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A New Transformer-Based Approach for Text Detection in Shaky and Non-shaky Day-Night Video 1

7th Asian Conference on pattern recognition, ACPR 2023

作者： Halder, Arnab Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Blumenstein, Michael Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kula Lumpur Malaysia Nanjing University Nanjing China University of Technology Sydney Sydney Australia

ISBN: (数字)9783031476372

ISBN: (纸本)9783031476365

Text detection in shaky and non-shaky videos is challenging because of variations caused by day and night videos. In addition, moving objects, vehicles, and humans in the video make the text detection problems more challenging in contrast to text detection in normal natural scene images. Motivated by the capacity of the transformer, we propose a new transformer-based approach for detecting text in both shaky and non-shaky day-night videos. To reduce the effect of object movement, poor quality, and other challenges mentioned above, the proposed work explores temporal frames for obtaining activation frames based on similarity and dissimilarity measures. For estimating similarity and dissimilarity, our method extracts luminance, contrast, and structural features. The activation frames are fed to the transformer which comprises an encoder, decoder, and feed-forward network for text detection in shaky and non-shaky day-night video. Since it is the first work, we create our own dataset for experimentation. To show the effectiveness of the proposed method, experiments are conducted on a standard dataset called the ICDAR-2015 video dataset. The results on our dataset and standard dataset show that the proposed model is superior to state-of-the-art methods in terms of recall, precision, and F-measure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Chemical activation

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A New StyleGAN Latent Space Based Model for Image Style Transfer 27th

A New StyleGAN Latent Space Based Model for Image Style Tran...

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27th International Conference on pattern recognition, ICPR 2024

作者： Dey, Rakesh Palaiahnakote, Shivakumara Bhattacharya, Saumik Chanda, Sukalpa Pal, Umapada Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata Baranagar India School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Electrical and Electronics Communication IIT-Kharagpur Kharagpur India Østfold University College Halden Norway

ISBN: (纸本)9783031781940

Cross-domain image style transfer task is an attractive topic for several applications, such as image-to-image style transfer, text-to-image style transfer, artistic image generation, etc. In cross-domain image style transfer tasks (e.g., image-to-image style transfer, artistic image-to-image style transfer, text-to-image style transfer, etc.), training becomes cumbersome due to differences in data distribution across domains and complex model architectures. Unlike existing domain adaptation and domain-independent methods that focus on robust and sufficient feature extraction, this work focuses on disentangling the latent space through latent optimization. For this purpose here we propose a new idea of styled image generation from the latent space of StyleGAN which works well for image-to-image and text-to-image style transfer. We critically analyzed the low-dimensional latent structure and its effect on cross-domain image style transfer tasks and finally proposed a method along with a latent optimizing procedure to overcome the problem of style transfer. The experimental results on different standard datasets show that the proposed model is robust, effective, and generic compared to the state-of-the-art models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Blind Image Inpainting via Omni-dimensional Gated Attention and Wavelet Queries

Blind Image Inpainting via Omni-dimensional Gated Attention ...

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IEEE computer Society Conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Shruti S. Phutke Ashutosh Kulkarni Santosh Kumar Vipparthi Subrahmanyam Murala Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Rupnagar Punjab

Blind image inpainting is a crucial restoration task that does not demand additional mask information to restore the corrupted regions. Yet, it is a very less explored research area due to the difficulty in discriminating between corrupted and valid regions. There exist very few approaches for blind image inpainting which sometimes fail at producing plausible inpainted images. Since they follow a common practice of predicting the corrupted regions and then inpaint them. To skip the corrupted region prediction step and obtain better results, in this work, we propose a novel end-to-end architecture for blind image inpainting consisting of wavelet query multi-head attention transformer block and the omni-dimensional gated attention. The proposed wavelet query multi-head attention in the transformer block provides encoder features via processed wavelet coefficients as query to the multi-head attention. Further, the proposed omni-dimensional gated attention effectively provides all dimensional attentive features from the encoder to the respective decoder. Our proposed approach is compared numerically and visually with existing state-of-the-art methods for blind image inpainting on different standard datasets. The comparative and ablation studies prove the effectiveness of the proposed approach for blind image inpainting. The testing code is available at : https://***/shrutiphutke/Blind_Omni_Wav_Net

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A New Lightweight Attention-Based Model for Emotion recognition on Distorted Social Media Face Images 1

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7th Asian Conference on pattern recognition, ACPR 2023

作者： Roy, Ayush Shivakumara, Palaiahnakote Pal, Umapada Gornale, Shivanand S. Liu, Cheng-Lin Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kula Lumpur Malaysia Department of Computer Science Rani Channamma University Belagavi India Institute of Automation of Chinese Academy of Sciences Beijing China

