Left Ventricular Parameter Regression from Deep Feature Maps of a Jointly Trained Segmentation CNN

2020 ◽  
pp. 395-404
Author(s):  
Sofie Tilborghs ◽  
Frederik Maes
Keyword(s):  
Left Ventricular ◽  
Feature Maps ◽  
Deep Feature ◽  
Left Ventricular Parameter

scholarly journals High-Resolution SAR Image Classification Using Multi-Scale Deep Feature Fusion and Covariance Pooling Manifold Network

2021 ◽  
Vol 13 (2) ◽  
pp. 328
Author(s):  
Wenkai Liang ◽  
Yan Wu ◽  
Ming Li ◽  
Yice Cao ◽  
Xin Hu
Keyword(s):  
High Resolution ◽  
Image Classification ◽  
Feature Fusion ◽  
Representation Learning ◽  
Sar Image ◽  
Gabor Filtering ◽  
Feature Maps ◽  
Sar Images ◽  
Multi Scale ◽  
Deep Feature

The classification of high-resolution (HR) synthetic aperture radar (SAR) images is of great importance for SAR scene interpretation and application. However, the presence of intricate spatial structural patterns and complex statistical nature makes SAR image classification a challenging task, especially in the case of limited labeled SAR data. This paper proposes a novel HR SAR image classification method, using a multi-scale deep feature fusion network and covariance pooling manifold network (MFFN-CPMN). MFFN-CPMN combines the advantages of local spatial features and global statistical properties and considers the multi-feature information fusion of SAR images in representation learning. First, we propose a Gabor-filtering-based multi-scale feature fusion network (MFFN) to capture the spatial pattern and get the discriminative features of SAR images. The MFFN belongs to a deep convolutional neural network (CNN). To make full use of a large amount of unlabeled data, the weights of each layer of MFFN are optimized by unsupervised denoising dual-sparse encoder. Moreover, the feature fusion strategy in MFFN can effectively exploit the complementary information between different levels and different scales. Second, we utilize a covariance pooling manifold network to extract further the global second-order statistics of SAR images over the fusional feature maps. Finally, the obtained covariance descriptor is more distinct for various land covers. Experimental results on four HR SAR images demonstrate the effectiveness of the proposed method and achieve promising results over other related algorithms.

scholarly journals Weakly Supervised Localization Using Deep Feature Maps

2016 ◽  
pp. 714-731 ◽  
Author(s):  
Archith John Bency ◽  
Heesung Kwon ◽  
Hyungtae Lee ◽  
S. Karthikeyan ◽  
B. S. Manjunath
Keyword(s):  
Feature Maps ◽  
Deep Feature ◽  
Weakly Supervised

scholarly journals Fruit classification using deep feature maps in the presence of deceptive similar classes

2021 ◽  
Author(s):  
Mohit Dandekar ◽  
Narinder Singh Punn ◽  
Sanjay Kumar Sonbhadra ◽  
Sonali Agarwal ◽  
Rage Uday Kiran
Keyword(s):  
Feature Maps ◽  
Deep Feature

scholarly journals MUSICAL: Multi-Scale Image Contextual Attention Learning for Inpainting

2019 ◽  
Author(s):  
Ning Wang ◽  
Jingyuan Li ◽  
Lefei Zhang ◽  
Bo Du
Keyword(s):  
Image Inpainting ◽  
Superior Performance ◽  
Background Information ◽  
Feature Maps ◽  
Background Region ◽  
Multi Scale ◽  
The Arts ◽  
Deep Feature ◽  
Style Consistency ◽  
Similar Texture

