However , for some complex and/or low-quality images , the alignments estimated by the content-based attention network are not accurate enough , and so , the generated glimpse vector is also not powerful enough to represent the predicted character at current time step . To solve this problem , in the paper we propose a memory-augmented attention model for scene text recognition . The proposed memory-augmented attention network ( MAAN ) feeds the part of character sequence already generated and all attended alignment history to the attention model when predicting the character at current time step . The proposed technique has been evaluated over two related but very different computer vision challenges , namely , scene text detection and scene text recognition . Experiments over a number of public datasets demonstrate the effectiveness of our proposed image synthesis technique - the use of our synthesized images in deep network training is capable of achieving similar or even better scene text detection and scene text recognition performance as compared with using real images . Scene text detection and recognition are popular research topics in computer vision due to its various applications such as autonomous driving , blind assistance and text translation . However , many methods currently can only detect or recog-nize the text of one language . We present a new feature representation method for scene text recognition problem , particularly focusing on improving scene character recognition . Many existing methods rely on Histogram of Oriented Gradient ( HOG ) or part-based models , which do not span the feature space well for characters in natural scene images , especially given large variation in fonts with cluttered backgrounds . This general method is not only limited to gait-based age estimation , but also can be used for face-based age estimation . ( 3 ) In “ FACLSTM : ConvLSTM with focused attention for scene text recognition ” , Wang et al. tackle scene text recognition problem from a spatiotemporal prediction perspective . They propose the ConvLSTM model for reading scene text from 2D space , by which attention mechanism and character center masks are further adopted for enhancing the recognition performance . The learning machines always learn from the properties associated to provided samples . The numerous approaches are designed in recent years for scene text extraction and recognition and the efforts are underway to improve the accuracy . The convolutional approach provided reasonable results on non-cursive text analysis appeared in natural images . First , the dusty image is enhanced by enhancement algorithm and then text are detected and recognized in the enhanced image . This paper presents literature review of work on scene images text recognition in past few years . Their corresponding advantages and disadvantages and overviews on various text related application are also present . Synth-Text Transfer Network utilizes a style transfer approach to synthesis images with arbitrary text content with preserving the texture of the referenced style image in the target dataset . The large amount of synthesized images can help to alleviate the overfitting problem and improve the accuracy in latter scene text image recognition tasks . In addition , our proposed method is flexible and fast , which has a relatively fast speed among regular style transfer approaches . As for scene text recognition , the changing character types , sharpness , sizes , and font families make it difficult to accurately recognize the text . In this paper , we have carried out work on both scene text location and recognition . We apply the general objects location methods to the scene text location task . However , when applied to unconstrained images , the recognition rates drop drastically . Therefore , the employment of object recognition-based techniques has become state of the art in scene text recognition applications . This paper presents a scene text recognition method tailored to ancient coin legends and compares the results achieved in character and word recognition experiments to a standard OCR engine . With the development of technology , deep learning has achieved good results in the field of image recognition . Therefore , this paper mainly summarizes the representative achievements in scene text recognition field based on deep learning method . This paper focuses on scene text detection . Scene text detection is challenging because scene texts have different sizes , width - height aspect ratios , font styles , lighting , perspective distortion , orientation , etc . As the orientation information is useful for scene text recognition and other tasks , scene text detection is different from common object detection tasks that the text orientation should be also be predicted in addition to the axis - aligned bounding box information . In recent years the field of computer vision has seen an explosion of success stories involving convolutional neural networks ( convnets ) . Such architectures currently provide top results for general object classification , general object detection , feature matching , stereo matching , scene recognition , pose estimation , action recognition and many other tasks . Pedestrian detection is a canonical case of object detection with relevant applications in car safety , surveillance , and robotics . section : Related Work In recent years , scene text detection and recognition has drawn more and more attention . But scene text detection remains a difficult problem due to its complicated orientation and background . By gradually expanding the detected areas from small kernels to large and complete instances via multiple semantic segmentation maps , our method is robust to shapes and can easily distinguish those text instances which are very close or even partially intersected . The experiments on scene text detection benchmarks demonstrate the superior performance of the proposed method . There are multiple directions to explore in the future . One of the advantages of DCNNs is their representation robustness to object locations , which is useful for object recognition tasks . However , this also discards spatial information , which is useful when dealing with topological information of the image ( e.g. scene parsing , face recognition ) . Adopting graphical models ( GMs ) to incorporate spatial and contextual information into the DCNNs is expected to improve the performance of DCNN-based computer vision tasks . Exploiting multiple modalities for semantic scene parsing has been shown to improve accuracy over the singlemodality scenario . However multimodal datasets often suffer from problems such as data misalignment and label inconsistencies , where the existing methods assume that corresponding regions in two modalities must have identical labels . Exploiting multiple modalities for semantic scene parsing has been shown to improve accuracy over the single modality scenario . Existing methods , however , assume that corresponding regions in two modalities have the same label . For better training MPF-RNN , we propose a new strategy that considers accumulative loss at multiple recurrent steps to improve performance of the MPF-RNN on parsing small objects . With these two novel components , MPF-RNN has achieved significant improvement over strong baselines ( VGG16 and Res101 ) on five challenging scene parsing benchmarks , including traditional SiftFlow , Barcelona , CamVid , Stanford Background as well as the recently released large-scale ADE20 K . Whose call is it in complicated scene classification ? . Scene semantic parsing is a challenging problem in the field of computer vision . Most approaches exploit low-level features to describe the whole scene . Recent advancement in real-time 3D scanning has upraised the need to maximally crunch the 3D data in minimum possible time . PointNet is a modern end-to-end deep learning architecture which directly utilizes point cloud without transforming it to regular formats for object- classification , part segmentation and scene semantic parsing . In this paper , we have proposed a novel set of hyper-parameters for PointNet which significantly reduced the size of network while achieving the same accuracy in 3D object classification as of the original network . The main disadvantage of this strategy is that errors in the initial proposals ( e.g : super - pixels ) may lead to poor predictions , no matter how good the feature extraction process is . Pinheiro and Collobert [ reference ] employed an RNN to model the spatial dependencies during scene parsing . In contrast to their approach , we show that a typical graphical model such as a CRF can be formulated as an RNN to form a part of a deep network , to perform end - to - end training combined with a CNN . On the CIFAR - 10 and CIFAR - 100 datasets , dasNet outperforms the previous state - of - the - art model on unaugmented datasets . 1 Introduction Deep convolutional neural networks ( CNNs ) [ 1 ] with max - pooling layers [ 2 ] trained by backprop [ 3 ] on GPUs [ 4 ] have become the state - of - the - art in object recognition [ 5 , 6 , 7 , 8 ] , segmentation / detection [ 9 , 10 ] , and scene parsing [ 11 , 12 ] ( for an extensive review see [ 13 ] ) . The system first uses four fish-eye lenses mounted around a vehicle to capture images . Then , according to the pattern of image acquisition , camera calibration , image stitching , and scene generation , the 3-D surround driving environment is created . To achieve the real-time and easy-to-handle performance , we only use one image to finish the camera calibration through a special designed checkerboard . The proposed method comprises a single-stream network that iteratively refines the scene graph with a nested graph neural network . We demonstrate that our approach achieves competitive performance with the state-of-the-art for scene graph generation on the Visual Genome dataset , while requiring fewer parameters than other methods . We also show that ILAC can improve regular object detectors by incorporating relational image-level information . Then , it further predicts subject-object relations based on both the visual and semantic features of entities under certain contextual area , and fairly ranks the relation predictions for those with a few