Recently, Transformer-based methods have achieved excellent results in various computer vision tasks, including Single Image Super-Resolution (SISR). In SwinIR, the mechanism of cross-window connection and local self-attention of Swin Transformer are introduced into the SISR task, achieving breakthrough improvements. However, the local self-attention mechanism of Swin Transformer has a limited spatial range of input pixels, which limits the ability of the super-resolution network to extract features in a wide range. Aiming at this problem, an enhanced CNN and Transformer hybrid module is designed for feature extraction by combining self-attention, spatial attention and channel attention. Taking advantage of their complementary strengths, the range of activated pixels is expanded while still maintaining a strong capability for local feature characterization. In addition, simply extending the activation pixel range without constraints is not conducive to reconstruction. Aiming at this problem, the Neural Window Fully-connected Conditional Random Fields (NeW FC-CRFs) are integrated for feature fusion. The shallow features are inputted into NeW FC-CRFs along with deep features, allowing for the utilization of multi-level information during the fusion process. In summary, we propose the Hybrid Attention Super Resolution Network with Conditional Random Field (HANCRF). Extensive experiments show that HANCRF achieves competitive results with a small number of parameters.
Visible-infrared person re-identification (VI-ReID) aims to search person images across cameras of different modalities, which can address the limitation of visible-based ReID in dark environments. It is a very challenging task, as images of the same identity have huge discrepancy in different modalities. To address this problem, a cross-modality ReID model based on sample diversity and identity consistency is proposed in this paper. For sample diversity, auxiliary images are introduced based on the idea of information transfer. The auxiliary images combine the information of visible images and infrared images, and can improve the diversity of input data and robustness of the network. For identity consistency, homogeneous distance loss and heterogeneous distance loss are developed from four different perspectives to shorten the distance between the samples of same identities. Extensive experimental results demonstrate the effectiveness of the proposed method.
In order to improve the diagnostic effect of MRI images, a multiparametric magnetic resonance imaging (MRI) based classification method was proposed in this paper. The study included 85 patients. The radiomics method was used to extract morphological and texture features, while Apparent diffusion coefficient (ADC) was used as functional feature.Three classification methods, including Linear Discriminate Analysis (LDA), Support Vector Machine (SVM) and Random Forest (RF), were used to distinguish benign and malignant of pulmonary lesions. The performance of multiparametric MRI sequences and single sequences were compared. The experimental results shown that multiparametric MRI classification with SVM classifier had best performence (AUC=0.82±0.03), indicating that multiparametric MR diagnosis has great potential.
Lane markings detection is a very important part of the ADAS to avoid traffic accidents. In order to obtain accurate lane markings, in this work, a novel and efficient algorithm is proposed, which analyses the waveform generated from the road image after inverse perspective mapping (IPM). The algorithm includes two main stages: the first stage uses an image preprocessing including a CNN to reduce the background and enhance the lane markings. The second stage obtains the waveform of the road image and analyzes the waveform to get lanes. The contribution of this work is that we introduce local and global features of the waveform to detect the lane markings. The results indicate the proposed method is robust in detecting and fitting the lane markings.
In this paper, a computer image-processing algorithm for analyzing the morphological characteristics of bile ducts in Magnetic Resonance Cholangiopancreatography (MRCP) images was proposed. The algorithm consisted of mathematical morphology methods including erosion, closing and skeletonization, and a spline curve fitting method to obtain the length and curvature of the center line of the bile duct. Of 10 cases, the average length of the bile duct was 14.56 cm. The maximum curvature was in the range of 0.111~2.339. These experimental results show that using the computer image-processing algorithm to assess the morphological characteristics of the bile duct is feasible and further research is needed to evaluate its potential clinical values.
A microaccelerometer based on gallium arsenide (GaAs) resonant-tunneling diodes (RTDs) is demonstrated. The input acceleration signal can be transformed into an output electrical signal using the meso-piezoresistive effects of the RTDs located at the root of the detection beams. Finite element simulations were performed to design, analyze, and optimize the structures of the accelerometer. The accelerometer was fabricated using a combination of GaAs IC surface and bulk micromachining techniques. Vibrating tests and shock tests were conducted to investigate the accelerometer characteristics. The experimental results revealed that the sensitivity of the RTD accelerometer was 7.91 mV/g. The noise resolution was ∼1.264 mg/√Hz, and the working frequency was up to 3 kHz.
Breast cancer occurs with high frequency among women. In most cases, the main early signs appear as mass and
calcification. Distinguishing masses from normal tissues is still a challenging work as mass varies with shapes, margins
and sizes. In this paper, a novel method for mass detection in mammograms was presented. First, morphology operators
are employed to locate mass candidates. Then anisotropic diffusion was applied to make mass region display better
multiple concentric layers (MCL). Finally an extended concentric morphology model (ECMM) criterion combining
MCL criterion and template matching was proposed to detect masses. This method was examined on 170 images from
Digital Database for Screening Mammography (DDSM) database. The detection rate is 93.92% at 1.88 false positives
per image (FPs/I), demonstrating the effectiveness of the proposed method.
A new context-aware scale-invariant feature transform (CASIFT) approach is proposed, which is designed for the use in traffic sign recognition (TSR) systems. The following issues remain in previous works in which SIFT is used for matching or recognition: (1) SIFT is unable to provide color information; (2) SIFT only focuses on local features while ignoring the distribution of global shapes; (3) the template with the maximum number of matching points selected as the final result is instable, especially for images with simple patterns; and (4) SIFT is liable to result in errors when different images share the same local features. In order to resolve these problems, a new CASIFT approach is proposed. The contributions of the work are as follows: (1) color angular patterns are used to provide the color distinguishing information; (2) a CASIFT which effectively combines local and global information is proposed; and (3) a method for computing the similarity between two images is proposed, which focuses on the distribution of the matching points, rather than using the traditional SIFT approach of selecting the template with maximum number of matching points as the final result. The proposed approach is particularly effective in dealing with traffic signs which have rich colors and varied global shape distribution. Experiments are performed to validate the effectiveness of the proposed approach in TSR systems, and the experimental results are satisfying even for images containing traffic signs that have been rotated, damaged, altered in color, have undergone affine transformations, or images which were photographed under different weather or illumination conditions.
Calcification detection in mammogram is important in breast cancer diagnosis. A
super-resolution reconstruction method is proposed to reconstruct mammogram image from one
single low resolution mammogram based on the compressed sensing by the contourlet transform.
The initial estimation of the super-resolution mammogram is obtained by the interpolation method
of the low resolution mammogram reconstructed by compressed sensing, then contourlet
transform is applied respectively to the initial estimation and the reconstructed low resolution
mammogram. From the statistical characteristics of the mutiscale frequency bands between the
initial estimation and the reconstructed low resolution mammogram, the thresholds are estimated
to integrate the high frequency of the initial estimation and the low frequency of the reconstructed
low resolution mammogram. The super-resolution mammogram is achieved through the
reconstruction of contourlet inverse transform. The proposed method can retrieve some details of
the low resolution images. The calcification in mammogram can be detected efficiently.
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