This paper addresses the design concept of a spatially and spectrally augmented computer vision approach toward enhanced image analysis and understanding. The concept of spatially augmented computer vision refers to the inclusion of the stereo disparity measure (1/2-D) along with the two-dimensional (2-D) spatial coordinates of the images, together yielding the augmented (2 1/2-D) representation. The concept of spectrally augmented computer vision refers to the implementation of the multiresolution concept of the wavelet theory to analyze and assess in detail the local properties of the 2-D images. The principal objective in applying this augmented, and more revealing, computer vision approach is to provide the added dimension in spatial and spectral resolutions for the enhanced understanding of images. To this end, imaging techniques are developed to exploit, in an optimal fashion, the information acquired by the camera system to yield useful descriptions of the viewed scenes. Experimental results are provided in support of this research direction.