scholarly journals A Coarse-to-Fine Approach for 3D Facial Landmarking by Using Deep Feature Fusion

Symmetry ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 308 ◽  
Author(s):  
Kai Wang ◽  
Xi Zhao ◽  
Wanshun Gao ◽  
Jianhua Zou
Keyword(s):  
Feature Fusion ◽  
State Of The Art ◽  
Facial Feature ◽  
Feature Points ◽  
Lighting Condition ◽  
Facial Landmarks ◽  
3D Data ◽  
Deep Feature ◽  
Local Patch ◽  
Coarse To Fine

Facial landmarking locates the key facial feature points on facial data, which provides not only information on semantic facial structures, but also prior knowledge for other kinds of facial analysis. However, most of the existing works still focus on the 2D facial image which may suffer from lighting condition variations. In order to address this limitation, this paper presents a coarse-to-fine approach to accurately and automatically locate the facial landmarks by using deep feature fusion on 3D facial geometry data. Specifically, the 3D data is converted to 2D attribute maps firstly. Then, the global estimation network is trained to predict facial landmarks roughly by feeding the fused CNN (Convolutional Neural Network) features extracted from facial attribute maps. After that, input the local fused CNN features extracted from the local patch around each landmark estimated previously, and other local models are trained separately to refine the locations. Tested on the Bosphorus and BU-3DFE datasets, the experimental results demonstrated effectiveness and accuracy of the proposed method for locating facial landmarks. Compared with existed methods, our results have achieved state-of-the-art performance.

Accurate landmarking from 3D facial scans by CNN and cascade regression

2018 ◽  
Vol 16 (02) ◽  
pp. 1840007 ◽  
Author(s):  
Wang Kai ◽  
Jun An ◽  
Xi Zhao ◽  
Jianhua Zou
Keyword(s):  
Mapping Function ◽  
Facial Feature ◽  
Feature Points ◽  
Lighting Condition ◽  
The Face ◽  
Geometry Feature ◽  
The Mean ◽  
Scan Data ◽  
Cascade Regression ◽  
Coarse To Fine

Facial landmarking locates the key facial feature points on facial data, which provides not only information on semantic facial structures, but also prior knowledge for other types of facial analysis. However, most of the existing works still focus on the 2D facial image which is quite sensitive to the lighting condition changes. In order to address this limitation, this paper proposed a coarse-to-fine method only based on the 3D facial scan data extracted from professional equipment to automatically and accurately estimate the landmark localization. Specifically, we firstly trained a convolutional neural network (CNN) to initialize the face landmarks instead of the mean shape. Then the proposed cascade regression networks learn the mapping function between 3D facial geometry feature and landmarks location. Tested on Bosphorus database, the experimental results demonstrated effectiveness and accuracy of the proposed method for [Formula: see text] landmarks. Compared with other methods, the results in several points demonstrate state-of-the-art performance.

scholarly journals Age prediction based on a small number of facial landmarks and texture features

2021 ◽  
pp. 1-11
Author(s):  
Mengjie Wang ◽  
Weiyang Chen
Keyword(s):  
Essential Feature ◽  
Texture Feature ◽  
Prediction Method ◽  
Texture Features ◽  
Facial Feature ◽  
Support Vector ◽  
Feature Points ◽  
Facial Landmarks ◽  
Geometric Point ◽  
Age Prediction

BACKGROUND: Age is an essential feature of people, so the study of facial aging should have particular significance. OBJECTIVE: The purpose of this study is to improve the performance of age prediction by combining facial landmarks and texture features. METHODS: We first measure the distribution of each texture feature. From a geometric point of view, facial feature points will change with age, so it is essential to study facial feature points. We annotate the facial feature points, label the corresponding feature point coordinates, and then use the coordinates of feature points and texture features to predict the age. RESULTS: We use the Support Vector Machine regression prediction method to predict the age based on the extracted texture features and landmarks. Compared with facial texture features, the prediction results based on facial landmarks are better. This suggests that the facial morphological features contained in facial landmarks can reflect facial age better than facial texture features. Combined with facial landmarks and texture features, the performance of age prediction can be improved. CONCLUSIONS: According to the experimental results, we can conclude that texture features combined with facial landmarks are useful for age prediction.

scholarly journals ART-UP: A Novel Method for Generating Scanning-Robust Aesthetic QR Codes

2021 ◽  
Vol 17 (1) ◽  
pp. 1-23
Author(s):  
Mingliang Xu ◽  
Qingfeng Li ◽  
Jianwei Niu ◽  
Hao Su ◽  
Xiting Liu ◽  
...  
Keyword(s):  
State Of The Art ◽  
Visual Quality ◽  
Qr Code ◽  
Quick Response ◽  
Estimation Model ◽  
Qr Codes ◽  
Excellent Performance ◽  
Novel Method ◽  
Coarse To Fine

Quick response (QR) codes are usually scanned in different environments, so they must be robust to variations in illumination, scale, coverage, and camera angles. Aesthetic QR codes improve the visual quality, but subtle changes in their appearance may cause scanning failure. In this article, a new method to generate scanning-robust aesthetic QR codes is proposed, which is based on a module-based scanning probability estimation model that can effectively balance the tradeoff between visual quality and scanning robustness. Our method locally adjusts the luminance of each module by estimating the probability of successful sampling. The approach adopts the hierarchical, coarse-to-fine strategy to enhance the visual quality of aesthetic QR codes, which sequentially generate the following three codes: a binary aesthetic QR code, a grayscale aesthetic QR code, and the final color aesthetic QR code. Our approach also can be used to create QR codes with different visual styles by adjusting some initialization parameters. User surveys and decoding experiments were adopted for evaluating our method compared with state-of-the-art algorithms, which indicates that the proposed approach has excellent performance in terms of both visual quality and scanning robustness.

