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27th International Conference on pattern recognition, ICPR 2024

作者： Roy, Ayush Palaiahnakote, Shivakumara Pal, Umapada Antonacopoulos, Apostolos Ramachandra, Raghavendra Computer Vision and Pattern Recognition Indian Statistical Institute Kolkata India School of Science Engineering and Environment University of Salford Manchester United Kingdom Norwegian University of Science and Technology Trondheim Norway

ISBN: (纸本)9783031784460

Text-line segmentation is still considered challenging for complex background scene images. The success of text detection and recognition depends on the success of the text segmentation. This study presents a new method for text segmentation to facilitate reliable detection and recognition. Therefore, we introduce a new model called Pixel Correlation and Gaussian Attention Driven Network (PCGAUNet) for text segmentation. To extract pixel correlation, we modified the MultiResUnet architecture, which leverages pixel-wise correlation to effectively highlight foreground pixels. In addition, the proposed model utilizes the prior spatial statistics of bottleneck features to create a learnable Gaussian distribution, which guides the decoder for accurate text segmentation. Experimental results on three standard scene text segmentation datasets, ICDAR13 FST, Total Text, and COCO-TS, show that the proposed model outperforms existing methods. Furthermore, the results for the underwater dataset UTS-55 show that our model is robust and generic. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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XLSI: A New Xception and Log Polar Transform Based Approach for Scene Text Script Identification 27th

XLSI: A New Xception and Log Polar Transform Based Approach ...

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27th International Conference on pattern recognition, ICPR 2024

作者： Roy, Ayush Palaiahnakote, Shivakumara Pal, Umapada Antonacopoulos, Apostolos Blumenstein, Michael Computer Vision and Pattern Recognition Indian Statistical Institute Kolkata Kolkata India School of Science Engineering and Environment University of Salford Manchester United Kingdom University of Technology Sydney Sydney Australia

ISBN: (纸本)9783031784941

Script identification of text in natural scene images is challenging due to complex backgrounds, arbitrary orientations, different-sized characters, varying fonts, and multiple styles. Most existing methods are not effective in the presence of the above challenges. This paper introduces a new approach based on the Xception architecture and employing the log-polar transformed original image as an additional input, enabling the extraction of cues that are invariant to rotation, scaling, but are sensitive to script. The rationale behind the proposed work is that the combination of global features with text style features makes a significant difference in discriminating between different scripts. To combine the features extracted by Xception from the input image and log the polar transform of the input image, the proposed method introduces a style-enhanced fusion block. In addition, to further improve the performance of script identification, the proposed approach uses a new receptive channel selective focal attention module. Comparative evaluation results on three benchmark datasets, namely CVSI 2015, SIW-13, and MLe2e show that the proposed method outperforms the state-of-the-art in terms of classification rate. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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DATR: Domain Agnostic Text Recognizer 27th

DATR: Domain Agnostic Text Recognizer

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27th International Conference on pattern recognition, ICPR 2024

作者： Purkayastha, Kunal Sarkar, Shashwat Palaiahnakote, Shivakumara Pal, Umapada Ghosal, Palash Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata 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

ISBN: (纸本)9783031784460

Recognizing text extracted from multiple domains is complex and challenging because complexities vary from one domain to another. Most existing methods focus either on natural scene text or specific text type but not text of multiple domains, namely, scene, underwater, and drone texts. In addition, the state-of-the-art models ignore the vital cues that exist in multiple instances of the text. This paper presents a new method called the Student-Teacher-Assistant (STA) network, which involves dual CLIP models to exploit cues in multiple text instances. The model that uses ResNet50 in its image encoder is called helper CLIP, while the model that uses ViT in its image encoder is called primary CLIP. The proposed work processes both models simultaneously to extract visual and textual features through image and text encoders. Our work uses cosine similarity for the randomly chosen input image to detect instances similar to the input image. The input and similar instances are supplied to primary and helper CLIPs for visual and textual feature extraction. The outputs of dual CLIPs are fused in a different way through the alignment step for recognizing text accurately, irrespective of domains. To demonstrate the proposed model’s significance, experiments are conducted on a set of standard natural scene text datasets (regular and irregular), underwater images, and drone images. The results on three different domains show that the proposed model outperforms the state-of-the-art recognition models. The datasets and code for public use in training and testing shall be made available on GitHub. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Underwater imaging

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A New HourGlass Network for Detecting Text in Shaky and Non-shaky Video Frames 27th

A New HourGlass Network for Detecting Text in Shaky and Non-...

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27th International Conference on pattern recognition, ICPR 2024

作者： Halder, Arnab Palaiahnakote, Shivakumara Pal, Umapada Blumenstein, Michael Gornale, Shivanand S. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata Kolkata India School of Science Engineering and Environment University of Salford Manchester United Kingdom University of Technology Sydney Sydney Australia Department of Computer Science Rani Channamma University Belagavi India

ISBN: (纸本)9783031784972

Shaky and non-shaky videos are quite common in real-time applications such as surveillance and monitoring vehicles and human movements in protected areas. As a result, text detection in such videos is a formidable challenge due to motion blur, noise, shaky cameras, poor quality and poor visibility. In contrast to existing text detection methods, which focus on text detection in scene images or specific types of images, the present work focuses on text detection in both shaky and non-shaky video frames. Inspired by the impressive performance of the HourGlass network for adverse situations, we explore the HourGlass network for successful text detection in shaky, non-shaky video frames and natural scene images. To improve the performance of the HourGlass network, we employ the Real-Time Model (RTMHead) for predicting text precisely and the Cross Stage Partial Network (CSPNet), which is a neck architecture for robust feature fusion. In addition, the integration of the SiLu activation function with the HourGlass network improves the discriminative power ability. To test the efficacy of the proposed method, we conducted experiments on shaky and non-shaky video frames, as well as ICDAR 2015 video frames. Furthermore, to show the effectiveness of the proposed method, we used Total-Text scene images for experimentation. The results on different datasets and a comparative study with the state-of-the-art models show that the proposed model outperforms the existing methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

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A New Impressive and Expressive Features Based Model for Personality Traits Identification 27th

A New Impressive and Expressive Features Based Model for Per...

