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Telugu Word Segmentation Using Fringe Maps 4th

Telugu Word Segmentation Using Fringe Maps

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4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, ICVGIP 2018

作者： Devarapalli, Koteswara Rao Negi, Atul Department of Computer Science and Engineering Mahatma Gandhi Institute of Technology Hyderabad500075 India School of Computer and Information Sciences University of Hyderabad Gachibowli Hyderabad500046 India

ISBN: (纸本)9789811393600

In this paper, we propose a word segmentation method that is based on fringe maps on Telugu script. Our objective is to create a data set of word images for enabling direct training for recognition on those. the standard methods employed for the task of word segmentation in Telugu OCR systems are projection profiles and run-length smearing. However those methods have their limitations. In this work a different application of fringe maps is shown for line segmentation into words. Fringes were previously applied successfully for carrying out classification and line segmentation. Telugu script, which has consonant modifiers that are usually placed below or below-right to the base consonants. this kind of orthographic property leads to characters that may touch each other. One way to deal with touched characters is to make use of segmentation free methods, which do not need prior segmentation of word images into characters or connected components. the novelty of our method is that we analyze fringe maps of document images to find an appropriate fringe value threshold and apply it for word segmentation of Telugu documents. Encouraging results are observed with our fringe value threshold based word segmentation. We observe that choosing higher threshold fringe values leads to under-segmentation of words, whereas lower values cause over-segmentation of words. Our word segmentation approach is successfully compared with the widely used projection profiles based word segmentation method. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Optical character recognition

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Real time System Implementation for Stereo 3D Mapping and Visual Odometry

Real time System Implementation for Stereo 3D Mapping and Vi...

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International image processing, Applications and Systems conference (IPAS)

作者： Yashwant Temburu Mandar Datar Simranjeet Singh Vaibhav Malviya Sachin Patkar Indian Institute of Technology Bombay Mumbai India

ISBN: (数字)9781728175744

ISBN: (纸本)9781728175751

Depth Estimation and localisation is a critical and computation heavy task. Due to its computation complexity, it is hard to achieve the high rate performance on the normal CPU. We here have as purpose compact, low power architecture for real time stereo depth estimation and stereo visual odometry that can be easily used on the UAV (unmanned aerial vehicle) and other autonomous navigation vehicles. A novel implementation of Stereo odometry based on careful feature selection and tracking [2] on GPU is described. It accelerates core computations like feature tracking, RANSAC based non linear solver using the GPU. 3D Mapping using stereo disparity estimation based on More Global Matching (MGM) a variant of SGM is implemented on FPGA. A Pipeline Architecture is introduced to increase throughput of the 3D map by leveraging multiple ARM cores. the programs are tested on Jetson Nano (an embedded GPU) and Ultra96 (ARM-FPGA Soc) which have less form factor and consume less power. Update rate of 20 is achieved for 6 degrees of freedom pose on Jetson Nano (60 on i7 core with Nvidia 1050ti) and an update rate of 16 fps is achieved for 3D point cloud on Ultra96 making the system desirable for above mentioned applications.

关键词： three-dimensional displays graphics processing units computer architecture Real-time systems Unmanned aerial vehicles Acceleration Visual odometry

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A Secure and Light Weight User Authentication System Based on Online Signature Verification for Resource Constrained Mobile Networks 4th

A Secure and Light Weight User Authentication System Based o...

引用

4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, ICVGIP 2018

作者： Vorugunti, Chandra Sekhar Guru, D.S. Pulabaigari, Viswanath Indian Institute of Information Technology-SriCity Chittoor India University of Mysore Mysore India

ISBN: (纸本)9789811393600

the rapid advances in mobile and networking technologies results in usage of mobiles for critical applications like m-commerce, m-payments etc. Even though mobile based services offer many benefits, authenticating the user logging into the system is a big challenge. To mitigate this concern, secure mobile applications based on user online signature verification (OSV) has been proposed. Unfortunately, these models would intensify the substantial computational overhead on thin and resource-constrained mobile devices. this summarizes for a critical need of OSV models which are computationally efficient and achieves higher classification accuracy. Recently, several OSV models have been defined in the literature. However, these models are not computationally effective for resource-constrained mobile devices, because the proposed verification models ought to require not only higher feature dimension but also heavy weight writer specific parameter fixation logic. In this manuscript, we propose an efficient and light weight OSV model for resource-constrained mobile devices. Our approach employs dimensionality reduction based on DBSCAN clustering technique and user specific parameter selection. thorough experimental analysis are conducted on benchmarking online signature datasets MCYT-100 (DB1) and MCYT-330 (DB2) datasets which confirms the efficiency of proposed model with latest OSV models. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Mobile security

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HSD-CNN: Hierarchically self decomposing CNN architecture using class specific filter sensitivity analysis 2018

HSD-CNN: Hierarchically self decomposing CNN architecture us...

