Carlos A. B. Mello

Bio: Carlos A. B. Mello is an academic researcher from Federal University of Pernambuco. The author has contributed to research in topics: Image segmentation & Thresholding. The author has an hindex of 13, co-authored 78 publications receiving 538 citations. Previous affiliations of Carlos A. B. Mello include Universidade de Pernambuco.

A SVM based off-line handwritten digit recognizer

Abstract: This paper presents an efficient method for handwritten digit recognition. The proposed method makes use of Support Vector Machines (SVM), benefitting from its generalization power. The method presents improved recognition rates when compared to Multi-Layer Perceptron (MLP) classifiers, other SVM classifiers and hybrid classifiers. Experiments and comparisons were done using a digit set extracted from the NIST SD19 digit database. The proposed SVM method achieved higher recognition rates and it outperformed other methods. It is also shown that although using solely SVMs for the task, the new method does not suffer when considering processing time.

Generation of images of historical documents by composition

Abstract: This paper describes a system for efficient storage, indexing and network transmission of images of historical documents. The documents are first decomposed into their features such as paper texture, colours, typewritten parts, pictures, etc. Document retrieval forces the re-assembling of the document, synthetising an image visually close to the original document. The information needed to build the final image occupies, in average, 2 Kbytes performing a very efficient compression scheme.

Perceptual analysis of the reading of dermatopathology virtual slides by pathology residents.

Abstract: Context.—The process by which pathologists arrive at a given diagnosis—a combination of their slide exploration strategy, perceptual information gathering, and cognitive decision making—has not been thoroughly explored, and many questions remain unanswered. Objective.—To determine how pathology residents learn to diagnose inflammatory skin dermatoses, we contrasted the slide exploration strategy, perceptual capture of relevant histopathologic findings, and cognitive integration of identified features between 2 groups of residents, those who had and those who had not undergone their dermatopathology rotation. Design.—Residents read a case set of 20 virtual slides (10 depicting nodular and diffuse dermatitis and 10 depicting subepidermal vesicular dermatitis), using an in-house–developed interface. We recorded residents' reports of diagnostic findings, conjectured diagnostic hypotheses, and final (or differential) diagnosis for each case, and time stamped each interaction with the interface. We created sear...

A new thresholding algorithm for document images based on the perception of objects by distance

Abstract: In this work a new method to enhance and binarize document images with several kind of degradation is proposed. The method is based on the idea that by the absolute difference between a document image and its background it is possible to effectively emphasize the text and attenuate degraded regions. To generate the background of a document our work was inspired on the human visual system and on the perception of objects by distance. Snellen's visual acuity notation was used to define how far an image must be from an observer so that the details of the characters are not perceived anymore, remaining just the background. A scheme that combines k-means clustering algorithm and Otsu's thresholding method is also used to perform binarization. The proposed method has been tested on two different datasets of document images DIBCO 2011 and a real historical document image dataset with very satisfactory results.

Parameter tuning for document image binarization using a racing algorithm

Abstract: It is investigated the use of I/F-Race to tune document image binarization methods.The method combines visual perception with the minimization of an energy function.Our experiments show that I/F-Race suggests promising parametric configurations.The binarization algorithm configured by I/F-Race outperforms other recent methods. Binarization of images of old documents is considered a challenging task due to the wide diversity of degradation effects that can be found. To deal with this, many algorithms whose performance depends on an appropriate choice of their parameters have been proposed. In this work, it is investigated the application of a racing procedure based on a statistical approach, named I/F-Race, to suggest the parameters for two binarization algorithms reasoned (i) on the perception of objects by distance (POD) and (ii) on the POD combined with a Laplacian energy-based technique. Our experiments show that both algorithms had their performance statistically improved outperforming other recent binarization techniques. The second proposal presented herein ranked first in H-DIBCO (Handwritten Document Image Binarization Contest) 2014.

The Self-Organizing Map

Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

Statistical Pattern Recognition

Abstract: This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

A Survey of Digital Map Processing Techniques

Abstract: Maps depict natural and human-induced changes on earth at a fine resolution for large areas and over long periods of time. In addition, maps—especially historical maps—are often the only information source about the earth as surveyed using geodetic techniques. In order to preserve these unique documents, increasing numbers of digital map archives have been established, driven by advances in software and hardware technologies. Since the early 1980s, researchers from a variety of disciplines, including computer science and geography, have been working on computational methods for the extraction and recognition of geographic features from archived images of maps (digital map processing). The typical result from map processing is geographic information that can be used in spatial and spatiotemporal analyses in a Geographic Information System environment, which benefits numerous research fields in the spatial, social, environmental, and health sciences. However, map processing literature is spread across a broad range of disciplines in which maps are included as a special type of image. This article presents an overview of existing map processing techniques, with the goal of bringing together the past and current research efforts in this interdisciplinary field, to characterize the advances that have been made, and to identify future research directions and opportunities.