Publications

In this work, we propose a system for shot comparison directly working on the MPEG-1 stream in the compressed domain, extracting both color, texture and motion features considering all frames with a reasonable computational cost, and results comparable to those obtained on uncompressed keyframes. In particular a summary descriptor for each Group Of Pictures (GOP) is computed and employed for shot characterization and comparison. The Mallows distance allows to match different length clips in a unified framework.

Sketch-based image retrieval systems need to handle two main problems. First of all, they have to recognize shapes similar but not necessarily identical to the user’s query. Hence, exact object identification techniques do not fit in this case. The second problem is the selection of the image features to compare with the user’s sketch. In domain-independent visual repositories, real-life images with non-uniform background and possible occluding objects make this second task particularly hard. We address the second problem proposing a variant of the well-known Generalized Hough Transform (GHT), which is a robust object identification technique for unsegmented images. Moreover, we solve the first problem modifying the GHT to deal with an inexact matching problem. In this paper, we show how this idea can be efficiently and accurately realized. Experimental results are shown with two different databases of real, unsegmented images.

This paper proposes the use of a mixture of Von Mises distributions to detect abnormal behaviors of moving people. The mixture is created from an unsupervised training set by exploiting k-medoids clustering algorithm based on Bhattacharyya distance between distributions. The extracted medoids are used as modes in the multi-modal mixture whose weights are the priors of the specific medoid. Given the mixture model a new trajectory is verified on the model by considering each direction composing it as independent. Experiments over a real scenario composed of multiple, partially-overlapped cameras are reported.

In this paper, we present the dynamic pictorial ontology paradigm for video annotation. Ontologies are often used to describe a given domain for different goals, including description of multimedia data. In the case of video annotation, the visual knowledge cannot be described using only abstract concepts but is more effectively represented in a visual form. To this aim, we introduce visual concepts, elicited from the data set as the most representative prototypes that specialize abstract concepts. The ontology created is intrinsically dynamic since it must embrace the perceptual and visual experience during annotation. Thus visual concepts can change, adapting to the multimedia content analyzed. Motivation for this new ontology paradigm are discussed together with a proposal of a framework for ontology creation, maintenance, and automatic annotation of video. The creation and usage of dynamic pictorial ontologies have been tested for soccer domain exploiting low level perceptual features and higher level domain features.

The worldwide incidence of cutaneous melanoma has increased dramatically over the past decades. It is well known that a good prognosis of melanoma is only expected for thin lesions. Preventive effort has therefore been concentrated on identification of early lesions facilitated by the introduction and dissemination of standardized clinical criteria and by the use of dermoscopy (epiluminescence microscopy). However, the interpretation of dermoscopic criteria is often confusing especially for the inexperienced observer. This chapter summarizes recent computer techniques to help the clinician in these tasks.

The paper presents the design and implementation of a system of autonomous navigation of a mobile robot with stereo vision. © 2007 IEEE.

Color is one of the most meaningful features used in content based retrieval of visual data. In video content based retrieval, color features computed on selected frames are integrated with other low-level features concerning texture, shape and motion in order to find clip similarities. For example, the Scalable Color feature defined in the MPEG-7 standard exploits HSV histograms to create color feature vectors. HSV is a widely adopted space in image and video retrieval, but its quantization for histogram generation can create misleading errors in classification of achromatic and low saturated colors. In this paper we propose an Enhanced HSV Histogram with achromatic point detection based on a single Hue and Saturation parameter that can correct this limitation. The enhanced histograms have proven to be effective in color analysis and they have been used in a system for automatic clip annotation called PEANO, where pictorial concepts are extracted by a clip clustering and used for similarity based automatic annotation.

-