SEMANTIC IMAGE SEGMENTATION VIA A DENSE PARALLEL NETWORK

Image segmentation has been an important area of study in computer vision. Image segmentation is a challenging task, since it involves pixel-wise annotation, i.e. labeling each pixel according to the class to which it belongs. In image classification task, the goal is to predict to which class an entire image belongs. Thus, there is more focus on the abstract features extracted by Convolutional Neural Networks (CNNs), with less emphasis on the spatial information. In image segmentation task, on the other hand, the abstract information and spatial information are needed at the same time. One class of work in image segmentation focuses on ``recovering” the high-resolution features from the low resolution ones. This type of network has an encoder-decoder structure, and spatial information is recovered by feeding the decoder part of the model with previous high-resolution features through skip connections. Overall, these strategies involving skip connections try to propagate features to deeper layers. The second class of work, on the other hand, focuses on ``maintaining high resolution features throughout the process. In this thesis, we first review the related work on image segmentation and then introduce two new models, namely Unet-Laplacian and Dense Parallel Network (DensePN). The Unet-Laplacian is a series CNN model, incorporating a Laplacian filter branch. This new branch performs Laplacian filter operation on the input RGB image, and feeds the output to the decoder. Experiments results show that, the output of the Unet-Laplacian captures more of the ground truth mask, and eliminates some of the false positives. We then describe the proposed DensePN, which was designed to find a good balance between extracting features through multiple layers and keeping spatial information. DensePN allows not only keeping high-resolution feature maps but also feature reuse at deeper layers to solve the image segmentation problem. We have designed the Dense Parallel Network based on three main observations that we have gained from our initial trials and preliminary studies. First, maintaining a high resolution feature map provides good performance. Second, feature reuse is very efficient, and allows having deeper networks. Third, having a parallel structure can provide better information flow. Experimental results on the CamVid dataset show that the proposed DensePN (with 1.1M parameters) provides a better performance than FCDense56 (with 1.5M parameters) by having less parameters at the same time

Impact damage behavior of lightweight CFRP protection suspender on railway vehicles

The aim of the paper is to evaluate the impact damage behavior of a carbon fiber reinforced polymers (CFRP) protection suspender, a component on the railway vehicles that can prevent the falling joist and bolster from touching the rails and to avoid the derailment of trains. A three-dimensional impact model of CFRP protection suspender which considers the bolt preloads was established in ABAQUS/Explicit

Invited Article: High-pressure techniques for condensed matter physics at low temperature

Condensed matter experiments at high pressure accentuate the need for accurate pressure scales over a broad range of temperatures, as well as placing a premium on a homogeneous pressure environment. However, challenges remain in diamond anvil cell technology, including both the quality of various pressure transmitting media and the accuracy of secondary pressure scales at low temperature. We directly calibrate the ruby fluorescence R1 line shift with pressure at T=4.5 K using high-resolution x-ray powder diffraction measurements of the silver lattice constant and its known equation of state up to P=16 GPa. Our results reveal a ruby pressure scale at low temperatures that differs by 6% from the best available ruby scale at room T. We also use ruby fluorescence to characterize the pressure inhomogeneity and anisotropy in two representative and commonly used pressure media, helium and methanol:ethanol 4:1, under the same preparation conditions for pressures up to 20 GPa at T=5 K. Contrary to the accepted wisdom, both media show equal levels of pressure inhomogeneity measured over the same area, with a consistent Delta P/P per unit area of +/- 1.8 %/(10^(4) µm^(2)) from 0 to 20 GPa. The helium medium shows an essentially constant deviatoric stress of 0.021 +/- 0.011 GPa up to 16 GPa, while the methanol:ethanol mixture shows a similar level of anisotropy up to 10 GPa, above which the anisotropy increases. The quality of both pressure media is further examined under the more stringent requirements of single crystal x-ray diffraction at cryogenic temperature. For such experiments we conclude that the ratio of sample-to-pressure chamber volume is a critical parameter in maintaining sample quality at high pressure, and may affect the choice of pressure medium