Automatic evaluation of ocular versions in images
Abstract
A vision that comply with the basic visual demands, such as short, long, and lateral vision, needs that ocular muscles work perfectly. People with defective ocular muscles may have several problems including strabismus. This paper presents a method to automate the examination of ocular versions based on images of the patient’s face.
References
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Banerji, S., Sinha, A., and Liu, C. (2013). Haarhog: Improving the hog descriptor for image classification. In 2013 IEEE International Conference on Systems, Man, and Cybernetics, pages 4276–4281.
Banić, N. and Lončarić, S. (2014). Color Badger: A Novel Retinex-Based Local Tone Mapping Operator, pages 400–408. Springer International Publishing, Cham.
Bicas, H. E. A. (2003). Oculomotricidade e seus fundamentos. Arquivos Brasileiros de Oftalmologia, 66:687 – 700.
Breiman, L. (2001). Random forests. Machine Learning, 45(1):5–32.
De Almeida, J. D. S., Silva, A. C., Teixeira, J. A. M., Paiva, A. C., and Gattass, M. (2015). Computer-aided methodology for syndromic strabismus diagnosis. Journal of digital imaging, 28(4):462–473.
Zheng, Z., Yang, J., and Yang, L. (2005). A robust method for eye features extraction on color image. Pattern Recognition Letters, 26(14):2252 – 2261.
Published
2017-07-02
How to Cite
PINHEIRO, Jullyana Fialho; DE ALMEIDA, João Dallyson Sousa; TEIXEIRA, Jorge Antonio Meireles; BRAZ JUNIOR, Geraldo.
Automatic evaluation of ocular versions in images. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (SBCAS), 17. , 2017, São Paulo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 1869-1872.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2017.3697.
