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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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CodePhys: Robust Video-Based Remote Physiological Measurement Through Latent Codebook Querying

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Chu, Shuyang Xia, Menghan Yuan, Mengyao Liu, Xin Seppanen, Tapio Zhao, Guoying Shi, Jingang Xi'an Jiaotong University School of Software Engineering Xi'an China Tencent Ai Lab Shenzhen China Lappeenranta-Lahti University of Technology Lut Computer Vision and Pattern Recognition Laboratory Lappeenranta53850 Finland University of Oulu Center for Machine Vision and Signal Analysis Finland

Remote photoplethysmography (rPPG) aims to measure non-contact physiological signals from facial videos, which has shown great potential in many applications. Most existing methods directly extract video-based rPPG features by designing neural networks for heart rate estimation. Although they can achieve acceptable results, the recovery of rPPG signal faces intractable challenges when interference from real-world scenarios takes place on facial video. Specifically, facial videos are inevitably affected by non-physiological factors (e.g., camera device noise, defocus, and motion blur), leading to the distortion of extracted rPPG signals. Recent rPPG extraction methods are easily affected by interference and degradation, resulting in noisy rPPG signals. In this paper, we propose a novel method named CodePhys, which innovatively treats rPPG measurement as a code query task in a noise-free proxy space (i.e., codebook) constructed by ground-truth PPG signals. We consider noisy rPPG features as queries and generate high-fidelity rPPG features by matching them with noise-free PPG features from the codebook. Our approach also incorporates a spatial-aware encoder network with a spatial attention mechanism to highlight physiologically active areas and uses a distillation loss to reduce the influence of non-periodic visual interference. Experimental results on four benchmark datasets demonstrate that CodePhys outperforms state-of-the-art methods in both intra-dataset and cross-dataset settings. © 2025 IEEE.

关键词： Heart

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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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Interaction Confidence Attention for Human–Object Interaction Detection

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International Journal of computer vision 2025年 1-20页

作者： Zhang, Hong-Bo Lin, Wang-Kai Su, Hang Lei, Qing Liu, Jing-Hua Du, Ji-Xiang Department of Computer Science and Technology Huaqiao University Xiamen361021 China Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen361021 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Xiamen361021 China Fujian Province University Xiamen361021 China

In human–object interaction (HOI) detection task, ensuring that interactive pairs receive higher attention weights while reducing the weight of non-interaction pairs is imperative for enhancing HOI detection accuracy. Guiding attention learning is also a key aspect of existing transformer-based algorithms. To tackle this challenge, this study proposes a novel approach termed Interaction Confidence Score Learning Attention (ICSLA), which introduces weakening and augmentation operations into the original attention weight calculation and feature extraction processes. In ICSLA, feature learning is coupled with confidence score learning, simultaneously. Leveraging ICSLA, a new and universal decoder is devised, establishing a transformer-based one-stage HOI detection architecture. Experimental results demonstrate the effectiveness of the proposed method in improving HOI detection accuracy, offering valuable insights for further optimization of attention mechanisms. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Augmentation operation Human–object interaction detection Interaction confidence score learning attention Weakening operation

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CodePhys: Robust Video-based Remote Physiological Measurement through Latent Codebook Querying

arXiv

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arXiv 2025年

作者： Chu, Shuyang Xia, Menghan Yuan, Mengyao Liu, Xin Seppanen, Tapio Zhao, Guoying Shi, Jingang The School of Software Engineering Xi’an Jiaotong University Xi’an China The Tencent AI Lab Shenzhen China The Computer Vision and Pattern Recognition Laboratory Lappeenranta-Lahti University of Technology LUT Lappeenranta53850 Finland The Center for Machine Vision and Signal Analysis University of Oulu Finland

关键词： Heart

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SIR-HCL: Semantic-Inconsistency Reasoning and Hybrid Contrastive Learning for Efficient Cross-Emotion Anomaly Detection

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Liu, Xin Chen, Qiyan Cheung, Yiu-Ming Peng, Shu-Juan Huaqiao University Department of Computer Science Xiamen361021 China Hong Kong Baptist University Department of Computer Science SAR Hong Kong Hong Kong Xiamen Key Laboratory of Computer Vision and Pattern Recognition Xiamen361021 China Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Xiamen361021 China Huaqiao University Department of Artificial Intelligence Xiamen China Fujian Province University Key Laboratory of Computer Vision and Machine Learning Huaqiao University Xiamen361021 China

