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Gaussian Multiscale Aggregation Applied to Segmentation in Hand Biometrics
Group of Biometrics, Biosignals and Security, Universidad Politécnica de Madrid, Campus de Montegancedo s/n, 28223 Pozuelo de Alarcón, Madrid, Spain
* Author to whom correspondence should be addressed.
Received: 14 October 2011; in revised form: 7 November 2011 / Accepted: 22 November 2011 / Published: 28 November 2011
Abstract: This paper presents an image segmentation algorithm based on Gaussian multiscale aggregation oriented to hand biometric applications. The method is able to isolate the hand from a wide variety of background textures such as carpets, fabric, glass, grass, soil or stones. The evaluation was carried out by using a publicly available synthetic database with 408,000 hand images in different backgrounds, comparing the performance in terms of accuracy and computational cost to two competitive segmentation methods existing in literature, namely Lossy Data Compression (LDC) and Normalized Cuts (NCuts). The results highlight that the proposed method outperforms current competitive segmentation methods with regard to computational cost, time performance, accuracy and memory usage.
Keywords: hand biometrics; multiscale aggregation; image segmentation; image processing; biometrics; security
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MDPI and ACS Style
Sierra, A.S.; Ávila, C.S.; Casanova, J.G.; Pozo, G.B. Gaussian Multiscale Aggregation Applied to Segmentation in Hand Biometrics. Sensors 2011, 11, 11141-11156.
Sierra AS, Ávila CS, Casanova JG, Pozo GB. Gaussian Multiscale Aggregation Applied to Segmentation in Hand Biometrics. Sensors. 2011; 11(12):11141-11156.
Sierra, Alberto de Santos; Ávila, Carmen Sánchez; Casanova, Javier Guerra; Pozo, Gonzalo Bailador del. 2011. "Gaussian Multiscale Aggregation Applied to Segmentation in Hand Biometrics." Sensors 11, no. 12: 11141-11156.