Wyniki wyszukiwania - Biblioteka Nauki

Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

Ograniczanie wyników

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: iris segmentation

Sortuj według:

Ogranicz wyniki do:

Efficient iris segmentation method with support vector domain description

100%

Tang R. , Han J. , Zhang X.

2009

tom Vol. 39, nr 2

365-374

With the aim to improve the performance of iris segmentation method to process images with heterogeneous characteristics, the authors introduce a new method inspired by the support vector domain description (SVDD). A local geometric moment function is used to extract shape features of the iris borders. Then, these features are fed into the trained SVDD classifier for borders recognition followed by the application of Hough transform to solve circumference parameters of iris. The performance of the proposed method and the most cited methods, Daugman's method and Wilders' method, had been tested on the UBIRIS database. Compared with the two existing methods, our proposal is not only comparable to them when the iris image has good quality, but has better segmentation performance in the case of poor quality images. The experimental results show that the method proposed does have a higher robustness and is less dependent on the quality of raw iris image.

An effective iris segmentation scheme for noisy images

75%

Jan F. , Min-Allah N.

tom Vol. 40, no. 3

1064--1080

Iris segmentation plays a critical role in the iris biometric systems. It has two modules: iris localization and noise detection. The first module demarcates the actual iris' inner and outer boundaries in input eyeimages. The second module detects and removes noise in the valid iris part. Researchers devised numerous iris segmentation and/or localization schemes, which are based on the histogram and thresholding, circular Hough transform (CHT), Integro-differential operator (IDO), active contour models, graph-cuts, or deep learning. It is observed that most contemporary schemes perform poorly when confronted with images containing noisy factors such as the eyebrows, eyelashes, contact lenses, non-uniform illumination, light reflections, defocus and/or eyeglasses. The performance of CHT and IDO against noise is found robust, but these operators are computationally expensive. On the other hand, the histogram and thresholding-based schemes are considered fast, but these are less robust against noise. Besides, most contemporary schemes mark iris contours with a circle approximation and offer no noise removal strategy. To address these issues, this study offers an effective iris segmentation algorithm. First, it applies an optimized coarse-to-fine scheme based on an adaptive threshold to mark iris inner boundary. Next, it detects and marks eyelashes adaptively. After that, it marks iris outer boundary via an optimized coarse- to-fine scheme. Then, it regularizes the non-circular iris' contours using the Fourier series. Finally, eyelids and reflections are marked in the iris polar form. The proposed scheme shows better results on the CASIA-Iris-Interval V3.0, IITD V1.0, and MMU V1.0 iris databases.