Text detection and recognition in images and video sequences
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Current document archives are enormously large and constantly increasing and that makes it practically impossible to make use of them efficiently. To analyze and interpret large volumes of speech and text of these archives in multiple languages and produce ...
Skin cancer is a spreading disease in the western world. Early detection and treatment are crucial for improving the patient survival rate. In this paper we present two algorithms for computer assisted diagnosis of melanomas. The first is the support vecto ...
Malignant melanoma is the most deadly for of skin lesion. Early diagnosis is of critical importance to patient survival. Visual recognition algorithms could potentially be of great help for physicians in a computer assisted diagnosis system. Previous work ...
A distinctive feature of intelligent systems is their capability to analyze their level of expertise for a given task; in other words, they know what they know. As a way towards this ambitious goal, this paper presents a recognition algorithm able to measu ...
Text categorization is intrinsically a supervised learning task, which aims at relating a given text document to one or more predefined categories. Unfortunately, labeling such databases of documents is a painful task. We present in this paper a method tha ...
The recognition of events in multimedia data is a challenging area of research. The growth in the amount of multimedia data being produced and stored increases the need for systems capable of automatically analysing this data. This analysis can aid in effi ...
This paper proposes an algorithm for distributed classification, based on a SVM scheme. The contribution of each support vector is approximated by low complexity distributed thresholding over sub-dictionaries, whose union forms a redundant dictionary of at ...
In this work, we propose different strategies for efficiently integrating an automated speech recognition module in the framework of a dialogue-based vocal system. The aim is the study of different ways leading to the improvement of the quality and robustn ...
We present a novel approach for contextual segmentation of complex visual scenes, based on the use of bags of local invariant features (visterms) and probabilistic aspect models. Our approach uses context in two ways: (1) by using the fact that specific le ...
In this paper, we advance the state of the art in variational image segmentation through the fusion of bottom-up segmentation and top-down classification of object behavior over an image sequence. Such an approach is beneficial for both tasks and is carrie ...