scholarly journals Towards Scalable Economic Photovoltaic Potential Analysis Using Aerial Images and Deep Learning

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3800
Author(s):  
Sebastian Krapf ◽  
Nils Kemmerzell ◽  
Syed Khawaja Haseeb Khawaja Haseeb Uddin ◽  
Manuel Hack Hack Vázquez ◽  
Fabian Netzler ◽  
...  
Keyword(s):  
Deep Learning ◽  
System Analysis ◽  
State Of The Art ◽  
Critical Role ◽  
Semantic Segmentation ◽  
Energy System ◽  
Aerial Images ◽  
Potential Analysis ◽  
3D Data ◽  
Challenges And Opportunities

Roof-mounted photovoltaic systems play a critical role in the global transition to renewable energy generation. An analysis of roof photovoltaic potential is an important tool for supporting decision-making and for accelerating new installations. State of the art uses 3D data to conduct potential analyses with high spatial resolution, limiting the study area to places with available 3D data. Recent advances in deep learning allow the required roof information from aerial images to be extracted. Furthermore, most publications consider the technical photovoltaic potential, and only a few publications determine the photovoltaic economic potential. Therefore, this paper extends state of the art by proposing and applying a methodology for scalable economic photovoltaic potential analysis using aerial images and deep learning. Two convolutional neural networks are trained for semantic segmentation of roof segments and superstructures and achieve an Intersection over Union values of 0.84 and 0.64, respectively. We calculated the internal rate of return of each roof segment for 71 buildings in a small study area. A comparison of this paper’s methodology with a 3D-based analysis discusses its benefits and disadvantages. The proposed methodology uses only publicly available data and is potentially scalable to the global level. However, this poses a variety of research challenges and opportunities, which are summarized with a focus on the application of deep learning, economic photovoltaic potential analysis, and energy system analysis.

scholarly journals LPI Radar Waveform Recognition Based on Multi-Resolution Deep Feature Fusion

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 26138-26146
Author(s):  
Xue Ni ◽  
Huali Wang ◽  
Fan Meng ◽  
Jing Hu ◽  
Changkai Tong
Keyword(s):  
Feature Fusion ◽  
Deep Feature

scholarly journals Remote Sensing Image Retrieval with Gabor-CA-ResNet and Split-Based Deep Feature Transform Network

2021 ◽  
Vol 13 (5) ◽  
pp. 869
Author(s):  
Zheng Zhuo ◽  
Zhong Zhou
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
State Of The Art ◽  
Remote Sensing Image ◽  
Storage Space ◽  
Remote Sensing Images ◽  
Retrieval Method ◽  
Organization Management ◽  
Deep Feature ◽  
Feature Transform

In recent years, the amount of remote sensing imagery data has increased exponentially. The ability to quickly and effectively find the required images from massive remote sensing archives is the key to the organization, management, and sharing of remote sensing image information. This paper proposes a high-resolution remote sensing image retrieval method with Gabor-CA-ResNet and a split-based deep feature transform network. The main contributions include two points. (1) For the complex texture, diverse scales, and special viewing angles of remote sensing images, A Gabor-CA-ResNet network taking ResNet as the backbone network is proposed by using Gabor to represent the spatial-frequency structure of images, channel attention (CA) mechanism to obtain stronger representative and discriminative deep features. (2) A split-based deep feature transform network is designed to divide the features extracted by the Gabor-CA-ResNet network into several segments and transform them separately for reducing the dimensionality and the storage space of deep features significantly. The experimental results on UCM, WHU-RS, RSSCN7, and AID datasets show that, compared with the state-of-the-art methods, our method can obtain competitive performance, especially for remote sensing images with rare targets and complex textures.

scholarly journals A Multi-Branch Feature Fusion Strategy Based on an Attention Mechanism for Remote Sensing Image Scene Classification

2021 ◽  
Vol 13 (10) ◽  
pp. 1950
Author(s):  
Cuiping Shi ◽  
Xin Zhao ◽  
Liguo Wang
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Feature Fusion ◽  
State Of The Art ◽  
Rapid Development ◽  
Remote Sensing Image ◽  
Classification Performance ◽  
Attention Mechanism ◽  
Scene Classification

In recent years, with the rapid development of computer vision, increasing attention has been paid to remote sensing image scene classification. To improve the classification performance, many studies have increased the depth of convolutional neural networks (CNNs) and expanded the width of the network to extract more deep features, thereby increasing the complexity of the model. To solve this problem, in this paper, we propose a lightweight convolutional neural network based on attention-oriented multi-branch feature fusion (AMB-CNN) for remote sensing image scene classification. Firstly, we propose two convolution combination modules for feature extraction, through which the deep features of images can be fully extracted with multi convolution cooperation. Then, the weights of the feature are calculated, and the extracted deep features are sent to the attention mechanism for further feature extraction. Next, all of the extracted features are fused by multiple branches. Finally, depth separable convolution and asymmetric convolution are implemented to greatly reduce the number of parameters. The experimental results show that, compared with some state-of-the-art methods, the proposed method still has a great advantage in classification accuracy with very few parameters.

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.

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