ZONING DESIGN FOR HAND­WRITTEN NUMERAL RECOGNITION

Microsoft, Motorola, Siemens, Hitachi, IAPR, NICI, IUF In the field of Optical Character Recognition (OCR), zoning is used to extract topological information from patterns. In this paper zoning is considered as the result of an optimisation problem and a new technique is presented for automatic zoning. More precisely, local analysis of feature distribution based on Shannon's entropy estimation is performed to determine "core" zones of patterns. An iterative region­growing procedure is applied on the "core" zones to determine the final zoning

A PERTURBATION­BASED APPROACH FOR MULTI­CLASSIFIER SYSTEM DESIGN

Microsoft, Motorola, Siemens, Hitachi, IAPR, NICI, IUF This paper presents a perturbation­based approach useful to select the best combination method for a multi­classifier system. The basic idea is to simulate small variations in the performance of the set of classifiers and to evaluate to what extent they influence the performance of the combined classifier. In the experimental phase, the Behavioural Knowledge Space and the Dempster­Shafer combination methods have been considered. The experimental results, carried out in the field of hand­written numeral recognition, demonstrate the effectiveness of the new approach

A comprehensive approach to establish the impact of worksites air emissions

Worksite activities are time-limited events associated with continuous releases of airborne pollutants, such as carbon monoxide, particulate matter, and NOx, and they impact potentially vast areas. The side-effects on the environment can be severe, and they are subject of literature studies, with the final aim of proposing solutions that may improve the management of air emissions. No general assessment method or approach is yet available to estimate their effects on the environment and workers’ health. In this work, a general procedure that can be potentially applied to every type of worksite is proposed (i.e., construction sites, upgrading of chemical plants, road sites, etc..). The approach involves a detailed assessment of emissions and their expected pollutant concentrations. A dedicated mathematical model has been defined to assess pollutant emissions over time, consistent with all the different phases of foreseen activities. Emissions are defined on base of the GANTT descriptions of the activities and air pollutant dispersion is simulated with a dedicated model. Finally, the obtained results are evaluated against air quality thresholds as defined by laws and conditioning the human health risks for workers and citizens potentially exposed to pollutants

Attosecond pulse shaping around a Cooper minimum

High harmonic generation (HHG) is used to measure the spectral phase of the recombination dipole matrix element (RDM) in argon over a broad frequency range that includes the 3p Cooper minimum (CM). The measured RDM phase agrees well with predictions based on the scattering phases and amplitudes of the interfering s- and d-channel contributions to the complementary photoionization process. The reconstructed attosecond bursts that underlie the HHG process show that the derivative of the RDM spectral phase, the group delay, does not have a straight-forward interpretation as an emission time, in contrast to the usual attochirp group delay. Instead, the rapid RDM phase variation caused by the CM reshapes the attosecond bursts.Comment: 5 pages, 5 figure

Semantic segmentation of conjunctiva region for non-invasive anemia detection applications

Technology is changing the future of healthcare, technology-supported non-invasive medical procedures are more preferable in the medical diagnosis. Anemia is one of the widespread diseases affecting the wellbeing of individuals around the world especially childbearing age women and children and addressing this issue with the advanced technology will reduce the prevalence in large numbers. The objective of this work is to perform segmentation of the conjunctiva region for non-invasive anemia detection applications using deep learning. The proposed U-Net Based Conjunctiva Segmentation Model (UNBCSM) uses fine-tuned U-Net architecture for effective semantic segmentation of conjunctiva from the digital eye images captured by consumer-grade cameras in an uncontrolled environment. The ground truth for this supervised learning was given as Pascal masks obtained by manual selection of conjunctiva pixels. Image augmentation and pre-processing was performed to increase the data size and the performance of the model. UNBCSM showed good segmentation results and exhibited a comparable value of Intersection over Union (IoU) score between the ground truth and the segmented mask of 96% and 85.7% for training and validation, respectively