We study the task of image inpainting, where an image with missing region is recovered with plausible context. Recent approaches based on deep neural networks have exhibited potential for producing elegant detail and are able to take advantage of background information, which gives texture information about missing region in the image. These methods often perform pixel/patch level replacement on the deep feature maps of missing region and therefore enable the generated content to have similar texture as background region. However, this kind of replacement is a local strategy and often performs poorly when the background information is misleading. To this end, in this study, we propose to use a multi-scale image contextual attention learning (MUSICAL) strategy that helps to flexibly handle richer background information while avoid to misuse of it. However, such strategy may not promising in generating context of reasonable style. To address this issue, both of the style loss and the perceptual loss are introduced into the proposed method to achieve the style consistency of the generated image. Furthermore, we have also noticed that replacing some of the down sampling layers in the baseline network with the stride 1 dilated convolution layers is beneficial for producing sharper and fine-detailed results. Experiments on the Paris Street View, Places, and CelebA datasets indicate the superior performance of our approach compares to the state-of-the-arts. 

scholarly journals Wildfire Smoke Detection Based on Depthwise Separable Convolutions and Target-Awareness

2020 ◽  
Author(s):  
Yunji Zhao ◽  
Haibo zhang ◽  
Xinliang Zhang ◽  
Wei Qian
Keyword(s):  
Real Time ◽  
Early Warning Systems ◽  
Small Sample ◽  
Complex Environments ◽  
Smoke Detection ◽  
Feature Maps ◽  
Wildfire Smoke ◽  
Deep Feature ◽  
Fire Smoke ◽  
Fire Spreading

Since smoke usually occurs before a flame arises, fire smoke detection is especially significant for early warning systems. In this paper, a DSATA(Depthwise Separability And Target Awareness) algorithm based on depthwise separability and target awareness is proposed. Existing deep learning methods with convolutional neural networks pretrained by abundant and vast datasets are always used to realize generic object recognition tasks. In the area of smoke detection, collecting large quantities of smoke data is a challenging task for small sample smoke objects. The basis is that the objects of interest can be arbitrary object classes with arbitrary forms. Thus, deep feature maps acquired by target-aware pretrained networks are used in modelling these objects of arbitrary forms to distinguish them from unpredictable and complex environments. In this paper, this scheme is introduced to deal with smoke detection. The depthwise separable method with a fixed convolution kernel replacing the training iterations can improve the speed of the algorithm to meet the enhanced requirements of real-time fire spreading for detecting speed. The experimental results demonstrate that the proposed algorithm can detect early smoke, is superior to the state-of-the-art methods in accuracy and speed, and can also realize real-time smoke detection.

scholarly journals GEOMETRIC AND NON-LINEAR RADIOMETRIC DISTORTION ROBUST MULTIMODAL IMAGE MATCHING VIA EXPLOITING DEEP FEATURE MAPS

2020 ◽  
Vol V-3-2020 ◽  
pp. 233-240
Author(s):  
M. Chen ◽  
Y. Zhao ◽  
T. Fang ◽  
Q. Zhu ◽  
S. Yan ◽  
...  
Keyword(s):  
Image Matching ◽  
Prior Information ◽  
Geometric Distortion ◽  
Feature Maps ◽  
Ground Sample ◽  
Matching Methods ◽  
Ground Control Points ◽  
Deep Feature ◽  
Non Linear ◽  
Multimodal Images

Abstract. Image matching is a fundamental issue of multimodal images fusion. Most of recent researches only focus on the non-linear radiometric distortion on coarsely registered multimodal images. The global geometric distortion between images should be eliminated based on prior information (e.g. direct geo-referencing information and ground sample distance) before using these methods to find correspondences. However, the prior information is not always available or accurate enough. In this case, users have to select some ground control points manually to do image registration and make the methods work. Otherwise, these methods will fail. To overcome this problem, we propose a robust deep learning-based multimodal image matching method that can deal with geometric and non-linear radiometric distortion simultaneously by exploiting deep feature maps. It is observed in our study that some of the deep feature maps have similar grayscale distribution and correspondences can be found from these maps using traditional geometric distortion robust matching methods even significant non-linear radiometric difference exists between the original images. Therefore, we can only focus on the geometric distortion when we deal with deep feature maps, and then only focus on non-linear radiometric distortion in patches similarity measurement. The experimental results demonstrate that the proposed method performs better than the state-of-the-art matching methods on multimodal images with both geometric and non-linear radiometric distortion.

