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The problem posed by complex, articulated or deformable objects has been at the focus of much tracking research for a considerable length of time. However, it remains a major challenge, fraught with numerous difficulties. The increased ubiquity of technology in all realms of our society has made the need for effective solutions all the more urgent. In this article, we describe a novel method which systematically addresses the aforementioned difficulties and in practice outperforms the state of the art. Global spatial flexibility and robustness to deformations are achieved by adopting a pictorial structure based geometric model, and localized appearance changes by a subspace based model of part appearance underlain by a gradient based representation. In addition to one-off learning of both the geometric constraints and part appearances, we introduce a continuing learning framework which implements information discounting i.e., the discarding of historical appearances in favour of the more recent ones. Moreover, as a means of ensuring robustness to transient occlusions (including self-occlusions), we propose a solution for detecting unlikely appearance changes which allows for unreliable data to be rejected. A comprehensive evaluation of the proposed method, the analysis and discussing of findings, and a comparison with several state-of-the-art methods demonstrates the major superiority of our algorithm.Image fusion is a process that integrates similar types of images collected from heterogeneous sources into one image in which the information is more definite and certain. Hence, the resultant image is anticipated as more explanatory and enlightening both for human and machine perception. Different image combination methods have been presented to consolidate significant data from a collection of images into one image. As a result of its applications and advantages in variety of fields such as remote sensing, surveillance, and medical imaging, it is significant to comprehend image fusion algorithms and have a comparative study on them. This paper presents a review of the present state-of-the-art and well-known image fusion techniques. The performance of each algorithm is assessed qualitatively and quantitatively on two benchmark multi-focus image datasets. We also produce a multi-focus image fusion dataset by collecting the widely used test images in different studies. The quantitative evaluation of fusion results is performed using a set of image fusion quality assessment metrics. The performance is also evaluated using different statistical measures. Another contribution of this paper is the proposal of a multi-focus image fusion library, to the best of our knowledge, no such library exists so far. The library provides implementation of numerous state-of-the-art image fusion algorithms and is made available publicly at project website.In this paper, we provide an overview on the foundation and first results of a very recent quantum theory of color perception, together with novel results about uncertainty relations for chromatic opposition. The major inspiration for this model is the 1974 remarkable work by H.L. Resnikoff, who had the idea to give up the analysis of the space of perceived colors through metameric classes of spectra in favor of the study of its algebraic properties. This strategy permitted to reveal the importance of hyperbolic geometry in colorimetry. Starting from these premises, we show how Resnikoff's construction can be extended to a geometrically rich quantum framework, where the concepts of achromatic color, hue and saturation can be rigorously defined. Moreover, the analysis of pure and mixed quantum chromatic states leads to a deep understanding of chromatic opposition and its role in the encoding of visual signals. We complete our paper by proving the existence of uncertainty relations for the degree of chromatic opposition, thus providing a theoretical confirmation of the quantum nature of color perception.The audiovisual entertainment industry has entered a race to find the video encoder offering the best Rate/Distortion (R/D) performance for high-quality high-definition video content. The challenge consists in providing a moderate to low computational/hardware complexity encoder able to run Ultra High-Definition (UHD) video formats of different flavours (360°, AR/VR, etc.) with state-of-the-art R/D performance results. It is necessary to evaluate not only R/D performance, a highly important feature, but also the complexity of future video encoders. New coding tools offering a small increase in R/D performance at the cost of greater complexity are being advanced with caution. We performed a detailed analysis of two evolutions of High Efficiency Video Coding (HEVC) video standards, Joint Exploration Model (JEM) and Versatile Video Coding (VVC), in terms of both R/D performance and complexity. The results show how VVC, which represents the new direction of future standards, has, for the time being, sacrificed R/D performance in order to significantly reduce overall coding/decoding complexity.In dynamic MRI, sufficient temporal resolution can often only be obtained using imaging protocols which produce undersampled data for each image in the time series. This has led to the popularity of compressed sensing (CS) based reconstructions. One problem in CS approaches is determining the regularization parameters, which control the balance between data fidelity and regularization. We propose a data-driven approach for the total variation regularization parameter selection, where reconstructions yield expected sparsity levels in the regularization domains. The expected sparsity levels are obtained from the measurement data for temporal regularization and from a reference image for spatial regularization. Two