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Recent Trends in image processing and Pattern Recognition 1

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丛书名： Communications in computer and Information Science

1000年

作者： KC Santosh Aaisha Makkar Myra Conway Ashutosh K. Singh Antoine Vacavant Anas Abou el Kalam Mohamed-Rafik Bouguelia Ravindra Hegadi

ISBN: (数字)9783031530821

ISBN: (纸本)9783031530814

this book constitutes the refereed proceedings of the 6th International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2023, held in Derby, UK, during December 2023, in collaboration with the Applied AI Research Lab at the University of South Dakota.;the 62 full papers included in this book were carefully reviewed and selected from 216 submissions. the papers are organized in the following topical sections:;Volume I:;Artificial intelligence and applied machine learning; applied image processing and pattern recognition; and biometrics and applications.;Volume II:;Healthcare informatics; pattern recognition in blockchain, IOT, cyber plus network security, and cryptography.

关键词： computer Imaging, vision, Pattern Recognition and graphics Artificial Intelligence computer System Implementation computer Applications computer Applications

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Document image Binarization Using U-Net 7th

Document Image Binarization Using U-Net

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Kotecha, Dhara Joshi, Manjunath V. Dhirubhai Ambani Institute of Information and Communication Technology Gandhinagar India

ISBN: (纸本)9789811586965

In this paper, we propose an algorithm to binarize the degraded document images. We incorporate U-Net for the task at hand. We model document image binarization as a classification problem wherein we generate an image which is a result of classification of each pixel as text or background. Optimizing the cross entropy loss function, we translate the input degraded image to the corresponding binarized image. Our approach of using U-Net ensures low level feature transfer from the input degraded image to the output binarized image and thus it is better than using a simple convolution neural network. Our method tries to obtain the desired results with suitable generalization. the results obtained are significantly better than the state-of-the-art techniques and the approach is simpler than other deep learning approaches for document image binarization. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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An interactive framework for reconstructing 3D neuronal structures 14

An interactive framework for reconstructing 3D neuronal stru...

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Kumar, Kanuj Natarajan, Vijay Sikdar, S.K. Srinivas, Kalyan V. Computer Science and Automation Indian Institute of Science Bangalore India Supercomputer Education and Research Centre Indian Institute of Science Bangalore India Molecular Biophysics Unit Indian Institute of Science Bangalore India

ISBN: (纸本)1595930361

Neuron reconstruction is a complex and tedious task and neurobiologists today have to rely on time-consuming manual methods. Methods that entail manual tracing may introduce systematic inaccuracies. this necessitates the use of automated methods for reconstruction of neuronal structures. Despite recent advancements, the automation of reconstruction process either does not exhibit robustness against noise of microscopy images or fails to capture precise dendritic structures. this motivates the development of semiautomated methods that require crucial but minimal expert user input. In this paper, we present a fast and interactive framework for reconstruction of neuronal structures that employs automatic methods while allowing a user to provide expert input if the results are unsatisfactory. the framework is designed as a multi-stage pipeline, where in the user provides numerical input parameters to guide the reconstruction process and validates the output of automated methods visually. the user is also assisted by the framework in calculating the optimal values of required parameters for reconstruction. the framework is also able to handle discontinuities and produce a connected geometric model of the neuron structure. Copyright 2014 ACM.

关键词： Automation

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Element-Wise Layer Normalization for Continuous Signal Representation 23

Element-Wise Layer Normalization for Continuous Signal Repre...

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15th International conference on Digital image processing, ICDIP 2023

作者： Chen, Weifeng Ding, Hui He, Zhifen Li, Bo Liu, Bin Wang, Kang School of Mathematics and Information Science Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China Jiangxi Science and Technology Infrastructure Center Jiangxi Computing Center Jiangxi Computing Center Nanchang China

