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Computer Vision Metrics: Chapter One (Part D)

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For Part C of Chapter One, please click here.

Bibliography references are set off with brackets, i.e. "[XXX]". For the corresponding bibliography entries, please click here.


Correspondence

Correspondence, or feature matching, is common to most depth-sensing methods. For a taxonomy of stereo feature matching algorithms, see Scharstein and Szeliski [440]. Here, we discuss correspondence along the lines of feature descriptor methods and triangulation as applied to stereo, multi-view stereo, and structured light.

Subpixel accuracy is a goal in most depth-sensing methods, so several algorithms exist [468]. It’s popular to correlate two patches or intensity templates by fitting the surfaces to find the highest match; however, Fourier methods are also used to correlate phase [467, 469], similar to the intensity correlation methods.

For stereo systems, the image pairs are rectified prior to feature matching so that the features are expected to be found along the same line at about the same scale, as shown in Figure 1-11; descriptors with little or no rotational invariance are suitable [215, 120]. A feature descriptor such as a correlation template is fine, while a powerful method such as the SIFT feature description method [161] is overkill. The feature descriptor region may be a rectangle favoring disparity in the x-axis and expecting little variance in the y-axis, such as a rectangular 3x9 descriptor shape. The disparity is expected in the x-axis, not the y-axis. Several window sizing methods for the descriptor shape are used, including fixed size and adaptive size [440].

Multi-view stereo systems are similar to stereo; however, the rectification stage may not be as accurate, since motion between frames can include scaling, translation, and rotation. Since scale and rotation may have significant correspondence problems between frames, other approaches to feature description have been applied to MVS, with better results. A few notable feature descriptor methods applied to multi-view and wide baseline stereo include the MSER [194] method (also discussed in Chapter 6), which uses a blob-like patch, and the SUSAN [164, 165] method (also discussed in Chapter 6), which defines the feature based on an object region or segmentation with a known centroid or nucleus around which the feature exists.

For structured light systems, the type of light pattern will determine the feature, and correlation of the phase is a popular method [469]. For example, structured light methods that rely on phase-shift patterns using phase correlation [467]...