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Efficient High-Resolution image-to-image Translation Using Multi-Scale Gradient U-Net 1

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6th International conference on computer vision and image processing, CVIP 2021

作者： Laxman, Kumarapu Dubey, Shiv Ram Kalyan, Baddam Kojjarapu, Satya Raj Vineel Indian Institute of Information Technology Sri City Chittoor India Indian Institute of Information Technology Allahabad Prayagraj India

ISBN: (数字)9783031113468

ISBN: (纸本)9783031113451

Recently, Conditional Generative Adversarial Network (Conditional GAN) has shown very promising performance in several image-to-image translation applications. However, the uses of these conditional GANs are quite limited to low-resolution images, such as 256 × 256. the Pix2Pix-HD is a recent attempt to utilize the conditional GAN for high-resolution image synthesis. In this paper, we propose a Multi-Scale Gradient based U-Net (MSG U-Net) model for high-resolution image-to-image translation up to 2048 × 1024 resolution. the proposed model is trained by allowing the flow of gradients from multiple-discriminators to a single generator at multiple scales. the proposed MSG U-Net architecture leads to photo-realistic high-resolution image-to-image translation. Moreover, the proposed model is computationally efficient as compared to the Pix2Pix-HD with an improvement in the inference time nearly by 2.5 times. We provide the code of MSG U-Net model at https://***/laxmaniron/MSG-U-Net. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Comparitive Analysis of Various Transfer Learning Apporaches in Deep CNNs for image Classification 6th

Comparitive Analysis of Various Transfer Learning Apporaches...

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6th International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2023

作者： Tyagi, Arnav Khandelwal, Rishabh Shelke, Nitin Arvind Singh, Jagendra Rajpal, Dev Gaware, Ishaan Rajendra School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9783031530814

Deep learning has become an effective approach over the past few years to addressing intricate computer vision problems, and Convolutional Neural Networks (CNNs) have been the primary driving force behind this progress. Developing CNNs, however, comes with the obstacle of requiring huge, labeled datasets. Gathering and annotating a large dataset for any specific job is costly and time-consuming. To overcome this challenge, researchers can employ transfer learning, a technique that involves using pre-trained deep learning models on extensive datasets. this study primarily aims to investigate the application of various transfer learning methods in conjunction with Deep Convolutional Neural Networks (CNNs) for image classification. the research utilizes the Visual Object Classes Challenge 2012 (VOC2012) dataset as the foundation for its analysis. To classify a diverse range of object images, the study applies well-established transfer learning techniques, specifically fine-tuning pre-trained CNN models. Model performance is assessed through metrics like FPS (frames per second) and mAP% (mean average precision). A variety of models, including VGG19, SqueezeNet, YOLOv7, and Inception-ResNet-v2, are tested to determine the most suitable model for the dataset. the study’s findings demonstrate that employing transfer learning with CNNs can substantially enhance image classification accuracy on the dataset while reducing model development time. Notably, the Inception-ResNet-v2 model emerged as the top-performer, achieving an mAP score of 77%. this research underscores the potential of transfer learning as a powerful tool in the realm of image classification. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image classification

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MMAG: Mutually Motivated Attention Gates for Simultaneous Extraction of Contextual and Spatial Information from a Monocular image 14

MMAG: Mutually Motivated Attention Gates for Simultaneous Ex...

