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A New Benchmark Dataset for Indonesian Traditional Woven Fabric image Recognition and image Retrieval 9

A New Benchmark Dataset for Indonesian Traditional Woven Fab...

引用

9th International conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2022

作者： Tena, Silvester Hartanto, Rudy Ardiyanto, Igi Universitas Gadjah Mada Department of Electrical Engineering and Information Technology Yogyakarta Indonesia

ISBN: (数字)9781665471503

ISBN: (纸本)9781665471480

This study aims to develop the TenunIkatNet dataset as one of the benchmarks for image recognition and retrieval problems, especially in Indonesian traditional fabrics. These are due to the present absence of the dataset for traditional fibers, especially the tenun ikat (woven) fabrics of East Nusa Tenggara (ENT). The TenunIkatNet dataset contains 120 types of fabric each being captured 40 times, leading to the collection of 4,800 original images at traditional shops and craftsmen. These shooting variations are often internally and externally carried out within the mini-studio box and another fabric background, before being hanged, and worn on the body. In this condition, the utilized Nikon D5600 camera produced 24 megapixels of RGB (red, green, blue) images, with the pre-processing and visual procedural application obtaining a 256x256 display according to the input feature extraction method. Furthermore, the data augmentation processes such as flipping, random rotating, zooming, and shear range, increased the number of images, with each type of tenun ikat fabric being randomly augmented by two images. This led to the production of 9,600 images based on the number of training datasets. Despite this, the testing and validation data were 960 images each. The TenunIkatNet dataset is expected to develop image recognition, classification, and retrieval algorithms, while also being useful for the preservation of local culture, as a knowledge base in the fields of education, crafts, and trade. In this report, two pre-trained Convolution Neural Network (CNN) methods with a transfer learning procedure, namely ResNet101 and DenseNet201. The results showed that the retrieval accuracy of two pre-trained models is 100% in the top-1. While the retrieval accuracy of DenseNet201 outperformed the ResNet101 method in the top-5 at 44.75% and 44.38%, respectively. Subsequently, the method had higher retrieval accuracy than ResNet101, although the query time was slower. © 2022 ieee.

关键词： image recognition

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Exploring the Newly Introduced Data Augmentation Technique in TensorFlow Library

Exploring the Newly Introduced Data Augmentation Technique i...

引用

ieee Signal processing and communications Applications (SIU)

作者： Onur Ardiç Gökçen Çetinel Elektrik-Elektronik Mühendisliği Sakarya Üniversitesi Sakarya TÜRKİYE Üretim Mühendisliği Toyota Otomotiv Sanayi Türkiye A.Ş. Sakarya TÜRKİYE

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

Libraries such as TensorFlow provide various tools for implementing live data augmentation methods. However, in practical applications, common situations such as objects arriving at different angles can negatively impact system performance. For such factors, there is no dedicated live data augmentation method in the TensorFlow library. In this study, a new live data augmentation method that can perform real-time random rotation operations in the user-specified angle and axis range has been developed to overcome this deficiency. This method, which is integrated into the TensorFlow library, is much more flexible than the existing method, which can only perform a 90° rotation on the X-Y axis. Artificial intelligence training was performed on standard (existing method) and modified (new method) model master data by labeling 2800 objects on 1060 images of parts obtained from a private automotive manufacturing facility. The training results were tested for five different scenarios on an additional 115 image data collected from the production facility at different times. It was found that the model trained with the new method outperformed the model trained with the existing method by up to 2.6%. This study will significantly contribute to the detection of moving parts with higher accuracy, especially in vision-based systems.

关键词： Training System performance Signal processing Data augmentation Libraries Production facilities Real-time systems

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External Sparsity Combined with Internal Low-Rankness for image Denoising 7

External Sparsity Combined with Internal Low-Rankness for Im...

