Human pose estimation based on parallel atrous convolution and body structure constraints

Human pose estimation is still a challenging task in computer vision, especially in the case of camera view transformation, joints occlusions and overlapping, the task will be of ever-increasing difficulty to achieve success. Most existing methods pass the input through a network, which typically consists of high-to-low resolution sub-networks that are connected in series. Still, during the up-sampling process, the spatial relationships and details might be lost. This paper designs a parallel atrous convolutional network with body structure constraints (PAC-BCNet) to address the problem. Among the mentioned techniques, the parallel atrous convolution (PAC) is constructed to deal with scale changes by connecting multiple different atrous convolution sub-networks in parallel. And it is used to extract features from different scales without reducing the resolution. Besides, the body structure constraints (BC), which enhance the correlation between each keypoint, are constructed to obtain better spatial relationships of the body by designing keypoints constraints sets and improving the loss function. In this work, a comparative experiment of the serial atrous convolution, the parallel atrous convolution, the ablation study with and without body structure constraints are conducted, which reasonably proves the effectiveness of the approach. The model is evaluated on two widely used human pose estimation benchmarks (MPII and LSP). The method achieves better performance on both datasets.

Electrocardio Panorama: Synthesizing New ECG views with Self-supervision

Multi-lead electrocardiogram (ECG) provides clinical information of heartbeats from several fixed viewpoints determined by the lead positioning. However, it is often not satisfactory to visualize ECG signals in these fixed and limited views, as some clinically useful information is represented only from a few specific ECG viewpoints. For the first time, we propose a new concept, Electrocardio Panorama, which allows visualizing ECG signals from any queried viewpoints. To build Electrocardio Panorama, we assume that an underlying electrocardio field exists, representing locations, magnitudes, and directions of ECG signals. We present a Neural electrocardio field Network (Nef-Net), which first predicts the electrocardio field representation by using a sparse set of one or few input ECG views and then synthesizes Electrocardio Panorama based on the predicted representations. Specially, to better disentangle electrocardio field information from viewpoint biases, a new Angular Encoding is proposed to process viewpoint angles. Also, we propose a self-supervised learning approach called Standin Learning, which helps model the electrocardio field without direct supervision. Further, with very few modifications, Nef-Net can synthesize ECG signals from scratch. Experiments verify that our Nef-Net performs well on Electrocardio Panorama synthesis, and outperforms the previous work on the auxiliary tasks (ECG view transformation and ECG synthesis from scratch). The codes and the division labels of cardiac cycles and ECG deflections on Tianchi ECG and PTB datasets are available at https://github.com/WhatAShot/Electrocardio-Panorama.

A Petri Nets Evolution Method that Supports BPMN Model Changes

The correctness of the business process modelling notation (BPMN) is essential for software success, and the BPMN formalization is the foundation of the correctness verification process. However, dynamically adapting the formalized BPMN model to changes in the BPMN model and protecting tokens from being lost in the remapping formalization are the main limitations of the BPMN formalization under changing business requirements. To overcome these limitations, an approach for evolving a Petri nets model according to the BPMN changes is proposed in this paper. In this approach, a check algorithm is designed to identify the differences between the original BPMN model and the updated BPMN model. Then, the evolution rules of the extended Petri nets (EPN) model are defined according to the results of the checking program. Finally, these evolution rules are described in the query/view/transformation operational mapping (QVTo) language and implemented in the Eclipse platform. We demonstrate the effectiveness of the evolution of the BPMN formalization using a case study of the Web Payment business system. Moreover, the dynamic evolution of the BPMN formalization can maintain the consistency between the original model and the updated model, and this consistency has been successfully verified.