How can a single fully convolutional neural network (FCN) perform on object detection? We introduce DenseBox, a unified end-to-end FCN framework that directly predicts bounding boxes and object class confidences through all locations and scales of an image. Our contribution is two-fold. First, we show that a single FCN, if designed and optimized carefully, can detect multiple different objects ...

Three most long and dense VLBI nutation series obtained at the Goddard Space Flight Center, Institute of Applied Astronomy, and U.S. Naval Observatory were used for investigation of the Free Core Nutation (FCN) contribution to the celestial pole offset. Some recent studies have showed that the FCN period or/and phase does not remain constant, but varies in a rather wide range of about 410–490 d...

In this paper, we study the influence of both long and short skip connections on Fully Convolutional Networks (FCN) for biomedical image segmentation. In standard FCNs, only long skip connections are used to skip features from the contracting path to the expanding path in order to recover spatial information lost during downsampling. We extend FCNs by adding short skip connections, that are sim...

Semantic Segmentation using deep convolutional neural network pose more complex challenge for any GPU intensive work, as it has to compute million of parameters resulting to huge consumption of memory. Moreover, extracting finer features and conducting supervised training tends to increase the complexity furthermore. With the introduction of Fully Convolutional Neural Network, which uses finer ...

The sympathetic nervous system (SNS) drive on white adipose tissue (WAT) was varied to test its effects on fat cell number (FCN) under conditions in which lipolysis would be minimized and therefore partially separable from SNS trophic effects. The inguinal subcutaneous WAT (IWAT) pad of Siberian hamsters was chosen because 1) it is innervated by the SNS, 2) short day (SD) exposure increases its...

Soybean is an important crop, so it very to forecast soybean price trend, which can stabilize the market. This paper presents a Synthesis Method with Multistage Model (SMwMM) in order identify and trend China. In previous work,Toeplitz Inverse Covariance-based Clustering(TICC) has been applied cluster prices of four variables. The research have found that there are patterns market price, could ...

Diagnosing and quantifying plant-parasitic nematodes is critical for efficient nematode management. Several studies have been performed intending to demonstrate nematode quantification via real-time quantitative PCR. However, most of the studies used dilution of DNA templates to make standard curves, while few studies used samples with different nematode numbers to make the standard curve, resu...

Convolutional neural networks (CNNs) have demonstrated their ability object detection of very high resolution remote sensing images. However, CNNs have obvious limitations for modeling geometric variations in remote sensing targets. In this paper, we introduced a CNN structure, namely deformable ConvNet, to address geometric modeling in object recognition. By adding offsets to the convolution l...

We propose a model with two-stage process for abdominal segmentation on CT volumes. First, in order to capture the details of organs, a full convolution-deconvolution network (FCN-DecNet) is constructed with multiple new unpooling, deconvolutional, and fusion layers. Then, we optimize the coarse segmentation results of FCN-DecNet by multiscale weights probabilistic atlas (MS-PA), which uses spa...

Over the past decade, multivariate time series classification has been receiving a lot of attention. We propose augmenting the existing univariate time series classification models, LSTM-FCN and ALSTM-FCN with a squeeze and excitation block to further improve performance. Our proposed models outperform most of the state of the art models while requiring minimum preprocessing. The proposed model...