They were chosen to represent a wide variety of image types, e.g., indoor, outdoor, portraits, etc.
This paper describes a general purpose segmentation algorithm capable of creating parcel boundaries from a wide range of image types.
Due to the law of reflection, the converging and diverging behavior of concave and convex mirrors causes that curved mirrors show different image types.
Heterogeneity analyses of both image types can simultaneously reveal subtle quantitative and qualitative features (gradients, boundaries, random or patchy patterns at successive scales) linked to ecological processes.
The result (k,n -visual secret sharing schemes are more general than most existing schemes in terms of the secret/share image types.
These converging and diverging behaviors cause that the curved mirrors show different image types depending on the distance between the object and the mirror.
Wiener spectra and modulation transfer function were compared for both image types.
Application of this method to segment closed-contour features in several ophthalmic image types is shown.
The possibilities for this methodology are limitless, and can be applied to a broad spectrum of image types.
Further down the road, Pachikov is working on image-recognition technology that can identify different image types and facial emotions.
T2-weighted images are superior to other MR image types for the determination of diffuse intrinsic pontine glioma intratumoral heterogeneity.
