151 research outputs found
Inner and Inter Label Propagation: Salient Object Detection in the Wild
In this paper, we propose a novel label propagation based method for saliency
detection. A key observation is that saliency in an image can be estimated by
propagating the labels extracted from the most certain background and object
regions. For most natural images, some boundary superpixels serve as the
background labels and the saliency of other superpixels are determined by
ranking their similarities to the boundary labels based on an inner propagation
scheme. For images of complex scenes, we further deploy a 3-cue-center-biased
objectness measure to pick out and propagate foreground labels. A
co-transduction algorithm is devised to fuse both boundary and objectness
labels based on an inter propagation scheme. The compactness criterion decides
whether the incorporation of objectness labels is necessary, thus greatly
enhancing computational efficiency. Results on five benchmark datasets with
pixel-wise accurate annotations show that the proposed method achieves superior
performance compared with the newest state-of-the-arts in terms of different
evaluation metrics.Comment: The full version of the TIP 2015 publicatio
Post-processing Procedures for Passive GPS based Travel Survey
AbstractA challenge in posteriori data processing for passive GPS based travel survey, which constitute the heart of this paper, is to develop a series of methods to automatically restore the sequences of data points, both in space and time. It means the trips and activities occurred in the survey time should be identifiable chronologically and those identified by the program should respect this definition convention. Reference to the research results of our colleagues, and by combining the experiences of other French travel survey and personal mobility survey at Lille, a series of methods has been developed and put into application. The data outcome is ready for further applications
AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time
Accurate whole-body multi-person pose estimation and tracking is an important
yet challenging topic in computer vision. To capture the subtle actions of
humans for complex behavior analysis, whole-body pose estimation including the
face, body, hand and foot is essential over conventional body-only pose
estimation. In this paper, we present AlphaPose, a system that can perform
accurate whole-body pose estimation and tracking jointly while running in
realtime. To this end, we propose several new techniques: Symmetric Integral
Keypoint Regression (SIKR) for fast and fine localization, Parametric Pose
Non-Maximum-Suppression (P-NMS) for eliminating redundant human detections and
Pose Aware Identity Embedding for jointly pose estimation and tracking. During
training, we resort to Part-Guided Proposal Generator (PGPG) and multi-domain
knowledge distillation to further improve the accuracy. Our method is able to
localize whole-body keypoints accurately and tracks humans simultaneously given
inaccurate bounding boxes and redundant detections. We show a significant
improvement over current state-of-the-art methods in both speed and accuracy on
COCO-wholebody, COCO, PoseTrack, and our proposed Halpe-FullBody pose
estimation dataset. Our model, source codes and dataset are made publicly
available at https://github.com/MVIG-SJTU/AlphaPose.Comment: Documents for AlphaPose, accepted to TPAM
Assessing the causality between thyroid and breast neoplasms: A bidirectional Mendelian randomization study
AimThis study aimed to evaluate the association between thyroid neoplasms (TN) and the risk of developing breast neoplasms (BN) by assessing data on single nucleotide polymorphisms (SNPs) obtained from the Deutsches Krebsforschungszentrum (DKFZ) and Breast Cancer Association (BCAC).MethodsData on SNPs associated with TN and BN were obtained from DKFZ and BCAC, respectively. Secondary data analysis of all pooled data from genome-wide association studies (GWAS) was performed to identify the genetic loci closely associated with TN or BN as instrumental variables (IVs). To evaluate the causal relationship between TN and BN, a bidirectional Mendelian randomization (MR) analysis was performed using MR Egger regression, weighted median, inverse variance weighted (IVW) random effects model, simple mode, weighted mode, maximum likelihood, penalized weighted median, IVW radial, IVW fixed effects, and robust adjusted profile scores (RAPS) method.ResultsThe MR in this study demonstrated a modest reverse causal relationship between TN and BN but a significant positive causal relationship between BN and TN.ConclusionsThe MR of this study provided genetic evidence suggesting an association between BN and TN; however, further research is warranted to explore the potential mechanism of interaction between these two malignancies. Moreover, general breast screening should be performed in individuals with TN, but TN screening should be reinforced in individuals with BN
Optimized Control Strategy for Photovoltaic Hydrogen Generation System with Particle Swarm Algorithm
Distributed generation is a vital component of the national economic sustainable development strategy and environmental protection, and also the inevitable way to optimize energy structure and promote energy diversification. The power generated by renewable energy is unstable, which easily causes voltage and frequency fluctuations and power quality problems. An adaptive online adjustment particle swarm optimization (AOA-PSO) algorithm for system optimization is proposed to solve the technical issues of large-scale wind and light abandonment. Firstly, a linear adjustment factor is introduced into the particle swarm optimization (PSO) algorithm to adaptively adjust the search range of the maximum power point voltage when the environment changes. In addition, the maximum power point tracking method of the photovoltaic generator set with direct duty cycle control is put forward based on the basic PSO algorithm. Secondly, the concept of recognition is introduced. The particles with strong recognition ability directly enter the next iteration, ensuring the search accuracy and speed of the PSO algorithm in the later stage. Finally, the effectiveness of the AOA-PSO algorithm is verified by simulation and compared with the traditional control algorithm. The results demonstrate that the method is effective. The system successfully tracks the maximum power point within 0.89 s, 1.2 s faster than the traditional perturbation and observation method (TPOM), and 0.8 s faster than the incremental admittance method (IAM). The average maximum power point is 274.73 W, which is 98.87 W higher than the TPOM and 109.98 W more elevated than the IAM. Besides, the power oscillation range near the maximum power point is small, and the power loss is slight. The method reported here provides some guidance for the practical development of the system
- …