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A robust sclera segmentation algorithm
Sclera segmentation is shown to be of significant importance for eye and iris biometrics. However, sclera segmentation has not been extensively researched as a separate topic, but mainly summarized as a component of a broader task. This paper proposes a novel sclera segmentation algorithm for colour images which operates at pixel-level. Exploring various colour spaces, the proposed approach is robust to image noise and different gaze directions. The algorithm’s robustness is enhanced by a two-stage classifier. At the first stage, a set of simple classifiers is employed, while at the second stage, a neural network classifier operates on the probabilities’ space generated by the classifiers at stage 1. The proposed method was ranked the 1st in Sclera Segmentation Benchmarking Competition 2015, part of BTAS 2015, with a precision of 95.05% corresponding to a recall of 94.56%
An Improved Algorithm for Eye Corner Detection
In this paper, a modified algorithm for the detection of nasal and temporal
eye corners is presented. The algorithm is a modification of the Santos and
Proenka Method. In the first step, we detect the face and the eyes using
classifiers based on Haar-like features. We then segment out the sclera, from
the detected eye region. From the segmented sclera, we segment out an
approximate eyelid contour. Eye corner candidates are obtained using Harris and
Stephens corner detector. We introduce a post-pruning of the Eye corner
candidates to locate the eye corners, finally. The algorithm has been tested on
Yale, JAFFE databases as well as our created database
A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head
Purpose: To develop a deep learning approach to de-noise optical coherence
tomography (OCT) B-scans of the optic nerve head (ONH).
Methods: Volume scans consisting of 97 horizontal B-scans were acquired
through the center of the ONH using a commercial OCT device (Spectralis) for
both eyes of 20 subjects. For each eye, single-frame (without signal
averaging), and multi-frame (75x signal averaging) volume scans were obtained.
A custom deep learning network was then designed and trained with 2,328 "clean
B-scans" (multi-frame B-scans), and their corresponding "noisy B-scans" (clean
B-scans + gaussian noise) to de-noise the single-frame B-scans. The performance
of the de-noising algorithm was assessed qualitatively, and quantitatively on
1,552 B-scans using the signal to noise ratio (SNR), contrast to noise ratio
(CNR), and mean structural similarity index metrics (MSSIM).
Results: The proposed algorithm successfully denoised unseen single-frame OCT
B-scans. The denoised B-scans were qualitatively similar to their corresponding
multi-frame B-scans, with enhanced visibility of the ONH tissues. The mean SNR
increased from dB (single-frame) to dB
(denoised). For all the ONH tissues, the mean CNR increased from (single-frame) to (denoised). The MSSIM increased from
(single frame) to (denoised) when compared with
the corresponding multi-frame B-scans.
Conclusions: Our deep learning algorithm can denoise a single-frame OCT
B-scan of the ONH in under 20 ms, thus offering a framework to obtain superior
quality OCT B-scans with reduced scanning times and minimal patient discomfort
Effective segmentation of sclera, iris and pupil in noisy eye images
In today’s sensitive environment, for personal authentication, iris recognition is the most attentive technique among the various biometric technologies. One of the key steps in the iris recognition system is the accurate iris segmentation from its surrounding noises including pupil and sclera of a captured eye-image. In our proposed method, initially input image is preprocessed by using bilateral filtering. After the preprocessing of images contour based features such as, brightness, color and texture features are extracted. Then entropy is measured based on the extracted contour based features to effectively distinguishing the data in the images. Finally, the convolution neural network (CNN) is used for the effective sclera, iris and pupil parts segmentations based on the entropy measure. The proposed results are analyzed to demonstrate the better performance of the proposed segmentation method than the existing methods.
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