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Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks [article]

Ian J. Goodfellow, Yaroslav Bulatov, Julian Ibarz, Sacha Arnoud, Vinay Shet
2014 arXiv   pre-print
In this paper, we address an equally hard sub-problem in this domain viz. recognizing arbitrary multi-digit numbers from Street View imagery.  ...  In this paper we propose a unified approach that integrates these three steps via the use of a deep convolutional neural network that operates directly on the image pixels.  ...  In this paper, we focus on recognizing multi-digit numbers from Street View panoramas.  ... 
arXiv:1312.6082v4 fatcat:evebdvkt2vbbroop6jnia66454

Urban Zoning Using Higher-Order Markov Random Fields on Multi-View Imagery Data [chapter]

Tian Feng, Quang-Trung Truong, Duc Thanh Nguyen, Jing Yu Koh, Lap-Fai Yu, Alexander Binder, Sai-Kit Yeung
2018 Lecture Notes in Computer Science  
In the proposed HO-MRF, top-view satellite data is segmented via a multi-scale deep convolutional neural network (MS-CNN) and used in lowerorder potentials.  ...  This paper proposes a method for automatic urban zoning using higher-order Markov random fields (HO-MRF) built on multi-view imagery data including street-view photos and top-view satellite images.  ...  We also developed a multi-scale deep convolutional neural network used for classifying satellite image pixels.  ... 
doi:10.1007/978-3-030-01237-3_38 fatcat:b7mokyzqn5cbvi6yoyyrrrmgjm

Implementation of Training Convolutional Neural Networks [article]

Tianyi Liu, Shuangsang Fang, Yuehui Zhao, Peng Wang, Jun Zhang
2015 arXiv   pre-print
Deep learning refers to the shining branch of machine learning that is based on learning levels of representations. Convolutional Neural Networks (CNN) is one kind of deep neural network.  ...  Then we applied the particular convolutional neural network to implement the typical face recognition problem by java. Then, a parallel strategy was proposed in section4.  ...  Chen takes much effort to offer us guide and help. So the first person we must offer our thanks to is Mr.Chen. At the same time, our major team leader Tianyi Liu, also deserves our sincerest thanks.  ... 
arXiv:1506.01195v2 fatcat:exhz7obrsfddxnnzozfzhcxfh4

Classification of Traffic Vehicle Density Using Deep Learning

Abdul Kholik, Agus Harjoko, Wahyono Wahyono
2020 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)  
soft-computing problems, This research uses the convolutional neural network architecture.  ...  This research tries to change the supporting parameters on the convolutional neural network to further calibrate the maximum accuracy.  ...  Deep Learning introduces the Convolutional Neural Network (CNN) method which has excellent performance in pattern recognition and image classification.  ... 
doi:10.22146/ijccs.50376 fatcat:h7n3wigolraoziuh2toaknsbpe

Object Recognition Using Deep Neural Networks: A Survey [article]

Soren Goyal, Paul Benjamin
2014 arXiv   pre-print
Recognition of objects using Deep Neural Networks is an active area of research and many breakthroughs have been made in the last few years.  ...  The paper briefly describes the history of research in Neural Networks and describe several of the recent advances in this field.  ...  Networks using DropConnect [31] 2013 SVHN 98% Human Performance [49] 2012 97.84% Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks [50]  ... 
arXiv:1412.3684v1 fatcat:zh2jlxncbzgofipvkgbfwsmt4i

Multiple Object Recognition with Visual Attention [article]

Jimmy Ba, Volodymyr Mnih, Koray Kavukcuoglu
2015 arXiv   pre-print
We evaluate the model on the challenging task of transcribing house number sequences from Google Street View images and show that it is both more accurate than the state-of-the-art convolutional networks  ...  The proposed model is a deep recurrent neural network trained with reinforcement learning to attend to the most relevant regions of the input image.  ...  We evaluate the model on the task of transcribing multi-digit house numbers from publicly available Google Street View imagery.  ... 
arXiv:1412.7755v2 fatcat:zlrvslglofhtfpfmrwrsdymqtm

Table of Contents

2018 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA)  
for disabled 183 Android Application for Object Recognition using Neural Networks for the Visually Impaired. 184 BEHAVIOR ANALYSIS OF VISUALIZATION IMAGES USING DEEP LEARNING APPROACH 185 Multi-Instance  ...  Human Identification using Convolutional Neural Network in Smart Health Applications 304 Isolated Spoken Marathi Words Recognition using HMM 305 Detection of pitch frequency of Indian classical music  ... 
doi:10.1109/iccubea.2018.8697655 fatcat:jvjgmcrh3fhxtkf4kyydawnkiq

Integrating Aerial and Street View Images for Urban Land Use Classification

Rui Cao, Jiasong Zhu, Wei Tu, Qingquan Li, Jinzhou Cao, Bozhi Liu, Qian Zhang, Guoping Qiu
2018 Remote Sensing  
We use a deep neural network to extract semantic features from sparsely distributed street view images and interpolate them in the spatial domain to match the spatial resolution of the aerial images, which  ...  In this paper, deep neural network-based approaches are presented to label urban land use at pixel level using high-resolution aerial images and ground-level street view images.  ...  In our study, semantic features of street view images are firstly extracted by Places-CNN which is a deep convolutional neural network used for ground-level scene recognition.  ... 
doi:10.3390/rs10101553 fatcat:r5uar374wfdi7hdgw3e33qoycy

