convolution

(redirected from Convolution kernel)
Also found in: Dictionary, Thesaurus, Legal, Encyclopedia.

convolution

 [kon″vo-lu´shun]
a tortuous irregularity or elevation caused by the infolding of a structure upon itself.

con·vo·lu·tion

(kon'vō-lū'shŭn),
1. A coiling or rolling of an organ.
2. Specifically, a gyrus of the cerebral cotex or folia of the cerebellar cortex.
[L. convolutio]

convolution

/con·vo·lu·tion/ (-loo´shun) a tortuous irregularity or elevation caused by the infolding of a structure upon itself.
Broca's convolution  the inferior frontal gyrus of the left hemisphere of the cerebrum.
Heschl's convolutions  transverse temporal gyri; see temporal gyrus, under gyrus.

convolution

(kŏn′və-lo͞o′shən)
n.
1. A form or part that is folded or coiled.
2. One of the convex folds of the surface of the brain.

con′vo·lu′tion·al adj.

convolution

[kon′vəlo̅o̅′shən]
Etymology: L, convolutus, rolled together
a tortuous irregularity or elevation caused by a structure being infolded on itself, such as the gyri of the cerebrum. See also gyrus.

convolution

(1) A redundancy or folding of tissue native to an organ. 
(2) Gyrus, brain.

convolution

An elevation on the surface of a structure and an infolding of the tissue upon itself

con·vo·lu·tion

(kon-vŏ-lū'shŭn)
1. A coiling or rolling of an organ.
2. Specifically, a gyrus of the cerebral or cerebellar cortex.
[L. convolutio]

convolution

a tortuous irregularity or elevation caused by the infolding of a structure upon itself.
References in periodicals archive ?
Under the assumption that the convolution kernel is known, they propose generating an extrapolated image from the blurred one using tiles, which they define as rectangular image blocks that follow certain patterns nearing the edges.
In theory the operator consists of a pair of 2 x 2 convolution kernels as shown below.
The generic convolution kernels for GE were replaced in order to improve the spatial decorrelation with image-specific kernels constructed by using either PCA or FastICA analyses methods.
It used four different convolution kernels to find edge map from all directions [5].
For example, you can add standard image-processing techniques, including smoothing, crisping, edge detection, convolution kernels, and filtering.