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检索条件"任意字段=Proceedings of the Fourteenth Indian Conference on Computer Vision, Graphics and Image Processing"

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Edge Detection in Speckled SAR images with Improved ROEWA

Edge Detection in Speckled SAR Images with Improved ROEWA

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： Jennifer Ranjani M. Gokila S.J. Thiruvengadam Department of Information Technology Thiagarajar College of Engineering Madurai India Department of Electronics and Communication Engineering Thiagarajar College of Engineering Madurai India

Automatic detection of sustained intensity changes (edges) in SAR images for applications requiring localization and identification of objects is complicated by the nature of the speckle. The speckle is characterized by a high degree of correlation and multiplicative signal dependence. Ratio based detectors like MRoA and RGoA edge detector use predefined thresholds. The modified RGoA detector defines an automatic threshold determining method. But all these edge detectors, apart from detecting the object edges, detect a number of false edges. In this paper, a new ROEWA based algorithm that automatically discriminates the object boundaries and the false edges is proposed. Otsupsilas nonparametric and unsupervised principle for automatic threshold selection is introduced in this classification process. An optimal threshold is selected by maximizing the seperability of the classes in gray level. Real SAR images are used to verify our method and the results are compared with the existing methods for edge detection. Experimental results show that the proposed method is robust and efficient.

关键词： image edge detection Detectors Speckle Object detection Optical scattering Reflectivity Radar scattering Radar detection image segmentation Histograms

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Fuzzy video summarization using key frame extraction

Fuzzy video summarization using key frame extraction

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Aditi Kapoor K. K. Biswas M. Hanmandlu Amar Nath and Shashi Khosla School of Information Technology Indian Institute of Technology Delhi Hauz Khas New Delhi

In this paper we propose to summarize videos based on key frames. We improve upon the histogram and pixel difference based approach with fuzzy rule based approach and also give a new approach which reduces the computation of framewise differences. We test our methods using fidelity ratio and compression ratio on videos of sports from YouTube and UCF sports dataset, videos of commercials and sitcoms. The results of our methods are seen to be comparable to other state of the art approaches.

关键词： Videos High definition video Redundancy image segmentation Histograms Irrigation Europe

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StegoHunter: Steganalysis of LSB Embedded images Based on Stego-Sensitive Threshold Close Color Pair Signature

StegoHunter: Steganalysis of LSB Embedded Images Based on St...

引用

indian conference on computer vision, graphics & image processing (ICVGIP)

作者： S. Geetha S.S. Sivatha Sindhu R. Renganathan P. Janaki Raman N. Kamraj Department of Information Technology Thiagarajar College of Engineering Madurai Tamil Nadu India

This paper proposes a reliable framework for the detection of the least significant bit (LSB) steganography using digital media files as cover objects. Steganographic methods attempt to insert data in multimedia signals in an undetectable fashion. However, these methods often disrupt the underlying signal characteristics, thereby allowing detection under careful steganalysis. Under repeated embedding, disruption of the signal characteristics is the highest for the first embedding and decreases subsequently. This principle is used to derive a steganalysis tool that detects the presence of hidden messages in uncompressed twenty-four bits BMP image. This work presents close color pair analysis with stego-sensitive threshold (CCPASST) to detect stego-objects with even 10% payload. In earlier works 20% payload was detected through close color pair analysis. The new framework exploits the first-order statistics of structural similarity index measure of the samples to calculate the threshold. The literature contains only one other detector specialized with variable threshold, and the one presented here is substantially more sensitive. Simulation results with the stego-sensitive threshold applied on well-known LSB steganographic technique indicate that this approach is superior to the earlier methods and is able with promising accuracy to distinguish between clean and stego images.

关键词： Steganography image color analysis Statistical analysis Pixel Discrete cosine transforms Reliability engineering Payloads Frequency estimation computer vision computer graphics

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TransDocUNet: A Transformer-based UNet Architecture for Degraded Document image Binarization 23

TransDocUNet: A Transformer-based UNet Architecture for Degr...

