Semantic-Aware Image Analysis

Extracting and utilizing high-level semantic information from images is one of the important goals of computer vision. The ultimate objective of image analysis is to be able to understand each pixel of an image with regard to high-level semantics, e.g. the objects, the stuff, and their spatial, functional and semantic relations. In recent years, thanks to large labeled datasets and deep learning, great progress has been made to solve image analysis problems, such as image classification, object detection, and object pose estimation. In this work, we explore several aspects of semantic-aware image analysis. First, we explore semantic segmentation of man-made scenes using fully connected conditional random fields which can model long-range connections within the image of man-made scenes and make use of contextual information of scene structures. Second, we introduce a semantic smoothing method by exploiting the semantic information to accomplish semantic structure-preserving image smoothing. Semantic segmentation has achieved significant progress recently and has been widely used in many computer vision tasks. We observe that high-level semantic image labeling information can provide a meaningful structure prior to image smoothing naturally. Third, we present a deep object co-segmentation approach for segmenting common objects of the same class within a pair of images. To address this task, we propose a CNN-based Siamese encoder-decoder architecture. The encoder extracts high-level semantic features of the foreground objects, a mutual correlation layer detects the common objects, and finally, the decoder generates the output foreground masks for each image. Finally, we propose an approach to localize common objects from novel object categories in a set of images. We solve this problem using a new common component activation map in which we treat the class-specific activation maps as components to discover the common components in the image set. We show that our approach can generalize on novel object categories in our experiments

Design of a Turbulence Generator of Medium Consistency Pulp Pumps

The turbulence generator is a key component of medium consistency centrifugal pulp pumps, with functions to fluidize the medium consistency pulp and to separate gas from the liquid. Structure sizes of the generator affect the hydraulic performance. The radius and the blade laying angle are two important structural sizes of a turbulence generator. Starting with the research on the flow inside and shearing characteristics of the MC pulp, a simple mathematical model at the flow section of the shearing chamber is built, and the formula and procedure to calculate the radius of the turbulence generator are established. The blade laying angle is referenced from the turbine agitator which has the similar shape with the turbulence generator, and the CFD simulation is applied to study the different flow fields with different blade laying angles. Then the recommended blade laying angle of the turbulence generator is formed to be between 60° and 75°

A Clustering Algorithm to Organize Satellite Hotspot Data for the Purpose of Tracking Bushfires Remotely

This paper proposes a spatiotemporal clustering algorithm and its implementation in the R package spotoroo. This work is motivated by the catastrophic bushfires in Australia throughout the summer of 2019-2020 and made possible by the availability of satellite hotspot data. The algorithm is inspired by two existing spatiotemporal clustering algorithms but makes enhancements to cluster points spatially in conjunction with their movement across consecutive time periods. It also allows for the adjustment of key parameters, if required, for different locations and satellite data sources. Bushfire data from Victoria, Australia, is used to illustrate the algorithm and its use within the package

Performance-based seismic isolation design using the theory of spatially concave friction distribution

Seismic isolation devices were designed to protect three similar building structures, containing different objects with different fragilities, in a strong earthquake region. And a performance-based assessment framework, established by the PEER, was used to identify the seismic isolation efficiency of these devices. It optimized the ratios of spring part, viscous damping part and friction part in the seismic isolation devices, aiming at different functional buildings. Results show that a spatially concave friction distribution, combined with a weak spring, not only can reduce the structural acceleration response during earthquakes, but also decrease the structural residual displacement after earthquakes. Moreover, the spatially concave friction distribution can dissipate earthquake energy, but cannot hinder the recentering of structure like that of general uniform friction distributions. Consequently, the spatially concave friction distribution can partly or fully replace the viscous dampers, which are more expensive and short-lived. The reasonable combination of different components in the seismic isolation devices can satisfy different seismic requirements, aiming at different functional buildings

Theory of the special Smith-Purcell radiation from a rectangular grating

The recently uncovered special Smith-Purcell radiation (S-SPR) from the rectangular grating has significantly higher intensity than the ordinary Smith-Purcell radiation (SPR). Its monochromaticity and directivity are also much better. Here we explored the mechanism of the S-SPR by applying the fundamental electromagnetic theory and simulations. We have confirmed that the S-SPR is exactly from the radiating eigen modes of the grating. Its frequency and direction are well correlated with the beam velocity and structure parameters, which indicates its promising applications in tunable wave generation and beam diagnostic