Automation of image mining is one of the most important strategic goals in image analysis , recognition and understanding science and technologies . The main subgoals are developing and applying of mathematical theory for constructing image models accepted by efficient pattern recognition algorithms and for standardized representation and selection of image analysis transforms . Automation of image-mining is possible by combined application techniques for image analysis , understanding and recognition . This paper describes a generic image processing language IPL , and a programming environment supporting the language primitives for an image information system . The central notion of IPL is that it allows the user to navigate through the image database and manipulate images using generalized icons . This paper first introduces the combination of virtual network and electronic scanner microscope , and uses nonlinear Burgers method to establish virtual network partial differential equations and the algorithm of scanning electron microscopy , and uses MATLAB software to draw the image of algorithm . It verifies the reliability of program . The method used through two stages of statistical analysis and classification using ANN . The result of statistical analysis showed that NDMI , TCW and MAVI vegetation index were not correlated with water content data so that the three images were used as model input image for vegetation moisture parameter ; while for the parameter of vegetation density obtained SR has the best correlation with data LAI equal to 0,7351 , so that SR then used as image of model input for image of vegetation density . While the surface temperature parameters obtained from thermal image processing using Single Channel Algorithm . This paper proposes arbitrary listening-point generation of sound without source localization , and a theory based on the ray-space representation of light rays using sub-band signal processing . An array of beam-formed dynamic microphone-arrays ( MAs ) , are set and each MA generates a multi frequency layer sound-image ( SImage ) by scanning the viewing range of a camera . Each layer is captured by active microphones in MA for a given frequency range which makes the correct interval to capture that frequency layer , and a sound wave ray-space . In the first case study , located in the west Paris area , France , a model including an optimisation component was proposed to find the best operational exploitation of groundwater and surface water with minimal cost . In the second example , a numerical tool , SimAGE , was developed to explore water management scenarios at the catchment scale and to assess their environmental and economic impacts . In the process of low voltage current transformer surface crack detection , aimed at the problem of inaccurate edge localization of penetration algorithm and poor crack connectivity detected by adaptive Canny algorithm , this paper proposes a new crack detection method based on the fusion of percolation algorithm and adaptive Canny operator . The crack gray-scale image is processed by the percolation algorithm to get binary image of crack and this binary image is named Image-A , while the crack gray-scale image is also processed by the adaptive Canny operator to get binary image of crack and this binary image is named Image-B. Using wavelet transform to extract low-frequency and high-frequency components of Image-A and Image-B , and the low frequency and high frequency components of the two binary images are respectively superposed . The superposed low frequency and high frequency images are inverse transformed to obtain a fusion image . Automatic computation of best views of objects is very useful . For example , they can be used as the starting point of a scene exploration , or to enrich galleries of objects available through Internet by adding an image a model that may help to decide if it is worth downloading . To select the most interesting viewpoint of an object , we use the so-called viewpoint entropy . I-frame is an image type which is defined in MPEG.It is used in many occasions , such as multimedia system based on video searching , playing video in special way . In this paper , a method is given to count the distribution of I-frame in MPEG-2 TS stream . While neural networks have been applied in a variety of situations , they have yet to become widely adopted . This situation is reminiscent of the state of image models before the broad adoption of convolutional neural networks and is due , in part , to a dearth of empirical evidence that neural architectures with the appropriate inductive bias can be successful in this domain . Recently , large scale quantum chemistry calculation and molecular dynamics simulations coupled with advances in high throughput experiments have begun to generate data at an unprecedented rate . Convolutional neural networks are widely adopted in Acoustic Scene Classification ( ASC ) tasks , but they generally carry a heavy computational burden . In this work , we propose a lightweight yet high-performing baseline network inspired by MobileNetV2 , which replaces square convolutional kernels with unidirectional ones to extract features alternately in temporal and frequency dimensions . Furthermore , we explore a dynamic architecture space built on the basis of the proposed baseline with the recent Neural Architecture Search ( NAS ) paradigm , which first trains a supernet that incorporates all candidate networks and then applies a well-known evolutionary algorithm NSGA-II to discover more efficient networks with higher accuracy and lower computational cost . On the test set , the detection accuracy is 98.00\% for a vanilla convolutional neural network ( CNN ) model , 99.07\% for Xception model , 97.00\% for ResNet50 model , and 99.17\% for MobileNetV2 model . The MobileNetV2 model acquires the highest accuracy , and the closest to the highest is the Xception model . The proposed approach has a potential impact on creating a tool for automatically detecting retinal diseases . To address these issues , a novel efficient network architecture , termed pyramid attention recurrent networks ( PAR-Net ) , is proposed for real-time guidewire segmentation and tracking . The proposed PAR-Net contains three major modules , namely pyramid attention module , recurrent residual module and pre-trained MobileNetV2 encoder . Specifically , a hybrid loss function of both reinforced focal loss and dice loss is proposed to better address the issues of class imbalance and misclassified examples . We also perform extensive experiments on the classification and recognition tasks to show the superiority of our method . For example , the grafted MobileNetV2 outperforms the non-grafted MobileNetV2 by about 7 percent on CIFAR-100 dataset . We achieve state-of-the-art pruning results for ResNet-50 with higher accuracy on ImageNet . Furthermore , we show that the light weight MobileNetV2 can further be compressed on ImageNet without a significant drop in performance . Models were evaluated on both NIH-CXR and RIH-CXR test sets based on the area under the receiver operating characteristic curve ( AUROC ) . DenseNet and MobileNetV2 models achieved AUROCs of 0.900 and 0.893 for normal versus abnormal classification on NIH-CXR and AUROCs of 0.960 and 0.951 on RIH-CXR . For the 14 pathological findings in NIH-CXR , MobileNetV2 achieved an AUROC within 0.03 of DenseNet for each finding , with an average difference of 0.01 . Together , these optimizations enable state-of-the-art throughput of 1,998 images/s on the CryptoNets network . Using the client-aided model , we also present homomorphic evaluation of ( to our knowledge ) the largest network to date , namely , pre-trained MobileNetV2 models on the ImageNet dataset , with 60.4%/82.7 % top-1/top-5 accuracy and an amortized runtime of 381 ms/image . FM images enabled very fast training over a few epochs while neither IA nor raw images produced any meaningful training for such low-complexity network . Furthermore , the use of FM images was 7 × to 11 × faster to train per epoch while using 123 × less parameters than a reduced-complexity MobileNetV2 , at comparable performance ( AUC of 0.79 vs 0.80 ) . MobileNetV3-Large detection is over 25 % faster at roughly the same accuracy as MobileNetV2 on COCO detection . MobileNetV3-Large LR-ASPP is 34 % faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation . Extensive experiments show that ISBNet achieves extremely efficient inference in terms of parameter size and FLOPs comparing to existing networks . For example , ISBNet takes only 8.70 % parameters and 31.01 % FLOPs of the efficient network MobileNetV2 with comparable accuracy on CIFAR-10 . In this work , we propose approaches based on U-Net and UNet++ architecture to automate the segmentation task of EndoCV2020 . We use the EfficientNet as our encoder to extract powerful features for our decoders . Data augmentation and pre-trained weights are employed to prevent overfilling and improve generalization . Specifically , we utilize a revised ResNet152 as our backbone to improve the accuracy of the angle-closure identification . For scleral spur localization , we adopt EfficientNet as encoder because of its powerful feature extraction potential . By combining the skip-connect module and pyramid pooling module , the network is able to collect semantic cues in feature maps from multiple dimensions and scales . In this paper , we propose a novel convolutional-recurrent neural network ( CRNN ) for text recognition . Particularly , we adapt the EfficientNet architecture for extracting deep features and propose multi-head attention mechanisms to improve character localization . The experiments show that our EfficientNet-like feature extractor clearly outperforms other previous CNN feature extractors like VGG and ResNet . The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes . Under comparable training settings and flops , the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs . After convergence , the results are aggregated using an average fusion layer . As pre-trained CNN we use in the work the recent state-of-the-art EfficientNet models . In the experiments , we assess the method on four remote sensing datasets acquired over different locations of the earth ’s surface and are labeled by different experts . First , we propose a weighted bi-directional feature pyramid network ( BiFPN ) , which allows easy and fast multi-scale feature fusion ; Second , we propose a compound scaling method that uniformly scales the resolution , depth , and width for all backbone , feature network , and box/class prediction networks at the same time . Based on these optimizations and EfficientNet backbones , we have developed a new family of object detectors , called EfficientDet , which consistently achieve much better efficiency than prior art across a wide spectrum of resource constraints . In particular , with single-model and single-scale , our EfficientDetD7 achieves state-of-the-art 52.2 AP on COCO test-dev with 52 M parameters and 325B FLOPs , being 4x – 9x smaller and using 13x – 42x fewer FLOPs than previous detector . 9 % and 8 % higher than when using it for TL via FT or RI respectively . The EfficientNet B4 obtained an accuracy of 82.29 % for FE , 66.35 % for FT and 50.00 % ( as good as a random guess ) for RI . We present a simple , yet effective and flexible method for action recognition supporting multiple sensor modalities . Multivariate signal sequences are encoded in an image and are then classified using a recently proposed EfficientNet CNN architecture . Our focus was to find an approach that generalizes well across different sensor modalities without specific adaptions while still achieving good results . In this paper , we address the anti-spoofing problem by proposing two end-to-end systems of convolutional neural networks . One model is developed based on the EfficientNet B0 network which has been modified in the final dense layers . The second one , is a very light model of the MobileNet V2 , which has been contracted , modified and retrained efficiently on the data being created based on the Rose-Youtu dataset , for this purpose . In this paper , we propose a new anomaly detection framework applied to the detection of aggressive driving behavior . Our contribution consists in the development of a 3D neural network architecture , based on the state-of-the-art EfficientNet 2D image classifier , for the aggressive driving detection in videos . We propose an EfficientNet3D CNN feature extractor for video analysis , and we compare it with existing feature extractors .
