Tracking is one of the most important but still difficult tasks in computer vision and pattern recognition. The main difficulties in the tracking field are appearance variation and occlusion. Most traditional tracking methods set the parameters or templates to track target objects in advance and should be modified accordingly. Thus, we propose a new and robust tracking method using a Fully Conv...

We present a technique for adding global context to fully convolutional networks for semantic segmentation. The approach is simple, using the average feature for a layer to augment the features at each location. In addition, we study several idiosyncrasies of training, significantly increasing the performance of baseline networks (e.g. from FCN Long et al. (2014)). When we add our proposed glob...

After a general overview in a previous paper [10], in which we proposed Didactic Networks (DN) as a new way for developing and exploiting web-learning content, we offer here a deeper study showing how to use them for web-learning design and content generation based on Instructional Theory with the coherence guaranty of the RST [3]. By using a set of expressivity patterns, it is possible to obta...

Automatic labeling of anatomical structures in brain images plays an important role in neuroimaging analysis. Among all methods, multi-atlas based segmentation methods are widely used, due to their robustness in propagating prior label information. However, non-linear registration is always needed, which is time-consuming. Alternatively, the patch-based methods have been proposed to relax the r...

The changing frequency of extreme climate events generally has profound impacts on our living environment and decision-makers. Based on the daily temperature and precipitation data collected from 753 stations in China during 1961–2005, the geographically weighted regression GWR model is used to investigate the relationship between the index of frequency of extreme precipitation FEP and other cl...

Recording and sharing of educational or lecture videos has increased in recent years. Within these recordings, we find a large number math-oriented lectures tutorials which attract students all levels. Many the topics covered by recordings are better explained using handwritten content on whiteboards chalkboards. Hence, numbers that feature instructor writing surface. In this work, propose nove...

Pancreas segmentation in computed tomography imaging has been historically difficult for automated methods because of the large shape and size variations between patients. In this work, we describe a custom-build 3D fully convolutional network (FCN) that can process a 3D image including the whole pancreas and produce an automatic segmentation. We investigate two variations of the 3D FCN archite...

Mass segmentation is an important task in mammogram analysis, providing effective morphological features and regions of interest (ROI) for mass detection and classification. Inspired by the success of using deep convolutional features for natural image analysis and conditional random fields (CRF) for structural learning, we propose an end-to-end network for mammographic mass segmentation. The n...

This paper extends fully-convolutional neural networks (FCN) for the clothing parsing problem. Clothing parsing requires higher-level knowledge on clothing semantics and contextual cues to disambiguate fine-grained categories. We extend FCN architecture with a side-branch network which we refer outfit encoder to predict a consistent set of clothing labels to encourage combinatorial preference, ...

In this study, the iron-based carbon composite (hereafter FCN-x, x = 0, 400, 500, and 600 calcination) was synthesized by a simple high-temperature pyrolysis method using iron-containing sludge coagulant generated from wastewater treatment settling ponds in chemical plants. The FCN-x used for adsorptive reduction of aqueous phase Cr(VI) effectively. characterized scanning electron microscopy (S...