Head pose estimation is not only a crucial challenge for many real-world applications, such as driver attention detection analysis, but it represents an interesting strategy to support biometric frameworks as well. This paper aims to estimate head pose from a single image by applying notions of network curvature. In the real world, many complex networks have groups of nodes that are well connected to each other with significant functional roles. Similarly, the interactions of facial landmarks can be represented as complex dynamic systems modeled by weighted graphs. The functionality of such a system is therefore intrinsically linked to the topology and geometry of the underlying graph. In this work, using the geometric notion of Ollivier-Ricci curvature (ORC) on weighted graphs as input to the XGBoost regression model, we show that the intrinsic geometric basis of ORC offers a natural approach to discovering underlying common structure within a pool of poses. Experiments on the BIWI, AFLW2000 and Pointing '04 datasets show that the ORC_XGB method performs well compared to state-of-the-art methods, both landmark-based and image-only.

Advanced surveillance applications often require the ability to re-identify an individual. In the typical context of a sensor network, this means to recognize a subject acquired at one location among a feasible set of candidates acquired at another locations and/or over time. Usually this does not necessarily imply to know the identity, and actually it might remain unknown. This task is especially challenging in applications targeted at crowded environments. Face and gait are contactless biometrics which are particularly suited to be used for re-identification, but even “soft” biometrics have also been considered to this aim. This paper presents a comparison of approaches to re-identification, with some characteristic examples in literature, while a complete overview of the main researches is out of our scope. The goal is to provide an objective estimate of both the state-of-the-art and the potential of such techniques to further improve them and to extend the applicability of re-identification systems.

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\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.

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.

This paper illustrates a method, called HEAT, for image indexing based on texture information. The texture's 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.

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.

This paper describes an algorithm for image compression called B-tree triangular coding (BTTC). An image is considered as a discrete 3D surface, which is then represented by a set of polyhedrons. The image domain is partitioned into right-angled triangles constituting the polyhedrons' bases. Each polyhedron is characterized by the vertices of its triangular upper face, near the approximated surface. During the approximation process the polyhedrons are organized into a B-tree that will be represented as a binary string; the B-tree's leaves are the polyhedrons needed to restore the image during decompression. The computing time is O(nlogn) for compression and /spl Theta/(n) for decompression, where n is the number of pixels. Especially in decompression, this is a very fast method if compared to the standard techniques (e.g. JPEG).