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proceedings of the fourteenth indian conference on computer vision, graphics and image processing

作者： Risab Biswas Soumik Sarkhel Swalpa Kumar Roy Umapada Pal Artificial Intelligence Group Optiks Innovations Pvt. Ltd. (P360) India Department of Computer Science and Engineering Alipurduar Government Engineering and Management College India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9798400716256

The enhancement of historical document images is critical for improving the quality and legibility of scanned or captured document images. Convolutional-based techniques previously generated competitive results for document image binarization, however, due to their inherent locality, these models are often limited in explicitly expressing long-range dependency. Transformers (ViT) have evolved as an alternative design with a global self-attention mechanism to tackle this issue, however, they can result in restricted localization capabilities due to a lack of low-level details. To address this problem, we propose TransDocUNet, a CNN-Transformer hybrid UNet architecture for document image binarization that merits both attention and convolution capabilities in a U-Net architecture and serves as a strong alternative to the existing solutions. The experimental results, obtained using the DIBCO/H-DIBCO datasets, highlight that our proposed method outperforms all the existing competing methods in terms of both objective quality metrics and visual quality assessment, achieving state-of-the-art performance in document image binarization. In addition, we undertake an ablation study to understand the role of dilation in the CNN to capture feature dependencies while reducing the computational cost as well. The findings helped us arrive at the final model and provide valuable insights into the importance of acquiring both global and local contextual information for tasks like enhancing document images.

关键词： Binarization CNN Deep Learning Document image Enhancement TransUNet UNet vision Transformer

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Segmenting printed text and handwritten annotation by Spectral Partitioning

Segmenting printed text and handwritten annotation by Spectr...

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Shilpa Pandey Gaurav Harit Computer Science & Engineering Indian Institute of Technology Jodhpur India

This paper addresses the problem of segmenting handwritten annotations on scientific research papers. The motivation of this work is to geometrically segment the complex cases of handwritten annotations, including marks, cuts and special symbols along, with the regular text. Our work particularly focuses on documents that have multi-oriented handwritten [1] annotations rather than annotations in controlled scenario [2]. Spectral Partitioning is adopted as the segmentation scheme to separate the printed text and annotations. A new feature Envelope Straightness is developed and included in our feature set. This leads to an improvement of accuracy over the state-of-the-art features. The experiments are performed on two datasets: 40 documents authored by two writers from IAM dataset, comprising only printed and handwritten text and a self created dataset of 40 scientific papers from various proceedings annotated by a reader, comprising varied types of annotations. In the framework of spectral partitioning, our feature set has achieved a recall of 98.39% for printed text and precision of 85.40% for handwritten annotations on our dataset. For IAM dataset our feature set has achieved a recall of 81.89% for printed text and a precision of 69.67% for handwritten annotations. The results achieved on both dataset are better compared with results obtained using [3] [1].

关键词： Feature extraction Shape Layout image segmentation Noise measurement Clustering algorithms

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Source color error analysis for robust separation of reflection components

Source color error analysis for robust separation of reflect...

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Soma Biswas Khurram Shafique Department of Electrical Engineering Indian Institute of Science Bengaluru India Object Video Inc. Virginia United States

In this paper, we address the problem of separating the diffuse and specular reflection components of complex textured surfaces from a single color image. Unlike most previous approaches that assume accurate knowledge of illumination source color for this task, we analyze errors in source color information to perform robust separation. The analysis leads to a simple, efficient and robust algorithm to estimate the diffuse and specular components using the estimated source color. The algorithm is completely automatic and does not need explicit color segmentation or color boundary detection as required by many existing methods. Results on complex textured images show the effectiveness of the proposed algorithm for robust reflection component separation.

关键词： image color analysis Lighting Vectors Robustness Algorithm design and analysis Error analysis Surface texture

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Robust Fall Detection Using Human Shape and Multi-class Support Vector Machine

Robust Fall Detection Using Human Shape and Multi-class Supp...

