1,449 research outputs found
Quantization-free parameter space reduction in ellipse detection
Ellipse modeling and detection is an important task in many computer vision and pattern recognition applications. In this thesis, four Hough-based transform algorithms have been carefully selected, studied and analyzed. These techniques include the Standard Hough Transform, Probabilistic Hough Transform, Randomized Hough Transform and Directional Information for Parameter Space Decomposition. The four algorithms are analyzed and compared against each other in this study using synthetic ellipses. Objects such as noise have been introduced to distract ellipse detection in some of the synthetic ellipse images. To complete the analysis, real world images were used to test each algorithm resulting in the proposal of a new algorithm. The proposed algorithm uses the strengths from each of the analyzed algorithms. This new algorithm uses the same approach as the Directional Information for Parameter Space Decomposition to determine the ellipse center. However, in the process of collecting votes for the ellipse center, pairs of unique edge points voted for the center are also kept in an array. A minimum of two pairs of edge points are required to determine the ellipse. This significantly reduces the usual five dimensional array requirement needed in the Standard Hough Transform. We present results of the experiments with synthetic images demonstrating that the proposed method is more effective and robust to noise. Real world applications on complex real world images are also performed successfully in the experiment
Pupil Center Detection Approaches: A comparative analysis
In the last decade, the development of technologies and tools for eye
tracking has been a constantly growing area. Detecting the center of the pupil,
using image processing techniques, has been an essential step in this process.
A large number of techniques have been proposed for pupil center detection
using both traditional image processing and machine learning-based methods.
Despite the large number of methods proposed, no comparative work on their
performance was found, using the same images and performance metrics. In this
work, we aim at comparing four of the most frequently cited traditional methods
for pupil center detection in terms of accuracy, robustness, and computational
cost. These methods are based on the circular Hough transform, ellipse fitting,
Daugman's integro-differential operator and radial symmetry transform. The
comparative analysis was performed with 800 infrared images from the
CASIA-IrisV3 and CASIA-IrisV4 databases containing various types of
disturbances. The best performance was obtained by the method based on the
radial symmetry transform with an accuracy and average robustness higher than
94%. The shortest processing time, obtained with the ellipse fitting method,
was 0.06 s.Comment: 15 pages, 9 figures, submitted to the journal "Computaci\'on y
Sistemas
Eye Corner Detection
Detection of corners of the eye is a good research topic. It plays an important role in multiple tasks performed in the field of Computer Vision. It also plays a key role in biometric systems. In this the- sis, initially, the existing corner detection methods are discussed. Using Hough transform line, circle and ellipse were found out in the given image. The proposed work includes, finding the eye region in the given face image using Template Matching method. Later on, we fit a rectangle to the matched eye region. And then, we find out the corners of the rectangle and approximate them to be the corners of the eye
Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images
Iris centre localization in low-resolution visible images is a challenging
problem in computer vision community due to noise, shadows, occlusions, pose
variations, eye blinks, etc. This paper proposes an efficient method for
determining iris centre in low-resolution images in the visible spectrum. Even
low-cost consumer-grade webcams can be used for gaze tracking without any
additional hardware. A two-stage algorithm is proposed for iris centre
localization. The proposed method uses geometrical characteristics of the eye.
In the first stage, a fast convolution based approach is used for obtaining the
coarse location of iris centre (IC). The IC location is further refined in the
second stage using boundary tracing and ellipse fitting. The algorithm has been
evaluated in public databases like BioID, Gi4E and is found to outperform the
state of the art methods.Comment: 12 pages, 10 figures, IET Computer Vision, 201
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