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27th International Conference on pattern recognition, ICPR 2024

作者： Biswas, Kunal Palaiahnakote, Shivakumara Pal, Umapada Chanda, Sukalpa Wu, Xiao-Jun Jadavpur University Kolkata India School of Science Engineering and Environment University of Salford Manchester United Kingdom Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Østfold University College Halden Norway Jiangnan University Wuxi China

ISBN: (纸本)9783031781858

Personality traits identification is challenging due to unpredictable changes in the foreground and background of images. In this work, we propose a new deep learning model for personality traits image classification by exploiting visual and textual information from dating images. We believe that there is a strong correlation between the features (visual + textual) of dating images and personality traits images. The features which draw attention are defined as impressive features of dating images and features that convey emotions are expressive features of personality traits images. This observation motivated us to combine the features of dating images to improve the performance of personality traits images classification. To affirm the above observation, we propose multiple convolutional layers followed by max pool layers which extract features from dating and personality traits images simultaneously. To integrate the strengths of impressive and expressive features, the proposed work introduces a dual fusion approach, which fuses features and modalities at different levels. The experiments are conducted on different standard datasets of personality traits images to demonstrate the effectiveness of impressive features in terms of personality trait image classification. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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A New Attention Based UNet and Gated Edge Attention Network for Retinal Vessel Segmentation 27th

A New Attention Based UNet and Gated Edge Attention Network ...

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27th International Conference on pattern recognition, ICPR 2024

作者： Roy, Ayush Palaiahnakote, Shivakumara Pal, Umapada Chanda, Sukalpa Department of Electrical Engineering Jadavpur University Kolkata India School of Science Engineering and Environment University of Salford Salford United Kingdom Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata Kolkata India Østfold University College Halden Norway

ISBN: (纸本)9783031781032

Early diagnosis of retinal diseases is crucial for preventing blindness. However, due to background variations and degradation in the images, retinal vessel segmentation has become challenging. As a result, accurate segmentation of the retinal vessels is essential for enhancing diagnosis to identify the disease. To achieve this, inspired by the special ability of the attention mechanism, which detects vital regions, and UNet, which segments the vital region, we propose a combination of a new attention mechanism and modified UNet for segmenting vessels in retina images. In the proposed segmentation model, the convolutional blocks have been modified to capture multiscale spatial information by varying the convolution dilation rates. Similarly, the Trainable tanh activation (T-Tanh) is adapted in a new way to identify changes in the flow of the feature gradients to differentiate between the retinal vessel pixels and the background. Furthermore, to make the segmentation robust, the Gated Edge Attention (GEA) network is proposed. The effectiveness of the segmentation is demonstrated by testing on two benchmark datasets, namely, STARE and CHASE. The results show that the performance of the proposed method is superior to the state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Ophthalmology

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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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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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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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Non-Uniform Illumination Attack for Fooling Convolutional Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Jain, Akshay Dubey, Shiv Ram Singh, Satish Kumar Santosh, K.C. Chaudhuri, Bidyut Baran Indian Institute of Information Technology Allahabad Computer Vision and Biometrics Lab Department of Information Technology Uttar Pradesh Prayagraj211015 India University of South Dakota AI Research Lab Department of Computer Science VermillionSD57069 United States Indian Statistical Institute Computer Vision and Pattern Recognition Unit Kolkata700108 India

Convolutional Neural Networks (CNNs) have made remarkable strides;however, they remain susceptible to vulnerabilities, particularly to image perturbations that humans can easily recognize. This weakness, often termed as 'attacks,' underscores the limited robustness of CNNs and the need for research into fortifying their resistance against such manipulations. This study introduces a novel Non-Uniform Illumination (NUI) attack technique, where images are subtly altered using varying NUI masks. Extensive experiments are conducted on widely-accepted datasets including CIFAR10, TinyImageNet, CalTech256 and NWPU-RESISC45 focusing on image classification with 12 different NUI masks. The resilience of VGG, ResNet, MobilenetV3-small, InceptionV3 and efficientNet b0 models against NUI attacks are evaluated. Our results show a substantial decline in the CNN models' classification accuracy when subjected to NUI attacks, due to changes in the image pixel value distribution, indicating their vulnerability under non-uniform illumination. To mitigate this, a defense strategy is proposed, including NUI-attacked images, generated through the new NUI transformation, into the training set. The results demonstrate a significant enhancement in CNN model performance when confronted with perturbed images affected by NUI attacks. This strategy seeks to bolster CNN models' resilience against NUI attacks. A comparative study with other attack techniques shows the effectiveness of the NUI attack and defense technique. © 2020 IEEE.

关键词： Convolutional neural networks

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