Front Matter: Volume 10033

2016 Eighth International Conference on Digital Image Processing (ICDIP 2016)  
SPIE uses a seven-digit CID article numbering system structured as follows:  The first five digits correspond to the SPIE volume number.  The last two digits indicate publication order within the volume  ...  using a Base 36 numbering system employing both numerals and letters.  ...  by fine tuning deep convolutional neural networks [10033-91] 10033 2E Fast image clustering based on convolutional neural network and binary K-means [10033-78] 10033 2F SOFM-type artificial neural  ... 
doi:10.1117/12.2257252 fatcat:v2ipfp2mp5gedjypzpecahpo7e

Effective semantic pixel labelling with convolutional networks and Conditional Random Fields

Sakrapee Paisitkriangkrai, Jamie Sherrah, Pranam Janney, Anton Van-Den Hengel
2015 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
Large amounts of available training data and increasing computing power have led to the recent success of deep convolutional neural networks (CNN) on a large number of applications.  ...  The CRF infers a labelling that smooths regions while respecting the edges present in the imagery.  ...  Mnih and Hinton learn discriminative image features using deep neural networks to detect roads and buildings from noisy labels [19, 18] . Labels were post-processed using a CRF defined over pixels.  ... 
doi:10.1109/cvprw.2015.7301381 dblp:conf/cvpr/Paisitkriangkrai15a fatcat:awuebtezp5atnbq3vf47bwut5a

Learning Dense Stereo Matching for Digital Surface Models from Satellite Imagery [article]

Wayne Treible, Scott Sorensen, Andrew D. Gilliam, Chandra Kambhamettu, Joseph L. Mundy
2018 arXiv   pre-print
Digital Surface Model generation from satellite imagery is a difficult task that has been largely overlooked by the deep learning community.  ...  In this work we present neural network tailored for Digital Surface Model generation, a ground truthing and training scheme which maximizes available hardware, and we present a comparison to existing methods  ...  For a comparison of multi-view reconstruction using satellite images we refer the reader to [14] Satellites typically do not take wide baseline synchronized stereo imagery due to a number of physical  ... 
arXiv:1811.03535v2 fatcat:xdeihsbpcvfxla46iq4wm7folm

GasBotty: Multi-Metric Extraction in the Wild

Kevin Dick, Joshua B. Tanner, Francois Charih, James R. Green
2022 IEEE Access  
Street View House Numbers [SVHN]); however, extraction and assignment of the contextual meaning of those values are far more difficult given the diversity and unstructured nature of advertisements.  ...  The lived environment, particularly when proximal to roadways, is filled with multi-digit and multi-numbered values corresponding to advertised commodities.  ...  Early examples include Matan's ZIP code reader using space displacement neural networks that preceded the advent of LeCun's convolutional neural networks (CNNs) [5], [20].  ... 
doi:10.1109/access.2022.3156578 fatcat:h4koirjpqjdtdefwydfhz5sxoq

Using Convolutional Neural Networks to Map Houses Suitable for Electric Vehicle Home Charging

James Flynn, Cinzia Giannetti
2021 AI  
A unique dataset comprised of open source Google Street View images was used to train and compare three deep neural networks and represents the first attempt to classify residential driveways from streetscape  ...  In this multi-disciplinary paper, we demonstrate a novel end-to-end workflow using deep learning to perform automated surveys of urban areas to identify residential properties suitable for EV charging.  ...  This paper also presents the first case of using deep Convolutional Neural Networks (CNN's) to classify residential off-street parking from remotely sourced imagery.  ... 
doi:10.3390/ai2010009 fatcat:6sl3o5hnvngblau6bq7b6xru4e

Ontological supervision for fine grained classification of Street View storefronts

Yair Movshovitz-Attias, Qian Yu, Martin C. Stumpe, Vinay Shet, Sacha Arnoud, Liron Yatziv
2015 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
For businesses with street side locations one can leverage the abundance of street level imagery, such as Google Street View, to automate the process.  ...  Our learner, which is based on the GoogLeNet/Inception Deep Convolutional Network architecture and classifies 208 categories, achieves human level accuracy.  ...  Here, we focus on reviewing related work on analysis of street view data, fine-grained classification and the use of Deep Convolutional Networks. Analyzing Street View Data.  ... 
doi:10.1109/cvpr.2015.7298778 dblp:conf/cvpr/Movshovitz-Attias15 fatcat:6y4arr2hsndy7lfojhqbamvsuy

Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN)

Savita Ahlawat, Amit Choudhary, Anand Nayyar, Saurabh Singh, Byungun Yoon
2020 Sensors  
Convolutional neural networks (CNNs) are very effective in perceiving the structure of handwritten characters/words in ways that help in automatic extraction of distinct features and make CNN the most  ...  Our aim in the proposed work is to explore the various design options like number of layers, stride size, receptive field, kernel size, padding and dilution for CNN-based handwritten digit recognition.  ...  Shallow neural network vs deep neural network. Factors Shallow Neural Network (SNN) Deep Neural Network (DNN) Number of hidden layers -single hidden layer (need to be fully connected).  ... 
doi:10.3390/s20123344 pmid:32545702 fatcat:xxv3sappxzacpnmy6g7kr5p3hy
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