image registration is a crucial task in signalprocessing, but it often encounters issues with stability and efficiency. Non-learning registration approaches rely on optimizing similarity metrics between fixed and mov...
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No-reference super-resolution (SR) image quality assessment (NR-SRIQA) aims to evaluate the quality of SR images without relying on any reference images. Currently, most previous methods usually utilize a certain hand...
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No-reference super-resolution (SR) image quality assessment (NR-SRIQA) aims to evaluate the quality of SR images without relying on any reference images. Currently, most previous methods usually utilize a certain handcrafted perceptual statistical features to quantify the degradation of SR images and a simple regression model to learn the mapping relationship from the features to the perceptual quality. Although these methods achieved promising performance, they still have some limitations: 1) the handcrafted features cannot accurately quantify the degradation of SR images;2) the complex mapping relationship between the features and the quality scores cannot be well approximated by a simple regression model. To alleviate the above problems, we propose a novel stacking regression framework for NR-SRIQA. In the proposed method, we use a pre-trained VGGNet to extract the deep features for measuring the degradation of SR images, and then develop a stacking regression framework to establish the relationship between the learned deep features and the quality scores to achieve the NR-SRIQA. The stacking regression integrates two base regressors, namely Support Vector Regression (SVR) and K-Nearest Neighbor (K-NN) regression, and a simple linear regression as a meta-regressor. Thanks to the feature representation capability of deep neural networks (DNNs) and the complementary features of the two base regressors, the experimental results indicate that the proposed stacking regression framework is capable of yielding higher consistency with human visual judgments on the quality of SR images than other state-of-the-art SRIQA methods. (C) 2020 Elsevier B.V. All rights reserved.
Content-based image retrieval (CBIR) has made notable progress thanks to deep learning methods, particularly convolutional neural networks (CNNs). These methods have demonstrated competitive performance in feature ext...
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Multimodal sentiment analysis (MSA) tasks leverage diverse data sources, including text, audio, and visual data, to infer users' sentiment states. Previous research has mainly focused on capturing the differences ...
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Biometric identification is the technology that differentiates individuals by body parts or behavioral characteristics. Hand has been proved to be a successful biometric for verification and identification because of ...
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An insufficient number of training samples is a common problem in neural network applications. While data augmentation methods require at least a minimum number of samples, we propose a novel, rendering-based pipeline...
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Many keypoint detection and description methods have been proposed for image matching or registration. While these methods demonstrate promising performance for single-modality image matching, they often struggle with...
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Many keypoint detection and description methods have been proposed for image matching or registration. While these methods demonstrate promising performance for single-modality image matching, they often struggle with multimodal data because the descriptors trained on single-modality data tend to lack robustness against the non-linear variations present in multimodal data. Extending such methods to multimodal image matching often requires well-aligned multimodal data to learn modality-invariant descriptors. However, acquiring such data is often costly and impractical in many real-world scenarios. To address this challenge, we propose a modality-invariant feature learning network (MIFNet) to compute modality-invariant features for keypoint descriptions in multimodal image matching using only single-modality training data. Specifically, we propose a novel latent feature aggregation module and a cumulative hybrid aggregation module to enhance the base keypoint descriptors trained on single-modality data by leveraging pre-trained features from Stable Diffusion models. We validate our method with recent keypoint detection and description methods in three multimodal retinal image datasets (CF-FA, CF-OCT, EMA-OCTA) and two remote sensing datasets (Optical-SAR and Optical-NIR). Extensive experiments demonstrate that the proposed MIFNet is able to learn modality-invariant feature for multimodal image matching without accessing the targeted modality and has good zero-shot generalization ability. The source code will be made publicly available.
Automatic classification of breast histopathology images plays a key role in computer-aided breast cancer diagnosis. However, feature-based classification methods rely on the accurate cell segmentation and feature ext...
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Automatic classification of breast histopathology images plays a key role in computer-aided breast cancer diagnosis. However, feature-based classification methods rely on the accurate cell segmentation and feature extraction. Due to overlapping cells, dust, impurities and uneven irradiation the accurate segmentation and efficient feature extraction are still challenging. In order to overcome the above difficulties and limited breast histopathology images, in this paper, a hybrid structure which includes a double deep transfer learning ((DTL)-T-2) and interactive cross-task extreme learning machine (ICELM) is proposed based on feature extraction and representation ability of CNN and classification robustness of ELM. First, high level features are extracted using deep transfer learning and double-step deep transfer learning. Then, the high level feature sets are jointly used as regularization terms to further improve classification performance in interactive cross task extreme learning machine. The proposed method was tested on 134 breast cancer histopathology images. Results show that our method has achieved remarkable performance in classification accuracy (96.67%, 96.96%, 98.18%). From the experiment result, the proposed method is promising for providing an efficient tool for breast cancer classification in clinical settings. (C) 2019 Elsevier Ltd. All rights reserved.
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