引用

7th International conference on Computer and communications, ICCC 2021

作者： Ou, Yang Li, Bailin Luo, Jianqiao Liao, Jiacai Southwest Jiaotong University School of Mechanical Engineering Chengdu China Hunan University College of Mechanical and Vehicle Engineering Changsha China

ISBN: (纸本)9781665409506

Nonlocal self-similarity (NSS) as a remarkable prior has been successfully applied to image processing including image denoising. For each local patch in an image, we can search for many nonlocally similar patches under NSS prior and stack them into a group, and then process the group instead of a patch to better capture the nonlocal structure. Most existing methods consider the NSS either of the internal degraded image or from an external clean corpus, which may limit the denoising performance. In this paper, we propose a hybrid model to catch both the external and internal NSS. Specifically, we develop a model with two regularizers, the internal NSS is exploited by a low-rankness regularizer, and the external NSS is exploited by a sparse regularizer. An alternating minimization method is developed to solve our model. Experimental results demonstrate that our algorithm achieves better results compared with several state-of-the-art denoising methods. © 2021 ieee.

关键词： image denoising

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NLP Meets Vision for visual Interpretation - A Retrospective Insight and Future directions

NLP Meets Vision for Visual Interpretation - A Retrospective...

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ieee International conference on Digital Futures and Transformative Technologies (ICoDT2)

作者： Jamshed, Ahmed Fraz, Muhammad Moazam Natl Univ Sci & Technol NUST Sch Elect Engn & Comp Sci Islamabad Pakistan

ISBN: (纸本)9781665412858

Recent advances in the field of NLP (Natural Language processing) and CV (Computer Vision) have sparked a lot of curiosity among researchers to test the limitations of latest Deep learning techniques by employing them in more complex AI tasks. One such kind of task is VQA (visual Question Answering) which is inherently divided into many layers of complexities. Some questions are simple having obvious answers while some are more complex which need logical reasoning, common sense and factual knowledge. Starting simple and gradually incorporating complexity, is always a good idea in scientific research and development. At first, datasets were simpler consisting of simple question-answer pairs with images depicting simpler concepts and relatively naive VQA models were trained on them. Slowly, with time, the VQA datasets got more complicated and tangled demanding more cognitive capabilities from VQA models. This evolution pushed the VQA models to be more efficient in matching human cognitive abilities, using reasoning based on common sense and factual knowledge. In this survey, we will first discuss some of the famous datasets in the domain of VQA and then we will discuss some of the crucial advancements in the VQA architectures and what is currently being done for integrating common sense and knowledge into these models. Moreover, reasoning is very crucial for truly intelligent systems but representations in deep learning models are inherently very fuzzy and vague. We need models that can transparently generate reasoning about their predictions like old school expert systems which used to work on symbolic knowledge, so the architectures based on the amalgam of deep learning techniques and Symbolic representations would also be a part of our discussion. We will also shed some light on the impact of transformers in the field of deep learning and how these transformer based models are quickly becoming state-of-the-art in almost every deep learning task.

关键词： visual Question Answering image Semantic Understanding Natural Language processing Transformers Graph Convolutional Networks

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Fast and Context-Aware Framework for Space-Time Video Super-Resolution

Fast and Context-Aware Framework for Space-Time Video Super-...

引用

ieee visual communications and image processing (VCIP)

作者： Xueheng Zhang Li Chen Li Song Zhiyong Gao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781728173221

Increasing the spatial resolution and frame rate of a video simultaneously has attracted attention in recent years. The current one-stage space-time video super-resolution (STVSR) methods are difficult to deal with large motion and complex scenes, and are time-consuming and memory intensive. We propose an efficient STVSR framework, which can correctly handle complicated scenes such as occlusion and large motion and generate results with clearer texture. In REDS dataset, our method outperforms all existing one-stage methods. Our method is lightweight and can generate 720p frames at 16fps on a NVIDIA GTX 1080 Ti GPU.

关键词： visual communication Superresolution Graphics processing units Spatial resolution image reconstruction

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Exploring Versatility of Primary visual Cortex Inspired Feature Extraction Hardware Model through Various Network Architectures 4

Exploring Versatility of Primary Visual Cortex Inspired Feat...

