Abstract
The modeling of the spatial distribution of image properties is important for many pattern recognition problems in science and engineering. Mathematical methods are needed to quantify the variability of this spatial distribution based on which a decision of classification can be made in an optimal sense. However, image properties are often subject to uncertainty due to both incomplete and imprecise information. This paper presents an integrated approach for estimating the spatial uncertainty of vagueness in images using the theory of geostatistics and the calculus of probability measures of fuzzy events. Such a model for the quantification of spatial uncertainty is utilized as a new image feature extraction method, based on which classifiers can be trained to perform the task of pattern recognition. Applications of the proposed algorithm to the classification of various types of image data suggest the usefulness of the proposed uncertainty modeling technique for texture feature extraction.
Highlights
There are different types of images used in the diverse applications of image classification; to name a few, medical images, biological images, remote-sensing images, scene images, and so on
Mitochondria are considered as the powerhouse of the cell because they function as the platform for generating the production of chemical energy
In order to simulate and model mitochondria using a large amount of images, the first task in image processing is the automated detection of this organelle
Summary
There are different types of images used in the diverse applications of image classification; to name a few, medical images, biological images, remote-sensing images, scene images, and so on. A critical challenge in the discriminative quantification of image properties of various regions of interest is that they are usually subject to noise and vague boundaries between the objects and background, which are often found in medical, life-science, and natural data [1]–[7]. These factors adversely affect the classification performance. To deal with the uncertainty of image information, statistical measures of sets of images are often utilized to construct probability models of images, in which they are considered as random variables. An approach for handling imprecision rather than randomness in images is to consider them as fuzzy events so that some non-probabilistic measure of uncertainty can be established
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
