Image processing and its potential application in companies with digital strategy
DOI:
https://doi.org/10.26439/interfases2017.n10.1767Keywords:
image processing, retail marketing, machine learning, predictive analyticsAbstract
Peruvian retail market today, more than ever, has turned the phrase “everything goes through the eyes” into a competitive tool. The design and optimization of space, as well as visual merchandising, are techniques that impact the sale new concepts such as omnicanality and buying experience are fed by data analytics in order to describe the commercial mode; and new qualitative sources of information, among them color theory, specially help to understand and predict the impact of future decisions on the point of sale. This paper describes the utility of image processing techniques to innovate the retail market in the effort to extract useful information from advertising pieces frequently used in this sector.
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Álvarez, J. (Mayo del 2016). Perfil del ama de casa en Lima Metropolitana. Informe Gerencial de Marketing. Base de datos Marketing Data Plus. Apoyo Opinión y Mercado S. A.
Cámara Peruana de Venta Directa – Capedevi (mayo del 2017). Venta Directa al cierre del primer trimestre 2017. Boletín Mensual, 53. Recuperado de http://www.capevedi.com/Public/boletines.html
Cuesta, U., Niño, J. y Rodríguez, J. (2017). The Cognitive Processing of an Educational App with Electroencephalogram and “Eye Tracking”. Comunicar, 25(52), 41-50. DOI:10.3916/C52-2017-04
Dalal, N., y Triggs, B. (2005). Histograms of Oriented Gradients for Human Detection. En 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) (Vol. 1, 886-893). IEEE. DOI:10.1109/CVPR.2005.177
Euromonitor International. (Agosto del 2017). Digital Consumer Profiles: How Latin Americans will shop and spend digitally. Análisis del consumidor. Base de datos Euromonitor International.
Hidalgo, I., y Sánchez, R. (2015). Reconocimiento de caracteres mediante imágenes en contadores de gas en entornos reales (Trabajo de fin de grado). Universidad Complutense de Madrid, España.
Kaur, S. (2016). An automatic number plate recognition system under image processing. International Journal of Intelligent Systems and Applications, 8(3), 14-25. DOI: 10.5815/ijisa.2016.03.02
Kwan, S. P. (2009, June 25). Methods and apparatus for improved image processing to provide retroactive image focusing and improved depth of field in retail imaging systems. Google Patents. Recuperado de https://www.google.ch/patents/US20090160975
Montoya, C., Cortés, J., y Chaves, J. (2014). Sistema automático de reconocimiento de frutas basado en visión por computador. Ingeniare. Revista Chilena de Ingeniería, 22(4), 504-516. DOI:10.4067/S0718-33052014000400006
Rublee, E., Rabaud, V., Konolige, K., y Bradski, G. (2011). ORB: An efficient alternative to SIFT or SURF. En 2011 International Conference on Computer Vision (2564-2571). IEEE. DOI: 10.1109/ ICCV.2011.6126544
Seeman, M. (2013). Image processing for improved perception and interaction. Information Sciences and Technologies Bulletin of the ACM Slovakia, 5(3), 8-12.
Shrivastava, N., y Tyagi, V. (2016). An integrated approach for image retrieval using local binary pattern. Multimedia Tools and Applications, 75(11), 6569-6583. DOI: 10.1007/s11042-015-2589-2
Wang, Y. (2014). An analysis of the Viola-Jones face detection algorithm. Image Processing On Line, 4, 128-148. DOI:10.5201/ipol.2014.104
Zielke, S. (2010). How price image dimensions influence shopping intentions for different store formats. European Journal of Marketing, 44(6), 748-770. DOI:10.1108/03090561011032702
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Last updated 03/05/21
