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alt="images"/> can be expressed as a linear combination of images, images, and images as shown below.

      (1.37)equation

equation equation

      For the sake of comparing Eqs. (1.29) and (1.38) from the viewpoint of the notational logic, Eq. (1.29) is written again below.

      Here, it is instructive to pay attention to the interchanged location of the superscript (a) in Eqs. (1.38) and (1.39). In Eq. (1.39), images must not bear (a) because it is a vector that is specified without necessarily knowing anything about the observation frame images, whereas images must necessarily bear (a) because it is one of the basis vectors of images. In Eq. (1.38), on the other hand, images must necessarily bear (a) because it represents the appearance of images as observed in images, whereas images must not bear (a) because it is not tied up to any reference frame as explained above and expressed by Eq. (1.36).

      1.6.1 Dot Product

      Consider two vectors images and images, which are resolved as follows in a reference frame images:

      The dot product of images and images can be expressed as

      On the other hand, according to Eq. (1.24),

      (1.43)equation

      Hence, Eq. (1.42) becomes

      Equation (1.45) can also be written as follows in terms of images and images, which are the column matrix representations of images and images in images:

equation

      Equation (1.46) shows that the dot product of two vectors is equivalent to the inner product of their column matrix representations in a reference frame such as images.

      1.6.2 Cross Product and Skew Symmetric Cross Product Matrices

      Consider the same two vectors images and images, which are expressed by Eqs. (1.40) and (1.41) as resolved in the reference frame images. Their cross product can be expressed as

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