Near Set Evaluation And Recognition (NEAR) System - Rough Fuzzy Image Analysis: Foundations and Methodologies - page 129

near set sense) in this system are subimages of the images being processed and the probe
functions (features) are image processing functions defined on the subimages. The system
was written in C++ and was designed to facilitate the addition of new processing tasks and
probe functions ∗ . Currently, the system performs five major tasks: displaying equivalence
and tolerance classes for an image; performing segmentation evaluation; measuring the
nearness of two images; and displaying the output of processing an image using an individual
probe functions. This report is organized as follows: Section 7.2 gives some background on
near set theory, and Section 7.3 demonstrates the application of near set theory to images.
Finally, Sections 7.5-7.8 describe the operation of the GUI.

FIGURE 7.1: NEAR system GUI.

7.2 Near sets

Near set theory focuses on sets of perceptual objects with matching descriptions. The
discovery of near sets begins with the selection of probe functions that provide a basis for
describing and discerning a nities between sample objects. A probe function is a real-
valued function representing features of physical objects. Specifically, let O represent the
set of all objects. The description of an object x∈O is given by

φ B (x) = (φ (x),φ (x),...,φ i (x),...,φ l (x)),

1 2

∗ Parts of the Graphical User Interface (GUI) were inspired by the GUI reported in (Christoudias,
Georgescu, and Meer, 2002) and the wxWidgets example in (wxWidgets, 2009).