Exploiting Multiple Detections for Person Re-Identification

Re-identification systems aim at recognizing the same individuals in multiple cameras, and one of the most relevant problems is that the appearance of same individual varies across cameras due to illumination and viewpoint changes. This paper proposes the use of cumulative weighted brightness transfer functions (CWBTFs) to model these appearance variations. Different from recently proposed methods which only consider pairs of images to learn a brightness transfer function, we exploit such a multiple-frame-based learning approach that leverages consecutive detections of each individual to transfer the appearance. We first present a CWBTF framework for the task of transforming appearance from one camera to another. We then present a re-identification framework where we segment the pedestrian images into meaningful parts and extract features from such parts, as well as from the whole body. Jointly, both of these frameworks contribute to model the appearance variations more robustly. We tested our approach on standard multi-camera surveillance datasets, showing consistent and significant improvements over existing methods on three different datasets without any other additional cost. Our approach is general and can be applied to any appearance-based metho

Multi-descriptor random sampling for patch-based face recognition

While there has been a massive increase in research into face recognition, it remains a challenging problem due to conditions present in real life. This paper focuses on the inherently present issue of partial occlusion distortions in real face recognition applications. We propose an approach to tackle this problem. First, face images are divided into multiple patches before local descriptors of Local Binary Patterns and Histograms of Oriented Gradients are applied on each patch. Next, the resulting histograms are concatenated, and their dimensionality is then reduced using Kernel Principle Component Analysis. Once completed, patches are randomly selected using the concept of random sampling to finally construct several sub-Support Vector Machine classifiers. The results obtained from these sub-classifiers are combined to generate the final recognition outcome. Experimental results based on the AR face database and the Extended Yale B database show the effectiveness of our proposed technique

Automated Detection and Segmentation of Nonmass-Enhancing Breast Tumors with Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Nonmass-enhancing (NME) lesions constitute a diagnostic challenge in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast. Computer-aided diagnosis (CAD) systems provide physicians with advanced tools for analysis, assessment, and evaluation that have a significant impact on the diagnostic performance. Here, we propose a new approach to address the challenge of NME lesion detection and segmentation, taking advantage of independent component analysis (ICA) to extract data-driven dynamic lesion characterizations. A set of independent sources was obtained from the DCE-MRI dataset of breast cancer patients, and the dynamic behavior of the different tissues was described by multiple dynamic curves, together with a set of eigenimages describing the scores for each voxel. A new test image is projected onto the independent source space using the unmixing matrix, and each voxel is classified by a support vector machine (SVM) that has already been trained with manually delineated data. A solution to the high false-positive rate problem is proposed by controlling the SVM hyperplane location, outperforming previously published approaches.European Unions Horizon 2020 Research and Innovation Programme under the Marie Skodowska-Curie grant agreement No. 656886Austrian National Bank "Jubilaeumsfond" Project 162192020-Research and Innovation Framework Programme PHC-11-2015 667211-2Siemens AustriaNovomedGuerbet, FranceNIH/NCI Cancer Center Support Grant P30CA00874

Blood Oxygen Level-Dependent (BOLD) MRI in Glomerular Disease

Renal hypoxia has recently been implicated as a key contributor and indicator of various glomerular diseases. As such, monitoring changes in renal oxygenation in these disorders may provide an early diagnostic advantage that could prevent potential adverse outcomes. Blood oxygen level-dependent magnetic resonance imaging (BOLD MRI) is an emerging noninvasive technique for assessing renal oxygenation in glomerular disease. Although BOLD MRI has produced promising initial results for the use in certain renal pathologies, the use of BOLD imaging in glomerular diseases, including primary and secondary nephrotic and nephritic syndromes, is relatively unexplored. Early BOLD studies on primary nephrotic syndrome, nephrotic syndrome secondary to diabetes mellitus, and nephritic syndrome secondary to systemic lupus erythematosus have shown promising results to support its future clinical utility. In this review, we outline the advancements made in understanding the use of BOLD MRI for the assessment, diagnosis, and screening of these pathologies

Outlier detection and robust normal-curvature estimation in mobile laser scanning 3D point cloud data

This paper proposes two robust statistical techniques for outlier detection and robust saliency features, such as surface normal and curvature, estimation in laser scanning 3D point cloud data. One is based on a robust z-score and the other uses a Mahalanobis type robust distance. The methods couple the ideas of point to plane orthogonal distance and local surface point consistency to get Maximum Consistency with Minimum Distance (MCMD). The methods estimate the best-fit-plane based on most probable outlier free, and most consistent, points set in a local neighbourhood. Then the normal and curvature from the best-fit-plane will be highly robust to noise and outliers. Experiments are performed to show the performance of the algorithms compared to several existing well-known methods (from computer vision, data mining, machine learning and statistics) using synthetic and real laser scanning datasets of complex (planar and non-planar) objects. Results for plane fitting, denoising, sharp feature preserving and segmentation are significantly improved. The algorithms are demonstrated to be significantly faster, more accurate and robust. Quantitatively, for a sample size of 50 with 20% outliers the proposed MCMD_Z is approximately 5, 15 and 98 times faster than the existing methods: uLSIF, RANSAC and RPCA, respectively. The proposed MCMD_MD method can tolerate 75% clustered outliers, whereas, RPCA and RANSAC can only tolerate 47% and 64% outliers, respectively. In terms of outlier detection, for the same dataset, MCMD_Z has an accuracy of 99.72%, 0.4% false positive rate and 0% false negative rate; for RPCA, RANSAC and uLSIF, the accuracies are 97.05%, 47.06% and 94.54%, respectively, and they have misclassification rates higher than the proposed methods. The new methods have potential for local surface reconstruction, fitting, and other point cloud processing tasks

Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies

Urban environments are regions of complex and diverse architecture. Their reconstruction and representation as three-dimensional city models have attracted the attention of many researchers and industry specialists, as they increasingly recognise the potential for new applications requiring detailed building models. Nevertheless, despite being investigated for a few decades, the comprehensive reconstruction of buildings remains a challenging task. While there is a considerable body of literature on this topic, including several systematic reviews summarising ways of acquiring and reconstructing coarse building structures, there is a paucity of in-depth research on the detection and reconstruction of façade openings (i.e., windows and doors). In this review, we provide an overview of emerging applications, data acquisition and processing techniques for building façade reconstruction, emphasising building opening detection. The use of traditional technologies from terrestrial and aerial platforms, along with emerging approaches, such as mobile phones and volunteered geography information, is discussed. The current status of approaches for opening detection is then examined in detail, separated into methods for three-dimensional and two-dimensional data. Based on the review, it is clear that a key limitation associated with façade reconstruction is process automation and the need for user intervention. Another limitation is the incompleteness of the data due to occlusion, which can be reduced by data fusion. In addition, the lack of available diverse benchmark datasets and further investigation into deep-learning methods for façade openings extraction present crucial opportunities for future research

Automatic Super-Surface Removal in Complex 3D Indoor Environments Using Iterative Region-Based RANSAC

Removing bounding surfaces such as walls, windows, curtains, and floor (i.e., super-surfaces) from a point cloud is a common task in a wide variety of computer vision applications (e.g., object recognition and human tracking). Popular plane segmentation methods such as Random Sample Consensus (RANSAC), are widely used to segment and remove surfaces from a point cloud. However, these estimators easily result in the incorrect association of foreground points to background bounding surfaces because of the stochasticity of randomly sampling, and the limited scene-specific knowledge used by these approaches. Additionally, identical approaches are generally used to detect bounding surfaces and surfaces that belong to foreground objects. Detecting and removing bounding surfaces in challenging (i.e., cluttered and dynamic) real-world scene can easily result in the erroneous removal of points belonging to desired foreground objects such as human bodies. To address these challenges, we introduce a novel super-surface removal technique for 3D complex indoor environments. Our method was developed to work with unorganized data captured from commercial depth sensors and supports varied sensor perspectives. We begin with preprocessing steps and dividing the input point cloud into four overlapped local regions. Then, we apply an iterative surface removal approach to all four regions to segment and remove the bounding surfaces. We evaluate the performance of our proposed method in terms of four conventional metrics: specificity, precision, recall, and F1 score, on three generated datasets representing different indoor environments. Our experimental results demonstrate that our proposed method is a robust super-surface removal and size reduction approach for complex 3D indoor environments while scoring the four evaluation metrics between 90% and 99%

Jeremy Bentham and Australia

Jeremy Bentham and Australia is a collection of scholarship inspired by Bentham’s writings on Australia. These writings are available for the first time in authoritative form in Panopticon versus New South Wales and other writings on Australia, a volume in The Collected Works of Jeremy Bentham published by UCL Press. In the present collection, a distinguished group of authors reflect on Bentham’s Australian writings, making original contributions to existing debates and setting agendas for future ones. In the first part of the collection, the works are placed in their historical contexts, while the second part provides a critical assessment of the historical accuracy and plausibility of Bentham’s arguments against transportation from the British Isles. In the third part, attention turns to Bentham’s claim that New South Wales had been illegally founded and to the imperial and colonial constitutional ramifications of that claim. Here, authors also discuss Bentham’s work of 1831 in which he supports the establishment of a free colony on the southern coast of Australia. In the final part, authors shed light on the history of Bentham’s panopticon penitentiary scheme, his views on the punishment and reform of criminals and what role, if any, religion had to play in that regard, and discuss apparently panopticon-inspired institutions built in the Australian colonies. This collection will appeal to readers interested in Bentham’s life and thought, the history of transportation from the British Isles, and of British penal policy more generally, colonial and imperial history, Indigenous history, legal and constitutional history, and religious history

Jeremy Bentham and Australia: Convicts, utility and empire

Jeremy Bentham and Australia is a collection of scholarship inspired by Bentham’s writings on Australia. These writings are available for the first time in authoritative form in Panopticon versus New South Wales and other writings on Australia, a volume in The Collected Works of Jeremy Bentham published by UCL Press. In the present collection, a distinguished group of authors reflect on Bentham’s Australian writings, making original contributions to existing debates and setting agendas for future ones. In the first part of the collection, the works are placed in their historical contexts, while the second part provides a critical assessment of the historical accuracy and plausibility of Bentham’s arguments against transportation from the British Isles. In the third part, attention turns to Bentham’s claim that New South Wales had been illegally founded and to the imperial and colonial constitutional ramifications of that claim. Here, authors also discuss Bentham’s work of 1831 in which he supports the establishment of a free colony on the southern coast of Australia. In the final part, authors shed light on the history of Bentham’s panopticon penitentiary scheme, his views on the punishment and reform of criminals and what role, if any, religion had to play in that regard, and discuss apparently panopticon-inspired institutions built in the Australian colonies. This collection will appeal to readers interested in Bentham’s life and thought, the history of transportation from the British Isles, and of British penal policy more generally, colonial and imperial history, Indigenous history, legal and constitutional history, and religious history