Understanding and interpreting classification decisions of automated image classification systems is of high value in many applications, as it allows to verify the reasoning of the system and provides additional information to the human expert. Although machine learning methods are solving very successfully a plethora of tasks, they have in most cases the disadvantage of acting as a black box, ...

We consider two popular, permutation-based, step-down procedures of p-values adjustment in multiple testing problems known as min P and max T and intended for strong control of the family-wise error rate, under the so-called subset pivotality property (SPP). We examine key but subtle issues involved in ascertaining validity of these methods, and also introduce a new, slightly narrower notion of...

PURPOSE To assess within-subject variability of retinal nerve fiber layer (RNFL) and choroidal layer thickness in longitudinal repeat optical coherence tomography (OCT) images with point-to-point measurement comparison made using nonrigid surface registration. METHODS Nine repeat peripapillary OCT images were acquired over 3 weeks from 12 eyes of 6 young, healthy subjects using a 1060-nm prot...

Quality scalability is an important feature of image and video coding systems. In JPEG2000, quality scalability is achieved through the use of quality layers that are formed in the encoder through rate-distortion optimization techniques. Quality layers provide optimal rate-distortion representations of the image when the codestream is transmitted and/or decoded at layer boundaries. Nonetheless,...

How to develop slim and accurate deep neural networks has become crucial for realworld applications, especially for those employed in embedded systems. Though previous work along this research line has shown some promising results, most existing methods either fail to significantly compress a well-trained deep network or require a heavy retraining process for the pruned deep network to re-boost...

A recently developed application of computer vision is pathfinding in self-driving cars. Semantic scene understanding and semantic segmentation, as subfields vision, are widely used autonomous driving. segmentation for uses deep learning methods various large sample datasets to train a proper model. Due the importance this task, accurate robust models should be trained perform properly differen...