Image thresholding using fuzzy entropies.

An image can be regarded as a fuzzy subset of a plane. A fuzzy entropy measuring the blur in an image is a functional which increases when the sharpness of its argument image decreases. We generalize and extend the relation "sharper than" between fuzzy sets in view of implementing the properties of a relation "sharper than" between images. We show that there are infinitely many implementations of this relation into an ordering between fuzzy sets (equivalently, images). Relying upon these orderings, we construct classes of fuzzy entropies which are useful for image thresholding by cost minimization. Assuming the image to be a degraded version of an ideal two level image (object/background), a fuzzy entropy can be introduced in a cost functional to force the fitting function to be as close as possible to a two-valued function. The minimization problem is numerically solved, and the results obtained on a synthetic image are reported.

From local maxima to connected skeletons.

In picture processing it is often convenient to deal with a stick-like version (skeleton) of binary digital images. Although skeleton connectedness is not necessary for storage and retrieval purposes, this property is desirable when a structural description of images is of interest. In this paper a parallel procedure is described which, applied to a connected image, originates a connected skeleton made by the union of simple digital arcs. The procedure involves a step by step propagation of the background over the image. At every step, contour elements either belonging to the significant convex regions of the current image or being local maxima of the original image are selected as skeleton elements. Since the final set so obtained is not ensured to be connected, the configurations in correspondence of which disconnections appear are investigated and the procedures to avoid this shortcoming are given. The presence of the whole set of local maxima among the skeleton elements ensures the possibility of recovering the original image by means of a reverse distance transform. The details of the program implementing the proposed algorithm on a parallel processor are finally included.

Self modulating, derivative optical spectroscopy. Part II: experimental.

The present paper follows a previous one that dealt with the theoretical principles of a self modulating derivative optical spectrometer (SMODOS). In this paper the complete description is given of a prototype of such an instrument, namely, of its optical as well as electronic components. The project criteria are thoroughly discussed, and some examples of performance that demonstrate the power of the method are presented.