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Filters
Bilinear CNNs for Fine-grained Visual Recognition
[article]
2017
arXiv
pre-print
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B-CNNs belong to the class of orderless texture representations but unlike prior work they can be trained in an end-to-end manner. ...
These networks represent an image as a pooled outer product of features derived from two CNNs and capture localized feature interactions in a translationally invariant manner. ...
Fig. 2 . 2 Flow of gradients in a B-CNN.
which are aggregated and weighted by the Gaussian mixture model (GMM) posteriors θ(x). ...
arXiv:1504.07889v6
fatcat:qzgah7mwebeitom5kzmiouvosq
CardiacNET: Segmentation of Left Atrium and Proximal Pulmonary Veins from MRI Using Multi-view CNN
[chapter]
2017
Lecture Notes in Computer Science
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The created catheter prototype has an outer diameter of 9 Fr (3 mm) and a steerable distal end that can be deflected in 3D space via four braided high-tensile Spectra ® fiber tendons. ...
The catheter's steerable structure is tendon driven and consists of miniature deflectable, helical segments created by a precise rapid prototyping technique. ...
Jim Trotter for their invaluable assistance in designing the robotic actuator and conducting the experiments. ...
doi:10.1007/978-3-319-66185-8_43
fatcat:mcbx2rqxnreqxafaqqk3c6tpeu
Gabor Convolutional Networks
2018
IEEE Transactions on Image Processing
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In this paper, we propose a new deep model, termed Gabor Convolutional Networks (GCNs or Gabor CNNs), which incorporates Gabor filters into DCNNs to enhance the resistance of deep learned features to the ...
Steerable properties dominate the design of traditional filters, e.g., Gabor filters, and endow features the capability of dealing with spatial transformations. ...
The reason might lie in that our method can capture nonlinear features based on steerable filters, which benefits the food recognition suffering from the noise problems. V. ...
doi:10.1109/tip.2018.2835143
pmid:29870353
fatcat:ux2b5cngund2phleok3oposuqe
DISTRIBUTED SENSOR NETWORKS: A CELLULAR NONLINEAR NETWORK PERSPECTIVE
2003
International Journal of Neural Systems
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This article shows that those networks can be considered as cellular nonlinear networks (CNNs), and that their analysis and design may greatly benefit from the rich theoretical results available for CNNs ...
Advances in hardware technology and engineering design have led to dramatic reductions in size, power consumption, and cost for digital circuitry, and wireless communications. ...
Acknowledgment The partial support of the DARPA/IXO-NEST Program (contract AF-F30602-01-2-0526) is gratefully acknowledged. ...
doi:10.1142/s0129065703001686
pmid:15031848
fatcat:cpyskajmrvdrlgvbua6jy5vwlu
Features and Classification Methods to Locate Deciduous Trees in Images
1999
Computer Vision and Image Understanding
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We compare features and classification methods to locate deciduous trees in images. ...
Our analysis of the relevance of 51 features from seven feature extraction methods based on the graylevel co-occurrence matrix, Gabor filters, fractal dimension, steerable filters, the Fourier transform ...
so far. ...
doi:10.1006/cviu.1999.0769
fatcat:c6ews3vbybbx7c4x5svqqs7lba
A Novel Approach for Detection of Pavement Crack and Sealed Crack Using Image Processing and Salp Swarm Algorithm Optimized Machine Learning
2022
Advances in Civil Engineering
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During the phase of periodic survey, sealed crack and crack in asphalt pavement surface should be detected accurately. ...
Because crack and sealed crack are both line-based defects and may resemble each other in shape, this study puts forward an innovative method based on computer vision for detecting sealed crack and crack ...
(x, y, σ) zx � − x 2πσ 4 exp − x 2 + y 2 2σ 2 ⎡ ⎣ ⎤ ⎦ , G 90 � zG(x, y, σ) zy � − y 2πσ 4 exp − x 2 + y 2 2σ 2 ⎡ ⎣ ⎤ ⎦ . ( For an arbitrary orientation β, a Gaussian steerable filter is expressed ...
doi:10.1155/2022/9193511
fatcat:zng27yel3ngfjdyom7i3iplt74
Local Feature Descriptor for Image Matching: A Survey
2019
IEEE Access
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Image registration is an important technique in many computer vision applications such as image fusion, image retrieval, object tracking, face recognition, change detection and so on. ...
., how to detect features and how to describe them, play a fundamental and important role in image registration process, which directly influence the accuracy and robustness of image registration. ...
Several modifications in the SIFT model have proposed [51] - [54] in order to improve the repeatability of detection and effectiveness of matching under nonlinear intensity changes and big distortion ...
doi:10.1109/access.2018.2888856
fatcat:rshfx326izcfdgk5ed6umit7wi
Integrating Flexible Normalization into Mid-Level Representations of Deep Convolutional Neural Networks
[article]
2018
arXiv
pre-print
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Deep convolutional neural networks (CNNs) are becoming increasingly popular models to predict neural responses in visual cortex. ...
However, contextual effects, which are prevalent in neural processing and in perception, are not explicitly handled by current CNNs, including those used for neural prediction. ...