Automation of image mining is one of the most important strategic goals in image analysis , recognition and understanding science and technologies . The main subgoals are developing and applying of mathematical theory for constructing image models accepted by efficient pattern recognition algorithms and for standardized representation and selection of image analysis transforms . Automation of image-mining is possible by combined application techniques for image analysis , understanding and recognition .
This paper describes a generic image processing language IPL , and a programming environment supporting the language primitives for an image information system . The central notion of IPL is that it allows the user to navigate through the image database and manipulate images using generalized icons .
This paper first introduces the combination of virtual network and electronic scanner microscope , and uses nonlinear Burgers method to establish virtual network partial differential equations and the algorithm of scanning electron microscopy , and uses MATLAB software to draw the image of algorithm . It verifies the reliability of program .
The method used through two stages of statistical analysis and classification using ANN . The result of statistical analysis showed that NDMI , TCW and MAVI vegetation index were not correlated with water content data so that the three images were used as model input image for vegetation moisture parameter ; while for the parameter of vegetation density obtained SR has the best correlation with data LAI equal to 0,7351 , so that SR then used as image of model input for image of vegetation density . While the surface temperature parameters obtained from thermal image processing using Single Channel Algorithm .
This paper proposes arbitrary listening-point generation of sound without source localization , and a theory based on the ray-space representation of light rays using sub-band signal processing . An array of beam-formed dynamic microphone-arrays ( MAs ) , are set and each MA generates a multi frequency layer sound-image ( SImage ) by scanning the viewing range of a camera . Each layer is captured by active microphones in MA for a given frequency range which makes the correct interval to capture that frequency layer , and a sound wave ray-space .
In the first case study , located in the west Paris area , France , a model including an optimisation component was proposed to find the best operational exploitation of groundwater and surface water with minimal cost . In the second example , a numerical tool , SimAGE , was developed to explore water management scenarios at the catchment scale and to assess their environmental and economic impacts .
In the process of low voltage current transformer surface crack detection , aimed at the problem of inaccurate edge localization of penetration algorithm and poor crack connectivity detected by adaptive Canny algorithm , this paper proposes a new crack detection method based on the fusion of percolation algorithm and adaptive Canny operator . The crack gray-scale image is processed by the percolation algorithm to get binary image of crack and this binary image is named Image-A , while the crack gray-scale image is also processed by the adaptive Canny operator to get binary image of crack and this binary image is named Image-B. Using wavelet transform to extract low-frequency and high-frequency components of Image-A and Image-B , and the low frequency and high frequency components of the two binary images are respectively superposed . The superposed low frequency and high frequency images are inverse transformed to obtain a fusion image .
Automatic computation of best views of objects is very useful . For example , they can be used as the starting point of a scene exploration , or to enrich galleries of objects available through Internet by adding an image a model that may help to decide if it is worth downloading . To select the most interesting viewpoint of an object , we use the so-called viewpoint entropy .
I-frame is an image type which is defined in MPEG.It is used in many occasions , such as multimedia system based on video searching , playing video in special way . In this paper , a method is given to count the distribution of I-frame in MPEG-2 TS stream .
While neural networks have been applied in a variety of situations , they have yet to become widely adopted . This situation is reminiscent of the state of image models before the broad adoption of convolutional neural networks and is due , in part , to a dearth of empirical evidence that neural architectures with the appropriate inductive bias can be successful in this domain . Recently , large scale quantum chemistry calculation and molecular dynamics simulations coupled with advances in high throughput experiments have begun to generate data at an unprecedented rate .
