Publications

In this work we describe the Topological Tree (TT) as a knowledge representation method that relates some important visual and spatial features of image regions, namely the color similarity, the inclusion and the spatial adjacency. Starting from color-based region segmentation of an image into disjoint regions, their spatial relationships can be devised and described with graph-based methods. We are interested in the region’s propriety “to be included into” (in the sense of “surrounded by”) another region. This property could be very useful in biomedical imaging and in particular in the diagnosis of skin melanoma. The TT can be constructed after segmentation, by computing the spatial relationships of regions or can be generated directly during the segmentation: to this aim we present a novel recursive fuzzy c-means (FCM) clustering algorithm based on the PCA of the color space. In the paper, in addition to the TT definition and the construction algorithm description, some results are presented and discussed.

In this paper we present transcoding techniques for a video server architecture that enables the user to access live video streams by using different devices with different capabilities. For live videos, annotation methods cannot be exploited. Instead we propose methods of on-the-fly transcoding that adapt the video content with respect to the user resources and the video semantic. Thus we propose an object-based transcoding with "classes of relevance"(for instance People, Face and Background). To compare the different strategies we propose a metric based on the Weighted Mean Square Error that allows the analysis of different application scenarios by means of a class-wise distortion measure. The obtained results show that the use of semantic can improve the bandwidth to distortion ratio significantly.

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%.

In this paper we define a novel approach for image segmentation into regions which focuses on both visual and topological cues, namely color similarity, inclusion and spatial adjacency. Many color clustering algorithms have been proposed in the past for skin lesion images but none exploits explicitly the inclusion properties between regions. Our algorithm is based on a recursive version of fuzzy c-means (FCM) clustering algorithm in the 2D color histogram constructed by Principal Component Analysis (PCA) of the color space. The distinctive feature of the proposal is that recursion is guided by the evaluation of adjacency and mutual inclusion properties of extracted regions; then, the recursive analysis addresses only included regions or regions with a not-negligible size. This approach allows a coarse-to-fine segmentation which focuses the attention on the inner parts of the images, in order to highlight the internal structure of the object depicted in the image. This could be particularly useful in many applications, especially in the biomedical image analysis. In this work we apply the technique to the segmentation of skin lesions in dermatoscopic images. It could be a suitable support for the diagnosis of skin melanoma, since dermatologists are interested in the analysis of the spatial relations, the symmetrical positions and the inclusion of regions.

In this paper we present transcoding techniques for a video server architecture that enables the user to access live video streams by using different devices with different capabilities. For live videos, annotation methods cannot be exploited. Instead we propose methods of on-the-fly transcoding that adapt the video content with respect to the user resources and the video semantic. Thus we propose an object-based transcoding with "classes of relevance" (for instance People, Face and Background). To compare the different strategies we propose a metric based on the Weighted Mean Square Error that allows the analysis of different application scenarios by means of a class-wise distortion measure. The obtained results show that the use of semantic can improve the bandwidth to distortion ratio significantly.

Video-surveillance and traffic analysis systems can be heavily improved using vision-based techniques able to extract, manage and track objects in the scene. However, problems arise due to shadows. In particular, moving shadows can affect the correct localization, measurements and detection of moving objects. This work aims to present a technique for shadow detection and suppression used in a system for moving visual object detection and tracking. The major novelty of the shadow detection technique is the analysis carried out in the HSV color space to improve the accuracy in detecting shadows. Signal processing and optic motivations of the approach proposed are described. The integration and exploitation of the shadow detection module into the system are outlined and experimental results are shown and evaluated

Many approaches to moving object detection for traffic monitoring and video surveillance proposed in the literature are based on background suppression methods. How to correctly and efficiently update the background model and how to deal with shadows are two of the more distinguishing and challenging features of such approaches. This work presents a general-purpose method for segmentation of moving visual objects (MVOs) based on an object-level classification in MVOs, ghosts and shadows. Background suppression needs a background model to be estimated and updated: we use motion and shadow information to selectively exclude from the background model MVOs and their shadows, while retaining ghosts. The color information (in the HSV color space) is exploited to shadow suppression and, consequently, to enhance both MVOs segmentation and background update.