annotations . Experiments on the popular Visual Genome dataset show that RiFa achieves state-of-the-art performance under several challenging settings of scene graph task . Especially , it performs significantly better on capturing different semantic properties of relations , and obtains the best overall per relation performance . This work covers the problems of scene graph systems . Currently used systems like Open Inventor or OpenGL Performer cover current and coming developments only partially . Generating scene graph to describe the object interactions inside an image gains increasing interests these years . However , most of the previous methods use complicated structures with slow inference speed or rely on the external data , which limits the usage of the model in real-life scenarios . Generation of scene graphs and natural language captions from images for deep image understanding is an ongoing research problem . Scene graphs and natural language captions have a common characteristic in that they are generated by considering the objects in the images and the relationships between the objects . We also describe the changes of the AODV protocol by AODVUU . Then , we simulate the configurations of mobile nodes and scene generating by the network simulator NS2 . Lastly , we compare and analyze the performances of DSDV , AODV and AODVUU protocols from packet delivery rate to end-to-end average delay , together with the interpretation for it . According to the practical working condition , motion analysis of TBM erector was carried out , and motion model was founded . Some work of scene graph constructing , module converting and OpenGL-Performer-based program composing were introduced . Finally , a certain TBM motion simulation program was developed . The second , Proximal Relationship Ambiguity , arises when multiple subject-predicate-object triplets appear in close proximity with the same predicate , and the model struggles to infer the correct subject-object pairings ( e.g. mis-pairing musicians and their instruments ) . We propose a set of contrastive loss formulations that specifically target these types of errors within the scene graph generation problem , collectively termed the Graphical Contrastive Losses . These losses explicitly force the model to disambiguate related and unrelated instances through margin constraints specific to each type of confusion . Image contrast enhancement for outdoor vision is important for smart car auxiliary transport systems . The video frames captured in poor weather conditions are often characterized by poor visibility ... Rain streaks removal is an important issue of the outdoor vision system and recently has been investigated extensively . In the past decades , maximum a posterior and network-based architecture have been attracting considerable attention for this problem . The invention provides a transformation method for carrying out centralized power supply on an outdoor lighting system . The method includes the first step of preparing an AC/DC conversion circuit , a spare AC/DC conversion circuit , a centralized controller and the outdoor lighting system , the second step of allowing the input end of the AC/DC conversion circuit to be connected with a mains supply , the third step of allowing the positive pole of the output end of the AC/DC conversion circuit to be connected with a live wire of the outdoor lighting system , allowing the negative pole of the output end of the AC/DC conversion circuit to be connected with a null wire of the outdoor lighting system to provide stable DC voltage for the outdoor lighting system , the fourth step of allowing the spare AC/DC conversion circuit to be connected with the AC/DC conversion circuit in parallel , wherein the spare AC/DC conversion circuit and the AC/DC conversion circuit works alternately , and the fifth step of allowing the centralized controller to be electrically connected with the AC/DC conversion circuit , the spare AC/DC conversion circuit and the outdoor lighting system respectively so as to carry out centralized control on all components . This paper introduces these new soft technologies and their application to solve some key problems on outdoor lighting control . Then some cases are illustrated to describe the core technology and application , which is meaningful for the outdoor lighting systems . Foggy image enhancement is an important branch of digital image processing , which significantly benefits traffic and outdoor visual systems . To overcome the shortcomings of the existing foggy image enhancement algorithms , we have developed a method that combines Principal Component Analysis ( PCA ) , Multi-Scale Retinex ( MSR ) and Global Histogram Equalization ( GHE ) . In order to reduce the bad effect in the outdoor visibility system in foggy days and achieve the real-time dehazing effect , a fast and effective method , which is based on Bayer pattern image and dark channel prior , was developed to remove the haze from single image . The color image processing pipeline was improved through this method . The invention relates to the relevant technical field of outdoor illumination , in particular to