Functional brain networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used for Autism Spectrum Disorder (ASD) diagnosis. Typically, these networks are ...constructed by calculating functional connectivity (FC) between any pair of brain regions of interest (ROIs), i.e., using Pearson's correlation between rs-fMRI time series. However, this can only be called as a
representation of the functional interaction, because the relationship is investigated just between two ROIs. Brain disorders might not only affect low-order FC, but also
FC, i.e., the higher-level relationship among multiple brain regions, which might be more crucial for diagnosis. To comprehensively characterize such relationship for better diagnosis of ASD, we propose a multi-level, high-order FC network representation that can nicely capture complex interactions among brain regions. Then, we design a feature selection method to identify those discriminative multi-level, high-order FC features for ASD diagnosis. Finally, we design an ensemble classifier with multiple linear SVMs, each trained on a specific level of FC networks, for boosting the final classification accuracy. Experimental results show that the integration of both low-order and first-level high-order FC networks achieves the best ASD diagnostic accuracy (81%). We further investigated those selected discriminative low-order and high-order FC features and found that the high-order FC features can provide complementary information to the low-order FC features in the ASD diagnosis.
As the first step of vehicle detection and recognition system, how to quickly and accurately detect the vehicle in a picture is directly related to the subsequent vehicle application research. In ...order to improve the processing speed of vehicle detection, reduce the false alarm rate of detection, and get better results, the method is applied in real scene, this paper carried out in-depth research on this. Collect traffic and urban road surveillance videos as experimental data, of which 2000 were positive samples and 2000 were negative samples. Firstly, a vehicle image preprocessing is carried out on the collected experimental data, and the image feature is extracted based on gray image and improved AdaBoost algorithm, and then the image enhancement is realized by using multi-scale Retinex. Using this method, we can make the image processing accord with the nonlinear characteristics of the human eye to the brightness response, and avoid the distortion of the image directly processed by Fourier transform. In order to improve AdaBoost classifier, it is necessary to use local binary edge features and train the collected feature samples. In order to highlight the vehicle target and ignore the background, we need to use a selective graying way, which is based on the H component of HSV space. The experimental results show that the accuracy of AdaBoost classifier reaches 85.8%, the recall rate is 80.9%, and the comprehensive performance is very high, which can meet the performance requirements.
Many advanced super-resolution reconstruction methods have been proposed recently, but they often require high computational and memory resources, making them incompatible with low-power devices in ...reality. To address this problem, we propose a simple yet efficient super-resolution reconstruction method using waveform representation and multi-layer perceptron (MLP) for image processing. Firstly, we partition the original image and its down-sampled version into multiple patches and introduce WaveBlock to process these patches. WaveBlock represents patches as waveform functions with amplitude and phase and extracts representative feature representations by dynamically adjusting phase terms between tokens and fixed weights. Next, we fuse the extracted features through a feature fusion block and finally reconstruct the image using sub-pixel convolution. Extensive experimental results demonstrate that SRWave-MLP performs excellently in both quantitative evaluation metrics and visual quality while having significantly fewer parameters than state-of-the-art efficient super-resolution methods.
Most Siamese‐based trackers use classification and regression to determine the target bounding box, which can be formulated as a linear matching process of the template and search region. However, ...this only takes into account the similarity of features while ignoring the semantic object information, resulting in some cases in which the regression box with the highest classification score is not accurate. To address the lack of semantic information, an object tracking approach based on an ensemble semantic‐aware network and redetection (ESART) is proposed. Furthermore, a DarkNet53 network with transfer learning is used as our semantic‐aware model to adapt the detection task for extracting semantic information. In addition, a semantic tag redetection method to re‐evaluate the bounding box and overcome inaccurate scaling issues is proposed. Extensive experiments based on OTB2015, UAV123, UAV20L, and GOT‐10k show that our tracker is superior to other state‐of‐the‐art trackers. It is noteworthy that our semantic‐aware ensemble method can be embedded into any tracker for classification and regression task.