引用

4th International conference on Computing, Electronics and communications Engineering, iCCECE 2021

作者： Tran, Thi Diem Nakashima, Yasuhiko Nara Institute of Science and Technology School of Information Science Japan

ISBN: (纸本)9781665449113

Improving the performance of the network architectures that mimic brain operation is a research trend. Optimizing the latency on hardware circuits of the artificial neural network continues investigating. In the third generation, the Spiking Neural Networks (SNNs) with biological plausibility and similarity to the functionality of the human brain are emerging. A more comprehensive study is expected to understand the inherent behavior of SNNs, especially under adversarial attacks. This study concatenates the proposed SLIT layer with the convolutional neural networks (CNNs) to degrade the latency of deep neural networks on the hardware platform. The input data modified with the SLIT layer is applied to interrogate the adversarial attack on Spiking Neural Network. We estimate new topology with MNIST and CIFAR-10 datasets. Latency of the inference phase on CNNs for image classification application is assessed on the chip ZC7Z020-1CLG484C FPGA. Reducing latency in the range of 2.6% to 16% is observed from the Vitis AI platform. With white-box adversarial attack applications on SNNs, the accuracy of the proposal is approximately 70% higher robustness than the previous works. © 2021 ieee.

关键词： image classification

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Autonomous Localization and Navigation for Transmission Line Inspection of UAV

Autonomous Localization and Navigation for Transmission Line...

引用

2021 ieee International conference on Advances in Electrical Engineering and Computer Applications, AEECA 2021

作者： Wang, Jing The 54th Research Institute of China Electronics Technology Group Corporation State Key Laboratory of Satellite Navigation System and Equipment Technology Shijiazhuang China

ISBN: (纸本)9781665435611

Transmission lines bear incomparable responsibilities for power transmission. In order to prevent transmission line failures, regular inspection is required. Unmanned aerial vehicle (UAV), an intelligent, efficient, and safe inspection method, has solved many disadvantages of manual inspection, robot, and helicopter inspection. At present, GPS is utilized for positioning and tracking of UAV during inspection. However, it is necessary to find a more convenient and reliable positioning method in such place where there is no GPS signal. Therefore, UAV is combined with binocular vision to conduct transmission line inspection. For the line tracking during inspection, the line features extracted from a single frame are converted into point features in the parameter space of continuous frames. And a two-layer image pyramid hierarchical matching structure is designed to improve the algorithm efficiency. Considering about the demand for autonomous landing of UAV, the visual recognition and spatial pose acquisition algorithm of the cooperative target capable of visual guidance are studied. An experiment for visual tracking of transmission lines in continuous frames is designed. The adjustment of camera attitude is analyzed to verify the accuracy of the designed tracking algorithm. The robustness of the algorithm is verified by the strong and weak light environment respectively. Finally, the autonomous landing experiment of the moving ArUco target is completed. Based on the transmission line positioning and tracking algorithm, the autonomous navigation of UAV is developed, providing a theoretical basis and verification for transmission line inspection under low GPS signal environment. © 2021 ieee.

关键词： Stereo image processing

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A Review of image Classification and Object Detection on Machine learning and Deep Learning Techniques 5

A Review of image Classification and Object Detection on Mac...

引用

5th International conference on Electronics, Communication and Aerospace Technology, ICECA 2021

作者： Sandhya Devi, R.S. Vijay Kumar, V.R. Sivakumar, P. Kumaraguru College of Technology Department of EEE Coimbatore India Anna University Regional Campus Department of Electronics and Communication Engineering Coimbatore India PSG College of Technology Department of EEE Coimbatore India