Cross-emotion anomaly detection is an emerging and challenging research topic in cognitive analysis field, which aims at identifying the abnormal emotion pair whose semantic patterns are inconsistent across different emotional modalities. To the best of our knowledge, this topic has yet to be well studied, which could potentially benefit lots of valuable cognitive applications such as autistic children diagnosis and criminal deception detection. To this end, this paper proposes an efficient cross-emotion anomaly detection approach via semanticinconsistency reasoning and hybrid contrastive learning (SIR-HCL), which is the first attempt to detect the anomalous emotional pairs across the audio-visual emotions. First, the proposed framework utilizes dual-branch network to obtain the deep emotional features in each modality, and then employs the shared residual block to derive the semantically compatible features. Subsequently, an efficient hybrid contrastive learning approach is designed to enlarge the semantic-inconsistency among abnormal emotional pair with different affective classes, while enhancing the semantic-consistency and increasing the feature correlation between normal emotional pair from the same affective class. At the same time, an efficient bidirectional learning scheme is employed to significantly improve the data utilization and a two-component Beta Mixture Model is adaptively utilized to reason the anomalous emotion pairs. Extensive experiments evaluated on two benchmark datasets show that the proposed SIR-HCL method can well detect the anomalous emotional pairs across audio-visual emotional data, and brings substantial improvements over the state-of-the-art competing methods. © 2016 IEEE.

关键词： Contrastive Learning

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A novel technique for the watermarking of symbolically compressed documents

A novel technique for the watermarking of symbolically compr...

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2nd International Conference on Document Image Analysis for Libraries (DIAL 2006)

作者： Palit, Sarbani Garain, Utpal Computer Vision and Pattern Recognition Unit India

ISBN: (纸本)0769525318

Symbolic compression is a lossy compression technique which has been found to perform very efficiently for Indian language text document images. A novel and efficient watermarking technique is proposed for such documents compressed symbolically. The approach embeds the watermark into prototypes which are unaffected during compression, leading to an exact recovery of the watermark both from the compressed document and the reconstructed one. The watermark is perfectly resistant to JPEG conversion and reasonably resistant to other attacks. The proposed technique, for the first time, combines the compression and watermarking of Indian language text document images in a common framework.

关键词： document image lossy symbolic compression prototype watermark embedding decompression watermark extraction

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Skew angle detection of digitized Indian script documents

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IEEE TRANSACTIONS ON pattern ANALYSIS AND MACHINE INTELLIGENCE 1997年第2期19卷 182-186页

作者： Chaudhuri, BB Pal, U Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

Skew angle detection of scanned documents containing most popular Indian scripts (Devnagari and Bangla) is considered. Most characters in these scripts have horizontal lines at the top, called headlines. The character head lines mostly join one another in a word and the word appears as a single component. In the proposed method the components are at first labeled. The upper envelope of a component is found by columnwise scanning from an imaginary line above the component. Portions of upper envelope satisfying the properties of digital straight line are detected. They are clustered as belonging to single text lines. Estimates from individual clusters are combined to get the skew angle. Apart from accuracy and efficiency, an advantage of the method is that character segmentation and zone detection can be readily done from head line information, which is useful in Optical Character recognition approaches of these scripts.

关键词： document processing skew detection optical character recognition (OCR) document structure analysis digital library

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A novel multiseed nonhierarchical data clustering technique

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 1997年第5期27卷 871-877页

作者： Chaudhuri, D Chaudhuri, BB Computer Vision and Pattern Recognition Unit Indian Statistical Institute Calcutta India

Clustering techniques such as K-means and Forgy as well as their improved version ISODATA group data around one seed point for each cluster. It is well known that these methods do not work well if the shape of the cluster is elongated or nonconvex. We argue that for a elongated or nonconvex shaped cluster, more than one seed is needed, In this paper a multiseed clustering algorithm is proposed. A density based representative point selection algorithm is used to choose the initial seed points. To assign several seed points to one cluster, a minimal spanning tree guided novel technique is proposed. Also, a border point detection algorithm is proposed for the detection of shape of the cluster. This border in turn signifies whether the cluster is elongated or not. Experimental results show the efficiency of this clustering technique.

关键词： classification minimal spanning tree multiseed clustering pattern recognition

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