an outdoor illumination system and an intelligent control system . The outdoor illumination system comprises a light intensity sensor , an electric control device and an illumination device . This is the topic of this thesis : modeling priors using deep machine learning to solve typically ill-posed problems . More specifically , we will cover three research axes : 1 ) surface reconstruction using photometric cues , 2 ) outdoor illumination estimation from a single image and 3 ) camera calibration estimation from a single image with generic content . These three topics will be addressed from a data-driven perspective . We validate our method through a wide set of experiments on synthetic data , as well as on a novel dataset of real photographs with ground truth . Our approach finds applications in a variety of settings , ranging from outdoor light capture to image matching . Rendering Per-Pixel Lists for Single Pass A-Buffer Sylvain Lefebvre , Samuel Hornus , and Anass Lasram Reducing Texture Memory Usage by 2-Channel Color Encoding Krzysztof Kluczek Particle-Based Simulation of Material Aging Tobias Gunther , Kai Rohmer , and Thorsten Grosch Simple Rasterization-Based Liquids Martin Guay Lighting and Shading Physically Based Area Lights Micha Drobot High Performance Outdoor Light Scattering using Epipolar Sampling Egor Yusov Volumetric Light Effects in Killzone Shadow Fall Nathan Vos Hi-Z Screen-Space Cone-Traced Reflections Yasin Uludag TressFX : Advanced Real-Time Hair Rendering Timothy Martin , Wolfgang Engel , Nicolas Thibieroz , Jason Yang , and Jason Lacroix Wire Anti-Aliasing Emil Persson Image Space Screen Space Grass David Pangerl Screen Space Deformable Meshes via CSG with Per-Pixel Linked Lists Joao Raza and Gustavo Nunes Bokeh Effects on the SPU Serge Bernier Mobile Devices Realistic Real-Time Skin Rendering on Mobile Renaldas Zioma and Ole Ciliox Deferred Rendering Techniques on Mobile Devices Ashley Vaughan Smith Bandwidth Efficient Graphics with ARM(R ) Mali . We propose a data-driven learned sky model , which we use for outdoor lighting estimation from a single image . As no large-scale dataset of images and their corresponding ground truth illumination is readily available , we use complementary datasets to train our approach , combining the vast diversity of illumination conditions of SUN360 with the radiometrically calibrated and physically accurate Laval HDR sky database . This paper describes ( 1 ) a novel , effective algorithm for outdoor visual position estimation ; ( 2 ) the implementation of this algorithm in the Viper system ; and ( 3 ) the extensive tests that have demonstrated the superior accuracy and speed of the algorithm . The Viper system ( Visual Position Estimator for Rovers ) is geared towards robotic space missions , and the central purpose of the system is to increase the situational awareness of a rover operator by presenting accurate position estimates .
However , for some complex and/or low-quality images , the alignments estimated by the content-based attention network are not accurate enough , and so , the generated glimpse vector is also not powerful enough to represent the predicted character at current time step . To solve this problem , in the paper we propose a memory-augmented attention model for scene text recognition . The proposed memory-augmented attention network ( MAAN ) feeds the part of character sequence already generated and all attended alignment history to the attention model when predicting the character at current time step .
The proposed technique has been evaluated over two related but very different computer vision challenges , namely , scene text detection and scene text recognition . Experiments over a number of public datasets demonstrate the effectiveness of our proposed image synthesis technique - the use of our synthesized images in deep network training is capable of achieving similar or even better scene text detection and scene text recognition performance as compared with using real images .
Scene text detection and recognition are popular research topics in computer vision due to its various applications such as autonomous driving , blind assistance and text translation . However , many methods currently can only detect or recog-nize the text of one language .
We present a new feature representation method for scene text recognition problem , particularly focusing on improving scene character recognition . Many existing methods rely on Histogram of Oriented Gradient ( HOG ) or part-based models , which do not span the feature space well for characters in natural scene images , especially given large variation in fonts with cluttered backgrounds .
This general method is not only limited to gait-based age estimation , but also can be used for face-based age estimation . ( 3 ) In “ FACLSTM : ConvLSTM with focused attention for scene text recognition ” , Wang et al. tackle scene text recognition problem from a spatiotemporal prediction perspective . They propose the ConvLSTM model for reading scene text from 2D space , by which attention mechanism and character center masks are further adopted for enhancing the recognition performance .