ISBN: (纸本)9798400708237

Implicit neural representation (INR), sometimes also referred to coordinate-based representation or fitting, has gained the state-of-the-art performance in numerous research fields including computer vision and computer graphics due to the powerful continuous representation ability. Recent researches indicate that Fourier embedding is critical for INR to fit realistic images with high-frequency details. However, is Fourier embedding all we need for high-frequency coordinate (image) fitting? In this paper, we revisit the problem of coordinate fitting from a novel perspective of distribution mapping. Fourier embedding, as a preprocessing step of coordinate fitting, essentially performs the operation of mapping the uniform coordinate distribution to a normal distribution, and makes the learning of mapping function between two similar smooth distribution become easier. However, the number of discrete Fourier basis function affects the fitting performance dramatically and cannot be determined automatically. Based on the above analysis, a simple yet efficient INR coordinate fitting method is proposed in this paper, which demonstrates that the Fourier embedding is not the only way to improve INR. the proposed method only adds an element-wise layer normalization (ELN) module to the vanilla multi-layer perception (MLP) with ReLU activation. Experimental results on public database demonstrate that the proposed method outperforms the state-of-the-art methods using Fourier embedding. © 2023 ACM.

关键词： Mapping

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Automated Detection of Mine Water Bodies Using Landsat 8 OLI/TIRS in Jharia 7th

Automated Detection of Mine Water Bodies Using Landsat 8 OLI...

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Mukherjee, Jit Mukherjee, Jayanta Chakravarty, Debashish Advance Technology Development Centre Kharagpur India Department of Computer Science and Engineering Kharagpur India Department of Mining Engineering Kharagpur India Indian Institute of Technology Kharagpur KharagpurWest Bengal India

ISBN: (纸本)9789811586965

Mine water bodies create enormous water pollution due to heavy use of water in different stages of mining. Detection and monitoring of such water bodies are necessary for environmental benefits. In the past, mine water bodies are classified along with non mine water bodies through manual intervention. the motivation of this work is to automate the process of detection of mine water bodies from Landsat 8 OLI/TIRS images. First, automated water extraction index (AWEI) is used to detect water bodies in adaptive manner. Most mine water bodies can be found near mining regions. Further, mine water bodies are detected through connected component and bounding box analysis using this cue. Coal Mine Index (CMI) can differentiate coal mine regions from other land classes. the proposed method uses the feature space of CMI to detect mine water bodies in an automated fashion with average precision, and recall of respectively. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Coal mines

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LBP-SURF descriptor with color invariant and texture based features for underwater images 12

LBP-SURF descriptor with color invariant and texture based f...

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Prabhakar, C.J. Praveen Kumar, P.U. Department of P.G. Studies and Research in Computer Science Kuvempu University Shankaraghatta - 577451 Karnataka India

ISBN: (纸本)9781450316606

In this paper, we introduce LBP-SURF, a local image descriptor for underwater environment, which is very efficient to extract color invariant and texture based features of underwater images. the current state-of-the-art feature descriptors viz. SIFT, SURF, DAISY, GLOH and variants are well known techniques for detecting and describing features of objects captured in the out-of-water environment. these standard descriptors have been proven to be the most robust to geometric variations. Nearly, all these geometrical invariant approaches avoid dealing with color images due to the color constancy problem. In underwater images, variation in color is very high compared to variations in geometrical properties due to propagation properties of light. the literature survey reveals that, the texture parameters that remain constant for the scene patch for the whole underwater image sequence. this motivated us to consider texture and color invariant features of underwater images, instead of using the gray-based geometrical invariant features. We normalize the color image using comprehensive color image normalization method to render the color values changed by the various radiometric factors of underwater environment. Our method uses Speeded Up Robust Features (SURF) to detect interest points from the normalized image. the texture features are extracted, and description is stored using Center-Symmetric Local Binary Patterns (CS-LBP) descriptor. the combination of SURF and CS-LBP, called LBP-SURF is evaluated extensively to verify its effectiveness with datasets acquired in underwater environment. © 2012 ACM.

关键词： Color

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HDR-cGAN: Single LDR to HDR image translation using conditional GAN 21

HDR-cGAN: Single LDR to HDR image translation using conditio...

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12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Raipurkar, Prarabdh Pal, Rohil Raman, Shanmuganathan CVIG Lab IIT Gandhinagar Gujarat India

ISBN: (纸本)9781450391276

the prime goal of digital imaging techniques is to reproduce the realistic appearance of a scene. Low Dynamic Range (LDR) cameras are incapable of representing the wide dynamic range of the real-world scene. the captured images turn out to be either too dark (underexposed) or too bright (overexposed). Specifically, saturation in overexposed regions makes the task of reconstructing a High Dynamic Range (HDR) image from single LDR image challenging. In this paper, we propose a deep learning based approach to recover details in the saturated areas while reconstructing the HDR image. We formulate this problem as an image-to-image (I2I) translation task. To this end, we present a novel conditional GAN (cGAN) based framework trained in an end-to-end fashion over the HDR-REAL and HDR-SYNth datasets. Our framework uses an overexposed mask obtained from a pre-trained segmentation model to facilitate the hallucination task of adding details in the saturated regions. We demonstrate the effectiveness of the proposed method by performing an extensive quantitative and qualitative comparison with several state-of-the-art single-image HDR reconstruction techniques. © 2021 ACM.

