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Single Storage Semi-Global Matching for Real Time Depth processing 7th

Single Storage Semi-Global Matching for Real Time Depth Proc...

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Sawant, Prathmesh Temburu, Yashwant Datar, Mandar Ahmed, Imran Shriniwas, Vinayak Patkar, Sachin Department of Electrical Engineering Indian Institute of Technology Bombay Mumbai India Defence Research and Development Organization New Delhi India

ISBN: (纸本)9789811586965

Depth-map is the key computation in computer vision and robotics. One of the most popular approach is via computation of disparity-map of images obtained from Stereo Camera. Semi Global Matching (SGM) method is a popular choice for good accuracy with reasonable computation time. To use such compute-intensive algorithms for real-time applications such as for autonomous aerial vehicles, blind Aid, etc. acceleration using GPU, FPGA is necessary. In this paper, we show the design and implementation of a stereo-vision system, which is based on FPGA-implementation of More Global Matching (MGM)[7]. MGM is a variant of SGM. We use 4 paths but store a single cumulative cost value for a corresponding pixel. Our stereo-vision prototype uses Zedboard containing an ARM-based Zynq-SoC[10], ZED-stereo-camera/ELP stereo-camera/Intel RealSense D435i, and VGA for visualization. the power consumption attributed to the custom FPGA-based acceleration of disparity map computation required for depth-map is just 0.72 watt. the update rate of the disparity map is realistic 10.5 fps. © 2020, Springer Nature Singapore Pte Ltd.

关键词： System-on-chip

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Automatic Mixed Precision and Distributed Data-Parallel Training for Satellite image Classification using CNN 15

Automatic Mixed Precision and Distributed Data-Parallel Trai...

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15th International conference on Machine vision (ICMV)

作者： Nuwara, Yohanes Kitt, Wong W. Juwono, Filbert H. Ollivierre, Gregory Asia Pulp & Paper Sinarmas Land Plaza MH Thamrin Jakarta Indonesia Curtin Univ Malaysia CDT 250 Bellevue WA 98009 USA Univ Southampton Malaysia Iskandar Puteri 79100 Johor Malaysia OmegaCrop 71-75 Shelton St London England

ISBN: (纸本)9781510666184;9781510666191

Deep learning models for computer vision in remote sensing such as Convolutional Neural Network (CNN) has benefitted acceleration from the usage of multiple CPUs and GPUs. there are several ways to make the training stage more effective in terms of utilizing multiple cores at the same time by processing different image mini-batches with a duplicated model called Distributed Data Parallelization (DDP) and computing the parameters in a lower precision floating-point number called Automatic Mixed Precision (AMP). We would like to investigate the impact of DDP and AMP training modes on the overall utilization and memory consumption of CPU and GPU, as well as the accuracy of a CNN model. the study is performed on the EuroSAT dataset, a Sentinel-2-based benchmark satellite image dataset for image classification of land covers. We compare training using 1 CPU, using DDP, and using both DDP and AMP over 100 epochs using ResNet-18 architecture. the hardware that we used are Intel Xeon Silver 4116 CPU with 24 cores and an NVIDIA v100 GPU. We find that although parallelization of CPUs or DDP takes less time to train on the images, it can take 50 MB more memory than using only a single CPU. the combination of DDP and AMP can release memory up to 160 MB and reduce computation time by 20 seconds. the test accuracy is slightly higher for both DDP and DDP-AMP at 90.61% and 90.77% respectively than without DDP and AMP at 89.84%. Hence, training using Distributed Data Parallelization (DDP) and Automatic Mixed Precision (AMP) has more benefits in terms of lower GPU memory consumption, faster training execution time, faster convergence towards solutions, and finally, higher accuracy.

关键词： Automatic Mixed Precision Convolutional Neural Network Distributed Data-Parallel graphics processing Unit Remote Sensing

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Classification of indian monuments into architectural styles 6th

Classification of indian monuments into architectural styles

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6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Sharma, Saurabh Aggarwal, Priyal Bhattacharyya, Akanksha N. Indu, S. Department of Computer Science and Engineering Delhi Technological University Delhi India Department of Electronics and Communication Engineering Delhi Technological University Delhi India

ISBN: (纸本)9789811300196

We propose two novel approaches to classify indian monuments according to their distinct architectural styles. While the historical significance of most indian monuments is well documented, the details of their architectural styles are not as well recorded. Different indian architectural styles often show certain similar features which makes classification a difficult task. Previous work has focused on European architecture and standard datasets are available for the same, but no standard dataset exists for indian architecture. therefore, we have curated a dataset of indian monuments. In this paper, we propose two approaches to classify monuments according to their styles: Radon Barcodes and Convolutional Neural Networks. the first approach is fast and consumes less memory, but the second approach gives an accuracy of 82%, which is better than the 76% accuracy of the first method. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Network architecture

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Table detection in document images using header and trailer patterns 12

Table detection in document images using header and trailer ...