As shown in more detail for the population statistics in the main text, the single GSM model reduces dependencies and makes the marginal distributions closer to Gaussian, but not as much as the mixture ...
arXiv:1806.01823v3
fatcat:noyduukirnfcfh5cvea4rlewku
LGCN: Learnable Gabor Convolution Network for Human Gender Recognition in the Wild
2019
IEICE transactions on information and systems
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In LGCN, a learnable Gabor filter (LGF) is introduced and combined with the convolutional neural network (CNN). ...
Here, LGFs learn intrinsic parameters by using standard back propagation method, so that the values of those parameters are no longer fixed by experience as traditional methods, but can be modified by ...
Unlike these two types of method, the integrationbased methods attempt to combine the steerable handcrafted features with the powerful CNN. ...
doi:10.1587/transinf.2018edl8239
fatcat:6jcuv3hq4vaddalaq3mtm7ps2i
Objects Classification by Learning-Based Visual Saliency Model and Convolutional Neural Network
2016
Computational Intelligence and Neuroscience
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Therefore, in this paper, inspiring the completed processing that humans classify different kinds of objects, we bring forth a new classification method which combines visual attention model and CNN. ...
Our classification method has apparently advantages in biology. Experimental results demonstrated that our method made the efficiency of classification improve significantly. ...
Acknowledgments The work is supported by NSF of China (nos. 61117115 and 61201319) and sponsored by "the Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University ...
doi:10.1155/2016/7942501
pmid:27803711
pmcid:PMC5075645
fatcat:ljazgoox2jahtgrda7hwwzqh3e
Multi-Image-Feature-Based Hierarchical Concrete Crack Identification Framework Using Optimized SVM Multi-Classifiers and D–S Fusion Algorithm for Bridge Structures
2021
Remote Sensing
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Cracks in concrete can cause the degradation of stiffness, bearing capacity and durability of civil infrastructure. Hence, crack diagnosis is of great importance in concrete research. ...
It is generally acknowledged that the light intensities between non-cracked and cracked images are remarkably different, so the nonlinear filters can be utilized to distinguish the difference in light ...
It is generally acknowledged that the light intensities between non-cracked and cracked images are remarkably different, so the nonlinear filters can be utilized to distinguish the difference in light ...
doi:10.3390/rs13020240
fatcat:t5y3e2m4jbhahnzam3yflgyo7u
Integrating Flexible Normalization into Midlevel Representations of Deep Convolutional Neural Networks
2019
Neural Computation
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Deep convolutional neural networks (CNNs) are becoming increasingly popular models to predict neural responses in visual cortex. ...
However, contextual effects, which are prevalent in neural processing and in perception, are not explicitly handled by current CNNs, including those used for neural prediction. ...
This is particularly necessary in the context of deep CNNs, and even more so at middle levels. ...
doi:10.1162/neco_a_01226
pmid:31525314
fatcat:vomtxnprx5b3heeg7f7rzqlgyq
Deep Learning of Human Visual Sensitivity in Image Quality Assessment Framework
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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In this paper, we propose a novel convolutional neural networks (CNN) based FR-IQA model, named Deep Image Quality Assessment (DeepQA), where the behavior of the HVS is learned from the underlying data ...
In addition, DeepQA achieves the state-ofthe-art prediction accuracy among FR-IQA models. ...
The visual sensitivity map is obtained from the CNN models s 1 = CN N 1 (Î d ; θ 1 ) (2) s 2 = CN N 2 (Î d , e; θ 2 ) (3) where CN N 1 (·) and CN N 2 (·) indicate the CNN models of DeepQA-s and DeepQA ...
doi:10.1109/cvpr.2017.213
dblp:conf/cvpr/Kim017
fatcat:4joenw35lfeorlnrmvle7vcbhe
Head pose estimation in the wild using Convolutional Neural Networks and adaptive gradient methods
2017
Pattern Recognition
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The results show that joining CNNs and adaptive gradient methods leads to the state-of-the-art in unconstrained head pose estimation. ...
In order to handle these limitations we propose an approach based on Convolutional Neural Networks (CNNs) supplemented with the most recent techniques adopted from the deep learning community. ...
We can consider CNNs as part of these methods. Nonlinear methods have many advantages. ...
doi:10.1016/j.patcog.2017.06.009
fatcat:ycif4k5exjhgracjsqarh3jqam
Analyzing Filters Toward Efficient ConvNet
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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In this paper, in contrast to the activations, we focus on filters which are main components of ConvNets. ...
They render the filter bases formulated in a parameter-free form as well as the efficient representation for the FC layer. ...
[6] 13.19 steerable 5 [21] 4 [15] 2 [6] 13.34 steerable 7 [36] 4 [15] 2 [6] 13.26 steerable 6 [28] 3 [10] 2 [6] 13.40 steerable 6 [28] 5 [21] 2 [6] 13.26 steerable 6 [28] 4 ...
doi:10.1109/cvpr.2018.00589
dblp:conf/cvpr/Kobayashi18
fatcat:akixcxe67rdnnkg5mqjsj4d6ge
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