The learning machines always learn from the properties associated to provided samples . The numerous approaches are designed in recent years for scene text extraction and recognition and the efforts are underway to improve the accuracy . The convolutional approach provided reasonable results on non-cursive text analysis appeared in natural images .
First , the dusty image is enhanced by enhancement algorithm and then text are detected and recognized in the enhanced image . This paper presents literature review of work on scene images text recognition in past few years . Their corresponding advantages and disadvantages and overviews on various text related application are also present .
Synth-Text Transfer Network utilizes a style transfer approach to synthesis images with arbitrary text content with preserving the texture of the referenced style image in the target dataset . The large amount of synthesized images can help to alleviate the overfitting problem and improve the accuracy in latter scene text image recognition tasks . In addition , our proposed method is flexible and fast , which has a relatively fast speed among regular style transfer approaches .
As for scene text recognition , the changing character types , sharpness , sizes , and font families make it difficult to accurately recognize the text . In this paper , we have carried out work on both scene text location and recognition . We apply the general objects location methods to the scene text location task .
However , when applied to unconstrained images , the recognition rates drop drastically . Therefore , the employment of object recognition-based techniques has become state of the art in scene text recognition applications . This paper presents a scene text recognition method tailored to ancient coin legends and compares the results achieved in character and word recognition experiments to a standard OCR engine .
With the development of technology , deep learning has achieved good results in the field of image recognition . Therefore , this paper mainly summarizes the representative achievements in scene text recognition field based on deep learning method .
This paper focuses on scene text detection . Scene text detection is challenging because scene texts have different sizes , width - height aspect ratios , font styles , lighting , perspective distortion , orientation , etc . As the orientation information is useful for scene text recognition and other tasks , scene text detection is different from common object detection tasks that the text orientation should be also be predicted in addition to the axis - aligned bounding box information .
In recent years the field of computer vision has seen an explosion of success stories involving convolutional neural networks ( convnets ) . Such architectures currently provide top results for general object classification , general object detection , feature matching , stereo matching , scene recognition , pose estimation , action recognition and many other tasks . Pedestrian detection is a canonical case of object detection with relevant applications in car safety , surveillance , and robotics .
section : Related Work In recent years , scene text detection and recognition has drawn more and more attention . But scene text detection remains a difficult problem due to its complicated orientation and background .
By gradually expanding the detected areas from small kernels to large and complete instances via multiple semantic segmentation maps , our method is robust to shapes and can easily distinguish those text instances which are very close or even partially intersected . The experiments on scene text detection benchmarks demonstrate the superior performance of the proposed method . There are multiple directions to explore in the future .
One of the advantages of DCNNs is their representation robustness to object locations , which is useful for object recognition tasks . However , this also discards spatial information , which is useful when dealing with topological information of the image ( e.g. scene parsing , face recognition ) . Adopting graphical models ( GMs ) to incorporate spatial and contextual information into the DCNNs is expected to improve the performance of DCNN-based computer vision tasks .
Exploiting multiple modalities for semantic scene parsing has been shown to improve accuracy over the singlemodality scenario . However multimodal datasets often suffer from problems such as data misalignment and label inconsistencies , where the existing methods assume that corresponding regions in two modalities must have identical labels .
Exploiting multiple modalities for semantic scene parsing has been shown to improve accuracy over the single modality scenario . Existing methods , however , assume that corresponding regions in two modalities have the same label .
For better training MPF-RNN , we propose a new strategy that considers accumulative loss at multiple recurrent steps to improve performance of the MPF-RNN on parsing small objects . With these two novel components , MPF-RNN has achieved significant improvement over strong baselines ( VGG16 and Res101 ) on five challenging scene parsing benchmarks , including traditional SiftFlow , Barcelona , CamVid , Stanford Background as well as the recently released large-scale ADE20 K .
