Publications by Costantino Grana

In this paper we present a novel algorithm to synthesize an optimal decision tree from OR-decision tables, an extension of standard decision tables, complete with the formal proof of optimality and computational cost analysis. As many problems which require to recognize particular patterns can be modeled with this formalism, we select two common binary image processing algorithms, namely connected components labeling and thinning, to show how these can be represented with decision tables, and the benets of their implementation as optimal decision trees in terms of reduced memory accesses. Experiments are reported, to show the computational time improvements over state of the art implementations.

Image collections are searched in common retrieval systems in many different ways, but the typical presentation is by means of a grid styled view. In this paper we try to suggest a novel use of relevance feedback as a tool to warp the view and allow the user to spatially navigate the image collection, and at the same time focus on his retrieval aim. This is obtained by the use of a distance based space warping on the 2D projection of the distance matrix.

For quite some time, libraries, document and historical centers from opposite corners of the world have been the caretakers of our rich and assorted social legacy. They have protected and furnished access to the testimonies of knowledge, beauty and inspiration, such as sculptures, paintings, music and literature. The new information technologies have created unbelievable opportunities to make this common heritage more accessible for all. Culture is following the digital path and “memory institutions” are adapting the way in which they communicate with their public. Multimedia technologies have recently created the conditions for a true revolution in the cultural heritage area, with reference to the study, valorization, and fruition of artistic works. New multimedia technologies shall be able to be utilized to plan unique approaches to the perception and fulfillment of the masterful legacy, for instance, through smart cultural objects and new interfaces with the backing of items such as story-telling, gaming and learning.All the plurality of masterpieces (paintings, books, manuscripts, even photos of sculptures and architecture) can be effectively embedded into a unique ``paradigm'' through digitization. This allows a significant reduction in costs, an enormous expansion of public accessibility (and therefore income), and at the same time a tremendous freedom for data elaboration. In brief, digitization enhances pleasure for the public and usefulness to experts on cultural heritage assets.

Artistic image collections are usually managed via textual metadata into standard content management systems. More sophisticated searches can be performed using image retrieval technologies based on visual content. Nevertheless, the problem of the information presentation remains. In this paper we try to move beyond the classic grid-styled presentation model, suggesting a novel use of relevance feedback as a navigation tool. Relevance feedback is therefore used to warp the view and allow the user to spatially navigate the image collection, and at the same time focus on his retrieval aim. This is obtained exploiting a distance based space warping on the 2D projection of the distance matrix. Multitouch gestures are employed to provide feedbacks by natural interaction with the system.

This paper describes the design and development of a Veiling Luminance estimation system based on the use of a CMOS image sensor, fully implemented on FPGA. The system is composed of the CMOS Image sensor, FPGA, DDR SDRAM, USB controller and SPI (Serial Peripheral Interface) Flash. The FPGA is used to build a system-on-chip integrating a soft processor (Xilinx MicroBlaze) and all the hardware blocks needed to handle the external peripherals and memory. The soft processor is used to handle image acquisition and all computational tasks need to compute the Veiling Luminance value. The advantages of this single chip FPGA implementation include the reduction of the hardware requirements, power consumption, and system complexity. The problem of the high dynamic range images have been addressed with multiple acquisitions at different exposure times. Vignetting, radial distortion and angular weighting, as required by veiling luminance definition, are handled by a single integer look-up table (LUT) access. Results are compared with a state of the art certified instrument.

A CCD-based measuring instrument aimed at the veiling luminance estimation and the relative low-cost calibration method are described. The system may allow the estimation of the optimum luminance levels in road-tunnels lighting, thus both increasing the drivers safety and avoiding energy wasting hence unjustified higher lighting-costs.

The artistic content of historical manuscripts provides a lot of challenges in terms of automatic text extraction, picture segmentation and retrieval by similarity. In particular this work addresses the problem of automatic extraction of meaningful pictures, distinguishing them from handwritten text and floral and abstract decorations. The proposed solution firstly employs a circular statistics description of a directional histogram in order to extract text. Then visual descriptors are computed over the pictorial regions of the page: the semantic content is distinguished from the decorative parts using color histograms and a novel texture feature called Gradient Spatial Dependency Matrix. The feature vectors are finally processed using an embedding procedure which allows increased performance in later SVM classification. Results for both feature extraction and embedding based classification are reported, supporting the effectiveness of the proposal on high resolution replicas of artistic manuscripts.

Relevance feedback is a widely adopted approach to improve content-based information retrieval systems by keeping the user in the retrieval loop. Among the fundamental relevance feedback approaches, feature space warping has been proposed as an effective approach for bridging the gap between high-level semantics and the low-level features. Recently, combination of feature space warping and query point movement techniques has been proposed in contrast to learning based approaches, showing good performance under dierent data distributions. In this paper we propose to merge feature space warping and transductive learning, in order to benet from both the ability of adapting data to the user hints and the information coming from unlabeled samples. Experimental results on an image retrieval task reveal signicant performance improvements from the proposed method.

In this paper we present a novel dynamic programming algorithm to synthesize an optimal decision tree from OR-decision tables,an extension of standard decision tables,which allow to choose between several alternative actions in the same rule. Experiments are reported,showing the computational time improvements over state of the art implementations of connected components labeling,using this modelling technique.