Abstract
Deep learning has recently gained popularity in the field of video anomaly detection, with the development of various methods for identifying abnormal events in visual data. The growing need for automated systems to monitor video streams for anomalies, such as security breaches and violent behaviours in public areas, requires the development of robust and reliable methods. As a result, there is a need to provide tools to objectively evaluate and compare the real-world performance of different deep learning methods to identify the most effective approach for video anomaly detection. Current state-of-the-art metrics favour weakly-supervised strategies stating these as the best-performing approaches for the task. However, the area under the ROC curve, used to justify this statement, has been shown to be an unreliable metric for highly unbalanced data distributions, as is the case with anomaly detection datasets. This paper provides a new perspective and insights on the performance of video anomaly detection methods. It reports the results of a benchmark study with state-of-the-art methods using a novel proposed framework for evaluating and comparing the different models. The results of this benchmark demonstrate that using the currently employed set of reference metrics led to the misconception that weakly-supervised methods consistently outperform semi-supervised ones. © 2023 The Authors

Abstract

Abstract

Abstract

Abstract
In the context of biometrics, matching confidence refers to the confidence that a given matching decision is correct. Since many biometric systems operate in critical decision-making processes, such as in forensics investigations, accurately and reliably stating the matching confidence becomes of high importance. Previous works on biometric confidence estimation can well differentiate between high and low confidence, but lack interpretability. Therefore, they do not provide accurate probabilistic estimates of the correctness of a decision. In this work, we propose a probabilistic interpretable comparison (PIC) score that accurately reflects the probability that the score originates from samples of the same identity. We prove that the proposed approach provides optimal matching confidence. Contrary to other approaches, it can also optimally combine multiple samples in a joint PIC score which further increases the recognition and confidence estimation performance. In the experiments, the proposed PIC approach is compared against all biometric confidence estimation methods available on four publicly available databases and five state-of-the-art face recognition systems. The results demonstrate that PIC has a significantly more accurate probabilistic interpretation than similar approaches and is highly effective for multi-biometric recognition. The code is publicly-available1. © 2023 IEEE.

Abstract
Background Breast symmetry is an essential component of breast cosmesis. The Harvard Cosmesis scale is the most widely adopted method of breast symmetry assessment. However, this scale lacks reproducibility and reliability, limiting its application in clinical practice. The VECTRA (R) XT 3D (VECTRA (R)) is a novel breast surface imaging system that, when combined with breast contour measuring software (Mirror (R)), aims to produce a more accurate and reproducible measurement of breast contour to aid operative planning in breast surgery. Objectives This study aims to compare the reliability and reproducibility of subjective (Harvard Cosmesis scale) with objective (VECTRA (R)) symmetry assessment on the same cohort of patients. Methods Patients at a tertiary institution had 2D and 3D photographs of their breasts. Seven assessors scored the 2D photographs using the Harvard Cosmesis scale. Two independent assessors used Mirror (R) software to objectively calculate breast symmetry by analysing 3D images of the breasts. Results Intra-observer agreement ranged from none to moderate (kappa - 0.005-0.7) amongst the assessors using the Harvard Cosmesis scale. Inter-observer agreement was weak (kappa 0.078-0.454) amongst Harvard scores compared to VECTRA (R) measurements. Kappa values ranged 0.537-0.674 for intra-observer agreement (p < 0.001) with Root Mean Square (RMS) scores. RMS had a moderate correlation with the Harvard Cosmesis scale (r(s) = 0.613). Furthermore, absolute volume difference between breasts had poor correlation with RMS (R-2 = 0.133). Conclusion VECTRA (R) and Mirror (R) software have potential in clinical practice as objectifying breast symmetry, but in the current form, it is not an ideal test.