We present an efficient method for detecting and localizing anomalies in videos showing crowded scenes. Research on fully convolutional neural networks (FCNs) has shown the potentials of this technology for object detection and localization, especially in images. We investigate how to involve temporal data, and how to transform a supervised FCN into an unsupervised one such that the resulting F...

Mass segmentation provides effective morphological features which are important for mass diagnosis. In this work, we propose a novel end-to-end network for mammographic mass segmentation which employs a fully convolutional network (FCN) to model a potential function, followed by a CRF to perform structured learning. Because the mass distribution varies greatly with pixel position, the FCN is co...

Existing region-based object detectors are limited to regions with fixed box geometry to represent objects, even if those are highly non-rectangular. In this paper we introduce DP-FCN, a deep model for object detection which explicitly adapts to shapes of objects with deformable parts. Without additional annotations, it learns to focus on discriminative elements and to align them, and simultane...

Fully Convolution Networks (FCN) have achieved great success in dense prediction tasks including semantic segmentation. In this paper, we start from discussing FCN by understanding its architecture limitations in building a strong segmentation network. Next, we present our Improved Fully Convolution Network (IFCN). In contrast to FCN, IFCN introduces a context network that progressively expands...

Fully convolutional networks (FCNs) have been proven very successful for semantic segmentation, but the FCN outputs are unaware of object instances. In this paper, we develop FCNs that are capable of proposing instance-level segment candidates. In contrast to the previous FCN that generates one score map, our FCN is designed to compute a small set of instance-sensitive score maps, each of which...

Context. The free core nutation (FCN) can be observed by its associated resonance effects on the forced nutations of the Earth’s figure axis, as observed by very long baseline interferometry (VLBI), or on the diurnal tidal waves, retrieved from the time-varying surface gravity recorded by superconducting gravimeters (SG). Aims. In this paper, we study the sensitivity of both techniques to the F...

Abstract. From the analysis of VLBI observational data compiled by USNO (U.S. Naval Observatory) from MJD 44089.994 to 51618.250 (McCarthy, 2000), we showed that a strong peak around 400 sidereal days in the spectrum of its di erences from the IERS96 nutation theory could be explained by adding a model Free Core Nutation (FCN) term in the form of a single damped oscillation. Then we developed a...

The greenhouse whitefly, Trialeurodes vaporariorum Westwood, is the most common and abundant whitefly in Argentine horticultural greenhouse crops, especially in tomato (Solanum lycopersicum). Resistance in some wild tomato relatives, such as S. peruvianum, S. habrochaites and S. pennellii to the greenhouse whitefly has been described. The Mi gene confers effective resistance against several spe...

In recent years, Fully Convolutional Networks (FCN) have led to a great improvement of semantic labeling for various applications including multi-modal remote sensing data. Although different fusion strategies have been reported for multi-modal data, there is no in-depth study of the reasons of performance limits. For example, it is unclear, why an early fusion of multi-modal data in FCN does n...

Inspired by enormous success of fully convolutional network (FCN) in semantic segmentation, as well as the similarity between semantic segmentation and pixel-by-pixel polarimetric synthetic aperture radar (PolSAR) image classification, exploring how to effectively combine the unique polarimetric properties with FCN is a promising attempt at PolSAR image classification. Moreover, recent research...