Riccardo Distasi

A range/domain approximation error-based approach for fractal image compression

Fractals can be an effective approach for several applications other than image coding and transmission: database indexing, texture mapping, and even pattern recognition problems such as writer authentication. However, fractal-based algorithms are strongly asymmetric because, in spite of the linearity of the decoding phase, the coding process is much more time consuming. Many different solutions have been proposed for this problem, but there is not yet a standard for fractal coding. This paper proposes a method to reduce the complexity of the image coding phase by classifying the blocks according to an approximation error measure. It is formally shown that postponing range/spl bsol/slash domain comparisons with respect to a preset block, it is possible to reduce drastically the amount of operations needed to encode each range. The proposed method has been compared with three other fractal coding methods, showing under which circumstances it performs better in terms of both bit rate and/or computing time.

Image compression by B-tree triangular coding

This paper describes an algorithm for still image compression called B-tree triangular coding (BTTC). The coding scheme is based on the recursive decomposition of the image domain into right-angled triangles arranged in a binary tree. The method is attractive because of its fast encoding, O(n log n), and decoding, /spl Theta/(n), where n is the number of pixels, and because it is easy to implement and to parallelize. Experimental studies indicate that BTTC produces images of satisfactory quality from a subjective and objective point of view, One advantage of BTTC over JPEG is its shorter execution time.

FIRE: fractal indexing with robust extensions for image databases

As already documented in the literature, fractal image encoding is a family of techniques that achieves a good compromise between compression and perceived quality by exploiting the self-similarities present in an image. Furthermore, because of its compactness and stability, the fractal approach can be used to produce a unique signature, thus obtaining a practical image indexing system. Since fractal-based indexing systems are able to deal with the images in compressed form, they are suitable for use with large databases. We propose a system called FIRE, which is then proven to be invariant under three classes of pixel intensity transformations and under geometrical isometries such as rotations by multiples of /spl pi//2 and reflections. This property makes the system robust with respect to a large class of image transformations that can happen in practical applications: the images can be retrieved even in the presence of illumination and/or color alterations. Additionally, the experimental results show the effectiveness of FIRE in terms of both compression and retrieval accuracy.

Face authentication using speed fractal technique

Abstract In this paper, a new fractal based recognition method, Face Authentication using Speed Fractal Technique (FAST), is presented. The main contribution is the good compromise between memory requirements, execution time and recognition ratio. FAST is based on Iterated Function Systems (IFS) theory, largely studied in still image compression and indexing, but not yet widely used for face recognition. Indeed, Fractals are well known to be invariant to a large set of global transformations. FAST is robust with respect to meaningful variations in facial expression and to the small changes of illumination and pose. Another advantage of the FAST strategy consists in the speed up that it introduces. The typical slowness of fractal image compression is avoided by exploiting only the indexing phase, which requires time O ( D log ( D )), where D is the size of the domain pool. Lastly, the FAST algorithm compares well to a large set of other recognition methods, as underlined in the experimental results.

CONTEXT: a technique for image retrieval integrating CONtour and TEXTure information

Many intrinsically 2-dimensional visual signals can be effectively encoded in a 1D form. This simpler representation is well-suited to both pattern recognition and image retrieval tasks. In particular, this paper deals with contour and texture, combined together in order to obtain an effective technique for content-based image indexing. The proposed method, named CONTEXT, represents CONtours and TEXTures by a vector containing the location and energy of the signal maxima. Such a representation has been utilized as the feature extraction engine in an image retrieval system for image databases. The homogeneous treatment reserved to both contour and texture information makes the algorithm elegant and easy to implement and extend. The data used for experimentally assessing CONTEXT were contours and textures from various application domains, plus a database of medical images. The experiments reveal a high discriminating power which in turn yields a high perceived quality of the retrieval results.

A Hierarchical Representation for Content-based Image Retrieval

Abstract In this paper, we present a hierarchical entropy-based representation (HER) for one-dimensional signals. Any signal can be effectively represented by means of a vector containing the energy values related to its most important points, i.e. the maxima, together with their relative locations along the time axis. Such a representation has been applied to a database containing several shapes represented by their closed contour in curvilinear coordinates in order to perform content-based retrieval for time series. K-d-trees have been used as a spatial access method in order to improve the search performance. The results obtained from our experiments show that HER for indexing (HERI) achieves very good performance with few false alarms and false dismissals.

Towards a novel technique for identification based on eye tracking

In studies related to soft biometrics, the iris has been considered as a potential predictor for ethnicity and gender [1]. Qiu et al. have used specific features in the texture of the iris to determine ethnicity - Asian vs. Caucasian [2]. A similar approach has been used to determine gender by Thomas et al. [3]. More recently, the problem of predicting both gender and ethnicity using iris patterns has been addressed by Lagree et al. in 2011 [4]. While undoubtedly related to soft biometrics, however, these studies do not actually aim at identification, but rather at recognizing a single character. On the other hand, in [5], iris color is actually used as a soft biometry for indexing, in order to obtain a subset of identities where actual recognition will be performed.

Using fractal encoding for image indexing

As already documented in the literature, fractal image encoding can be exploited to obtain good image indexing systems that are able to deal with the images in compressed form, which makes them suitable for use with large databases. The system proposed in this paper has the additional interesting property of being invariant under two classes of pixel intensity transformations. This property makes the system robust with respect to several types of image transformations that can happen in practical applications: the images can be retrieved even in the presence of illumination and/or color alterations. The system can be queried via both a query by sketch and a query by example paradigms.

Multi-Object Segmentation for Assisted Image reConstruction

MOSAIC is a tool for jigsaw puzzle solving. It is designed to assist cultural heritage operators in reconstructing broken pictorial artifacts from their fragments. These undergo feature extraction and feature based indexing, so that any fragment can be the key to queries about color distribution, shape and texture. Query results are listed in order of similarity, which helps the user to locate fragments likely to be near the key fragment in the original picture. A complete working protocol is provided to bring the user from the raw materials to a working database. System performance has been assessed with both computer simulations and a real case study involving the reconstruction of a XV century fresco.

HEAT: HIERARCHICAL ENTROPY APPROACH FOR TEXTURE INDEXING IN IMAGE DATABASES

This paper illustrates a method, called HEAT, for image indexing based on texture information. The textures partitioning element is first put into 1-D form and then its Hierarchical Entropy-based Representation is obtained. This representation is used to index the texture in the space of features. The same representation is well suited for contour data, and it has invariance and robustness properties that make it attractive for incorporation into larger systems. A comparison with another performing method is carried out, and the experiments show that the two techniques have slightly different strong points, suggesting different fields of application. In the experimental section, a case study involving over 2500 mammographies from different sources is presented and discussed.