Convolutional neural networks are widely adopted in Acoustic Scene Classification ( ASC ) tasks , but they generally carry a heavy computational burden . In this work , we propose a lightweight yet high-performing baseline network inspired by MobileNetV2 , which replaces square convolutional kernels with unidirectional ones to extract features alternately in temporal and frequency dimensions . Furthermore , we explore a dynamic architecture space built on the basis of the proposed baseline with the recent Neural Architecture Search ( NAS ) paradigm , which first trains a supernet that incorporates all candidate networks and then applies a well-known evolutionary algorithm NSGA-II to discover more efficient networks with higher accuracy and lower computational cost .
On the test set , the detection accuracy is 98.00\% for a vanilla convolutional neural network ( CNN ) model , 99.07\% for Xception model , 97.00\% for ResNet50 model , and 99.17\% for MobileNetV2 model . The MobileNetV2 model acquires the highest accuracy , and the closest to the highest is the Xception model . The proposed approach has a potential impact on creating a tool for automatically detecting retinal diseases .
To address these issues , a novel efficient network architecture , termed pyramid attention recurrent networks ( PAR-Net ) , is proposed for real-time guidewire segmentation and tracking . The proposed PAR-Net contains three major modules , namely pyramid attention module , recurrent residual module and pre-trained MobileNetV2 encoder . Specifically , a hybrid loss function of both reinforced focal loss and dice loss is proposed to better address the issues of class imbalance and misclassified examples .
We also perform extensive experiments on the classification and recognition tasks to show the superiority of our method . For example , the grafted MobileNetV2 outperforms the non-grafted MobileNetV2 by about 7 percent on CIFAR-100 dataset .
We achieve state-of-the-art pruning results for ResNet-50 with higher accuracy on ImageNet . Furthermore , we show that the light weight MobileNetV2 can further be compressed on ImageNet without a significant drop in performance .
Models were evaluated on both NIH-CXR and RIH-CXR test sets based on the area under the receiver operating characteristic curve ( AUROC ) . DenseNet and MobileNetV2 models achieved AUROCs of 0.900 and 0.893 for normal versus abnormal classification on NIH-CXR and AUROCs of 0.960 and 0.951 on RIH-CXR . For the 14 pathological findings in NIH-CXR , MobileNetV2 achieved an AUROC within 0.03 of DenseNet for each finding , with an average difference of 0.01 .
Together , these optimizations enable state-of-the-art throughput of 1,998 images/s on the CryptoNets network . Using the client-aided model , we also present homomorphic evaluation of ( to our knowledge ) the largest network to date , namely , pre-trained MobileNetV2 models on the ImageNet dataset , with 60.4%/82.7 % top-1/top-5 accuracy and an amortized runtime of 381 ms/image .
FM images enabled very fast training over a few epochs while neither IA nor raw images produced any meaningful training for such low-complexity network . Furthermore , the use of FM images was 7 × to 11 × faster to train per epoch while using 123 × less parameters than a reduced-complexity MobileNetV2 , at comparable performance ( AUC of 0.79 vs 0.80 ) .
MobileNetV3-Large detection is over 25 % faster at roughly the same accuracy as MobileNetV2 on COCO detection . MobileNetV3-Large LR-ASPP is 34 % faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation .
Extensive experiments show that ISBNet achieves extremely efficient inference in terms of parameter size and FLOPs comparing to existing networks . For example , ISBNet takes only 8.70 % parameters and 31.01 % FLOPs of the efficient network MobileNetV2 with comparable accuracy on CIFAR-10 .
In this work , we propose approaches based on U-Net and UNet++ architecture to automate the segmentation task of EndoCV2020 . We use the EfficientNet as our encoder to extract powerful features for our decoders . Data augmentation and pre-trained weights are employed to prevent overfilling and improve generalization .
Specifically , we utilize a revised ResNet152 as our backbone to improve the accuracy of the angle-closure identification . For scleral spur localization , we adopt EfficientNet as encoder because of its powerful feature extraction potential . By combining the skip-connect module and pyramid pooling module , the network is able to collect semantic cues in feature maps from multiple dimensions and scales .
In this paper , we propose a novel convolutional-recurrent neural network ( CRNN ) for text recognition . Particularly , we adapt the EfficientNet architecture for extracting deep features and propose multi-head attention mechanisms to improve character localization . The experiments show that our EfficientNet-like feature extractor clearly outperforms other previous CNN feature extractors like VGG and ResNet .
The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes . Under comparable training settings and flops , the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs .
After convergence , the results are aggregated using an average fusion layer . As pre-trained CNN we use in the work the recent state-of-the-art EfficientNet models . In the experiments , we assess the method on four remote sensing datasets acquired over different locations of the earth ’s surface and are labeled by different experts .
First , we propose a weighted bi-directional feature pyramid network ( BiFPN ) , which allows easy and fast multi-scale feature fusion ; Second , we propose a compound scaling method that uniformly scales the resolution , depth , and width for all backbone , feature network , and box/class prediction networks at the same time . Based on these optimizations and EfficientNet backbones , we have developed a new family of object detectors , called EfficientDet , which consistently achieve much better efficiency than prior art across a wide spectrum of resource constraints . In particular , with single-model and single-scale , our EfficientDetD7 achieves state-of-the-art 52.2 AP on COCO test-dev with 52 M parameters and 325B FLOPs , being 4x – 9x smaller and using 13x – 42x fewer FLOPs than previous detector .
9 % and 8 % higher than when using it for TL via FT or RI respectively . The EfficientNet B4 obtained an accuracy of 82.29 % for FE , 66.35 % for FT and 50.00 % ( as good as a random guess ) for RI .
We present a simple , yet effective and flexible method for action recognition supporting multiple sensor modalities . Multivariate signal sequences are encoded in an image and are then classified using a recently proposed EfficientNet CNN architecture . Our focus was to find an approach that generalizes well across different sensor modalities without specific adaptions while still achieving good results .
In this paper , we address the anti-spoofing problem by proposing two end-to-end systems of convolutional neural networks . One model is developed based on the EfficientNet B0 network which has been modified in the final dense layers . The second one , is a very light model of the MobileNet V2 , which has been contracted , modified and retrained efficiently on the data being created based on the Rose-Youtu dataset , for this purpose .
In this paper , we propose a new anomaly detection framework applied to the detection of aggressive driving behavior . Our contribution consists in the development of a 3D neural network architecture , based on the state-of-the-art EfficientNet 2D image classifier , for the aggressive driving detection in videos . We propose an EfficientNet3D CNN feature extractor for video analysis , and we compare it with existing feature extractors .
5. The algebraic approaches and techniques in image analysis An Image Processing Language with Icon-Assisted Navigation Application of Virtual Network Electron Microscopy in Materials Science PEMODELAN SPASIAL KERENTANAN KEBAKARAN PADA VEGETASI MENGGUNAKAN CITRA LANDSAT 8DAN METODE ARTIFICIAL NEURAL NETWORK (Studi Kasus: Kabupaten Tanah Bumbu, Kalimantan Selatan) Arbitrary Listening-Point Generation Using Sub-Band Representation of Sound Wave Ray-Space Integrated approaches for water resources management: examples from France. Crack Detection Algorithm based on the Fusion of Percolation Theory and Adaptive Canny Operator Fast Adaptive Selection of Best Views Statistic Analysis of I-frame in MPEG-2 TS Stream Neural Message Passing for Quantum Chemistry Neural Architecture Search on Acoustic Scene Classification Comparisonal study of Deep Learning approaches on Retinal OCT Image Pyramid attention recurrent networks for real-time guidewire segmentation and tracking in intraoperative X-ray fluoroscopy Filter Grafting for Deep Neural Networks Group Pruning using a Bounded-Lp norm for Group Gating and Regularization Generalizable Inter-Institutional Classification of Abnormal Chest Radiographs Using Efficient Convolutional Neural Networks nGraph-HE2: A High-Throughput Framework for Neural Network Inference on Encrypted Data The Importance of the Instantaneous Phase for Face Detection using Simple Convolutional Neural Networks Searching for MobileNetV3 ISBNet: Instance-aware Selective Branching Networks A U-Net++ With Pre-Trained EfficientNet Backbone for Segmentation of Diseases and Artifacts in Endoscopy Images and Videos Identification of primary angle-closure on AS-OCT images with Convolutional Neural Networks Improving CRNN with EfficientNet-like feature extractor and multi-head attention for text recognition Designing Network Design Spaces Deep Learning approach for Multiple Source Classification in Remote Sensing Imagery EfficientDet: Scalable and Efficient Object Detection Deep Learning and Transfer Learning applied to Sentinel-1 DInSAR and Sentinel-2 optical satellite imagery for change detection Gimme Signals: Discriminative signal encoding for multimodal activity recognition Attention-Based Face AntiSpoofing of RGB Camera using a Minimal End-2-End Neural Network DriftNet: Aggressive Driving Behavior Classification using 3D EfficientNet Architecture
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ICASSP (5) ICCSA (3) ICML SSIAI EndoCV@ISBI SoICT
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