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Graphically Residual Attentive Network for tackling aerial image occlusion

computers and Electrical Engineering 2025年 125卷

作者： Pradhan, Praveen Kumar Purkayastha, Kunal Sharma, Aaditya Lochan Baruah, Udayan Sen, Biswaraj Ghosal, Palash Department of Information Technology Sikkim Manipal Institute of Technology Sikkim Manipal University Majitar Sikkim Rangpo737136 India Department of Computer Science and Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Majitar Sikkim Rangpo737136 India Centre for Computers and Communication Technology Chisopani Sikkim South Sikkim737136 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute West Bengal Kolkata700108 India Birangana Sati Sadhani Rajyik Vishwavidyalaya Assam Golaghat785621 India

Deep learning has rapidly advanced, enabling new applications such as object detection, text recognition, and occlusion handling. However, challenges remain in the detection of objects in complex environments such as aerial images where things like motion blur, low light, and significant occlusion occur. This paper addresses a similar challenge by introducing a novel supervised framework, the Graphically Residual Attentive Network (GRESIDAN). In the same model, GRESIDAN integrates three synergistic pipelines for object detection, occlusion detection, and occlusion removal. GRESIDAN uses a residually attentive block combining ResNet-18 and a multi-headed attention mechanism to improve feature extraction and detection accuracy in low-quality, occluded aerial images. A graphically attentive occlusion detection pipeline is implemented to handle occlusion, segment better, and mask out the occluder in the aerial image. The GRESIDAN model is validated on the COCO-2017 dataset and a custom private aerial object detection dataset, outperforming the state-of-the-art methods in handling occlusion and detecting objects. Our contributions provide a robust solution to the problem of detecting and handling occluded objects in aerial imagery, pushing the boundaries of automated visual recognition in challenging real-world scenarios. The code for public use in training and testing is available on GitHub. © 2025 Elsevier Ltd

关键词： Image segmentation

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Palmprint recognition using kernel PCA of Gabor features

Palmprint recognition using kernel PCA of Gabor features

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International International Symposium on computer and Information Sciences (ISCIS)

作者： Murat Ekinci Murat Aykut Computer Vision and Pattern Recognition Laboratory Department of Computer Engineering Karadeniz Technical University Trabzon Turkey

This paper presents a new method for automatic palmprint recognition based on kernel PCA method by integrating the Gabor wavelet representation of palm images. Gabor wavelets are first applied to derive desirable palmprint features. The Gabor transformed palm images exhibit strong characteristics of spatial locality, scale, and orientation selectivity. These images can produce salient features that are most suitable for palmprint recognition. The kernel PCA method then nonlinearly maps the Gabor-wavelet image into a high-dimensional feature space. The proposed algorithm has been successfully tested on two different public data sets from the PolyU palmprint databases for which the samples were collected in two different sessions.

关键词： Kernel Principal component analysis Image recognition Testing Biometrics Face recognition Lighting pattern recognition computer vision Image databases

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Proceedings of 2nd International Conference on computer vision & Image Processing 1

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丛书名： Advances in Intelligent Systems and Computing

1000年

作者： Bidyut B. Chaudhuri Mohan S. Kankanhalli Balasubramanian Raman

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Artificial intelligent techniques and its applications

Artificial intelligent techniques and its applications

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作者： Sundhararajan, Mahalingam Gao, Xiao-Zhi Vahdat Nejad, Hamed Department of Electronics and Communication Engineering Bharath Institute of Higher Education and Research Bharath University Chennai India Department of Machine Vision and Pattern Recognition Laboratory Lappeenranta University of Technology Finland Department of Computer Science and Engineering University of Birjand Birjand Iran

This special issue of the Journal of Intelligent & Fuzzy Systems is a selected collection of papers submitted to the IEEE International Conference on Algorithms, methodology, models and applications in emerging technologies and International Conference on Telecommunication, Power analysis and Computing Techniques and held from February 16-18, 2017 and April 6-8, 2017, Chennai, India. These papers have been reviewed and accepted for presentation at the conference and for publication in the Journal of Intelligent & Fuzzy Systems (JIFS). In this special issue there are 50 papers covering a wide range of tools, techniques and applications of artificial intelligent techniques and applications.

关键词： Fuzzy systems

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Diffusion-snakes: combining statistical shape knowledge and image information in a variational framework

Diffusion-snakes: combining statistical shape knowledge and ...

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IEEE Workshop on Variational and Level Set Methods in computer vision

作者： D. Cremers C. Schnorr J. Weickert Computer Vision Graphics and Pattern Recognition Group Department of Mathematics and Computer Science University of Mannheim Mannheim Germany

ISBN: (纸本)076951278X

We present a modification of the Mumford-Shah functional and its cartoon limit which allows the incorporation of statistical shape knowledge in a single energy functional. We show segmentation results on artificial and real-world images with and without prior shape information. In the case of occlusion and strongly cluttered background the shape prior significantly improves segmentation. Finally we compare our results to those obtained by a level-set implementation of geodesic active contours.

