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

LoopGaussian:Creating 3D Cinemagraph with Multi-view Images via Eulerian Motion Field

Cinemagraph is a unique form of visual media that combines elements of still photography and subtle motion to create a captivating experience. However, the majority of videos generated by recent works lack depth information and are confined to the constraints of 2D image space. In this paper, inspired by significant progress in the field of novel view synthesis (NVS) achieved by 3D Gaussian Splatting (3D-GS), we propose LoopGaussian to elevate cinemagraph from 2D image space to 3D space using 3D Gaussian modeling. To achieve this, we first employ the 3D-GS method to reconstruct 3D Gaussian point clouds from multi-view images of static scenes,incorporating shape regularization terms to prevent blurring or artifacts caused by object deformation. We then adopt an autoencoder tailored for 3D Gaussian to project it into feature space. To maintain the local continuity of the scene, we devise SuperGaussian for clustering based on the acquired features. By calculating the similarity between clusters and employing a two-stage estimation method, we derive an Eulerian motion field to describe velocities across the entire scene. The 3D Gaussian points then move within the estimated Eulerian motion field. Through bidirectional animation techniques, we ultimately generate a 3D Cinemagraph that exhibits natural and seamlessly loopable dynamics. Experiment results validate the effectiveness of our approach, demonstrating high-quality and visually appealing scene generation. The project is available at https://pokerlishao.github.io/LoopGaussian/

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