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Computer Vision Metrics: Chapter Two

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Image Pre-Processing

“I entered, and found Captain Nemo deep in algebraical calculations of x and other quantities.”
—Jules Verne, 20,000 Leagues Under The Sea

This chapter describes the methods used to prepare images for further analysis, including interest point and feature extraction. Some of these methods are also useful for global and local feature description, particularly the metrics derived from transforms and basis spaces. The focus is on image pre-processing for computer vision, so we do not cover the entire range of image processing topics applied to areas such as computational photography and photo enhancements, so we refer the interested reader to various other standard resources in Digital Image Processing and Signal Processing as we go along [4,9,325,326], and we also point out interesting research papers that will enhance understanding of the topics. Readers with a strong background in image processing may benefit from a light reading of this chapter.

Perspectives on Image Processing

Image processing is a vast field that cannot be covered in a single chapter. So why do we discuss image pre-processing in a book about computer vision? The reason is to advance the science of local and global feature description, as image pre-processing is typically ignored in discussions of feature description. Some general image processing topics are covered here in light of feature description, intended to illustrate rather than to proscribe, as applications and image data will guide the image pre-processing stage.

Some will argue that image pre-processing is not a good idea, since it distorts or changes the true nature of the raw data. However, intelligent use of image pre-processing can provide benefits and solve problems that ultimately lead to better local and global feature detection. We survey common methods for image enhancements and corrections that will affect feature analysis downstream in the vision pipeline in both favorable and unfavorable ways, depending on how the methods are employed.

Image pre-processing may have dramatic positive effects on the quality of feature extraction and the results of image analysis. Image pre-processing is analogous to the mathematical normalization of a data set, which is a common step in many feature descriptor methods. Or to make a musical analogy, think of image pre-processing as a sound system with a range of controls, such as raw sound with no volume controls; volume control with a simple tone knob; volume control plus treble, bass, and mid; or volume control plus...