Giovanni Gallo

A locally adaptive zooming algorithm for digital images

Abstract In this paper we address the problem of producing an enlarged picture from a given digital image (zooming). We propose a method that tries to take into account information about discontinuities or sharp luminance variations while doubling the input picture. This is realized by a nonlinear iterative procedure of the zoomed image and could hence be implemented with limited computational resources. The algorithm works on monochromatic images, RGB color pictures and Bayer data images acquired by CCD/CMOS camera sensor. Our experiments show that the proposed method beats in quality classical simple zooming techniques (e.g. pixel replication, simple interpolation). Moreover our algorithm is competitive both for quality and efficiency with bicubic interpolation.

SIFT Features Tracking for Video Stabilization

This paper presents a video stabilization algorithm based on the extraction and tracking of scale invariant feature transform features through video frames. Implementation of SIFT operator is analyzed and adapted to be used in a feature-based motion estimation algorithm. SIFT features are extracted from video frames and then their trajectory is evaluated to estimate interframe motion. A modified version of iterative least squares method is adopted to avoid estimation errors and features are tracked as they appear in nearby frames to improve video stability. Intentional camera motion is eventually filtered with adaptive motion vector integration. Results confirm the effectiveness of the method.

Modeling uncertain data with fuzzy B-splines

Abstract Fuzzy numbers are an alternative solution to the problem uncertain data modeling. The idea of fuzzy B-spline is here introduced: its power relies on the possibility of being used as approximating function both for fuzzy and crisp data. The fuzzy modeling of large collections of noisy data described in this paper, moreover, achieves a very high degree of compression with a relatively low computational complexity both for maintenance and interrogation of the model itself. An extensive description of the modeling technique is given, together with methods for interrogating the model. Experimental results are shown to prove the effectiveness of the proposed approach.

Efficient Algorithms and Bounds for Wu-Ritt Characteristic Sets

The concept of a characteristic set of an ideal was originally introduced by J.F. Ritt, in the late forties, and later, independently rediscovered by Wu Wen-Tsiin, in the late seventies. Since then Wu-Ritt Characteristic Sets have found wide applications in Symbolic Computational Algebra, Automated Theorem Proving in Elementary Geometries and Computer Vision. The original algorithm of Ritt, and subsequent modifications by Wu, has a non-elementary worst-case time complexity, and could be used for computing only an extended characteristic set. In this paper, we present optimal algorithms for computing a characteristic set with simple-exponential sequential and polynomial parallel time complexities. These algorithms are derived, via linear algebra, from simple-exponential degree bounds for a characteristic set. The degree bounds are obtained by using the recent effective version of Hilbert’s Nullstellensatz, due to D. Brownawell and J. Kollar, and a version of Bezout’s Inequality, due to J. Heintz.

Experimental methodology for digital breast shape analysis and objective surgical outcome evaluation

Outcome evaluation in cosmetic and reconstructive surgery of the breast is commonly performed visually or employing bi-dimensional photography. The reconstructive process in the era of anatomical implants requires excellent survey capabilities that mainly rely on surgeon experience. In this paper we present a set of parameters to unambiguously estimate the shape of natural and reconstructed breast. A digital laser scanner was employed on seven female volunteers. A graphic depiction of curvature of the thoracic surface has been the most interesting result. Further work is required to provide clinical and instrumental validation to our technique.

Digital Mosaic Frameworks - An Overview

Art often provides valuable hints for technological innovations especially in the field of Image Processing and Computer Graphics. In this paper we survey in a unified framework several methods to transform raster input images into good quality mosaics. For each of the major different approaches in literature the paper reports a short description and a discussion of the most relevant issues. To complete the survey comparisons among the different techniques both in terms of visual quality and computational complexity are provided.

Smart interpolation by anisotropic diffusion

To enlarge a digital image from a single frame preserving the perceptive cues is a relevant research issue. The best known algorithms take into account the presence of edges in the luminance channel, to interpolate correctly the samples/pixels of the original image. This approach allows the production of pictures where the interpolated artifacts (aliasing blurring effect,...) are limited but where high frequencies are not properly preserved. The zooming algorithm proposed in this paper on the other hand reduces the noise and enhances the contrast to the borders/edges of the enlarged picture using classical anisotropic diffusion improved by a smart heuristic strategy. The method requires limited computational resources and it works on gray-level images, RGB color pictures and Bayer data. Our experiments show that this algorithm outperforms in quality and efficiency the classical interpolation methods (replication, bilinear, bicubic).

Robust Watermarking for Images Based on Color Manipulation

In this paper we present a new efficient watermarking scheme for images. The basic idea of our method is to alter the colors of the given image in a suitable but imperceptible way. This is accomplished by moving the coordinates of each color in the color opponency space. The scheme is shown to be robust against a large class of image manipulations. The robustness of the scheme is also theoretically analyzed and it is shown to depend on the number of colors in the image being marked.

SVG rendering of real images using data dependent triangulation

This paper presents a novel technique to convert raster images in a Scalable Vector Graphic (SVG) format using Data Dependent Triangulation (DDT). The triangulation, a classical 3D graphic rendering approach, is here applied to digital images acquired by imaging consumer devices. Good quality rendering of real images has been obtained making use of some ad-hoc heuristics able to properly manage advanced SVG features (e.g. path, gradient, filter effects). Experiments and comparisons with existing techniques confirm the effectiveness of the proposed strategy.

An efficient Re-indexing algorithm for color-mapped images

The efficiency of lossless compression algorithms for fixed-palette images (indexed images) may change if a different indexing scheme is adopted. Many lossless compression algorithms adopt a differential-predictive approach. Hence, if the spatial distribution of the indexes over the image is smooth, greater compression ratios may be obtained. Because of this, finding an indexing scheme that realizes such a smooth distribution is a relevant issue. Obtaining an optimal re-indexing scheme is suspected to be a hard problem and only approximate solutions have been provided in literature. In this paper, we restate the re-indexing problem as a graph optimization problem: an optimal re-indexing corresponds to the heaviest Hamiltonian path in a weighted graph. It follows that any algorithm which finds a good approximate solution to this graph-theoretical problem also provides a good re-indexing. We propose a simple and easy-to-implement approximation algorithm to find such a path. The proposed technique compares favorably with most of the algorithms proposed in literature, both in terms of computational complexity and of compression ratio.