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Harit, Gaurav Bansal, Anukriti Indian Institute of Technology Jodhpur Rajasthan India

ISBN: (纸本)9781450316606

this paper presents a new approach to detect tabular structures present in document images and in low resolution video images. the algorithm for table detection is based on identifying the unique table start pattern and table trailer pattern. We have formulated perceptual attributes to characterize the patterns. the performance of our table detection system is tested on a set of document images picked from UW-III (University of Washington) dataset, UNLV dataset, video images of NPTEL videos, and our own dataset. Our approach demonstrates improved detection for different types of table layouts, with or without ruling lines. We have obtained correct table localization on pages with multiple tables aligned side-by-side. © 2012 ACM.

关键词： Light trailers

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the Hybrid Attention Single image Super Resolution Network with Conditional Random Field 15

The Hybrid Attention Single Image Super Resolution Network w...

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15th International conference on graphics and image processing, ICGIP 2023

作者： Wei, Jiayu Li, Yanfeng Chen, Houjin Sun, Jia Yao, Xu Beijing Jiaotong University China

ISBN: (纸本)9781510675018

Recently, Transformer-based methods have achieved excellent results in various computer vision tasks, including Single image Super-Resolution (SISR). In SwinIR, the mechanism of cross-window connection and local self-attention of Swin Transformer are introduced into the SISR task, achieving breakthrough improvements. However, the local self-attention mechanism of Swin Transformer has a limited spatial range of input pixels, which limits the ability of the super-resolution network to extract features in a wide range. Aiming at this problem, an enhanced CNN and Transformer hybrid module is designed for feature extraction by combining self-attention, spatial attention and channel attention. Taking advantage of their complementary strengths, the range of activated pixels is expanded while still maintaining a strong capability for local feature characterization. In addition, simply extending the activation pixel range without constraints is not conducive to reconstruction. Aiming at this problem, the Neural Window Fully-connected Conditional Random Fields (NeW FC-CRFs) are integrated for feature fusion. the shallow features are inputted into NeW FC-CRFs along with deep features, allowing for the utilization of multi-level information during the fusion process. In summary, we propose the Hybrid Attention Super Resolution Network with Conditional Random Field (HANCRF). Extensive experiments show that HANCRF achieves competitive results with a small number of parameters. © 2024 SPIE. All rights reserved.

关键词： Pixels

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Multi-epipolar plane image based 3D reconstruction using robust surface fitting 14

Multi-epipolar plane image based 3D reconstruction using rob...

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Choudhury, Chandrajit Amaldev, V. Chaudhuri, Subhasis Dept. of Electrical Engineering Indian Institute of Technology Bombay India

ISBN: (纸本)1595930361

In this paper, we present a novel method for 3D reconstruction from epipolar plane (EP) representation of images and surface fitting for multiview or lightfield images. the proposed method detects parallelograms in EP images using mean shift segmentation. the slopes of the parallel lines along the left and right boundaries of the parallelograms are computed with Hough transform to estimate depth. In addition we also introduce a new concept of estimating depth from multiple EP images(diagonal EP images), making the resultant depth map denser. Multi-view depth maps are then combined and surface fitting is done to obtain the final depth map. Our method uses simple techniques to construct a quality depth map without being computationally demanding. Copyright 2014 ACM.

关键词： Hough transforms

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Enhancing Video Surveillance with Deep Learning-Based Real-Time Handgun Detection and Tracking 1

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8th International conference on computer vision and image processing (CVIP)

作者： Yadav, Pavinder Gupta, Nidhi Sharma, Pawan Kumar Natl Inst Technol Hamirpur Hamirpur 177005 Himachal Prades India Natl Inst Technol Kurukshetra Kurukshetra 136119 Haryana India

ISBN: (数字)9783031581816

ISBN: (纸本)9783031581809;9783031581816

Handguns, pistols, and revolvers are commonly used in today's world for committing criminal acts, requiring the need for effective surveillance and control systems. However, despite the advancement of security systems, human monitoring and involvement are still necessary to effectively combat these crimes. this paper provides a robust automated handgun identification technique for recorded videos and live CCTV footage that may be used for both control and surveillance purposes. Automatic detection of firearms is crucial for improving people's protection and safety, however, it is a challenging task because of the numerous differences in design, size, and appearance of firearms. In recent years, object detectors have improved, yielding better findings and shorter inference times. the authors used cutting-edge object detector YOLOv7 for firearm detection. A varied and demanding dataset of 15,367 images for weapon identification is also proposed, which is carefully annotated for weapon localization and classification. After analysing the data, it is determined that the model achieves an accuracy rate of 96.80% and recall rate of 90.37%.

