术、宇宙射线阵雨或物理学中的粒子轨迹、生物学中的染色体、房屋、道路或不同用途的农业表面（例如航空摄影或合成孔径雷达等）。
这些 模式识别的过程通常先于图像恢复和增强、图像分割或特征提取等多个步骤，这些步骤可以用一般术语来描述。以与问题相关的术语进行的最终描述，甚至最终的图像重建，都脱离了这种普遍性，并且必须查阅应用领域的文献。
The ultimate goal of most image processing is to extract information about some high-level and application-dependent objects from an image available in low-level (pixel) form. The objects may be of every day interest like in robotics, cosmic ray showers or particle tracks like in physics, chromosomes like in biology, houses, roads, or differently used agricultural surfaces like in aerial photography or synthetic-aperture radar, etc.
This task of pattern recognition is usually preceded by multiple steps of image restoration and enhancement, image segmentation, or feature extraction, steps which can be described in general terms. The final description in problem-dependent terms, and even more so the eventual image reconstruction, escapes such generality, and the literature of application areas has to be consulted.