Whose call is it in complicated scene classification ? . Scene semantic parsing is a challenging problem in the field of computer vision . Most approaches exploit low-level features to describe the whole scene .
Recent advancement in real-time 3D scanning has upraised the need to maximally crunch the 3D data in minimum possible time . PointNet is a modern end-to-end deep learning architecture which directly utilizes point cloud without transforming it to regular formats for object- classification , part segmentation and scene semantic parsing . In this paper , we have proposed a novel set of hyper-parameters for PointNet which significantly reduced the size of network while achieving the same accuracy in 3D object classification as of the original network .
The main disadvantage of this strategy is that errors in the initial proposals ( e.g : super - pixels ) may lead to poor predictions , no matter how good the feature extraction process is . Pinheiro and Collobert [ reference ] employed an RNN to model the spatial dependencies during scene parsing . In contrast to their approach , we show that a typical graphical model such as a CRF can be formulated as an RNN to form a part of a deep network , to perform end - to - end training combined with a CNN .
On the CIFAR - 10 and CIFAR - 100 datasets , dasNet outperforms the previous state - of - the - art model on unaugmented datasets . 1 Introduction Deep convolutional neural networks ( CNNs ) [ 1 ] with max - pooling layers [ 2 ] trained by backprop [ 3 ] on GPUs [ 4 ] have become the state - of - the - art in object recognition [ 5 , 6 , 7 , 8 ] , segmentation / detection [ 9 , 10 ] , and scene parsing [ 11 , 12 ] ( for an extensive review see [ 13 ] ) .
The system first uses four fish-eye lenses mounted around a vehicle to capture images . Then , according to the pattern of image acquisition , camera calibration , image stitching , and scene generation , the 3-D surround driving environment is created . To achieve the real-time and easy-to-handle performance , we only use one image to finish the camera calibration through a special designed checkerboard .
The proposed method comprises a single-stream network that iteratively refines the scene graph with a nested graph neural network . We demonstrate that our approach achieves competitive performance with the state-of-the-art for scene graph generation on the Visual Genome dataset , while requiring fewer parameters than other methods . We also show that ILAC can improve regular object detectors by incorporating relational image-level information .
Then , it further predicts subject-object relations based on both the visual and semantic features of entities under certain contextual area , and fairly ranks the relation predictions for those with a few annotations . Experiments on the popular Visual Genome dataset show that RiFa achieves state-of-the-art performance under several challenging settings of scene graph task . Especially , it performs significantly better on capturing different semantic properties of relations , and obtains the best overall per relation performance .
This work covers the problems of scene graph systems . Currently used systems like Open Inventor or OpenGL Performer cover current and coming developments only partially .
Generating scene graph to describe the object interactions inside an image gains increasing interests these years . However , most of the previous methods use complicated structures with slow inference speed or rely on the external data , which limits the usage of the model in real-life scenarios .
Generation of scene graphs and natural language captions from images for deep image understanding is an ongoing research problem . Scene graphs and natural language captions have a common characteristic in that they are generated by considering the objects in the images and the relationships between the objects .
We also describe the changes of the AODV protocol by AODVUU . Then , we simulate the configurations of mobile nodes and scene generating by the network simulator NS2 . Lastly , we compare and analyze the performances of DSDV , AODV and AODVUU protocols from packet delivery rate to end-to-end average delay , together with the interpretation for it .
According to the practical working condition , motion analysis of TBM erector was carried out , and motion model was founded . Some work of scene graph constructing , module converting and OpenGL-Performer-based program composing were introduced . Finally , a certain TBM motion simulation program was developed .
The second , Proximal Relationship Ambiguity , arises when multiple subject-predicate-object triplets appear in close proximity with the same predicate , and the model struggles to infer the correct subject-object pairings ( e.g. mis-pairing musicians and their instruments ) . We propose a set of contrastive loss formulations that specifically target these types of errors within the scene graph generation problem , collectively termed the Graphical Contrastive Losses . These losses explicitly force the model to disambiguate related and unrelated instances through margin constraints specific to each type of confusion .
