scholarly journals Sensor-Based Auto-Focusing System Using Multi-Scale Feature Extraction and Phase Correlation Matching

Sensors ◽  
2015 ◽  
Vol 15 (3) ◽  
pp. 5747-5762 ◽  
Author(s):  
Jinbeum Jang ◽  
Yoonjong Yoo ◽  
Jongheon Kim ◽  
Joonki Paik
Keyword(s):  
Feature Extraction ◽  
Phase Correlation ◽  
Scale Feature ◽  
Multi Scale ◽  
Correlation Matching ◽  
Auto Focusing

scholarly journals A Multi-Scale Feature Extraction-Based Normalized Attention Neural Network for Image Denoising

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 319
Author(s):  
Yi Wang ◽  
Xiao Song ◽  
Guanghong Gong ◽  
Ni Li
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Image Denoising ◽  
Color Image ◽  
Rapid Development ◽  
Similarity Index ◽  
Structural Similarity ◽  
Convolutional Network ◽  
Scale Feature ◽  
Multi Scale

Due to the rapid development of deep learning and artificial intelligence techniques, denoising via neural networks has drawn great attention due to their flexibility and excellent performances. However, for most convolutional network denoising methods, the convolution kernel is only one layer deep, and features of distinct scales are neglected. Moreover, in the convolution operation, all channels are treated equally; the relationships of channels are not considered. In this paper, we propose a multi-scale feature extraction-based normalized attention neural network (MFENANN) for image denoising. In MFENANN, we define a multi-scale feature extraction block to extract and combine features at distinct scales of the noisy image. In addition, we propose a normalized attention network (NAN) to learn the relationships between channels, which smooths the optimization landscape and speeds up the convergence process for training an attention model. Moreover, we introduce the NAN to convolutional network denoising, in which each channel gets gain; channels can play different roles in the subsequent convolution. To testify the effectiveness of the proposed MFENANN, we used both grayscale and color image sets whose noise levels ranged from 0 to 75 to do the experiments. The experimental results show that compared with some state-of-the-art denoising methods, the restored images of MFENANN have larger peak signal-to-noise ratios (PSNR) and structural similarity index measure (SSIM) values and get better overall appearance.

scholarly journals Learning rich features with hybrid loss for brain tumor segmentation

2021 ◽  
Vol 21 (S2) ◽  
Author(s):  
Daobin Huang ◽  
Minghui Wang ◽  
Ling Zhang ◽  
Haichun Li ◽  
Minquan Ye ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Brain Tumor ◽  
Class Imbalance ◽  
Feature Representation ◽  
Loss Functions ◽  
Radiotherapy Planning ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Scale Feature ◽  
Multi Scale

Abstract Background Accurately segment the tumor region of MRI images is important for brain tumor diagnosis and radiotherapy planning. At present, manual segmentation is wildly adopted in clinical and there is a strong need for an automatic and objective system to alleviate the workload of radiologists. Methods We propose a parallel multi-scale feature fusing architecture to generate rich feature representation for accurate brain tumor segmentation. It comprises two parts: (1) Feature Extraction Network (FEN) for brain tumor feature extraction at different levels and (2) Multi-scale Feature Fusing Network (MSFFN) for merge all different scale features in a parallel manner. In addition, we use two hybrid loss functions to optimize the proposed network for the class imbalance issue. Results We validate our method on BRATS 2015, with 0.86, 0.73 and 0.61 in Dice for the three tumor regions (complete, core and enhancing), and the model parameter size is only 6.3 MB. Without any post-processing operations, our method still outperforms published state-of-the-arts methods on the segmentation results of complete tumor regions and obtains competitive performance in another two regions. Conclusions The proposed parallel structure can effectively fuse multi-level features to generate rich feature representation for high-resolution results. Moreover, the hybrid loss functions can alleviate the class imbalance issue and guide the training process. The proposed method can be used in other medical segmentation tasks.

scholarly journals SAR Image Change Detection Based on Multi-scale Feature Extraction

2021 ◽  
Vol 10 (4) ◽  
pp. 077-081
Author(s):  
Xiaoqian Yuan ◽  
Chao Chen ◽  
Shan Tian ◽  
Jiandan Zhong
Keyword(s):  
Feature Extraction ◽  
Change Detection ◽  
Speckle Noise ◽  
Kernel Matrix ◽  
Sar Image ◽  
Scale Feature ◽  
Difference Image ◽  
Multi Scale ◽  
Image Change Detection ◽  
The Difference

In order to improve the contrast of the difference image and reduce the interference of the speckle noise in the synthetic aperture radar (SAR) image, this paper proposes a SAR image change detection algorithm based on multi-scale feature extraction. In this paper, a kernel matrix with weights is used to extract features of two original images, and then the logarithmic ratio method is used to obtain the difference images of two images, and the change area of the images are extracted. Then, the different sizes of kernel matrix are used to extract the abstract features of different scales of the difference image. This operation can make the difference image have a higher contrast. Finally, the cumulative weighted average is obtained to obtain the final difference image, which can further suppress the speckle noise in the image.

Multi-scale feature extraction and recognition of slope damage in high fill channel based on Gabor-SVM method

2020 ◽  
Vol 38 (4) ◽  
pp. 4237-4246
Author(s):  
Mingtang Liu ◽  
Li Wang ◽  
Zening Qin ◽  
Jiaqi Liu ◽  
Jian Chen ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Scale Feature ◽  
Multi Scale

Learning natural scene categories by selective multi-scale feature extraction

2010 ◽  
Vol 28 (6) ◽  
pp. 927-939 ◽  
Author(s):  
Alessandro Perina ◽  
Marco Cristani ◽  
Vittorio Murino
Keyword(s):  
Feature Extraction ◽  
Natural Scene ◽  
Scale Feature ◽  
Multi Scale
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