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Image processing has attracted increasing attention in recent researches to solve domain shift problem where machine learning algorithms are applied to sets of unseen images. Domain shift problem occurs when the training (source domain) and test (target domain) sets are collected in different environmental conditions but in related domains. In this way, the adaptation across data distributions of the source and target datasets are suggested as domain adaptation framework to overcome the performance degradation. In this paper, a novel domain adaptation method referred as joint local and statistical discriminant learning via feature alignment (LSA), is proposed to find a cross-domain subspace by matching cross-domain distribution shift and adapting the class structures of the local and statistical distributions across the source and target domains, during the dimensionality reduction. Specifically, LSA projects samples into an embedded subspace in which the distances across the samples from same class are minimized and the distances across samples from different classes are maximized, at each local and statistical area, during alignment of marginal and conditional distributions. Furthermore, the class densities of samples based on manifold structure in different classes are preserved to provide more separability across various classes. To evaluate the proposed method, comprehensive experiments have been conducted on benchmark cross-domain object and digit recognition datasets. Experimental results have verified the superiority of LSA with a large margin in average classification accuracy against several state-of-the-art distribution matching and discriminant learning methods of domain adaptation. Moreover, the results have demonstrated the effectiveness of our proposed representation learning. Our source code is available at https://github.com/jtahmores/LSA. [ABSTRACT FROM AUTHOR]

Purpose: Face detection is a needed component for the automatic analysis and assistance of human activities during surgical procedures. Efficient face detection algorithms can indeed help to detect and identify the persons present in the room and also be used to automatically anonymize the data. However, current algorithms trained on natural images do not generalize well to the operating room (OR) images. In this work, we provide a comparison of state-of-the-art face detectors on OR data and also present an approach to train a face detector for the OR by exploiting non-annotated OR images. Methods: We propose a comparison of six state-of-the-art face detectors on clinical data using multi-view OR faces, a dataset of OR images capturing real surgical activities. We then propose to use self-supervision, a domain adaptation method, for the task of face detection in the OR. The approach makes use of non-annotated images to fine-tune a state-of-the-art detector for the OR without using any human supervision. Results: The results show that the best model, namely the tiny face detector, yields an average precision of 0.556 at intersection over union of 0.5. Our self-supervised model using non-annotated clinical data outperforms this result by 9.2%. Conclusion: We present the first comparison of state-of-the-art face detectors on OR images and show that results can be significantly improved by using self-supervision on non-annotated data. [ABSTRACT FROM AUTHOR]