Publications

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.

The ability to understand social signals of a person we are communicating with is the core of social intelligence. Social Intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. At the same time, human-centric multimedia applications for humans and about humans are becoming increasingly important. 3D modeled human-objects, like bodies, heads and faces are exploited for animation, security, and human computer interaction, while three dimensional motion of arms, legs and local body features is used for more complete human gesture, activity and behavior analysis. The Joint Human Gesture and Behavior Understanding (J-HGBU) workshop event consists of two parts focusing on these complementary challenges: the Workshop on Multimedia Access to 3D Human Objects (MA3HO'11) and the Workshop on Social Signal Processing (SSPW'11). © 2011 ACM.

Embedded smart cameras have limited processing power, memory and energy. In this paper, we introduce two methodologies to increase the energy-efficiency and the battery-life of an embedded smart camera by hardware-level operations when performing foreground object detection. We use the CITRIC platform as our embedded smart camera. We first perform down-sampling at hardware level on the micro-controller of the image sensor rather than performing software-level down-sampling at the main microprocessor of the camera board. In addition, we crop an image frame at hardware level by using the HREF and VSYNC signals at the micro-controller of the image sensor to perform foreground object detection only in the cropped search region instead of the whole image. Thus, the amount of data that is moved from the image sensor to the main memory at each frame, is greatly reduced. Thanks to reduced data transfer, better use of the memory resources and not occupying the main microprocessor with image down-sampling and cropping tasks, we obtain significant savings in energy consumption and battery-life. Experimental results show that hardware-level down-sampling and cropping, and performing detection in cropped regions provide 54.14% decrease in energy consumption, and 121.25% increase in battery-life compared to performing software-level down-sampling and processing whole frames. © 2011 IEEE.

A new evidential fusion architecture is proposed to build anhybrid articial intelligent system for people surveillance in wide open areas. Authorized people and intruders are identied and localized thanks to the joint employment of cameras and RFID tags. Complex Event Processing and Transferable Belief Model are exploited for handling noisy data and uncertainty propagation. Experimental results on complex synthetic scenarios demonstrate the accuracy of the proposed solution.

Embedded systems have limited processing power, memory and energy. When camera sensors are added to an embedded system, the problem of limited resources becomes even more pronounced. In this paper, we introduce two methodologies to increase the energy-efficiency and battery-life of an embeddedsmart camera by hardware-level operations when performingobject detection and tracking. The CITRIC platform is employedas our embedded smart camera. First, down-sampling is performed at hardware level on the micro-controller of the imagesensor rather than performing software-level down-sampling atthe main microprocessor of the camera board. In addition, instead of performing object detection and tracking on wholeimage, we first estimate the location of the target in the nextframe, form a search region around it, then crop the next frameby using the HREF and VSYNC signals at the micro-controllerof the image sensor, and perform detection and tracking onlyin the cropped search region. Thus, the amount of data thatis moved from the image sensor to the main memory at eachframe is optimized. Also, we can adaptively change the size ofthe cropped window during tracking depending on the objectsize. Reducing the amount of transferred data, better use ofthe memory resources, and delegating image down-samplingand cropping tasks to the micro-controller on the image sensor,result in significant decrease in energy consumption and increasein battery-life. Experimental results show that hardware-leveldown-sampling and cropping, and performing detection andtracking in cropped regions provide 41.24% decrease in energyconsumption, and 107.2% increase in battery-life. Compared toperforming software-level down-sampling and processing wholeframes, proposed methodology provides an additional 8 hours ofcontinuous processing on 4 AA batteries, increasing the lifetimeof the camera to 15.5 hours.

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 increasing pace of biotechnological advances produced an unprecedented amount of both experimental data and biological information mostly diffused on the web. However, the heterogeneity of the data organization and the different knowledge representations open the ways to new challenges in the integration and the extraction of biological information fundamental for correctly interpreter experimental results. In the present work we introduce a new methodology for quantitatively scoring the degree of biological correlation among biological terms occurring in biomedical abstracts. The proposed flow is based on the latent semantic analysis of biomedical literature coupled with the UMLS Metathesarurs and PubMed literature information. The results demonstrate that the structured and consolidated knowledge in the UMLS and pathway database efficiently improves the accuracy of the latent semantic analysis of biomedical literature. © Springer-Verlag Berlin Heidelberg 2013.