ISBN: (纸本)9781665435246

One of the known computer vision tasks is image recognition and classification. Computer vision - a field that automates tasks and works with multi - dimensional data, when combined with the power of artificial intelligence mimics human visual system. image processing and analysis is an integral part of computer vision. image classification, a vital part in image analysis is used to categorize pixels or classes of interest by extracting information classes from a multiband raster image. Automatic sorting of pixels and analogous multispectral reflectance values are widely used in land cover mapping cases where remote sensing and Geographical Information System (GIS) is integrated. Designing of image classification and processing procedure by considering various factors involved, decides the success of the classification approach being used. This paper provides a summarization of various classical machine learning based classification approaches and the major advanced deep learning based classification techniques, issues affecting the success of each classification approach and its applications. Convolutional Neural Network (CNN) is the most popular deep network architecture used to analyze visual imagery. This paper discusses waste object detection, a part of the waste segregation process based on CNN hierarchical image classification approach. Here, the system is able to detect the object and provides the relative match percentage of the object being detected. Open source software libraries such as Tensorflow and Spyder is used for this process © 2021 ieee.

关键词： image classification

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DEM Generation From Mono-Multispectral images via Uncalibrated Epipolar Rectification and 3D Registration

DEM Generation From Mono-Multispectral Images via Uncalibrat...

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Recent Advances in Systems Science and Engineering (RASSE), ieee International conference on

作者： Ashutosh Gupta Hemant K. Lalwani Subhajit Paul Debajyoti Dhar Signal and Image Processing Area Space Applications Centre (SAC) Indian Space Research Organisation (ISRO) Ahmedabad India

Generation of Digital Elevation Models (DEM) typically requires fine image matching between set of images acquired with a wide-baseline and appropriate B/H ratio. Wide baseline stereo imaging is an expensive process since it requires two or more acquisitions at the cost of nominal mono-imaging operations. Due to the wide-baseline, angular separation in views is required, which results in increased occlusion problems leading to holes in the generated DEM. Multiview images taken with wide baselines also have lower overlap area compared to images taken from narrow-baseline systems. Moreover, while making orthorectified maps, large tilts need to be corrected using processing techniques to obtain a top-down view. To alleviate these issues, we propose a novel DEM generation pipeline that utilizes the narrow-baseline information between bands of a single multispectral image. Our method can generate sub-pixel accurate disparity maps while tackling spectral information change. Final DEM is generated from the disparity by using reference Ground Control Points (GCPs) and an iterative control point registration procedure. We demonstrate the effectiveness of our DEM generation pipeline on Resourcesat-2 LISS- IV multispectral image with a BIH ratio as low as 0.018. Our results show that sub pixel disparity of the order of 1/50th of a pixel can be computed. A visual and quantitative comparison of the generated DEM with the wide-baseline DEM from Cartosat-l shows that the elevation information is estimated with good overall agreement even for a hilly region. Our technique makes it possible to extract elevation information from mono-multispectral images with unusually low B/H ratios, potentially enabling the generation of DEMs from multispectral images that would not normall be considered viable for this purpose.

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Driver's visual Input processing of Information Displayed on the Windshield 23

Driver's Visual Input Processing of Information Displayed on...

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23rd ieee International conference on Intelligent Transportation Systems (ITSC)

作者： Yamada, Nodoka Ueda, Kazutaka Nakao, Masayuki Univ Tokyo Grad Sch Engn Dept Mech Engn Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan

ISBN: (纸本)9781728141497

Heads-up displays (HUDs) that present information on the windshields of vehicles have increasingly attracted attention. However, the process by which information is obtained from HUDs while driving remains unclear;this includes the critical parameters that determine how the input process is performed. Therefore, it is necessary to identify critical parameters and explore visual information processing to enhance the user-friendly design of HUDs. In this study, we focused on differences in the projection method between the virtual image and the real image as a critical parameter of the input process. We used eye tracking to evaluate the time taken for the input process, namely the process of shifting gaze from the environment to the HUD image. The results showed that the real image had shorter input processing time than the virtual image when the display distance was short. Moreover, processing time became shorter as the display distance increased in the virtual image display, while there was no such change in the real image display. This suggests that the input process has at least two critical parameters, namely the display distance and the ease of spatial orientation.

关键词： heads-up displays virtual image real image visual input eye tracking spatial localization

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