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Image morphing techniques can create a smooth transition between two images. However, one of the main weakness of the image morphing technique is that intermediate images in the transition often have physically incorrect shading such as highlights and shadows. Moreover, we cannot alter viewing and lighting conditions when creating the intermediate images. That is because those images are obtained by simply interpolating pixel intensities of the two 2D images without knowledge of 3D object shape and reflectance properties. In this context, 3D shape morphing techniques have a definite advantage in that arbitrary viewing and illumination conditions can be used for creating new images. Unfortunately, previous 3D morphing techniques do not account for object surface reflectance properties or reflection models when generating intermediate images. This often results in undesired shading artifacts. In this paper, we consider a new approach for 3D shape and reflectance morphing of two real 3D objects. Our morphing method consists of two components: shape and reflectance property measurement, and smooth interpolation of those measured properties. The measured shape and reflectance parameters are used to compute intermediate shape and reflectance parameters. Finally, the computed shape and reflectance parameters are used to render intermediate images which represent a smooth transition between the two objects.

The Pap Smear test, or cervico-vaginal cytology, is globally the most used and suitable method for screening cervical cancer precursor lesions, with a significant impact in reducing the incidence and mortality rates. However, Pap test suffers from subjective variability and no specificity, being the most controversial point the persistence of false negatives; i.e., normal cytological report for a woman with existing dysplasia, pre-malignant, or malignant lesions of the cervix. This is due in large part to the vast number of cells that must be reviewed by a technician for determining the possible existence of a small number of malignant or pre-malignant cells.

Automated systems that include technician knowledge and interpretation could not only reduce sample examination time but also avoid misclassification of samples because of human errors.

Here we present part of our ongoing work toward automation of cervical screening process. Specifically, since in cytological studies nuclei are considered the most informative regions, and an accurate segmentation is needed for extracting meaningful cell features, we propose an automated nuclei detection algorithm that integrates color information, cytopathologists knowledge, and fuzzy systems. Results have shown that besides a high performance and efficiency, the speed of the algorithm is very high.