引用

14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Singh, Aditya Mohan, Saloni Kushwaha, Vandana Nandi, G.C. Center of Intelligent Robotics Indian Institute of Information Technology Allahabad Uttar Pradesh Prayagraj India

ISBN: (纸本)9798400716256

In order to effectively interact with its environment, an agent must possess the ability to comprehend the'what' and the'where.' Two vision-based approaches, namely semantic segmentation and depth estimation, can be employed to provide this information. this paper introduces a unified model that combines these two tasks and utilizes a shared latent space. the chosen approach for the model involves an encoder-decoder architecture accompanied by a novel attention gate mechanism called MMAG, which has been proven to be highly effective. Furthermore, a common skip connection method, in conjunction with the proposed attention gate, is included to emphasize crucial features for both prediction purposes. By leveraging the complementary information within the shared representations, the model is capable of generating more accurate predictions compared to other joint-prediction models, all while utilizing fewer network parameters. To enhance performance, dilated layers are incorporated, allowing for focused attention through different receptive fields. the model's performance has been evaluated on the NYU Depth v2 and Camvid datasets, demonstrating improved results on both tasks when compared to other state-ofthe-art models. With its reduced parameter count, this model is particularly suitable for low-cost robots and has undergone successful testing for inference on the LoCoBot robot. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Semantic Segmentation

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Low Resource Degraded Quality Document image Binarization - Domain Adaptation is the Way 13

Low Resource Degraded Quality Document Image Binarization - ...

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

作者： Kundu, Ahana Bhattacharya, Ujjwal Computer Vision and Pattern Recognition Indian Statistical Institute West Bengal Kolkata India

ISBN: (纸本)9781450398237

Usually, image binarization plays a crucial role in automatic analysis of degraded documents from their captured images. However, this binarization task is often difficult due to a number of reasons including the high similarity between noisy background and faded foreground pixels. the study presented here is particularly focused on binarization of images of low-resource degraded quality documents based on a set of recently collected image samples of several rare, ancient and severely degraded quality printed documents of Bangla, the 2nd and 5th most popular script of India and the world respectively. this new collection of degraded document image samples will henceforth be referred as 'ISIDDI2' and it consists of 139 images of Bangla old document pages. Samples of 'ISIDDI', another existing database of degraded Bangla document image samples, have also been used in the present study. A novel deep architecture based on attention UNET++ with dilated convolution operation is proposed for this binarization task. the model is optimized using human vision perceptible distance reciprocal distortion (DRD) loss. Since the binarization ground truth of samples of both 'ISIDDI2' and 'ISIDDI' are not available, the proposed network has been trained using samples of DIBCO and H-DIBCO datasets and an unsupervised domain adaptation (DA) module is employed for adaptation of the proposed architecture to the degradation patterns of 'ISIDDI2' or 'ISIDDI' samples. the proposed binarization strategy includes certain post-processing operation based on a modified k-neighbourhood based approach for recovery of broken characters. Results of our extensive experimentation show that the proposed binarization strategy has improved the binarization output of state-of-the-art methods on both ISIDDI2 and ISIDDI datasets. Also, its performance on well-known DIBCO samples is satisfactory. © 2022 ACM.

关键词： Network architecture

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A Multi-stage Feature Enhancement Network with Second-Attention for image Captioning 6

A Multi-stage Feature Enhancement Network with Second-Attent...

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6th International conference on Artificial Intelligence and Pattern Recognition, AIPR 2023

作者： Yang, Xiaobao Yang, Yang Wu, Junsheng Ma, Sugang Hou, Zhiqiang Song, Bohui Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China School of Computer Science Northwestern Polytechnical University Xi'an710071 China

ISBN: (纸本)9798400707674

In the past few years, self-attention in Transformer has been widely used in natural language processing (NLP) and computer vision (CV) due to its excellent ability to capture global information, especially in image captioning, where the use of self-attention can significantly improve the representation of visual information. However, the obtained image feature from Transformer-based models suffers from two main problems, one is information redundancy due to the global aggregation of self-attention, and the other is the lack of semantic information caused by single-scale feature extraction. therefore, we first propose second-attention in this paper, by redistributing image attention weights, second-attention can effectively remove irrelevant information and enhance the attention to essential objects and relations. Meanwhile, to enrich semantic information and further enhance the role of second-attention, we design a Multi-stage Feature Enhancement (MFE) network to improve the ability to represent visual information. After conducting extensive experiments on the MS COCO dataset, we achieve significant improvements in all popular benchmarks, particularly BLEU4 increased by 1.6%, CIDEr increased by 3.4%. © 2023 ACM.

