Marbling Grading Framework Applied on Meat Boutique Environment
Resumo
Bovine meat commercialization has an important role in the general food market scenario. The beef quality evaluation is realized through many ways, being one of the parameters the intramuscular fat amount (marbling). This evaluation is often made by a visual approach, so the process is subjective and susceptible to some errors sources. The use of Computer Vision techniques results in an automatized, non-subjective, fast and accurate method for evaluation. This paper presents the modeling and development of a Computer Vision System for Marbling evaluation, applied on a meat Boutique, localized in Londrina – PR. The proposed System uses a Computer Vision approach to control the features of the marbling analysis tool, aiming to satisfy sanitary requirements for non-contamination of the analyzed samples. Besides that, multiples samples on the scene are supported by our application. The proposed Computer Vision System has proved to be suitable for implantation in a production environment, like a meat Boutique.
Palavras-chave:
Computer Vision, Segmentation, Beef, Image, Food Quality
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Botero, A.; Saad-Sulonen, J. Co-designing for New Citycitizen Interaction Possibilities: Weaving Prototypes and Interventions in the Design and Development of Urban Mediator. Indiana University (2008), 266–269.
Caminha, C., Furtado, V., Vasconcelos, E.; Ayres, L. Uma ferramenta de autoria para criação de mapas colaborativos para aplicações em egov 2.0. Proc. XXX CSBC, (2010), 20– 23.
Catania, B., Guerrini, G., Belussi, A., Mandreoli, F., Martoglia, R.; Penzo, W. Wearable Queries: Adapting Common Retrieval Needs to Data and Users. Proc. 7th Int. Work. on Ranking in Databases, ACM (2013), 7:1–7:3.
Chourabi, H. et al. Understanding Smart Cities: An Integrative Framework. IEEE Computer Society (2012), 2289–2297.
Denton, D.W. Enhancing instruction through constructivism, cooperative learning, and cloud computing. TechTrends 56, 4 (2012), 34–41.
DiGiammarino, F.; Trudeau, L. Virtual networks. THE PUBLIC 5, (2008).
Doan, A., Ramakrishnan, R.; Halevy, A.Y. Crowdsourcing systems on the World-Wide Web. Communications of the ACM 54, 4 (2011), 86.
Doran, D., Gokhale, S.; Dagnino, A. Human Sensing for Smart Cities. Proc. 2013 Int. Conf. on Advances in Social Networks Analysis and Mining, ACM (2013), 1323–1330.
Erickson, L., Petrick, I., Trauth, E.; Erickson, L.B. Hanging with the right crowd: Matching crowdsourcing need to crowd characteristics. Proc. AMCIS 2012, (2012), 1–9.
Estelles-Arolas, E.; Gonzalez-Ladron-de-Guevara, F. Towards an integrated crowdsourcing definition. Journal of Information … 38, 2 (2012), 189–200.
Furtado, V., Caminha, C., Ayres, L.; Santos, H. Open government and citizen participation in law enforcement via crowd mapping. IEEE Intelligent Systems 27, 4 (2012), 63– 69.
Gil-Castiñeira, F. et al. RunWithUs: A Social Sports Application in the Ubiquitous Oulu Environment. Proc. 10th Int. Conf. on Mobile and Ubiquitous Multimedia, ACM (2011), 195–204.
Goodspeed, R., Spanring, C.; Reardon, T. Crowdsourcing As Data Sharing: A Regional Web-based Real Estate Development Database. Proc. 6th Int. Conf. on Theory and Practice of Electronic Governance, ACM (2012), 460–463.
Harris, C.G. You’re Hired ! An Examination of Crowdsourcing Incentive Models in Human Resource Tasks. Proc. Workshop on Crowdsourcing for Search and Data Mining, CSDM (2011), 15–18.
Hetmank, L. Components and Functions of Crowdsourcing Systems – A Systematic Literature Review. Proc. 11th Int. Conf. on Wirtschaftsinformatik, March (2013), 55–69.
Howe, J. Crowdsourcing: How the power of the crowd is driving the future of business. Random House, 2008.
Hupfer, S. et al. MoCoMapps: mobile collaborative mapbased applications. Proc. Conf. Computer Supported Cooperative Work Companion, (2012), 43–44.
Jollivet, P. Crowd sourced security, trust & cooperation for learning digital megacities: valuing social intangible assets for competitive advantage and harmonious development. Proc. Int. Conf. on Smart and Sustainable City, IET (2011), 52–52.
Manzoor, A. et al. Data Sensing and Dissemination Framework for Smart Cities. Proc. Int. Conf. on MOBILe Wireless MiddleWARE, Operating Systems, and Applications, EAI (2013), 156–165.
Nam, T.; Pardo, T.A. Conceptualizing Smart City with Dimensions of Technology, People, and Institutions. ACM (2011), 282–291.
Navarro, K.F. et al. SocialCycle what can a mobile app do to encourage cycling? Proc. 38th Annual IEEE Conf. on Local Computer Networks - Workshops, IEEE (2013), 24–30.
Pedersen, J. et al. Conceptual foundations of crowdsourcing: a review of IS research. Proc. 46th Hawaii Int. Conf. on System Sciences, (2013), 579–588.
Quercia, D. Urban: Crowdsourcing for the Good of London. Proc. Int. World Wide Web Conferences Steering Committee (2013), 591–592.
Ringas, D.; Christopoulou, E. Collective City Memory: Field Experience on the Effect of Urban Computing on Community. ACM (2013), 157–165.
Roitman, H. et al. Harnessing the Crowds for Smart City Sensing. Proc 1st Int. Workshop on Multimodal Crowd Sensing, ACM (2012), 17–18.
Shapiro, J.M. Smart cities: quality of life, productivity, and the growth effects of human capital. The review of economics and statistics 88, 2 (2006), 324–335.
Silva, T.H. et al. Challenges and opportunities on the large scale study of city dynamics using participatory sensing. Proc. IEEE Symp on Computers and Communications (ISCC), (2013), 528–534.
Vakali, A., Angelis, L.; Giatsoglou, M. Sensors talk and humans sense Towards a reciprocal collective awareness smart city framework. Proc. 2013 IEEE Int. Conf. on Communications Workshops (ICC), IEEE (2013), 189–193.
Viegas, F.B. et al. Manyeyes: a site for visualization at internet scale. Visualization and Computer Graphics, IEEE Transactions on 13, 6 (2007), 1121–1128.
Zeiger, F.; Huber, M. Demonstration Abstract: Participatory Sensing Enabled Environmental Monitoring in Smart Cities. Proc. 13th Int. Symp. on Information Processing in Sensor Networks, IEEE Press (2014), 337–338.
Publicado
17/05/2016
Como Citar
MASTELINI, Saulo Martiello; DA SILVA, Matheus Camilo; BARBON, Ana Paula Ayub da Costa; BARBON JR., Sylvio.
Marbling Grading Framework Applied on Meat Boutique Environment. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 12. , 2016, Florianópolis.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 542-549.
DOI: https://doi.org/10.5753/sbsi.2016.6005.