关键词： Image segmentation Level set Active contours Spline Shape control Active shape model Equations Statistical learning computer vision computer graphics

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Graph theoretical framework for simultaneously integrating visual and textual features for efficient web image clustering 08

Graph theoretical framework for simultaneously integrating v...

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17th International Conference on World Wide Web 2008, WWW'08

作者： Rege, Manjeet Dong, Ming Hua, Jing Machine Vision Pattern Recognition Lab. Department ot Computer Science Wayne State University Detroit MI 48202 United States Graphics and Imaging Lab. Department ot Computer Science Wayne State University Detroit MI 48202 United States

ISBN: (纸本)9781605580852

With the explosive growth of Web and the recent development in digital media technology, the number of images on the Web has grown tremendously. Consequently, Web image clustering has emerged as an important application. Some of the initial efforts along this direction revolved around clustering Web images based on the visual features of images or textual features by making use of the text surrounding the images. However, not much work has been done in using multimodal information for clustering Web images. In this paper, we propose a graph theoretical framework for simultaneously integrating visual and textual features for efficient Web image clustering. Specifically, we model visual features, images and words from surrounding text using a tripartite graph. Partitioning this graph leads to clustering of the Web images. Although, graph partitioning approach has been adopted before, the main contribution of this work lies in a new algorithm that we propose - Consistent Isoperimetric High-order Co-clustering (CIHC), for partitioning the tripartite graph. Computationally, CIHC is very quick as it requires a simple solution to a sparse system of linear equations. Our theoretical analysis and extensive experiments performed on real Web images demonstrate the performance of CIHC in terms of the quality, efficiency and scalability in partitioning the visual feature-image-word tripartite graph.

关键词： Digital storage

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Head detection and localization from sparse 3D data

Head detection and localization from sparse 3D data

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24th German Association for pattern recognition Symposium, DAGM 2002

作者： Clabian, Markus Rötzer, Harald Bischof, Horst Kropatsch, Walter Advanced Computer Vision GmbH Donau City Str.1 ViennaA-1220 Austria Institute for Computer Graphics and Vision Graz University of Technology Inffeldgasse 16 GrazA-8010 Austria Pattern Recognition and Image Processing Group Institute of Computer Aided Automation Computer Science Department Vienna University of Technology Favoritenstr. 9 ViennaA-1040 Austria

ISBN: (纸本)354044209X

Head detection is an important, but difficult task, if no restrictions such as static illumination, frontal face appearance or uniform background can be assumed. We present a system that is able to perform head detection under very general conditions by employing a 3D measurement system namely a structured light distance measurement. An algorithm of head detection from sparse 3D data (19x19 data points) is developed that reconstructs a 3D surface over the image plane and detects head hypotheses of ellipsoidal shape. We demonstrate that detection and rough localization is possible in up to 90% of the images. © Springer-Verlag Berlin Heidelberg 2002.

关键词： computers

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Image co-clustering with multi-modality features and user feedbacks 09

Image co-clustering with multi-modality features and user fe...

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17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums

作者： Chen, Yanhua Dong, Ming Wan, Wanggen Machine Vision and Pattern Recognition Lab. Department of Computer Science Wayne State University Detroit MI 48202 United States School of Communication and Info. Eng. Shanghai University Shanghai 200072 China

ISBN: (纸本)9781605586083

In Content-based Image Retrieval (CBIR) research, advanced technology that fuses the heterogeneous information into image clustering has drawn extensive attention recently. However, using multiple features for co-clustering images without any user feedbacks is a challenging problem. In this paper, we propose a Semi-Supervised Non-negative Matrix Factorization (SS-NMF) framework for image co-clustering. Our method computes new relational matrices by incorporating user provided feedbacks into images through simultaneous distance metric learning and feature selection for different low-level visual features. Using an iterative algorithm, we perform tri-factorizations of the new matrices to infer image clusters. Theoretically, we show the convergence and correctness of SS-NMF co-clustering and the advantages of SS-NMF co-clustering over existing approaches. Through extensive experiments conducted on image data sets, we demonstrate that SS-NMF provides an effective and efficient solution for image co-clustering. Copyright 2009 ACM.

关键词： Matrix algebra

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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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Automatic object segmentation from large scale 3D urban point clouds through manifold embedded mode seeking 11

Automatic object segmentation from large scale 3D urban poin...

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19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11

作者： Yu, Zhiding Xu, Chunjing Liu, Jianzhuang Au, Oscar C. Tang, Xiaoou Shenzhen Key Laboratory for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Department of Information Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781450306164

This paper presents a system that can automatically segment objects in large scale 3D point clouds obtained from urban ranging images. The system consists of three steps: The first one involves a ground detection process that can detect relatively complex terrain and separate it from other objects. The second step superpixelizes the remaining objects to speed up the segmentation process. In the final step, a manifold embedded mode seeking method is adopted to segment the point clouds. Even though the segmentation of urban objects is a challenging problem in terms of accuracy and problem scale, our system can efficiently generate very good segmentation results. The proposed manifold learning effectively improves the segmentation performance due to the fact that continuous artificial objects often have manifold-like structures. Copyright 2011 ACM.

关键词： Image segmentation

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