scholarly journals High-Resolution Remote Sensing Image Segmentation Framework Based on Attention Mechanism and Adaptive Weighting

2021 ◽  
Vol 10 (4) ◽  
pp. 241
Author(s):  
Yifan Liu ◽  
Qigang Zhu ◽  
Feng Cao ◽  
Junke Chen ◽  
Gang Lu
Keyword(s):  
Semantic Segmentation ◽  
Feature Representation ◽  
Feature Maps ◽  
Multi Scale ◽  
Network Layers ◽  
Branching Structures ◽  
Basic Task ◽  
Deep Feature ◽  
Pass Through ◽  
Filter Noise

Semantic segmentation has been widely used in the basic task of extracting information from images. Despite this progress, there are still two challenges: (1) it is difficult for a single-size receptive field to acquire sufficiently strong representational features, and (2) the traditional encoder-decoder structure directly integrates the shallow features with the deep features. However, due to the small number of network layers that shallow features pass through, the feature representation ability is weak, and noise information will be introduced to affect the segmentation performance. In this paper, an Adaptive Multi-Scale Module (AMSM) and Adaptive Fuse Module (AFM) are proposed to solve these two problems. AMSM adopts the idea of channel and spatial attention and adaptively fuses three-channel branches by setting branching structures with different void rates, and flexibly generates weights according to the content of the image. AFM uses deep feature maps to filter shallow feature maps and obtains the weight of deep and shallow feature maps to filter noise information in shallow feature maps effectively. Based on these two symmetrical modules, we have carried out extensive experiments. On the ISPRS Vaihingen dataset, the F1-score and Overall Accuracy (OA) reached 86.79% and 88.35%, respectively.

scholarly journals Cross-Domain Image Matching with Deep Feature Maps

2019 ◽  
Vol 127 (11-12) ◽  
pp. 1738-1750 ◽  
Author(s):  
Bailey Kong ◽  
James Supanc̆ic̆ ◽  
Deva Ramanan ◽  
Charless C. Fowlkes
Keyword(s):  
Image Matching ◽  
Feature Maps ◽  
Cross Domain ◽  
Deep Feature

scholarly journals Multi-Scale Geospatial Object Detection Based on Shallow-Deep Feature Extraction

2019 ◽  
Vol 11 (21) ◽  
pp. 2525 ◽  
Author(s):  
Dalal AL-Alimi ◽  
Yuxiang Shao ◽  
Ruyi Feng ◽  
Mohammed A. A. Al-qaness ◽  
Mohamed Abd Elaziz ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Object Detection ◽  
Extraction Methods ◽  
Aerial Images ◽  
Detection Accuracy ◽  
Feature Maps ◽  
Deep Convolutional Neural Networks ◽  
Multi Scale ◽  
Deep Feature ◽  
Deep Feature Extraction

Multi-class detection in remote sensing images (RSIs) has garnered wide attention and introduced several service applications in many fields, including civil and military fields. However, several reasons make detection from aerial images very challenging and more difficult than nature scene images: Objects do not have a fixed size, often appear at very various scales and sometimes appear in dense groups, like vehicles and storage tanks, and have different surroundings or background areas. Furthermore, all of this makes the manual annotation of objects very complex and costly. The powerful effect of the feature extraction methods on object detection and the successes of deep convolutional neural networks (CNN) extract deep features more than traditional methods. This study introduced a novel network structure and designed a unique feature extraction which employs squeeze and excitation network (SENet) and residual network (ResNet) to obtain feature maps, named a shallow-deep feature extraction (SDFE), that improves the resolution and the localization at the same time. Furthermore, this novel model reduces the loss of dense groups and small objects, and provides higher and more stable detection accuracy which is not significantly affected by changing the value of the threshold of the intersection over union (IoU) and overcomes the difficulties of RSIs. Moreover, this study introduced strong evidence about the factors that affect the detection of RSIs. The proposed shallow-deep and multi-scale (SD-MS) method outperforms other approaches for the given ten classes of the NWPU VHR-10 dataset.

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