Giuliana Ramella - Academia.edu (original) (raw)

Papers by Giuliana Ramella

Image scaling methods allow us to obtain a given image at a different, higher (upscaling) or lowe... more Image scaling methods allow us to obtain a given image at a different, higher (upscaling) or lower (downscaling), resolution to preserve as much as possible the original content and the quality of the image. In this paper, we focus on interpolation methods for scaling three-dimensional grayscale images. Within a unified framework, we introduce two different scaling methods, respectively based on the Lagrange and filtered de la Vallée Poussin type interpolation at the zeros of Chebyshev polynomials of the first kind. In both cases, using a non-standard sampling model, we take (via tensor product) the associated trivariate polynomial interpolating the input image. It represents a continuous approximate 3D image to resample at the desired resolution. Using discrete ∞ and 2 norms, we theoretically estimate the error achieved in output, showing how it depends on the error in the input and on the smoothness of the specific image we are processing. Finally, taking the special case of medical images as a case study, we experimentally compare the performances of the proposed methods and with the classical multivariate cubic and Lanczos interpolation methods.

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Summary form only given. The aim of this paper is to accomplish a prototype system for automatic ... more Summary form only given. The aim of this paper is to accomplish a prototype system for automatic data management able to support project activities in the field of architecture and engineering, in addition to improving the performance of existing software. This automatic data management consists of both consulting and sharing operations that favour the communication with the customer and the

arXiv (Cornell University), Nov 26, 2020

Skin lesion segmentation is one of the crucial steps for an efficient non-invasive computer-aided... more Skin lesion segmentation is one of the crucial steps for an efficient non-invasive computer-aided early diagnosis of melanoma. This paper investigates how to use color information, besides saliency, for determining the pigmented lesion region automatically. Unlike most existing segmentation methods using only the saliency to discriminate against the skin lesion from the surrounding regions, we propose a novel method employing a binarization process coupled with new perceptual criteria, inspired by the human visual perception, related to the properties of saliency and color of the input image data distribution. As a means of refining the accuracy of the proposed method, the segmentation step is preceded by a pre-processing aimed at reducing the computation burden, removing artifacts, and improving contrast. We have assessed the method on two public databases, including 1497 dermoscopic images. We have also compared its performance with classical and recent saliency-based methods designed explicitly for dermoscopic images. The qualitative and quantitative evaluation indicates that the proposed method is promising since it produces an accurate skin lesion segmentation and performs satisfactorily compared to other existing saliency-based segmentation methods.

The 3rd ACS/IEEE International Conference onComputer Systems and Applications, 2005.

Remote Sensing

Image resizing (IR) has a crucial role in remote sensing (RS), since an image’s level of detail d... more Image resizing (IR) has a crucial role in remote sensing (RS), since an image’s level of detail depends on the spatial resolution of the acquisition sensor; its design limitations; and other factors such as (a) the weather conditions, (b) the lighting, and (c) the distance between the satellite platform and the ground targets. In this paper, we assessed some recent IR methods for RS applications (RSAs) by proposing a useful open framework to study, develop, and compare them. The proposed framework could manage any kind of color image and was instantiated as a Matlab package made freely available on Github. Here, we employed it to perform extensive experiments across multiple public RS image datasets and two new datasets included in the framework to evaluate, qualitatively and quantitatively, the performance of each method in terms of image quality and statistical measures.

2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2016

A new technique for color quantization is suggested. First, pre-quantization is accomplished by m... more A new technique for color quantization is suggested. First, pre-quantization is accomplished by means of spatial resolution reduction, then, color aggregation is accomplished based on the distance between colors in the color space. Color aggregation is an iterated process where the number of iterations is given by the difference between the number of colors of the pre-quantized image, and the number of colors desired for the quantized image. Color mapping is finally accomplished. Performance evaluation is done in terms of generally adopted quality measures. Comparisons with other methods in the literature are also provided.

Proceedings of 13th International Conference on Digital Signal Processing

In a gray-tone digital picture, the skeleton is a set of digital lines mainly located in correspo... more In a gray-tone digital picture, the skeleton is a set of digital lines mainly located in correspondence with the regions having locally higher gray-values. We describe a sequential skeletonization algorithm based on the dilation of the bottom regions, accomplished by an ordered propagation technique through increasing gray-levels. The non-bottom regions are eroded by lowering the gray-value of their pixels, except

Proceedings 10th International Conference on Image Analysis and Processing

An algorithm to decompose hierarchically bidimensional patterns is introduced. The single-scale i... more An algorithm to decompose hierarchically bidimensional patterns is introduced. The single-scale input pattern is first transformed into a multi-scale data set. The multi-resolution skeleton is then computed and its hierarchical decomposition is obtained by using the notion of permanence. A constrained reverse distance transformation is applied to the skeleton components to reconstruct the regions into which the pattern is decomposed. A merging process then reduces the number of components to the most significant ones and improves decomposition stability.

Advances in Visual Form Analysis, 1997

Image and Vision Computing, 2007

Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2020

Segmenting skin lesions in dermoscopic images is a key step for the automatic diagnosis of melano... more Segmenting skin lesions in dermoscopic images is a key step for the automatic diagnosis of melanoma. In this framework, this paper presents a new algorithm that after a pre-processing phase aimed at reducing the computation burden, removing artifacts and improving contrast, selects the skin lesion pixels in terms of their saliency and color. The method is tested on a publicly available dataset and is evaluated both qualitatively and quantitatively.

Image and Vision Computing, vol 25, n. 6, pp. 1021-1031, 2007

In this paper we build a shape preserving resolution pyramid and use it in the framework of image... more In this paper we build a shape preserving resolution pyramid and use it in the framework of image segmentation via watershed transformation.
Our method is based on the assumption that the most significant image components perceived at high resolution will also be perceived at lower resolution. Thus, we detect the seeds for the watershed transformation at a low resolution, and use them to distinguish significant and non-significant seeds at any selected higher resolution. In this way, the watershed partition obtained at the selected pyramid level will include only the most significant components, and over-segmentation will be considerably reduced. Segmentations of the image at different scales will be available. Moreover, since the seeds can be detected at different pyramid levels, alternative segmentations of the image at a given resolution can be obtained, each characterized by a different level of detail.

VISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, vol. 4, pp. 452-459, 2020