TY - CHAP
T1 - Low-level vision on Warp and the Apply programming model
AU - Hamey, Len
AU - Webb, Jon
AU - Wu, I-Chen
PY - 1988
Y1 - 1988
N2 - In computer vision, the first, and often most time-consuming, step in image processing is image-to-image operations. In this step, an input image is mapped into an output image through some local operation that applies to a window around each pixel of the input image. Algorithms that fall into this class include: edge detection, smoothing, convolutions in general, contrast enhancement, color transformations, and thresholding. Collectively, we call these operations low-level vision. Low-level vision is often time-consuming simply because images are quite large—a typical size is 512 × 512 pixels, so the operation must be applied 262,144 times.
AB - In computer vision, the first, and often most time-consuming, step in image processing is image-to-image operations. In this step, an input image is mapped into an output image through some local operation that applies to a window around each pixel of the input image. Algorithms that fall into this class include: edge detection, smoothing, convolutions in general, contrast enhancement, color transformations, and thresholding. Collectively, we call these operations low-level vision. Low-level vision is often time-consuming simply because images are quite large—a typical size is 512 × 512 pixels, so the operation must be applied 262,144 times.
U2 - 10.1007/978-1-4613-1989-4_10
DO - 10.1007/978-1-4613-1989-4_10
M3 - Chapter
SN - 9781461291886
SN - 9780898382273
T3 - The Springer International Series in Engineering and Computer Science
SP - 185
EP - 199
BT - Parallel Computation and Computers for Artificial Intelligence
A2 - Kowalik, Janusz S.
PB - Kluwer Academic Publishers
CY - Norwell, Massachusetts
ER -