The series publishes 50-150 page publications on topics pertaining to computer vision and pattern recognition. The scope will largely follow the purview of premier computer science conferences, such as ICCV, CVPR, and ECCV. Potential topics include, but not are limited to:• Statistical Methods and Learning • Performance Evaluation• Video Analysis and Event Recognition

The series publishes 50-150 page publications on topics pertaining to computer vision and pattern recognition. The scope will largely follow the purview of premier computer science conferences, such as ICCV, CVPR, and ECCV. Potential topics include, but not are limited to:• Statistical Methods and Learning • Performance Evaluation• Video Analysis and Event Recognition

We present a review of the diversity of concepts and motivations for improving the concentration and resolution of timefrequency distributions (TFDs) along the individual components of the multi-component signals. The central idea has been to obtain a distribution that represents the signal's energy concentration simultaneously in time and frequency without blur and crosscomponents so that closely spaced components can be easily distinguished. The objective is the precise description of spectral content of a signal with respect to time, so that first, necessary mathematical and physical principles may be developed, and second, accurate understanding of a time-varying spectrum may become possible. The fundamentals in this area of research have been found developing steadily, with significant advances in the recent past.

No abstract

Multi-organ segmentation is a challenging task due to the label imbalance and structural differences between different organs. In this work, we propose an efficient cascaded V-Net model to improve the performance of multi-organ segmentation by establishing dense Block Level Skip Connections (BLSC) across cascaded V-Net. Our model can take full advantage of features from the first stage network and make the cascaded structure more efficient. We also combine stacked small and large kernels with an inception-like structure to help our model to learn more patterns, which produces superior results for multi-organ segmentation. In addition, some small organs are commonly occluded by large organs and have unclear boundaries with other surrounding tissues, which makes them hard to be segmented. We therefore first locate the small organs through a multi-class network and crop them randomly with the surrounding region, then segment them with a single-class network. We evaluated our model on SegTHOR 2019 challenge unseen testing set and Multi-Atlas Labeling Beyond the Cranial Vault challenge validation set. Our model has achieved an average dice score gain of 1.62 percents and 3.90 percents compared to traditional cascaded networks on these two datasets, respectively. For hard-to-segment small organs, such as the esophagus in SegTHOR 2019 challenge, our technique has achieved a gain of 5.