formulations are proposed. Simultaneous search for a parameter pair yielding expected sparsity in both domains (S-surface), and a sequential parameter selection using the S-curve method (Sequential S-curve). The approaches are evaluated using simulated and experimental DCE-MRI. In the simulated test case, both methods produce a parameter pair and reconstruction that is close to the root mean square error (RMSE) optimal pair and reconstruction. In the experimental test case, the methods produce almost equal parameter selection, and the reconstructions are of high perceived quality. Eeyarestatin 1 cost Both methods lead to a highly feasible selection of the regularization parameters in both test cases while the sequential method is computationally more efficient.We present a sample-efficient image segmentation method using active learning, we call it Active Bayesian UNet, or AB-UNet. This is a convolutional neural network using batch normalization and max-pool dropout. The Bayesian setup is achieved by exploiting the probabilistic extension of the dropout mechanism, leading to the possibility to use the uncertainty inherently present in the system. We set up our experiments on various medical image datasets and highlight that with a smaller annotation effort our AB-UNet leads to stable training and better generalization. Added to this, we can efficiently choose from an unlabelled dataset.Digital Breast Tomosynthesis is an X-ray imaging technique that allows a volumetric reconstruction of the breast, from a small number of low-dose two-dimensional projections. Although it is already used in the clinical setting, enhancing the quality of the recovered images is still a subject of research. The aim of this paper was to propose and compare, in a general optimization framework, three slightly different models and corresponding accurate iterative algorithms for Digital Breast Tomosynthesis image reconstruction, characterized by a convergent behavior. The suggested model-based implementations are specifically aligned to Digital Breast Tomosynthesis clinical requirements and take advantage of a Total Variation regularizer. We also tune a fully-automatic strategy to set a proper regularization parameter. We assess our proposals on real data, acquired from a breast accreditation phantom and a clinical case. The results confirm the effectiveness of the presented framework in reconstructing breast volumes, with particular focus on the masses and microcalcifications, in few iterations and in enhancing the image quality in a prolonged execution.The prevailing approach for three-dimensional (3D) medical image segmentation is to use convolutional networks. Recently, deep learning methods have achieved human-level performance in several important applied problems, such as volumetry for lung-cancer diagnosis or delineation for radiation therapy planning. However, state-of-the-art architectures, such as U-Net and DeepMedic, are computationally heavy and require workstations accelerated with graphics processing units for fast inference. However, scarce research has been conducted concerning enabling fast central processing unit computations for such networks. Our paper fills this gap. We propose a new segmentation method with a human-like technique to segment a 3D study. First, we analyze the image at a small scale to identify areas of interest and then process only relevant feature-map patches. Our method not only reduces the inference time from 10 min to 15 s but also preserves state-of-the-art segmentation quality, as we illustrate in the set of experiments with two large datasets.Multiparametric prostate magnetic resonance imaging (mpMRI) is widely used as a triage test for men at a risk of prostate cancer. However, the traditional role of mpMRI was confined to prostate cancer staging. Radiomics is the quantitative extraction and analysis of minable data from medical images; it is emerging as a promising tool to detect and categorize prostate lesions. In this paper we review the role of radiomics applied to prostate mpMRI in detection and localization of prostate cancer, prediction of Gleason score and PI-RADS classification, prediction of extracapsular extension and of biochemical recurrence. We also provide a future perspective of artificial intelligence (machine learning and deep learning) applied to the field of prostate cancer.The popularity of social networks (SNs), amplified by the ever-increasing use of smartphones, has intensified online cybercrimes. This trend has accelerated digital forensics through SNs. One of the areas that has received lots of attention is camera fingerprinting, through which each smartphone is uniquely characterized. Hence, in this paper, we compare classification-based methods to achieve smartphone identification (SI) and user profile linking (UPL) within the same or across different SNs, which can provide investigators with significant clues. We validate the proposed methods by two datasets, our dataset and the VISION dataset, both including original and shared images on the SN platforms such as Google Currents, Facebook, WhatsApp, and Telegram. The obtained results show that k-medoids achieves the best results compared with k-means, hierarchical approaches, and different models of convolutional neural network (CNN) in the classification of the images. The results show that k-medoids provides the values of F1-measure up to 0.91% for SI and UPL tasks. Moreover, the results prove the effectiveness of the methods which tackle the loss of image details through the compression process on the SNs, even for the images from the same model of smartphones. An important outcome of our work is presenting the inter-layer UPL task, which is more desirable in digital investigations as it can link user profiles on different SNs.
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