ISBN: (数字)9783031476372

ISBN: (纸本)9783031476365

The recognition of human emotions remains a challenging task for social media images. This is due to distortions created by different social media conflict with the minute changes in facial expression. This study presents a new model called the Global Spectral-Spatial Attention Network (GSSAN), which leverages both local and global information simultaneously. The proposed model comprises a shallow Convolutional Neural Network (CNN) with an MBResNext block, which integrates the features extracted from MobileNet, ResNet, and DenseNet for extracting local features. In addition, to strengthen the discriminating power of the features, GSSAN incorporates Fourier features, which provide essential cues for minute changes in the face images. To test the proposed model for emotion recognition using social media images, we conduct experiments on two widely-used datasets: FER-2013 and AffectNet. The same benchmark datasets are uploaded and downloaded to create a distorted social media image dataset to test the proposed model. Experiments on distorted social media images dataset show that the model surpasses the accuracy of SOTA models by 0.69% for FER-2013 and 0.51% for AffectNet social media datasets. The same inference can be drawn from the experiments on standard datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Face recognition

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PulmoNetX: A Hybrid vision Transformer Approach for Multi-scale Spatial Feature Reduction in Pneumonia Classification 27th

PulmoNetX: A Hybrid Vision Transformer Approach for Multi-sc...

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27th International Conference on pattern recognition, ICPR 2024

作者： Lasker, Asifuzzaman Ghosh, Mridul Obaidullah, Sk Md Chakraborty, Chandan Roy, Kaushik Pal, Umapada Department of Computer Science and Engineering Aliah University Kolkata700160 India Department of Computer Science Shyampur Siddheswari Mahavidyalaya Howrah711312 India Department of Computer Science and Engineering National Institute of Technical Teachers’ Training and Research Kolkata700106 India Department of Computer Science West Bengal State University Barasat700126 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9783031781650

An innovative deep learning structure, PulmoNetX, integrates the capabilities of Convolutional Neural Networks (CNNs) and vision Transformers (ViTs) to enhance pneumonia detection in chest X-ray imagery. During preprocessing, images are normalized in size, converted to grayscale, and subjected to contrast amplification to emphasize essential features. PulmoNetX employs a hybrid methodology to capture both the local and global characteristics of images, leading to significant advancements in diagnosing different pneumonia types, such as COVID-19-induced, viral, and bacterial pneumonia. Comparative studies reveal that PulmoNetX surpasses leading vision Transformer models in terms of precision, recall, F1-score, and overall accuracy, highlighting its advanced processing abilities and its promise as an effective diagnostic tool in X-ray lung disease detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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DITS: A New Domain Independent Text Spotter 27th

DITS: A New Domain Independent Text Spotter

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27th International Conference on pattern recognition, ICPR 2024

作者： Purkayastha, Kunal Sarkar, Shashwat Shivakumara, Palaiahnakote Pal, Umapada Ghosal, Palash Wu, Xiao-Jun Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata Baranagar India School of Science Engineering and Environment University of Salford Manchester United Kingdom Department of Information Technology Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim Gangtok India Jiangnan University Wuxi China

ISBN: (纸本)9783031784941

Text spotting in diverse domains, such as drone-captured images, underwater scenes, and natural scene images, presents unique challenges due to variations in image quality, contrast, text appearance, background complexity, and external factors like water surface reflections and weather conditions. While most existing approaches focus on text spotting in natural scene images, we propose a Domain-Independent Text Spotter (DITS) that effectively handles multiple domains. We innovatively combine the Real-ESRGAN, developed for regular image enhancement, with the DeepSolo, developed for scene text spotting, in an end-to-end fashion for text detection and spotting on images of different domains. The key idea behind our approach is that improving image quality and text-spotting accuracy are complementary goals. Real-ESRGAN enhances image quality, making the text more discernible, while DeepSolo, a state-of-the-art text spotting model, accurately localizes and recognizes text in the enhanced images. We validate the superiority of our proposed model by evaluating it on datasets from drone, underwater, and scene domains (ICDAR 2015, CTW1500, and Total-Text). Furthermore, we demonstrate the domain independence of our model through cross-domain validation, where we train on one domain and test on others. Our dataset and code will be publicly available on GitHub. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image quality

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Open-Set Plankton recognition

arXiv

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arXiv 2025年

作者： Kareinen, Joona Skyttä, Annaliina Eerola, Tuomas Kraft, Kaisa Lensu, Lasse Suikkanen, Sanna Lehtiniemi, Maiju Kälviäinen, Heikki Computer Vision and Pattern Recognition Laboratory Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Finnish Environment Institute Helsinki Finland

This paper considers open-set recognition (OSR) of plankton images. Plankton include a diverse range of microscopic aquatic organisms that have an important role in marine ecosystems as primary producers and as a base of food webs. Given their sensitivity to environmental changes, fluctuations in plankton populations offer valuable information about oceans’ health and climate change motivating their monitoring. Modern automatic plankton imaging devices enable the collection of large-scale plankton image datasets, facilitating species-level analysis. Plankton species recognition can be seen as an image classification task and is typically solved using deep learning-based image recognition models. However, data collection in real aquatic environments results in imaging devices capturing a variety of non-plankton particles and plankton species not present in the training set. This creates a challenging fine-grained OSR problem, characterized by subtle differences between taxonomically close plankton species. We address this challenge by conducting extensive experiments on three OSR approaches using both phyto- and zooplankton images analyzing also on the effect of the rejection thresholds for OSR. The results demonstrate that high OSR accuracy can be obtained promoting the use of these methods in operational plankton research. We have made the data publicly available to the research community. © 2025, CC BY.