引用

Proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： K. SaiRam Jayanta Mukherjee Amit Patra Partha Pratim Das Indian Institute of Technology Kharagpur

ISBN: (纸本)9781450366151

Conventional convolutional neural networks (CNN) are trained on large domain datasets and are hence typically over-represented and inefficient in limited class applications. An efficient way to convert such large many-class pre-trained networks into small few-class networks is through a hierarchical decomposition of its feature maps. To alleviate this issue, we propose an automated framework for such decomposition in Hierarchically Self Decomposing CNN (HSD-CNN), in four steps. HSD-CNN is derived automatically using a class-specific filter sensitivity analysis that quantifies the impact of specific features on a class prediction. the decomposed hierarchical network can be utilized and deployed directly to obtain sub-networks for a subset of classes, and it is shown to perform better without the requirement of retraining these sub-networks. Experimental results show that HSD-CNN generally does not degrade accuracy if the full set of classes is used. Interestingly, when operating on known subsets of classes, HSD-CNN has an improvement in accuracy with a much smaller model size requiring much fewer operations. HSD-CNN flow is verified on the CIFAR10, CIFAR100 and CALTECH101 datasets. We report accuracies up to 85.6% (94.75%) on scenarios with 13 (4) classes of CIFAR100, using a pre-trained VGG-16 network on the full dataset. In this case, the proposed HSD-CNN requires 3.97x fewer parameters and has 71.22% savings in operations, in comparison to baseline VGG-16 containing features for all 100 classes.

关键词： classification hierarchical neural networks CNN clustering model transfer sub-networks

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A Fast Method for Very Low Bit-Rate Coding of Hyperspectral images 11

A Fast Method for Very Low Bit-Rate Coding of Hyperspectral ...

引用

11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Kumar, Saurabh Chaudhuri, Subhasis Indian Inst Technol Mumbai Maharashtra India

ISBN: (纸本)9781450366151

In this work, we propose a computationally efficient compressive sensing based approach for very low bit rate lossy coding of hyperspectral (HS) image data by exploiting the redundancy inherent in this imaging modality. We divide the HS datacube into subsets of adjacent bands, each of which is encoded into a coded snapshot using a random code matrix. these coded snapshot images are encoded using the wavelet-based SPIHT compression technique. the decompression from the coded snapshots at the receiver is done using the orthogonal matching pursuit with the help of an overcomplete dictionary learned on a general purpose training dataset. We provide ample experimental results and performance comparisons to substantiate the usefulness of the proposed method. In the proposed technique the encoder is free from any decoder and it offers a significant saving in computation and yet yields a much higher compression quality.

关键词： image coding

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Multimodal Egocentric Activity Recognition Using Multi-stream CNN 11

Multimodal Egocentric Activity Recognition Using Multi-strea...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Imran, Javed Raman, Balasubramanian Indian Inst Technol Roorkee Dept Comp Sci & Engn Roorkee Uttar Pradesh India

ISBN: (纸本)9781450366151

Egocentric activity recognition (EAR) is an emerging area in the field of computer vision research. Motivated by the current success of Convolutional Neural Network (CNN), we propose a multi-stream CNN for multimodal egocentric activity recognition using visual (RGB videos) and sensor stream (accelerometer, gyroscope, etc.). In order to effectively capture the spatio-temporal information contained in RGB videos, two types of modalities are extracted from visual data: Approximate Dynamic image (ADI) and Stacked Difference image (SDI). these image-based representations are generated both at clip level as well as entire video level, and are then utilized to finetune a pretrained 2D-CNN called MobileNet, which is specifically designed for mobile vision applications. Similarly for sensor data, each training sample is divided into three segments, and a deep 1D-CNN network is trained (corresponding to each type of sensor stream) from scratch. During testing, the softmax scores of all the streams (visual + sensor) are combined by late fusion. the experiments performed on multimodal egocentric activity dataset demonstrates that our proposed approach can achieve state-of-the-art results, outperforming the current best handcrafted and deep learning based techniques.

关键词： Egocentric Activity Recognition Convolutional Neural Network Dynamic image Stacked Difference image Multimodal Fusion

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Parallel Multi-threaded Gridrec Algorithm for computer Tomography on GPU for Edge Computing

Parallel Multi-threaded Gridrec Algorithm for Computer Tomog...

