The iris is currently accepted as one of the most accurate traits for biometric purposes. However, for the sake of accuracy, iris recognition systems rely on good quality images and significantly deteriorate their results when images contain large noisy regions, either due to iris obstructions (eyelids or eyelashes) or reflections (specular or lighting). In this paper we propose an entropy-base...

Edge detection is a challenging, important task in image analysis. Various applications require real-time detection of long edges in large and noisy images, possibly under limited computational resources. While standard edge detection methods are computationally fast, they perform well only at low levels of noise. Modern sophisticated methods, in contrast, are robust to noise, but may be too sl...

-In this paper, we present a wavelet based edge detection technique. Edge detection is an important step in pattern recognition, image segmentation, and scene analysis. The conventional approaches to edge detection fail in presence of noise in images and may cause problems in many applications. But noise is very effectively reduced by wavelet filters without any significant loss in the image re...

This paper presents an algorithm for image registration and mosaicing on underwater sonar image sequences characterized by a high noise level, inhomogeneous illumination and low frame rate. Imaging geometry of acoustic cameras is significantly different from that of pinhole cameras. For a planar surface viewed through a pinhole camera undergoing translational and rotational motion, registration...

In this paper, a morphological-based system for detecting edges in reallife images is presented. The corner stone for this system is the hit-miss transform, which provides good performance in reallife images under noise conditions. The classical implementation of this transform suffers from drawbacks that are tackled in this paper. The new modified hit-miss transform is introduced to provide be...

The presence of noise represent a relevant issue in image feature extraction and classification. In deep learning, representation is learned directly from the data and, therefore, the classification model is influenced by the quality of the input. However, the ability of deep convolutional neural networks to deal with images that have a different quality when compare to those used to train the ...

Reconstruction of surfaces from images alone is usually diicult due to noise. Prior information such as smoothness, shape, size etc. may however be available. The Bayesian framework makes it possible for a formal integration of such prior information and the observed data. Furthermore, the development of the Markov Chain Monte Carlo method makes it possible for simulation from the posterior of ...

Some new techniques are proposed for estimating the quality of a noisy image of a natural scene. Analytical justifications are given which explain why these techniques work. Experimental results are provided which indicate that the techniques work well in practice. These techniques need only the images to be evaluated and do not use detailed information about the formation of the image. The foc...

bstract. We describe a method for automatically detecting treaks in printed images using adaptive window-based image proections and mutual information. The proposed approach accepts a canned image enclosing the defect and computes the projections cross the entire image at different window sizes. The resulting races collected from the projections are analyzed with a peak deection algorithm and s...