Co-saliency detection within a single image is a common vision problem that has not yet been well addressed. Existing methods often used a bottom-up strategy to infer co-saliency in an image in which salient regions a...
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Co-saliency detection within a single image is a common vision problem that has not yet been well addressed. Existing methods often used a bottom-up strategy to infer co-saliency in an image in which salient regions are firstly detected using visual primitives such as color and shape and then grouped and merged into a co-saliency map. However, co-saliency is intrinsically perceived complexly with bottom-up and top-down strategies combined in human vision. To address this problem, this study proposes a novel end-toend trainable network comprising a backbone net and two branch nets. The backbone net uses ground-truth masks as top-down guidance for saliency prediction, whereas the two branch nets construct triplet proposals for regional feature mapping and clustering, which drives the network to be bottom-up sensitive to co-salient regions. We construct a new dataset of 2019 natural images with co-saliency in each image to evaluate the proposed method. Experimental results show that the proposed method achieves state-of-the-art accuracy with a running speed of 28 fps.
The urgent demand for clean energy solutions has intensified the search for advanced storage materials, with rechargeable alkali-ion batteries(AIBs) playing a pivotal role in electrochemical energy storage. Enhancing ...
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The urgent demand for clean energy solutions has intensified the search for advanced storage materials, with rechargeable alkali-ion batteries(AIBs) playing a pivotal role in electrochemical energy storage. Enhancing electrode performance is critical to addressing the increasing need for high-energy and high-power AIBs. Next-generation anode materials face significant challenges, including limited energy storage capacities and complex reaction mechanisms that complicate structural ***-based materials have emerged as promising candidates for AIBs due to their inherent advantages. Recent research has increasingly focused on the development of heterojunctions as a strategy to enhance the performance of Sn-based anode materials. Despite significant advances in this field, comprehensive reviews summarizing the latest developments are still sparse. This review provides a detailed overview of recent progress in Sn-based heterojunction-type anode materials. It begins with an explanation of the concept of heterojunctions, including their fabrication, characterization, and classification. Cuttingedge research on Sn-based heterojunction-type anodes for AIBs is highlighted. Finally, the review summarizes the latest advancements in heterojunction technology and discusses future directions for research and development in this area.
Total shoulder arthroplasty is a standard restorative procedure practiced by orthopedists to diagnose shoulder arthritis in which a prosthesis replaces the whole joint or a part of the *** is often challenging for doc...
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Total shoulder arthroplasty is a standard restorative procedure practiced by orthopedists to diagnose shoulder arthritis in which a prosthesis replaces the whole joint or a part of the *** is often challenging for doctors to identify the exact model and manufacturer of the prosthesis when it is *** paper proposes a transfer learning-based class imbalance-aware prosthesis detection method to detect the implant’s manufacturer automatically from shoulder X-ray *** framework of the method proposes a novel training approach and a new set of batch-normalization,dropout,and fully convolutional layers in the head *** employs cyclical learning rates and weighting-based loss calculation *** modifications aid in faster convergence,avoid local-minima stagnation,and remove the training bias caused by imbalanced *** proposed method is evaluated using seven well-known pre-trained models of VGGNet,ResNet,and DenseNet *** is performed on a shoulder implant benchmark dataset consisting of 597 shoulder X-ray *** proposed method improves the classification performance of all pre-trained models by 10–12%.The DenseNet-201-based variant has achieved the highest classification accuracy of 89.5%,which is 10%higher than existing ***,to validate and generalize the proposed method,the existing baseline dataset is supplemented to six classes,including samples of two more implant *** results have shown average accuracy of 86.7%for the extended dataset and show the preeminence of the proposed method.
Most augmented reality (AR) pipelines typically involve the computation of the camera’s pose in each frame, followed by the 2D projection of virtual objects. The camera pose estimation is commonly implemented as SLAM...
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In the field of object detection for remote sensing images, especially in applications such as environmental monitoring and urban planning, significant progress has been made. This paper addresses the common challenge...
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Marine Object Detection, leveraging computer vision, plays a vital role in detecting objects in marine environments ranging from marine organisms to marine surveillance. However, marine environment presents ...
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Digital signatures, essential for establishing trust in the digital realm, have evolved in their application and importance alongside emerging technologies such as the Internet of Things (IoT), Blockchain, and cryptoc...
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Open-vocabulary object detection (OVD) models are considered to be Large Multi-modal Models (LMM), due to their extensive training data and a large number of parameters. Mainstream OVD models prioritize object coarse-...
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Deep Learning has recently been in trend when it comes to medical image analysis as it uses Convolution Neural Network (CNN), which utilizes multi-layer processing to extract intricate and complex features from the da...
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Images obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic ***,due to sensor limitations and imperfections during the image acq...
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Images obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic ***,due to sensor limitations and imperfections during the image acquisition and transmission phases,noise is introduced into the acquired image,which can have a negative impact on downstream analyses such as classification,target tracking,and spectral *** in hyperspectral images(HSI)is modelled as a combination from several sources,including Gaussian/impulse noise,stripes,and *** HSI restoration method for such a mixed noise model is ***,a joint optimisation framework is proposed for recovering hyperspectral data corrupted by mixed Gaussian-impulse noise by estimating both the clean data as well as the sparse/impulse noise ***,a hyper-Laplacian prior is used along both the spatial and spectral dimensions to express sparsity in clean image ***,to model the sparse nature of impulse noise,anℓ_(1)−norm over the impulse noise gradient is *** the proposed methodology employs two distinct priors,the authors refer to it as the hyperspectral dual prior(HySpDualP)*** the best of authors'knowledge,this joint optimisation framework is the first attempt in this *** handle the non-smooth and nonconvex nature of the generalℓ_(p)−norm-based regularisation term,a generalised shrinkage/thresholding(GST)solver is ***,an efficient split-Bregman approach is used to solve the resulting optimisation *** results on synthetic data and real HSI datacube obtained from hyperspectral sensors demonstrate that the authors’proposed model outperforms state-of-the-art methods,both visually and in terms of various image quality assessment metrics.
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