In face recognition, the dimensionality of raw data is very high, dimension reduction (Feature Extraction) should be applied before classification. There exist several feature extraction methods, commonly used are Pri...
详细信息
In this paper a framework for multichannel image restoration based on optimization of the structural similarity (SSIM) index is presented. The SSIM index describes the similarity of images more appropriately for the h...
详细信息
In automatic image annotation, it is often extracting low-level visual features from original image for the purpose of mapping to high level image semantic information. In this paper, we propose a novel method which i...
详细信息
Video frame interpolation is an important low-level vision task, which can increase frame rate for more fluent visual experience. Existing methods have achieved great success by employing advanced motion models and sy...
详细信息
In classification of multispectral remote sensing image, it is usually difficult to obtain higher classification accuracy if only consider image's spectral feature or texture feature alone. In this paper ,we prese...
详细信息
In order to improve the classifier performance in semantic image annotation, we propose a novel method which adopts learning vector quantization (LVQ) technique to optimize low level feature data extracted from given ...
详细信息
This paper proposes a key management mechanism by DH distributed key tree for the DHT network, which is based on the DHT network routing mechanism and network topology structure. The scheme uses the DH algorithm for k...
详细信息
In this paper, we propose a robust visual tracking algorithm based on online learning of a joint sparse dictionary. The joint sparse dictionary consists of positive and negative sub-dictionaries, which model foregroun...
详细信息
Malignant lung nodules can significantly affect patients' normal lives and, in severe cases, threaten their survival. Owing to the heterogeneity of computed tomography scans and the varying sizes of nodules, physi...
详细信息
Malignant lung nodules can significantly affect patients' normal lives and, in severe cases, threaten their survival. Owing to the heterogeneity of computed tomography scans and the varying sizes of nodules, physicians often face challenges in diagnosing this condition. Therefore, a novel adaptive multi-channel fusion network (AMCF-Net) is proposed for computer-aided diagnosis of lung nodules. First, a Multi-Channel Fusion Model module is designed, which divides the channels into two parts in specific proportions, effectively extracting multi-scale channel information while reducing network parameters. After the feature maps output at each layer of the AMCF-Net, a novel adaptive depth-wise separable convolution with a squeeze-and-excitation module is designed to adaptively integrate the feature maps of various stages of the AMCF-Net, ensuring that the key lesions of lung nodules are not lost during classification. Finally, a hybrid loss scheme based on an adaptive mixing ratio is proposed to solve the problem of an imbalanced number of positive and negative nodule samples in the dataset. The model achieved the following test results: an accuracy of 90.22%, a specificity of 98.19%, an F1-score of 86.57%, a sensitivity of 86.49%, and a G-mean of 87.72%. Compared with other advanced networks, AMCF-net delivers high-precision lung nodule classification with minimal inference cost. Related codes have been released at: https://***/GuYuIMUST/AMCF-net .
In this paper, we propose spatial localization of multiple sound sources using a spherical robot head equipped with four microphones. We obtain arrival time differences using phase difference candidates. Based on the ...
详细信息
暂无评论