Publications by Costantino Grana

We propose a new efficient approach for neighborhood exploration, optimized with decision tables and decision trees, suitable for local algorithms in image processing. In this work, it is employed to speed up two widely used thinning techniques. The performance gain is shown over a large freely available dataset of scanned document images.

This paper proposes a comparison of the two most advanced algorithms for connected components labeling, highlighting how they perform on a soft core SoC architecture based on FPGA. In particular we test our block based connected components labeling algorithm, optimized with decision tables and decision trees. The embedded system is composed of the CMOS image sensor, FPGA, DDR SDRAM, USB controller and SPI Flash. Results highlight the importance of caching and instructions and data cache sizes for high performance image processing tasks.

In this paper we detail a proposal of exploitation of expert-made commentaries in a unified system for illuminated manuscripts images analysis. In particular we will explore the possibility to improve the automatic segmentation of meaningful pictures, as well as the retrieval by similarity search engine, using clusters of keywords extracted from commentaries as semantic information.

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In this paper we define a new paradigm for 8-connection labeling, which employes a general approach to improve neighborhood exploration and minimizes the number of memory accesses. Firstly we exploit and extend the decision table formalism introducing OR-decision tables, in which multiple alternative actions are managed. An automatic procedure to synthesize the optimal decision tree from the decision table is used, providing the most effective conditions evaluation order. Secondly we propose a new scanning technique that moves on a 2x2 pixel grid over the image, which is optimized by the automatically generated decision tree.An extensive comparison with the state of art approaches is proposed, both on synthetic and real datasets. The synthetic dataset is composed of different sizes and densities random images, while the real datasets are an artistic image analysis dataset, a document analysis dataset for text detection and recognition, and finally a standard resolution dataset for picture segmentation tasks. The algorithm provides an impressive speedup over the state of the art algorithms.

This paper describes an interactive application for the exploration and annotation of illuminated manuscripts, which typically contain thousands of pictures, used to comment or embellish the manuscript Gothic text. The system is composed by a modern user interface for browsing, surfing and querying, an automatic segmentation module, to ease the initial picture extraction task, and a similarity based retrieval engine, used to provide visually assisted tagging capabilities. A relevance feedback procedure is included to further refine the results.

This paper describes a complete architecture for the interactive exploration and annotation of artistic collections. In particular the focus is on Renaissance illuminated manuscripts, which typically contain thousands of pictures, used to comment or embellish the manuscript Gothic text. The final aim is to create a human centered multimedia application allowing the non practitioners to enjoy these masterpieces and expert users to share their knowledge. The system is composed by a modern user interface for browsing, surfing and querying, an automatic segmentation module, to ease the initial picture extraction task, and a similarity based retrieval engine, used to provide visually assisted tagging capabilities. A relevance feedback procedure is included to further refine the results. Experiments are reported regarding the adopted visual features based on covariance matrices and the Mean Shift Feature Space Warping relevance feedback. Finally some hints on the user interface for museum installations are discussed.

In this paper a document analysis tool for historical manuscripts is proposed. The goal is to automatically segment layout components of the page, that is text, pictures and decorations. We specifically focused on the pictures, proposing a set of visual features able to identify significant pictures and separating them from all the floral and abstract decorations. The analysis is performed by blocks using a limited set of color and texture features, including a new texture descriptor particularly effective for this task, namely Gradient Spatial Dependency Matrix. The feature vectors are processed by an embedding procedure which allows increased performance in later SVM classification.

This paper proposes a comparison of color features for image retrieval. In particular the UCID image database has been employed to compare the retrieval capabilities of different color descriptors. The set of descriptors comprises global and spatially related features, and the tests show that HSV based global features provide the best performance at varying brightness and contrast settings.