An Alarm System For Segmentation Algorithm Based On Shape Model

It is usually hard for a learning system to predict correctly on rare events that never occur in the training data, and there is no exception for segmentation algorithms. Meanwhile, manual inspection of each case to locate the failures becomes infeasible due to the trend of large data scale and limited human resource. Therefore, we build an alarm system that will set off alerts when the segmentation result is possibly unsatisfactory, assuming no corresponding ground truth mask is provided. One plausible solution is to project the segmentation results into a low dimensional feature space; then learn classifiers/regressors to predict their qualities. Motivated by this, in this paper, we learn a feature space using the shape information which is a strong prior shared among different datasets and robust to the appearance variation of input data.The shape feature is captured using a Variational Auto-Encoder (VAE) network that trained with only the ground truth masks. During testing, the segmentation results with bad shapes shall not fit the shape prior well, resulting in large loss values. Thus, the VAE is able to evaluate the quality of segmentation result on unseen data, without using ground truth. Finally, we learn a regressor in the one-dimensional feature space to predict the qualities of segmentation results. Our alarm system is evaluated on several recent state-of-art segmentation algorithms for 3D medical segmentation tasks. Compared with other standard quality assessment methods, our system consistently provides more reliable prediction on the qualities of segmentation results.Comment: Accepted to ICCV 2019 (10 pages, 4 figures

Solving multiple-criteria R&D project selection problems with a data-driven evidential reasoning rule

In this paper, a likelihood based evidence acquisition approach is proposed to acquire evidence from experts'assessments as recorded in historical datasets. Then a data-driven evidential reasoning rule based model is introduced to R&D project selection process by combining multiple pieces of evidence with different weights and reliabilities. As a result, the total belief degrees and the overall performance can be generated for ranking and selecting projects. Finally, a case study on the R&D project selection for the National Science Foundation of China is conducted to show the effectiveness of the proposed model. The data-driven evidential reasoning rule based model for project evaluation and selection (1) utilizes experimental data to represent experts' assessments by using belief distributions over the set of final funding outcomes, and through this historic statistics it helps experts and applicants to understand the funding probability to a given assessment grade, (2) implies the mapping relationships between the evaluation grades and the final funding outcomes by using historical data, and (3) provides a way to make fair decisions by taking experts' reliabilities into account. In the data-driven evidential reasoning rule based model, experts play different roles in accordance with their reliabilities which are determined by their previous review track records, and the selection process is made interpretable and fairer. The newly proposed model reduces the time-consuming panel review work for both managers and experts, and significantly improves the efficiency and quality of project selection process. Although the model is demonstrated for project selection in the NSFC, it can be generalized to other funding agencies or industries.Comment: 20 pages, forthcoming in International Journal of Project Management (2019

Insignificant shadow detection for video segmentation

To prevent moving cast shadows from being misunderstood as part of moving objects in change detection based video segmentation, this paper proposes a novel approach to the cast shadow detection based on the edge and region information in multiple frames. First, an initial change detection mask containing moving objects and cast shadows is obtained. Then a Canny edge map is generated. After that, the shadow region is detected and removed through multiframe integration, edge matching, and region growing. Finally, a post processing procedure is used to eliminate noise and tune the boundaries of the objects. Our approach can be used for video segmentation in indoor environment. The experimental results demonstrate its good performance

Numerical study on the aerodynamic noise characteristics of CRH2 high-speed trains

The aerodynamic noise of high-speed trains not only causes interior noise pollution and reduces the comfort of passengers, but also seriously affects the normal life of residents. With the increase of running speed of trains, aerodynamic noises will be more than wheel-rail noises and become the main noise source of high-speed trains. This paper established a computational model for the aerodynamic noise of a CRH2 high-speed train with 3-train formation including 3 train bodies and 6 bogies, adopted the detached eddy simulation (DES) to conduct numerical simulation for the flow field around the high-speed train, applied Ffowcs Williams-Hawkings acoustic model to conduct unsteady computation for the aerodynamic noise of high-speed trains, and analyzed the far-field aerodynamic noise characteristics of high-speed trains. Studied results showed: The main energy of the complete train was mainly within the range of 613 Hz-2500 Hz when the high-speed train ran at the speed of 350 km/h. In the whole frequency domain, it was a broadband noise. Regarding the longitudinal observation point which was 25 m away from the center line of track and 6m away from the nose tip of head train, the sound pressure level of total noises reached the maximum value 88.9 dBA. The maximum sound pressure level of the noise observation point which was 7.5 m away from the center line of track was around the first bogie of head train. Various components made different contributions to the aerodynamic noise of the complete train, and the order was head train, mid train, bogie system (6 bogies) and tail train. The first bogie of head train made the greatest contribution to bogie system and was the main aerodynamic noise source of the complete train