关键词： Generative adversarial networks

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Blind 3D video watermarking based on 3D-HEVC encoder using depth 14

Blind 3D video watermarking based on 3D-HEVC encoder using d...

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Rana, Shuvendu Sur, Arijit Department of Computer Science and Engineering Indian Institute of Technology Guwahati Assam781039 India

ISBN: (纸本)1595930361

Due to enormous advancement of internet technology and display devices, 3D video becomes popular in recent times. To ensure secure media transmission, efficient authentication scheme for such 3D video sequence is a requirement. In recent past, watermarking is being regarded as a popular DRM tool for video authentication. It has been observed that video watermarking becomes a challenging task in the presence of advanced auto-stereoscopic display devices and MVD (Multi-view Video plus Depth) based encoding technique in case of 3D video. In this paper, depth image based rendering technique is proposed for blind 3D video watermarking. In this scheme, rendering technique is used to find the Z-axis of the stereo videos (left and right video). the connected regions of the Z-axis of a Group of Picture (GOP) have been filtered using the motion prediction of the video. Block DCT coefficients are used to embed the watermark signal with the selected Z-axis regions of the each video (left and right separately). A comprehensive set of experiments have been done to justify the robustness of the proposed scheme over existing schemes with respect to compression of the 3D-HEVC video codec. Copyright 2014 ACM.

关键词： Watermarking

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End-to-End GPU-Accelerated Low-Poly Remeshing using Curvature Map and Voronoi Tessellationg± 13

End-to-End GPU-Accelerated Low-Poly Remeshing using Curvatur...

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

作者： Chatterjee, Preetam Chayan Soni, Ashutosh Bhowmick, Partha Bhowmick, Iit Kharagpur Indian Institute of Technology West Bengal Kharagpur India ANSYS Software Pvt. Ltd Hinjewadi Maharashtra Pune India

ISBN: (纸本)9781450398237

We propose a novel algorithm for low-poly remeshing of 3D surfaces that runs fully in GPU. Since the input mesh is generally not well-organized, performing mesh simplification directly on the input mesh is liable to produce a low-poly mesh with a compromised face quality. Hence, instead of doing that, we voxelize the input mesh first. On the voxelized surface, we perform curvature estimation and discretization, Centroidal Voronoi Tessellation (CVT), and Delaunay triangulation to generate the output mesh. To control the vertex count, we exploit the curvature map, such that sufficiently large triangles constitute the low-curvature regions, whereas appropriately small triangles build up the high-curvature portions. We produce low-poly meshes at various levels of detail and measure their quality using Hausdorff error. We test our algorithm on different 3D scenes, and they are particularly chosen because they contain multiple objects with varied topologies. Upon testing on these datasets, our algorithm successfully achieves very low-poly approximation with a reasonable mesh quality, and at a reasonably faster pace, thus establishing its efficacy for mesh simplification. © 2022 ACM.

关键词： graphics processing unit

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Evidence Based image Selection for 3D Reconstruction 7th

Evidence Based Image Selection for 3D Reconstruction

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Yadavannavar, Smita C. Prabhu, Varad Vinod Tabib, Ramesh Ashok Patil, Ujwala Mudengudi, Uma KLE Technological University Hubli India

ISBN: (纸本)9789811586965

In this paper, we propose a framework for image selection using evidence theory, towards 3D reconstruction. the process of 3D reconstruction involves image acquisition, image selection, feature extraction, calculating camera parameters, and generation of the point cloud. Of these all, image selection plays a significant role, as it has a significant impact on the final 3D model. However, in large scale 3D reconstruction, image selection based on a single parameter is not sufficient. We measure the similarity between the images using multiple parameters and generate a combined confidence score towards discarding similar images. Experimental results show that the quality of 3D reconstruction is better using images selected by the proposed method. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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