Publications

In this paper we address the problem of fast segmenting moving objects in video acquired by moving camera or more generally with a moving background. We present an approach based on a color segmentation followed by a region-merging on motion through Markov Random Fields (MRFs). The technique we propose is inspired to a work of Gelgon and Bouthemy [6], that has been modified to reduce computational cost in order to achieve a fast segmentation (about ten frame per second). To this aim a modified region matching algorithm (namely Partitioned Region Matching) and an innovative arc-based MRF optimization algorithmwith a suitable definition of the motion reliability are proposed. Results on both synthetic and real sequences are reported to confirm validity of our solution.

In this paper we propose a vision-based system that lets the robot recognize an environment observed through the construction of a perspective structure which characterizes it. The individualization of the most significant characteristics of the perspective structure is performed by a geometric method that, using the information given by the image, represents the scene through elementary geometrical forms (such as straight lines) and, on the basis of this geometrical representation, it detects the perspective structure elements (e.g. the vanishing point). The method returns results that can help the robot to establish if he is really inside a corridor or in another place.

In this work we present a framework for on-the-fly video transcoding that exploits computer vision-based techniques to adapt the Web access to the user requirements. Theproposed transcoding approach aims at coping with both user bandwidth and resources capabilities, and with user interests in the video's content. We propose an object-basedsemantic transcoding that, according to the user-dened classes of relevance, applies different transcoding techniques to the objects segmented in a scene. Object extraction is provided by on-the-fly video processing, without manual annotation. Multiple transcoding policies are reviewed and a performance evaluation metric based on the Weighted Mean Square Error (and corresponding PSNR), that takes into account the perceptual user requirements by means of classes of relevance, is dened. Results are analyzed by varying transcoding techniques, bandwidth requirements and video types (with indoor and outdoor scenes), showing that the use of semantics can dramatically improve the bandwidth to distortion ratio.

In this work we present a transcoding framework and an object-based technique to adapt live and stored videos to the user bandwidth and resources capabilities.Multiple transcoding policies are reviewed and a performance evaluation metric based on the Weighted Mean Square Error that allows different classes of relevance is presented.We present results for different transcoding policies and for different bandwidth requirements, showing that the use of semantic can improve the bandwidth to distortion ratio.

An automated algorithm is presented to determine fragment DNA size from Atomic Force Microscope images. Several real and synthetic images were tested for different image and fragment sizes and different background noises. The automated approach allows to minimize processing time with respect to manual DNA sizing and to extract information that can be used to perform further analysis on the molecules. For computer-generated test images the percentage error in length estimation is less than 1% and its average value is 0.4%. For real images the deviation with respect to manually-performed length estimation is around 1%.