Image contrast enhancement for outdoor vision is important for smart car auxiliary transport systems . The video frames captured in poor weather conditions are often characterized by poor visibility ...
Rain streaks removal is an important issue of the outdoor vision system and recently has been investigated extensively . In the past decades , maximum a posterior and network-based architecture have been attracting considerable attention for this problem .
The invention provides a transformation method for carrying out centralized power supply on an outdoor lighting system . The method includes the first step of preparing an AC/DC conversion circuit , a spare AC/DC conversion circuit , a centralized controller and the outdoor lighting system , the second step of allowing the input end of the AC/DC conversion circuit to be connected with a mains supply , the third step of allowing the positive pole of the output end of the AC/DC conversion circuit to be connected with a live wire of the outdoor lighting system , allowing the negative pole of the output end of the AC/DC conversion circuit to be connected with a null wire of the outdoor lighting system to provide stable DC voltage for the outdoor lighting system , the fourth step of allowing the spare AC/DC conversion circuit to be connected with the AC/DC conversion circuit in parallel , wherein the spare AC/DC conversion circuit and the AC/DC conversion circuit works alternately , and the fifth step of allowing the centralized controller to be electrically connected with the AC/DC conversion circuit , the spare AC/DC conversion circuit and the outdoor lighting system respectively so as to carry out centralized control on all components .
This paper introduces these new soft technologies and their application to solve some key problems on outdoor lighting control . Then some cases are illustrated to describe the core technology and application , which is meaningful for the outdoor lighting systems .
Foggy image enhancement is an important branch of digital image processing , which significantly benefits traffic and outdoor visual systems . To overcome the shortcomings of the existing foggy image enhancement algorithms , we have developed a method that combines Principal Component Analysis ( PCA ) , Multi-Scale Retinex ( MSR ) and Global Histogram Equalization ( GHE ) .
In order to reduce the bad effect in the outdoor visibility system in foggy days and achieve the real-time dehazing effect , a fast and effective method , which is based on Bayer pattern image and dark channel prior , was developed to remove the haze from single image . The color image processing pipeline was improved through this method .
The invention relates to the relevant technical field of outdoor illumination , in particular to an outdoor illumination system and an intelligent control system . The outdoor illumination system comprises a light intensity sensor , an electric control device and an illumination device .
This is the topic of this thesis : modeling priors using deep machine learning to solve typically ill-posed problems . More specifically , we will cover three research axes : 1 ) surface reconstruction using photometric cues , 2 ) outdoor illumination estimation from a single image and 3 ) camera calibration estimation from a single image with generic content . These three topics will be addressed from a data-driven perspective .
We validate our method through a wide set of experiments on synthetic data , as well as on a novel dataset of real photographs with ground truth . Our approach finds applications in a variety of settings , ranging from outdoor light capture to image matching .
Rendering Per-Pixel Lists for Single Pass A-Buffer Sylvain Lefebvre , Samuel Hornus , and Anass Lasram Reducing Texture Memory Usage by 2-Channel Color Encoding Krzysztof Kluczek Particle-Based Simulation of Material Aging Tobias Gunther , Kai Rohmer , and Thorsten Grosch Simple Rasterization-Based Liquids Martin Guay Lighting and Shading Physically Based Area Lights Micha Drobot High Performance Outdoor Light Scattering using Epipolar Sampling Egor Yusov Volumetric Light Effects in Killzone Shadow Fall Nathan Vos Hi-Z Screen-Space Cone-Traced Reflections Yasin Uludag TressFX : Advanced Real-Time Hair Rendering Timothy Martin , Wolfgang Engel , Nicolas Thibieroz , Jason Yang , and Jason Lacroix Wire Anti-Aliasing Emil Persson Image Space Screen Space Grass David Pangerl Screen Space Deformable Meshes via CSG with Per-Pixel Linked Lists Joao Raza and Gustavo Nunes Bokeh Effects on the SPU Serge Bernier Mobile Devices Realistic Real-Time Skin Rendering on Mobile Renaldas Zioma and Ole Ciliox Deferred Rendering Techniques on Mobile Devices Ashley Vaughan Smith Bandwidth Efficient Graphics with ARM(R ) Mali .
