@conference {Conte19961213,
	title = {Automatic analysis of visual data in submarine pipeline inspection},
	booktitle = {Oceans Conference Record (IEEE)},
	volume = {3},
	year = {1996},
	note = {cited By 8},
	pages = {1213-1219},
	abstract = {An automatic system for analysis and interpretation of visual data from submarine pipeline inspection operates using images collected by cameras mounted on an unmanned Remotely Operated Vehicle (ROV) that moves along the pipeline. The system supports the operator in navigating the ROV and in detecting structural elements of the pipeline such as anodes, gravel heaps and reference bands. The system employs a feature based technique for recognizing the pipeline{\textquoteright}s profile and for detecting the structural elements. Information gathered by analyzing and interpreting the visual data can be used for automatic guidance and inspection by integrating the system into a larger architecture.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0030385353\&partnerID=40\&md5=d5723a92ebcff100f3868adc25135935},
	author = {Conte, G. and S.M. Zanoli and Perdon, A.M. and Guido Tascini and Primo Zingaretti}
}
@conference {Tascini19942378,
	title = {Attraction based recognition},
	booktitle = {Proceedings of the IEEE International Conference on Systems, Man and Cybernetics},
	volume = {3},
	year = {1994},
	note = {cited By 0},
	pages = {2378-2383},
	abstract = {An approach to image recognition is proposed, which is achieved through the correspondences between predicted and measured properties: points, lines, regions, color, shape, etc. Image parts are subjected to a series of transformations performed by the recognition process. In these transformations, called image chaining, there is a grouping of image parts or features due to a field continually perturbed by the recognition process. A fundamental role is played by the spatial correspondences together with an intentional behavior of the transformed visual data called attraction. After a definition of the attraction principle, the paper describes the attraction process, and justifies the image chaining concept. Finally, a series of processes - segmentation refinement, search space reduction, and perceptual organization - are interpreted in terms of the attraction principle.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0028742684\&partnerID=40\&md5=9925689ed71ffc413fb731f2205f4173},
	author = {Guido Tascini and Primo Zingaretti}
}
@conference {Tascini19941722,
	title = {Automatic quantitative analysis of lumbar bone radiographs},
	booktitle = {IEEE Nuclear Science Symposium \& Medical Imaging Conference},
	number = {pt 3},
	year = {1994},
	note = {cited By 1},
	pages = {1722-1726},
	abstract = {In the current radiological diagnosis the radiographs often appear as {\textquoteleft}rough{\textquoteright} means to be understood due to the bad quality of radiographic images. The aim of the work is to guarantee a diagnostic support even in presence of radiographic means only, with a little cost increment due to a computer system, by supplying a useful primary screening tool that automatically analyzes digitized radiographs and gives important features relevant from medical standpoint. The specific domain of image analysis concerns the osteoporosis which is a long term diseases requiring an accurate measurement of bone density and a periodic follow-up. The automatic image processing method described in the work goes through a series of steps: selection of the region of interest, first preprocessing, extraction of the vertebral left side, second preprocessing, extraction of upper and lower borders of the end plates, detection of the greatest uniform area inside the vertebral body, third preprocessing, correction of image disuniformity, detection of the end plates, feature detection (variance, mean-gray, concavity, body and tissue density, etc.). The automation of the process is complete and the results good agree with those obtained from human analysis.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0028277067\&partnerID=40\&md5=7871b50c44eb8cbdb7e70759b9b2eb0d},
	author = {Guido Tascini and Primo Zingaretti}
}