We innovatively propose object tracking using semantic‐aware ensemble learning for Siamese networks. We propose for the first time a semantic tag redetection method to rescore the tracker bounding boxes and replace the inaccurate bounding boxes.
Due to the presence of solar radiation, each object emits electromagnetic waves at different temperatures. Thermal infrared imaging is to image an object through a thermal infrared CCD, which can ...reflect the temperature field of the object. The image information is obtained by infrared radiation intensity distribution, and the infrared light invisible to the human eye is converted into a visible image. Thermal infrared has a wide range of applications in military, industrial, automotive, medical, and other fields. This technology is still a research hotspot at present, and it has high research value for any region. This paper designs a communication device architecture for an infrared thermal imaging target recognition and tracking system and optimizes and compares different target recognition and tracking algorithms. The test results show that the device system can reduce the system software frame processing time from 120 ms to an average of 28 ms, and the processing speed is increased by about 4.3 times.
Existing Siamese trackers usually do not update templates or adopt single‐updating strategies. However, historical information cannot be effectively utilized when using these strategies, and model ...drift from complex tracking challenges cannot be addressed. To address this issue, a novel tracking framework that learns the model update with local trusted templates is proposed in this paper. The authors propose a complementary confidence evaluation method to select local trusted templates in a sliding window. This provides high‐confidence historical information. The authors also propose a method including linear learning and deep learning to learn to model updates. Different from traditional update strategies, the authors’ method combines non‐linear and linear updates to obtain reliable templates with the most abundant historical information, which solves the complex tracking challenges to a certain extent. Finally, the adaptive fusion response maps of the two strategies determine the final tracking based on the confidence evaluation. Experimental results on NFS, UAVDT, UAV123, UAV20L and VOT2016 show that our method performs favourably when compared with current state‐of‐the‐art methods.
In the field of correlation filter object tracking, the traditional template-update method easily causes template drift, so it performs poorly in complex scenes. To enhance the robustness of the ...template, a novel incremental multi-template update strategy is proposed in this paper. We find that reliability varies among all historical filters and that highly reliable filters are key to achieving accurate tracking. The incremental multi-template update strategy combines the local maximum-reliability filter template with the historical filter template incrementally, which is obviously different from the traditional update method. We apply this strategy to two trackers with superior performance. The experimental results of three test benchmarks, including the VOT2016, OTB100 and UAV123 datasets, show that the performance of our trackers is superior to that of the state-of-the-art trackers.
Object tracking with robust scale estimation is a challenging task in computer vision. This paper presents a novel tracking algorithm that learns the translation and scale filters with a ...complementary scheme. The translation filter is constructed using the ridge regression and multidimensional features. A robust scale filter is constructed by the bidirectional scale estimation, including the forward scale and backward scale. Firstly, we learn the scale filter using the forward tracking information. Then the forward scale and backward scale can be estimated using the respective scale filter. Secondly, a conservative strategy is adopted to compromise the forward and backward scales. Finally, the scale filter is updated based on the final scale estimation. It is effective to update scale filter since the stable scale estimation can improve the performance of scale filter. To reveal the effectiveness of our tracker, experiments are performed on 32 sequences with significant scale variation and on the benchmark dataset with 50 challenging videos. Our results show that the proposed tracker outperforms several state-of-the-art trackers in terms of robustness and accuracy.
Abstract
This paper highlights the important impact of charitable giving and warm glow on the identification of the marginal cost of public funds (MCF). We employ the warm glow model of charitable ...giving to describe taxpayer behaviour, whereas we employ the standard model to evaluate social welfare. We first identify the impact theoretically. Then we conduct simulations to quantify its size numerically. The results of our numerical simulations show that the standard model underestimates the magnitude of MCF by at least 10 per cent. Our work suggests that adopting a non‐welfarist social welfare function can make a significant difference to the identification of MCF.
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