A study of the scale-invariant feature transform on a parallel pipeline

2014

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. Note ■ 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.

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Image Pre-Processing

2014

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Global and Regional Features

2014

This chapter covers the metrics of general feature description, often used for whole images and image regions, including textural, statistical, model based, and basis space methods. Texture, a key metric, is a well-known topic within image processing, and it is commonly divided into structural and statistical methods. Structural methods look for features such as edges and shapes, while statistical methods are concerned with pixel value relationships and statistical moments. Methods for modeling image texture also exist, primarily useful for image synthesis rather than for description. Basis spaces, such as the Fourier space, are also use for feature description. It is difficult to develop clean partitions between the related topics in image processing and computer vision that pertain to global vs. regional vs. local feature metrics; there is considerable overlap in the applications of most metrics. However, for this chapter, we divide these topics along reasonable boundaries, though those borders may appear to be arbitrary. Similarly, there is some overlap between discussions here on global and regional features and topics that were covered in Chapter 2 on image processing and that will be discussed in Chapter 6 on local features. In short, many methods are used for local, regional, and global feature description, as well as image processing, such as the Fourier transform and the LBP. But we begin with a brief survey of some key ideas in the field of texture analysis and general vision metrics. Historical Survey of Features To compare and contrast global, regional, and local feature metrics, it is useful to survey and trace the development of the key ideas, approaches, and methods used to describe features for machine vision. This survey includes image processing (textures and statistics) and machine vision (local, regional, and global features). Historically, the choice of feature metrics was limited to those that were computable at the time, given the limitations in compute performance, memory, and sensor technology. As time passed and technology • Transform or basis based, including methods such as Fourier, wavelets, Gabor filters, Zernike, and other basis spaces, which are treated here as a sub-class of the statistical methods (statistical moments); however, basis spaces are used in transforms for image processing and filtering as well.

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Interest Point Detector and Feature Descriptor Survey

2014