引用

IEEE International conference on Cyber Security and Cloud Computing (CSCloud)

作者： XINTONG CHEN YONGXIN ZHU XIAOYING ZHENG SI MIAO TIANHAO NAN WANYI LI School of Microelectronics Shanghai Jiao Tong University Shanghai China Chinese Academy of Sciences Shanghai Advanced Research Institute Shanghai China

ISBN: (数字)9781728165509

ISBN: (纸本)9781728165516

Tomography reconstruction is the process of quickly reconstructing the original image form the projection obtained by X-ray radiation. At present, the high-resolution detector of the Shanghai Synchrotron Radiation Facility (SSRF) can scan more than 4GB of tomographic data every 1.5 seconds, and the transmission speed is increased to more than 100GB s -1 . With the upgrade of high-resolution detectors and the increase of data transmission volume, the reconstruction computation on cloud has become a bottleneck in improving the speed of tomography reconstruction even if the fastest Gridrec algorithm is adopted. In this paper, we propose an improved serial Gridrec algorithm and a parallel Gridrec algorithm by improving the convolution kernel to optimize the speed of existing image reconstruction algorithms on low cost GPUs for edge computing. On these GPUs, the multi-threaded tomography reconstruction algorithm not only guarantees high-quality results, but also improves the reconstruction speed over original Gridrec algorithm by more than 11x, and over the classic FBP algorithm by more than 234x. Besides the significant speedup, our work would be the first parallel implementation of Gridrec algorithm on GPU for edge computing.

关键词： tomographic reconstruction GPU parallelization algorithm multithreading Multithreading GRAPPER PICK UP graphics processing Unit X-Ray Computed Tomography algorithms transmission speed Reconstruction algorithm high resolution detector Tomography Reconstructive Surgical Procedures X-rays Parallel Lines

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Color image Super Resolution in Real Noise 11

Color Image Super Resolution in Real Noise

引用

11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Mandal, Srimanta Purohit, Kuldeep Rajagopalan, A. N. DA IICT Gandhinagar Gujarat India IIT Madras Chennai Tamil Nadu India

ISBN: (纸本)9781450366151

In practice, images can contain different amounts of noise for different color channels, which is not acknowledged by existing super-resolution approaches. In this paper, we propose to super-resolve noisy color images by considering the color channels jointly. Noise statistics are blindly estimated from the input low-resolution image and are used to assign different weights to different color channels in the data cost. Implicit low-rank structure of visual data is enforced via nuclear norm minimization in association with adaptive weights, which is added as a regularization term to the cost. Additionally, multi-scale details of the image are added to the model through another regularization term that involves projection onto PCA basis, which is constructed using similar patches extracted across different scales of the input image. the results demonstrate the super-resolving capability of the approach in real scenarios.

关键词： Real Color image Noise Super Resolution Nuclear Norm PCA

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iPassenger: Smart passenger analytics system 6th

iPassenger: Smart passenger analytics system

引用

6th International conference on Advances in Visual Informatics, IVIC 2019

作者： Ab Wahab, Mohd Nadhir Mohamed, Ahmad Sufril Azlan Chung, Kong Chee School of Computer Sciences Universiti Sains Malaysia GelugorPenang11800 Malaysia

ISBN: (纸本)9783030340315

Automation with intelligence has gradually become the community’s talking point around the world. Nowadays, even public transportation such as bus is equipped with video surveillance cameras mainly for monitoring and security purposes. However, they can also be used for other purposes, such as analytics system through passengers counting. this is important to help the bus service provider to improve their fleet management operation by keep tracking of the passenger ridership information. Based on this information, the service provider company can be more flexible with bus scheduling by calculation of lines and stations efficiently. Hence, this paper proposed an analytics system through video footages acquired from daily operation. the incoming and outgoing passengers are tracked based on their head using blob detection and trained MobileNet SSD. the reference line is drawn to make sure that the detected passengers’ head crossed the line before it is considered either as incoming or outgoing passengers. Based on this video analysis, the number of passengers is counted concurrent with the location of the bus. this system is tested in several cases and managed to give proper information for the bus service provider. the data is stored in a cloud database for history, graphically visualize the passenger ridership in a day, month or year. therefore, this system is able to help the bus company to manage their resource efficiently and consequently improving their service quality and lower the cost of transportation. © Springer Nature Switzerland AG 2019.

关键词： Buses

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SpoofNET: Resolving Facial Makeup based Spoofs 11

SpoofNET: Resolving Facial Makeup based Spoofs

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Bhattacharjee, Avishek Banerjee, Samik Das, Sukhendu Indian Inst Technol Madras Chennai 600036 Tamil Nadu India

ISBN: (纸本)9781450366151

Face Recognition (FR) under adversarial conditions has been a big challenge for researchers in the computer vision and Machine Learning communities in the recent past. Most of state-of-the-art face recognition systems have been designed to overcome degradations in a face due to variations in pose, illumination, contrast, resolution, along with blur. However, interestingly none have addressed the fascinating issue of makeup as a spoof attack, which drastically changes the appearance of a face, making it difficult for even humans to detect and identify the impostor. In this paper, we propose a novel multi-component deep convolutional neural network (CNN) based architecture which performs the complex task of makeup removal from a disguised face, to reveal the original mugshot image of the impostor (i.e. without makeup). the proposed network also performs the hard tasks of FR on a disguised face in addition to recognition of identity and generation of the face of the spoofed target, by minimizing a novel multi-component objective function. Comparison of performance with a few recent state-of-the-art methods of FR over three benchmark datasets reveals the superiority of our proposed method for both synthesis as well as recognition (FR) tasks.

关键词： Face recognition

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