A New Computer-aided Technique for Planning the Aesthetic Outcome of Plastic Surgery

Plastic surgery plays a major role in today health care. Planning plastic face surgery requires dealing with the elusive concept of attractiveness for evaluating feasible beautification of a particular face. The existing computer tools essentially allow to manually warp 2D images or 3D face scans, in order to produce images simulating possible surgery outcomes. How to manipulate faces, as well as the evaluation of the results, are left to the surgeon's judgement. We propose a new quantitative approach able to automatically suggest effective patient-specific improvements of facial attractiveness. The general idea is to compare the face of the patient with a large database of attractive faces, excluding the facial feature to be improved. Then, the feature of the faces more similar is applied, with a suitable morphing, to the face of the patient. In this paper we present a first application of the general idea in the field of nose surgery. Aesthetically effective rhinoplasty is suggested on the base of the entire face profile, a very important 2D feature for rating face attractivenes

Generating indicative-informative summaries with SumUM

We present and evaluate SumUM, a text summarization system that takes a raw technical text as input and produces an indicative informative summary. The indicative part of the summary identifies the topics of the document, and the informative part elaborates on some of these topics according to the reader's interest. SumUM motivates the topics, describes entities, and defines concepts. It is a first step for exploring the issue of dynamic summarization. This is accomplished through a process of shallow syntactic and semantic analysis, concept identification, and text regeneration. Our method was developed through the study of a corpus of abstracts written by professional abstractors. Relying on human judgment, we have evaluated indicativeness, informativeness, and text acceptability of the automatic summaries. The results thus far indicate good performance when compared with other summarization technologies