关键词： Semantics

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Object Detection with YOLO Model on NAO Humanoid Robot 10th

Object Detection with YOLO Model on NAO Humanoid Robot

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10th Biennial International conference on Pattern Recognition and Machine Intelligence (PReMI)

作者： Biswas, Sougatamoy Nandy, Anup Naskar, Asim Kumar Natl Inst Technol Rourkela Rourkela Odisha India

ISBN: (纸本)9783031451690;9783031451706

Object detection is an important part in the field of robotics as it enables robots to understand their surroundings. the NAO humanoid robot is extensively used in human-robot interaction research. In this study a novel approach combining the VGG16 network and You Only Look Once (YOLO) algorithm is used for object detection using the NAO robot. YOLOv7 is selected for its best balanced information retention, quick inference, accurate localization, and identification of objects as compared to several bounding box algorithms. VGG16 network is adopted as a feature extractor to optimize the performance of object detection for NAO low-resolution camera images. Once feature extraction is completed then it's output layer is combined with our fine tuned YOLOv7 model for object detection. the fine-tuned YOLOv7 model is proposed with some pre-processing techniques such as image augmentation, angle movement, and scale resizing for the performance improvement. the efficiency of the proposed model is compared with the performance of other state-of-the-art models.

关键词： Object detection computer vision Humanoid robots Object recognition

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vision based Navigation, Guidance and Control of 6 DOF Robotic manipulator for Space Berthing application 23

Vision based Navigation, Guidance and Control of 6 DOF Robot...

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6th International conference on Advances in Robotics, AIR 2023

作者： Laha, Jayanta Kapoor, Abhinandan Maji, Amit Kejriwal, Ayush Tiwari, Sidhharth Yadav, Subash Kumar, Abhishek Nagaraj, B.P. Suresh Kumar, H.N. Indian Space Research Organization India

ISBN: (纸本)9781450399807

Berthing of the spacecraft with non cooperative targets are gaining interest in recent days. Space robotics is considered one of the most promising approaches for missions such as docking, re-fuelling, repairing, upgrading, transporting, rescuing, and orbital debris removal. Many enabling techniques have been developed in the past two decades and several technology demonstration missions have been completed. A number of manned on-orbit servicing missions were successfully accomplished but unmanned, fully autonomous, non-co-operative servicing missions have not been done yet. With the advent of advanced image processing algorithm and its hardware implementation made it possible to perform robotic arm actuations in space for berthing with on orbit satellite. Usually the robotic arm will be equipped by a small camera which will track the target. Here the usage of colour marker is useful as it can be detected and tracked faster with less amount of processing. One of the greatest challenges is to ensure the servicing spacecraft safely and reliably docks with the target spacecraft or capture the target to stabilize it for subsequent berthing. this is especially important if the target has an unknown motion and kinematics/ dynamics. © 2023 ACM.

关键词： Robotic arms

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MoMSGAN: Mode Collapse based Degradation Agnostic Multi-Scale Super-Resolution of Medical images 14

MoMSGAN: Mode Collapse based Degradation Agnostic Multi-Scal...

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Dey, Samiran Chakraborti, Tapabrata Basuchowdhuri, Partha Mitra, Debasis Augustine, Robin Saha, Sanjoy Kumar Indian Association for the Cultivation of Science India The Alan Turing Institute University College London United Kingdom Indian Institute of Engineering Science and Technology India Uppsala University Sweden Jadavpur University India

