People tracking has to face many issues in video surveillance scenarios. One of the most challenging aspect is
to re-identify people across different cameras. Humans, indeed, change appearance according to pose, clothes
and illumination conditions and thus defining features that are able to robustly describe people moving in
a camera network is a not trivial task. While color is widely exploited in the distinction and recognition of
objects, most of the color descriptors proposed so far are not robust in complex applications such as video
A new color based feature is introduced in this paper to describe the color appearance of the subjects.
For each target a probabilistic color histogram (PCH) is built by using a fuzzy K-Nearest Neighbors (KNN)
classifier trained on an ad-hoc dataset and is used to match two corresponding appearances of the same person
in different cameras of the network. The experimental results show that the defined descriptor is effective at
discriminating and re-identifying people across two different video cameras regardless of the viewpoint change
between the two views and outperforms state of the art appearance based techniques.
Human perception of image distortions has been widely explored in recent years, however, research has not
dealt with distortions due to geometric operations. As a consequence, there is a lack of objective visual quality
measures for this class of distortions. In this paper we propose a method of objectively assessing the perceptual
quality of geometrically distorted images. Our approach is based on the theory of Markov Random Fields. The
idea is that the potential function of the Markov Random Field describing the distortion gives an indication
of the degradation of the distorted image. This work can be seen as the first step toward the definition of an
objective metric for geometric distortions in images.
Human perception of image distortions has been widely explored in recent years, however, research has not dealt
with distortions due to geometric operations. In this paper, we present the results we obtained by means of
psychovisual experiments aimed at evaluating the way the human visual system perceives geometric distortions
in images. A mathematical model of the geometric distortions is first introduced, then the impact of the model
parameters on the visibility of the distortion is measured by means of both objective metrics and subjective