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： Homa Foroughi Alireza Rezvanian Amirhossien Paziraee Department of Computer Engineering Ferdowsi University of Mashhad Iran Department of Computer Engineering Azad University of Qazvin Iran Science and Research Branch Azad University of Qazvin Iran

Falls and resulting physical-psychological consequences in the elderly are a major health hazard and a serious obstacle for independent living. So development of intelligent video surveillance systems is so important due to providing safe and secure environments. To this end, this paper proposes a novel approach for human fall detection based on human shape variation. Combination of best-fit approximated ellipse around the human body, projection histograms of the segmented silhouette and temporal changes of head pose, would provide a useful cue for detection different behaviors. Extracted feature vectors are finally fed to a multi-class support vector machine for precise classification of motions and determination of a fall event. Unlike existent fall detection systems that only deal with limited movement patterns, we considered wide range of motions consisting of normal daily life activities, abnormal behaviors and also unusual events. Reliable recognition rate of experimental results underlines satisfactory performance of our system.

关键词： Robustness Humans Shape Support vector machines Senior citizens Hazards Intelligent systems Machine intelligence Video surveillance Histograms

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Depth estimation using Stereo Light Field Camerag± 13

Depth estimation using Stereo Light Field Camerag±

引用

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

作者： Nehra, Suresh Laha, Jayanta Mukhopadhyay, J. Biswas, P.K. Indian Institute of Technology Kharagpur India Indian Space Research Organisation India

ISBN: (纸本)9781450398237

Light field imaging has emerged as a new modality, enabling to capture the angular and spatial information of a scene. This additional angular information is used to estimate the depth of a 3-D scene. The continuum of virtual view-points in light field data efficiently handles occlusion and provides a robust depth estimate for smaller distances. However, a narrow baseline in a light field camera limits the depth estimation for larger distances. To have an efficient occlusion handling and increase the operating distances, we proposed a novel disparity based stereo light field depth estimation method. First, segments are obtained in central sub-aperture of left view and then estimate the disparity vector of these segments using left camera sub-aperture images. This handles occlusion efficiently. Then stereo disparity at boundaries of these segments exploiting the epipolar geometry inherent in a light field data. Finally this stereo disparity at boundaries is propagated to other pixels and normalized. We provided a synthetic stereo light field data-set having inherent characteristic of a light field. We have tested our approach on a variety of real-world scenes captured with Lytro Illum camera and also on synthetic images. The proposed method outperforms several state-of-the-art algorithms. © 2022 ACM.

关键词： Cameras

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RST invariant multi view 3D image watermarking using DWT and SVD

RST invariant multi view 3D image watermarking using DWT and...

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Sibaji Gaj Shuvendu Rana Anirban Lekharu Arijit Sur Prabin Kumar Bora Multimedia Lab Indian Institute of Technology Guwahati India Department of EEE Indian Institute of Technology Guwahati India

In this paper, a multi-view stereo image watermarking scheme is proposed to resist the RST (rotation, scaling and translation) attack. To make the scheme resilient to RST, the coefficients of Singular Value Decomposition (SVD) from both left and right views have been used for insertion of the watermark bits. 2D-DWT (Discrete wavelet transform) is used as a preprocessing step to get more correlated SVD coefficients of the left and right view such that the visual degradation due to embedding can be reduced. In this work, a blind embedding scheme is proposed by altering the selected SVD coefficients to improve the robustness of the embedding scheme. A comprehensive set of experiments have been performed to justify the robustness of the proposed scheme against RST attack. Moreover, this scheme can be used to detect the view swapping attack using DIBR technique.

关键词： Watermarking Three-dimensional displays Discrete wavelet transforms Robustness Matrix decomposition Resists

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A Robust Human Iris Verification Using a Novel Combination of Features

A Robust Human Iris Verification Using a Novel Combination o...

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Suvadip Mukherjee Bhabatosh Chanda Electronics and Communication Sciences Unit Indian Statistical Institute Kolkata India

This paper proposes a method for personal identification based on iris recognition. The iris segmentation is obtained by using an integro-differential operation. The segmented iris is then normalised and actually a small portion of the normalised portion is used for feature extraction. Three types of features are used: GLCM based features, Edge based features and Local Directional Pattern. We present a comparison of the performances of the above mentioned features. It is also shown experimentally that the half-way iris patterns exhibit a symmetry about the vertical axis. The multiclass recognition problem is reduced to a two class verification problem. Experimental results show that our proposed method has encouraging performance.

关键词： Iris recognition Feature extraction Vectors Accuracy image edge detection image segmentation Iris

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