关键词： Deep Learning Machine Learning Weapon Detection Surveillance System YOLO

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A Dataset and Model for Crossing indian Roads 13

A Dataset and Model for Crossing Indian Roads

引用

13th indian conference on computer vision, graphics, and image processing, ICVGIP 2022

作者： Brahmbhatt, Siddhi G H Patel College of Engineering and Technology Vallabh Vidyanagar Gujarat Anand India

ISBN: (纸本)9781450398237

Roads in medium-sized indian towns often have lots of traffic but no (or disregarded) traffic stops. this makes it hard for the blind to cross roads safely, because vision is crucial to determine when crossing is safe. Automatic and reliable image-based safety classifiers thus have the potential to help the blind to cross indian roads. Yet, we currently lack datasets collected on indian roads from the pedestrian point-of-view, labelled with road crossing safety information. Existing classifiers from other countries are often intended for crossroads, and hence rely on the detection and presence of traffic lights, which is not applicable in indian conditions. We introduce INDRA (indian Dataset for RoAd crossing), the first dataset capturing videos of indian roads from the pedestrian point-of-view. INDRA contains 104 videos comprising of 26k 1080p frames, each annotated with a binary road crossing safety label and vehicle bounding boxes. We train various classifiers to predict road crossing safety on this data, ranging from SVMs to convolutional neural networks (CNNs). the best performing model DilatedRoadCrossNet is a novel single-image architecture tailored for deployment on the Nvidia Jetson Nano. It achieves 79% recall at 90% precision on unseen images. Lastly, we present a wearable road crossing assistant running DilatedRoadCrossNet, which can help the blind cross indian roads in real-time. the project webpage is https://***/Website/*** © 2022 ACM.

关键词： Convolutional neural networks

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A Novel Self-Learning Network Integrating Contrastive Learning, Perceptual Learning and Masked image Modelling 15

A Novel Self-Learning Network Integrating Contrastive Learni...

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15th International conference on graphics and image processing, ICGIP 2023

作者： Chen, Yingxian Yang, Rui Lan, Rushi Guangxi Key Laboratory Image and Graphic Intelligent Processing Guilin University of Electronic Technology China

ISBN: (纸本)9781510675018

Unsupervised learning methods in computer vision have achieved remarkable success, exceeding the performance of supervised learning methods. It is noteworthy that current unsupervised learning methods share certain similarities, particularly in their data augmentation techniques. Masking, a type of data augmentation, can be utilized for both contrastive learning and masked image modelling. this paper presents a novel deep learning approach on visual unsupervised learning. It integrates previous methods such as contrastive learning, perceptual learning, self-distillation and masked image modelling. In our method, we treat the network that handles the original images as the teacher network, and the network that handles the masked images as the student network. the student network employs the representations extracted by the projection head for contrastive learning, while the features generated by the decoder are employed for masked image modeling. the process of self-knowledge distillation is facilitated by perceptual learning between the teacher and student networks. this model aligns with the main idea of contrastive learning, which aims to pull similar images closer while pushing dissimilar images further apart. Simultaneously, it reflects the main idea of masked image modelling, which enables the extraction of semantic information from large scale masked pixel reconstruction tasks. Additionally, we compare the effect of self-supervised methods to the performance of the model. Our results show that with only 75 epochs of fine-tuning, our 29M-parameter model achieves 78.5% top-1 accuracy on the imageNet-1k dataset. © 2024 SPIE. All rights reserved.

关键词： Unsupervised learning

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Enhancing shape from focus-measure-fusion and sparse representation 14

Enhancing shape from focus-measure-fusion and sparse represe...

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Shete, Snehal Bhavsar, Arnav Sao, Anil K. School of Computing and Electrical Engineering Indian Institute of Technology Mandi HP India

ISBN: (纸本)1595930361

Shape-from-focus (SFF) is extensively used in image processing for obtaining shape-maps using a sequence of images of a scene captured from same view point with different camera focus settings. Many focus measure operators have been proposed in the literature for SFF applications and their relative performance depends on the camera capabilities and scene properties. In this work, we propose a novel shape estimation method which fuses individual shape estimates obtained by multiple focus measures, and reconstructs an improved shape map in a patch-wise manner, within a sparse representation (SR) framework. the SR framework involves an over-complete dictionary containing shape patches, the linear combination of which is used to reconstruct each out-put shape patch. the experimental results demonstrate that the proposed approach estimates superior shape maps in both synthetic and real scenes as compared to the estimates obtained by individual focus measures. Copyright 2014 ACM.

关键词： Cameras

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