We propose a data-driven learned sky model , which we use for outdoor lighting estimation from a single image . As no large-scale dataset of images and their corresponding ground truth illumination is readily available , we use complementary datasets to train our approach , combining the vast diversity of illumination conditions of SUN360 with the radiometrically calibrated and physically accurate Laval HDR sky database .
This paper describes ( 1 ) a novel , effective algorithm for outdoor visual position estimation ; ( 2 ) the implementation of this algorithm in the Viper system ; and ( 3 ) the extensive tests that have demonstrated the superior accuracy and speed of the algorithm . The Viper system ( Visual Position Estimator for Rovers ) is geared towards robotic space missions , and the central purpose of the system is to increase the situational awareness of a rover operator by presenting accurate position estimates .
Memory-Augmented Attention Model for Scene Text Recognition Scene Text Synthesis for Efficient and Effective Deep Network Training MLTS: A Multi-Language Scene Text Spotter Region-Based Discriminative Feature Pooling for Scene Text Recognition Special focus on deep learning for computer vision Cursive Scene Text Analysis by Deep Convolutional Linear Pyramids A Review on Detection and Recognition of Text Synthesizing Scene Text Images for Recognition with Style Transfer Research on Text Location and Recognition in Natural Images with Deep Learning Reading Ancient Coin Legends: Object Recognition vs. OCR Scene Text Recognition Based on Deep Learning: A Brief Survey R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection Taking a deeper look at pedestrians Sliding Line Point Regression for Shape Robust Scene Text Detection Shape Robust Text Detection With Progressive Scale Expansion Network Deep Context Modeling for Semantic Segmentation Soft Correspondences in Multimodal Scene Parsing Cutting Edge: Soft Correspondences in Multimodal Scene Parsing Multi-Path Feedback Recurrent Neural Networks for Scene Parsing Object or background: Whose call is it in complicated scene classification? Time and Memory Efficient 3D Point Cloud Classification Conditional Random Fields as Recurrent Neural Networks Deep Networks with Internal Selective Attention through Feedback Connections 3-D Surround View for Advanced Driver Assistance Systems Image-Level Attentional Context Modeling Using Nested-Graph Neural Networks Unbiased Scene Graph Generation via Rich and Fair Semantic Extraction OpenSG: a scene graph system for flexible and efficient realtime rendering for virtual and augmented reality applications Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph Generation Deep Image Understanding Using Multilayered Contexts The research of routing algorithms based on NS2 in mobile ad hoc networks TBM motion simulation based on virtual reality system Graphical Contrastive Losses for Scene Graph Generation Single image dehazing through improved atmospheric light estimation Learning Bilevel Layer Priors for Single Image Rain Streaks Removal Transformation method for carrying out centralized power supply on outdoor lighting system New soft technologies in international outdoor lighting TALQ interface protocol Foggy image enhancement based on Principal Component Analysis A fast approach to remove the haze from a single bayer image Outdoor illumination system and intelligent control system Learning geometric and lighting priors from natural images Learning High Dynamic Range from Outdoor Panoramas GPU Pro 5 : Advanced Rendering Techniques Deep Sky Modeling for Single Image Outdoor Lighting Estimation Outdoor Visual Position Estimation for Planetary Rovers
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ICFHR ICME CVPR ICONIP (1) WML@ICDAR ICAAI ArXiv 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 24th International Conference on Pattern Recognition (ICPR) 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) WACV ICCV ChinaSIP 2015 IEEE International Conference on Computer Vision (ICCV) NIPS ECCV (1) BMEI MWSCAS GPU Pro 5: Advanced Rendering Techniques
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