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Recently, most background modeling approaches represent distributions of background changes by using parametric models such as Gaussian mixture models. Because of significant illumination changes and dynamic moving backgrounds with time, variations of background changes are hard to be modeled by parametric background models. Moreover, how to efficiently and effectively update parameters of parametric models to reflect background changes remains a problem. In this paper, we propose a novel coarse-to-fine detection theory algorithm to extract foreground objects on the basis of nonparametric background and foreground models represented by binary descriptors. We update background and foreground models by a first-in–first-out strategy to maintain the most recent observed background and foreground instances. As shown in the experiments, our method can achieve better foreground extraction results and fewer false alarms of surveillance videos with lighting changes and dynamic backgrounds in both collected and CDnet 2012 benchmark data sets. [ABSTRACT FROM AUTHOR]

We present a trajectory-based approach to detect salient regions in videos by dominant camera motion removal. Our approach is designed in a general way so that it can be applied to videos taken by either stationary or moving cameras without any prior information. Moreover, multiple salient regions of different temporal lengths can also be detected. To this end, we extract a set of spatially and temporally coherent trajectories of keypoints in a video. Then, velocity and acceleration entropies are proposed to represent the trajectories. In this way, long-term object motions are exploited to filter out short-term noises, and object motions of various temporal lengths can be represented in the same way. On the other hand, we are inspired by the observation that the trajectories in backgrounds, i.e., the nonsalient trajectories, are usually consistent with the dominant camera motion no matter whether the camera is stationary or not. We make use of this property to develop a unified approach to saliency generation for both stationary and moving cameras. Specifically, one-class SVM is employed to remove the consistent trajectories in motion. It follows that the salient regions could be highlighted by applying a diffusion process to the remaining trajectories. In addition, we create a set of manually annotated ground truth on the collected videos. The annotated videos are then used for performance evaluation and comparison. The promising results on various types of videos demonstrate the effectiveness and great applicability of our approach. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To study the protective effect of the Mixture of Shengmai Powder and Danshen Decoction (, abbreviated as the Mixture) in the rat model with type 2 diabetic cardiomyopathy in the rat model with type 2 diabetic cardiomyopathy, abbreviated as the Mixture) in the rat model with type 2 diabetic cardiomyopathy (DCM). METHODS: Forty-two SD rats with DCM model, established by the combination of insulin resistance by a high-fat diet with the damage of pancreatic islet [beta] cells by intraperitoneal injection of high dose streptozotocin (50 mg/kg) once, were evaluated in the damage of the myocardium by electrocardiogram at the end of 12 weeks of grouping and intervention administration; the extent of damage in the myocardial subcellular structure was observed by electron microscopy; the content of myocardial collagen in the left cardiac ventricle was quantified by Masson staining test; the myocardial cell apoptosis was determined by TUNEL; the changes in the mRNA expression levels of thrombospodin-1 (TSP-1) and tribbles homolog 3 (TRB-3) by real-time quantitative PCR, the expression levels of myocardial TSP-1, tumor growth factor[beta]1 (TGF-[beta]1), TRB-3, and chymase were detected by immunohistochemistry, and the changes in the expression levels of myocardial TSP-1, active-TGF-[beta]1 (A-TGF-[beta]1) and latent-TGF-[beta]1 (L-TGF-[beta]1) protein were tested by Western blotting. RESULTS: Compared with the control group, the myocardial tissue was less damaged, and the extent of damage in the myocardial subcellular structure was less; the collagen fiber content and the cell apoptosis were reduced; the expression levels of TSP-1mRNA and TRB-3 mRNA, the expression levels of myocardial TSP-1, TGF-[beta]1, TRB-3, and chymase, as well as the average expression levels of the myocardial TSP-1, A-TGF[beta]1, and L-TGF-[beta]1 protein were decreased in the Mixture group. CONCLUSION: The Mixture of Shengmai Powder and Danshen Decoction could inhibit the process of myocardial fibrosis in the rat myocardium of DCM through multiple pathways and significantly delay the genesis and progress of DCM in hyperglycemic rats. [ABSTRACT FROM AUTHOR]

We present an improved algorithm for two-image camera self-calibration and Euclidean structure recovery, where the effective focal lengths of both cameras are assumed to be the only unknown intrinsic parameters. By using the absolute quadric, it is shown that the effective focal lengths can be computed linearly from two perspective images without imposing scene or motion constraints. Moreover, a quadratic equation derived from the absolute quadric is proposed for solving the parameters of the plane at infinity from two images, which upgrades a projective reconstruction to a Euclidean reconstruction. [Copyright &y& Elsevier]

Histopathological images provide the medical evidences to help the disease diagnosis. However, pathologists are not always available or are overloaded by work. Moreover, the variations of pathological images with respect to different organs, cell sizes and magnification factors lead to the difficulty of developing a general method to solve the histopathological image classification problems. To address these issues, we propose a novel cross-scale fusion (CSF) transformer which consists of the multiple field-of-view patch embedding module, the transformer encoders and the cross-fusion modules. Based on the proposed modules, the CSF transformer can effectively integrate patch embeddings of different field-of-views to learn cross-scale contextual correlations, which represent tissues and cells of different sizes and magnification factors, with less memory usage and computation compared with the state-of-the-art transformers. To verify the generalization ability of the CSF transformer, experiments are performed on four public datasets of different organs and magnification factors. The CSF transformer outperforms the state-of-the-art task specific methods, convolutional neural network-based methods and transformer-based methods. The source code will be available in our GitHub https://github.com/nchucvml/CSFT.

Objective: Common bile duct (CBD) stones caused diseases are life-threatening. Because CBD stones locate in the distal part of the CBD and have relatively small sizes, detecting CBD stones from CT scans is a challenging issue in the medical domain., Methods and Procedures: We propose a deep learning based weakly-supervised method called multiple field-of-view based attention driven network (MFADNet) to detect CBD stones from CT scans based on image-level labels. Three dominant modules including a multiple field-of-view encoder, an attention driven decoder and a classification network are collaborated in the network. The encoder learns the feature of multi-scale contextual information while the decoder with the classification network is applied to locate the CBD stones based on spatial-channel attentions. To drive the learning of the whole network in a weakly-supervised and end-to-end trainable manner, four losses including the foreground loss, background loss, consistency loss and classification loss are proposed., Results: Compared with state-of-the-art weakly-supervised methods in the experiments, the proposed method can accurately classify and locate CBD stones based on the quantitative and qualitative results., Conclusion: We propose a novel multiple field-of-view based attention driven network for a new medical application of CBD stone detection from CT scans while only image-levels are required to reduce the burdens of labeling and help physicians automatically diagnose CBD stones. The source code is available at https://github.com/nchucvml/MFADNet after acceptance., Clinical Impact: Our deep learning method can help physicians localize relatively small CBD stones for effectively diagnosing CBD stone caused diseases., (This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.)

One of the major goals of healthcare systems is to automatically monitor patients of special needs and alarm the caregivers for providing assistant. In this paper, an efficient single-camera multidirectional wheelchair detector based on a cascaded decision tree (CDT) is proposed to detect a wheelchair and its moving direction simultaneously from video frames for a healthcare system. Our approach combines a decision tree structure and boosted-cascade classifiers to construct a new CDT that can perform early confidence decisions in a hierarchical manner to rapidly reject nonwheelchairs and decide the moving directions. We also impose the tracking history to guide detection routes in the CDT to further reduce detection time and increase detection accuracy. The experiments show over 92% detection rate under cluttered scenes.