ISBN: (纸本)9798400716256

Existing super-resolution (SR) models require separate training for different scales of SR because of the fixed upsampling levels in their architecture. We propose a novel one-time training approach for multi-scale SR models with a fixed upsampling unit, that can effectively super-resolve images to various scales. Another persisting problem relevant to medical image SR is that the models are biased to the level of degradation present in the low-resolution (LR) images and generate variations in anatomical structures and pathologies when applied to LR images corresponding to the same high-resolution (HR) image, varying only in degradation. But regardless of the degradation in the LR images, the anatomical structures and clinical findings in the SR images should remain consistent and unique to the patient's specific anatomy and health conditions. We thus propose a novel approach for GANs to blindly constrain the variations in super-resolved medical images. Further, we introduce an ensemble loss function to gain details and accurate textures, while enabling multi-scale SR and making the model unbiased towards degradation in LR images. Experimental results on lung CT scans demonstrate that our model, MoMSGAN, is an efficient one-time training approach for multi-scale SR and is also capable of reducing the degradation bias in reproducing accurate information in medical images, thus establishing state-of-the-art. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： computerized tomography

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Optimizing Knowledge Transfer in Sequential Models: Leveraging Residual Connections in Flow Transfer Learning for Lung Cancer Classification 14

Optimizing Knowledge Transfer in Sequential Models: Leveragi...

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Sharma, Richa Kumar, Santosh Shrivastava, Abhishek Bhatt, Tejasav Shri Shankaracharya Institute of Engineering and Technology Chhattisgarh Bhilai India IIIT-Naya Raipur Raipur India United College of Engineering and Research Uttar Pradesh Prayagraj India

ISBN: (纸本)9798400716256

Lung cancer is a prevalent and life-threatening disease characterized by abnormal cell growth in lung tissue. Early detection and accurate classification of lung cancer are crucial for timely treatment and improved patient outcomes. Our work proposes, a novel framework for accurate multi-class categorization of lung cancer using deep learning. the proposed framework uses a customized Densenet-201 model to leverage the knowledge of transfer learning, which acts as a parent architecture, enhanced by a residual structure as the child architecture. the performance of our model was examined by conducting experiments on the LCS25000 data set. the results demonstrate the outstanding accuracy of our proposed framework, achieving a remarkable accuracy of 95% on the test data set. this signifies the model's ability to accurately classify different types of lung cancer based on histopathology images. Our model also achieved remarkably well results in the TCGA lung cancer dataset, hence proving its generalization. these findings have important repercussions for improving pulmonary pathology diagnostic abilities and hold promise for enhancing patient care in the field of lung cancer diagnosis. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： image classification

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image Generation Method Based on Variable Scale Pyramid Attention with Text Data Enhancement 6

Image Generation Method Based on Variable Scale Pyramid Atte...

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6th International conference on Artificial Intelligence and Pattern Recognition, AIPR 2023

作者： Qiao, Pingan Lei, Ziying Zhang, Xiaofan School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (纸本)9798400707674

In recent years, research on methods for generating images from text based on generative adversarial networks has made some progress. the study of text-generated images is at the intersection of natural language processing and computer vision, a task that requires computers to be able to generate images that are consistent with the semantics of the text on the basis of understanding the semantics of the input text. Aiming at the problems of text-image matching and poor performance on image details in traditional text-generated image methods, a VT-AttnGAN (Variable Scale Pyramid Attention with Text Data Augmentation for Generative Adversarial Networks) text-based image generation method. A Vector Data Enhancement Network (TDEN) is designed to reduce the text dimension to enhance the model's ability to learn the probability distribution of text features, and optimize the alignment of image and text. In addition, to enhance the detailed features of the image, a Variable Scale Pyramid Attention Module (VSPAM) is proposed, which extracts the features at different scales of the initial image, and efficiently utilizes the useful information between the feature channels through the attention approach. We train and test on two datasets, CUB-200-2011 and Oxford-102, and the experimental results show that compared with AttnGAN, the model proposed in this paper solves the problems of image blurring and distortion, and has the image generation effect of text image alignment enhancement and picture integrity improvement. © 2023 ACM.

关键词： Generative adversarial networks

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