Abstract.Red eye artifacts are a well-known problem in digital photography. Small compact devices and point-and-click usage, typical of non-professional photography, greatly increase the likelihood for red eyes to appear in acquired images. Automatic detection of red eyes is a very challenging task, due to the variability of the phenomenon and the general difficulty in reliably discerning the shape of eyes. This paper presents a method for discriminating between red eyes and other objects in a set of red eye candidates. The proposed method performs feature-based image analysis and classification just considering the bag-of-keypoints paradigm. Experiments involving different keypoint detectors/descriptors are performed. Achieved results are presented, as well as directions for future work.
Abstract. The proposed paper describes a compression test analysis of JBIG standard algorithm. The aim of such work is to proof the effectiveness of this standard for images acquired through scanners and processed into a printer pipeline. The main issue of printer pipelines is the necessity to use a memory buffer to store scanned images for multiple prints. This work demonstrates that for very large scales the buffer can be fixed using medium compression case, using multiple scans in case of uncommon random patterns.
Since the large diffusion of mobile devices with embedded camera and flashgun, the red eye artifacts have de-facto become a critical problem. Red eyes are caused by the flash light reflected off the blood vessels of the human retina. This effect is more pronounced when the flash light is closer to the camera lens, which often occurs in compact imaging devices. To reduce these artifacts, most cameras have a red-eye flash mode which fires a series of pre-flashes prior picture acquisition. The biggest disadvantage of the pre-flash approach is power consumption (flash is the most power-consuming part of imaging devices), and thus it is not suitable for power-constrained systems (e.g., mobile devices). Moreover, this approach does not guarantee total prevention of red eye artifacts. Red eye removal must then be performed in post-processing, through the use of automatic correction algorithms. The aim of this Chapter is to depict the state of the art of automatic detection and correction of red eyes, taking into account strong points and drawbacks of the most well-known techniques, with particular emphasis on the image degradation risk associated to false positives in red eye detection and to wrong correction of red eyes. Furthermore the problem of estimating the quality of the final result, without reference image, is examined.
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