关键词： Biotic

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Diving into the Depths of Spotting Text in Multi-Domain Noisy Scenes

Diving into the Depths of Spotting Text in Multi-Domain Nois...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Alloy Das Sanket Biswas Umapada Pal Josep Lladós Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Computer Science Department Computer Vision Center Universitat Autónoma de Barcelona Barcelona Spain

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

When used in a real-world noisy environment, the capacity to generalize to multiple domains is essential for any autonomous scene text spotting system. However, existing state-of-the-art methods employ pretraining and fine-tuning strategies on natural scene datasets, which do not exploit the feature interaction across other complex domains. In this work, we explore and investigate the problem of domain-agnostic scene text spotting, i.e., training a model on multi-domain source data such that it can directly generalize to target domains rather than being specialized for a specific domain or scenario. In this regard, we present the community a text spotting validation benchmark called Under-Water Text (UWT) for noisy underwater scenes to establish an important case study. Moreover, we also design an efficient super-resolution based end-to-end transformer baseline called DA-TextSpotter which achieves comparable or superior performance over existing text spotting architectures for both regular and arbitrary-shaped scene text spotting benchmarks in terms of both accuracy and model efficiency. The dataset, code and pre-trained models have been released in our Github.

关键词： Training Codes Accuracy Superresolution Benchmark testing Transformers Data models

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Multi-scale Promoted Self-adjusting Correlation Learning for Facial Action Unit Detection

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IEEE Transactions on Affective Computing 2024年第2期16卷 697-711页

作者： Liu, Xin Yuan, Kaishen Niu, Xuesong Shi, Jingang Yu, Zitong Yue, Huanjing Yang, Jingyu Tianjin University School of Electrical and Information Engineering Tianjin300072 China Lappeenranta-Lahti University of Technology LUT Computer Vision and Pattern Recognition Laboratory School of Engineering Science Lappeenranta53850 Finland Beijing Institute for General Artificial Intelligence Beijing100080 China Xi'an Jiaotong University School of Software Engineering Xi'an710049 China Great Bay University Dongguan523000 China

Facial Action Unit (AU) detection is a crucial task in affective computing and social robotics as it helps to identify emotions expressed through facial expressions. Anatomically, there are innumerable correlations between AUs, which contain rich information and are vital for AU detection. Previous methods used fixed AU correlations based on expert experience or statistical rules on specific benchmarks, but it is challenging to comprehensively reflect complex correlations between AUs via hand-crafted settings. There are alternative methods that employ a fully connected graph to learn these dependencies exhaustively. However, these approaches can result in a computational explosion and high dependency with a large dataset. To address these challenges, this paper proposes a novel self-adjusting AU-correlation learning (SACL) method with less computation for AU detection. This method adaptively learns and updates AU correlation graphs by efficiently leveraging the characteristics of different levels of AU motion and emotion representation information extracted in different stages of the network. Moreover, this paper explores the role of multi-scale learning in correlation information extraction, and design a simple yet effective multi-scale feature learning (MSFL) method to promote better performance in AU detection. By integrating AU correlation information with multi-scale features, the proposed method obtains a more robust feature representation for the final AU detection. Extensive experiments show that the proposed method outperforms the state-of-the-art methods on widely used AU detection benchmark datasets, with only 28.7% and 12.0% of the parameters and FLOPs of the best method, respectively. The code for this method is available at https://***/linuxsino/Self-adjusting-AU. © 2010-2012 IEEE.

关键词： Contrastive Learning

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Multi-gradient-direction based deep learning model for arecanut disease identification

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CAAI Transactions on Intelligence Technology 2022年第2期7卷 156-166页

作者： S.B.Mallikarjuna Palaiahnakote Shivakumara Vijeta Khare M.Basavanna Umapada Pal B.Poornima Department of Computer Science and Engineering Bapuji Institute of Engineering and TechnologyDavanagereAffiliated to Visvesvaraya Theological UniversityBelagaviKarnatakaIndia Faculty of Computer Science and Information Technology University of MalayaKuala LumpurMalaysia Adani Institute of Infrastructure Engineering AhmedabadIndia Department of Computer Science Davanagere UniversityDavanagereKarnatakaIndia Computer Vision and Pattern Recognition Unit Indian Statistical InstituteKolkataWest BengalIndia

Arecanut disease identification is a challenging problem in the field of image *** this work,we present a new combination of multi-gradient-direction and deep con-volutional neural networks for arecanut disease identification,namely,rot,split and *** to the effect of the disease,there are chances of losing vital details in the *** enhance the fine details in the images affected by diseases,we explore multi-Sobel directional masks for convolving with the input image,which results in enhanced *** proposed method extracts arecanut as foreground from the enhanced images using Otsu ***,the features are extracted for foreground information for disease identification by exploring the ResNet *** advantage of the proposed approach is that it identifies the diseased images from the healthy arecanut *** results on the dataset of four classes(healthy,rot,split and rot-split)show that the proposed model is superior in terms of classification rate.

关键词： deep learning image analysis pattern recognition

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