@article {Tascini1996432,
	title = {Real-time inspection by submarine images},
	journal = {Journal of Electronic Imaging},
	volume = {5},
	number = {4},
	year = {1996},
	note = {cited By 5},
	pages = {432-442},
	abstract = {A real-time application of computer vision concerning tracking and inspection of a submarine pipeline is described. The objective is to develop automatic procedures for supporting human operators in the real-time analysis of images acquired by means of cameras mounted on underwater remotely operated vehicles (ROV). Implementation of such procedures gives rise to a human-machine system for underwater pipeline inspection that can automatically detect and signal the presence of the pipe, of its structural or accessory elements, and of dangerous or alien objects in its neighborhood. The possibility of modifying the image acquisition rate in the simulations performed on video-recorded images is used to prove that the system performs all necessary processing with an acceptable robustness working in real-time up to a speed of about 2.5 kn, widely greater than that the actual ROVs and the security features allow. {\textcopyright} 1996 SPIE and IS\&T.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0009091679\&partnerID=40\&md5=0be09172d776524c453eb82b24820faf},
	author = {Guido Tascini and Primo Zingaretti and Conte, G.}
}
@conference {Tascini1993322,
	title = {Retina vascular network recognition},
	booktitle = {Proceedings of SPIE - The International Society for Optical Engineering},
	volume = {1898},
	year = {1993},
	note = {cited By 5},
	pages = {322-329},
	abstract = {The analysis of morphological and structural modifications of the retina vascular network is an interesting investigation method in the study of diabetes and hypertension. Normally this analysis is carried out by qualitative evaluations, according to standardized criteria, though medical research attaches great importance to quantitative analysis of vessel color, shape and dimensions. The paper describes a system which automatically segments and recognizes the ocular fundus circulation and micro circulation network, and extracts a set of features related to morphometric aspects of vessels. For this class of images the classical segmentation methods seem weak. We propose a computer vision system in which segmentation and recognition phases are strictly connected. The system is hierarchically organized in four modules. Firstly the Image Enhancement Module (IEM) operates a set of custom image enhancements to remove blur and to prepare data for subsequent segmentation and recognition processes. Secondly the Papilla Border Analysis Module (PBAM) automatically recognizes number, position and local diameter of blood vessels departing from optical papilla. Then the Vessel Tracking Module (VTM) analyses vessels comparing the results of body and edge tracking and detects branches and crossings. Finally the Feature Extraction Module evaluates PBAM and VTM output data and extracts some numerical indexes. Used algorithms appear to be robust and have been successfully tested on various ocular fundus images.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0027873308\&partnerID=40\&md5=fe51e921b3d6ecbbbd0483a977269acd},
	author = {Guido Tascini and Passerini, G. and Paolo Puliti and Primo Zingaretti}
}
