Sentence examples for image operators from inspiring English sources
The implemented window-based image operators include generic image convolution, gray-level image morphology and template matching.
The architecture was specially designed to implement efficiently, both in performance and hardware resource utilization, window-based image operators under real-time constraints.
The design of translation invariant and locally defined binary image operators over large windows is made difficult by decreased statistical precision and increased training time.
In conclusion, the MO formulation of the interest point detection problem provides the appropriate framework for the automatic design of image operators that achieve interesting trade-offs between relevant performance criteria that are meaningful for a variety of vision tasks.
Experimental results obtained with respect to synthetic and handwritten music scores under varying image conditions show that the learned image operators are comparable with especially designed state-of-the-art heuristic algorithms.
Morphological image operators evaluate binary, grayscale and colour images, performing a wide range of algorithms such as edge detection, noise removal, image segmentation and corner detection [6, 21, 22].
The processing time for a 7×7 generic window-based image operator on 512×512 gray-level images is 8.35 ms.
In this work we show how to apply the image operator learning technique to the staff line removal problem.
This work considers stack filter design from training data under a general statistical framework developed in the context of morphological image operator design.
In this sense, image operator learning methods are concerned with estimating, from sample pairs of input-output images of a transformation, a local function that characterizes the image transformation.
In this paper, we present an appearance-based gaze estimation method based on a novel image operator, Gabor Directional Binary Pattern (GDBP), and Support Vector Regression (SVR) [9].
