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Computer Vision Metrics: Chapter Three

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Global and Regional Features

Measure twice, cut once.
—Carpenter’s saying

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 developed, the metrics have become more complex to compute, consuming larger memory footprints. The images are becoming multi-modal, combining intensity, color, multiple spectrums, depth sensor information, multiple-exposure settings, high dynamic range imagery, faster frame rates, and more precision and accuracy in x, y and Z depth. Increases in memory bandwidth and compute performance, therefore,...

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