We propose a solution for the computer-aided reconstruction
of strip-cut shredded documents. First of all, the visual content
of the strips is automatically extracted and represented by a
number of numerical features. Usually, the pieces of different pages
have been mixed. A grouping of the strips belonging to a same page
is thus realized by means of a clustering operator, to ease the successive
matching performed by a human operator with the help of a
In the forensics and investigative science fields there may arise the need of reconstructing documents which have been destroyed by means of a shredder. In a computer-based reconstruction, the pieces are described by numerical features, which represent the visual content of the strips. Usually, the pieces of different pages have been mixed. We propose an approach for the reconstruction which performs a first clustering on the strips to ease the successive matching, be it manual (with the help of a computer) or automatic. A number of features, extracted by means of image processing algorithms, have been selected for this aim. The results show the effectiveness of the features and of the proposed clustering algorithm.
In this paper a general architecture for the computer-aided reconstruction of strip-cut shredded documents is presented. The matching of the remnants is performed on the base of the visual content of the strips, described by means of automatically extracted numerical features. A clustering approach is adopted in order to reduce progressively the dimension of the sets of remnants in which the exhaustive search for the matching need to be performed.
We propose a novel method for motion analysis in video sequences.
It extends the co-occurrence matrix concept for texture analysis
to the temporal domain. The approach proved to be versatile in the
sense of targeting different motion analysis tasks. An application
of the method is in the compact representation of video sequences,
in particular temporal texture patterns.