Learning boosted asymmetric classifiers for object detection

http://ieeexplore.ieee.orgObject detection can be posted as those classification tasks where the rare positive patterns are to be distinguished from the enormous negative patterns. To avoid the danger of missing positive patterns, more attention should be payed on them. Therefore there should be different requirements for False Reject Rate (FRR) and False Accept Rate (FAR) , and learning a classifier should use an asymmetric factor to balance between FRR and FAR. In this paper, a normalized asymmetric classification error is proposed for the task of rejecting negative patterns. Minimizing it not only controls the ratio of FRR and FAR, but more importantly limits the upper-bound of FRR. The latter characteristic is advantageous for those tasks where there is a requirement for low FRR. Based on this normalized asymmetric classification error, we develop an asymmetric AdaBoost algorithm with variable asymmetric factor and apply it to the learning of cascade classifiers for face detection. Experiments demonstrate that the proposed method achieves less complex classifiers and better performance than some previous AdaBoost methods

Prevalence and Risk Factors of Postprocedure Depression in Patients with Atrial Fibrillation after Radiofrequency Ablation

Background. Recent studies have shown a bidirectional relationship between atrial fibrillation (AF) and psychological depression. However, little is known about the prevalence of postprocedure depression (PPD) in patients with AF at the time of radiofrequency (RF) ablation. Objective. To describe the prevalence and identify risk factors for PPD. Methods. This was a prospective cohort study, including 428 AF patients who were willing to undergo the first catheter ablation in our hospital from 1st April to 30th December 2019. The primary outcome was PPD, which was determined by Hospital Anxiety and Depression Scale-Depression. Results. The prevalence of PPD was 16.8% (72/428) in our cohort, without difference between men (16.0%, 41/256) and women (18.0%, 31/172) (P = 0.586) but with a great difference among different age groups (P = 0.016). On the univariable binary logistic regression analysis, age, a history of coronary heart disease, Observer’s Assessment of Alertness/Sedation (OAA/S) score when ablating at the specific position, and OAA/S score when pulling out the catheter sheath were associated with PPD. Subsequent multivariable logistic regression analysis indicated only age (OR 0.96, 95% CI: 0.94–0.99, P< 0.01) and OAA/S score when ablating at the specific position (OR 0.58, 95% CI: 0.39–0.88, P = 0.01) were independently associated with PPD. Conclusion. PPD is common in patients with AF after RF ablation. Younger age and lower OAA/S score when ablating at the specific position are its most significant risk factors. Intensive management of sedation may be of great importance for reducing PPD. This trial is registered with the Chinese Clinical Trial Registry (ChiCTR2200057810)

Robust 3D Face Recognition in Uncontrolled Environments

Most current 3D face recognition algorithms are designed based on the data collected in controlled situations, which leads to the un-guaranteed performance in practical systems. In this paper, we propose a Robust Local Log-Gabor Histograms (RLLGH) method to handle the uncontrolled problems encountered in 3D face recognition. In this challenging topic, large expressions and data noises are two main obstacles. To overcome the large expressions, we choose Log-Gabor features (LGF) to extract the distinctive and robust information embedded in 3D faces, which will be represented as 3D Log-Gabor faces. Data noises are summarized as distorted meshes, hair occlusions and misalignments. To overcome these problems, we introduce a Robust Local Histogram (RLH) strategy, which takes advantage of the robustness of the accurate local statistical information. The combination of LGF and RLH leads to RLLGH. The novelties of this paper come from 1) Our work aims at studying 3D face recognition performance in uncontrolled environments; 2) We find that embedding LGF into the LVC framework leads to robustness in handling large expression variations; 3) The RLH strategy gives a promising way to solve the data noises problem. Our experiments are based on the large expression subset in FRGC2.0 3D face database and the expression subset in CASIA 3D face database. Experimental results show the efficiency, robustness and generalization of our proposed method. 1