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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"

共 1494 条记录，以下是1411-1420 订阅

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An efficient approach to compute fractal dimension in texture image

An efficient approach to compute fractal dimension in textur...

引用

International conference on Pattern Recognition

作者： B.B. Chaudhuri N. Sarkar Electronics and Communication Sciences Unit Indian Statistical Institute Calcutta India

Fractal dimension is a feature used to characterize roughness and self-similarity in a picture. This feature is used in texture segmentation and classification, shape analysis and other problems. An efficient differen... 详细信息

关键词： Fractals Surface fitting Least squares methods Strips Shape image processing computer vision Equations Least squares approximation

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Efficient-PSP-Net: A Lightweight Model for the Segmentation of Liver and Tumor in CT Scans 22

Efficient-PSP-Net: A Lightweight Model for the Segmentation ...

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

作者： Chitimireddy Sindhura Harsha Vardhan Reddy Vengama Subrahmanyam Gorthi Electrical Engineering Indian Institute of Technology Tirupati IN Electrical Engineering Indian Institute of Technology - Tirupati IN

ISBN: (纸本)9781450398220

The segmentation of the liver and tumors present in the liver is an essential task for the diagnosis of liver cancer and surgical treatment planning. While most of the works were developed with the goal of improving segmentation accuracy, there is a high demand for the development of lightweight architectures to deploy in clinical settings with limited computational resources. Developing such resource-efficient models while maintaining the performance of the state-of-the-art architectures is challenging due to the high class imbalance and small observable changes between tumorous and non-tumorous regions, which would generally require extensive and complex deep learning architectures. The contributions of this paper are twofold; First, this paper proposes to encode information extracted at different scales using a “Pyramid Pooling Module” (PPM) architecture. While different variants of pyramidal architectures are widely used in the context of scene parsing in computer vision, this paper explores the use of the PPM architecture for the segmentation of liver and tumor. Second, the paper proposes using the popular lightweight “Efficient-Net” as the backbone network of PSP-Net for feature extraction, thereby reducing the overall model size while achieving relatively similar performance to state-of-the-art architectures. The performance of the proposed model is evaluated on the MICCAI 2017 LiTS dataset. The model achieved a DSC of 95.06% and 79.08% for liver and tumor, respectively, with only 1.76M parameters.

关键词： CT.

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Towards Realistic Underwater Dataset Generation and Color Restoration✱ 22

Towards Realistic Underwater Dataset Generation and Color Re...

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

作者： Neham Jain Gopi Raju Matta Kaushik Mitra IIT Madras IN

ISBN: (纸本)9781450398220

Recovery of true color from underwater images is an ill-posed problem. This is because the wide-band attenuation coefficients for the RGB color channels depend on object range, reflectance, etc. which are difficult to model. Also, there is backscattering due to suspended particles in water. Thus, most existing deep-learning based color restoration methods, which are trained on synthetic underwater datasets, do not perform well on real underwater data. This can be attributed to the fact that synthetic data cannot accurately represent real conditions. To address this issue, we use an image to image translation network to bridge the gap between the synthetic and real domains by translating images from synthetic underwater domain to real underwater domain. Using this multimodal domain adaptation technique, we create a dataset that can capture a diverse array of underwater conditions. We then train a simple but effective CNN based network on our domain adapted dataset to perform color restoration. Code and pre-trained models can be accessed at https://***/nehamjain10/TRUDGCR

关键词： underwater image restoration domain adaptation

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On unsupervised segmentation of color texture images

On unsupervised segmentation of color texture images

引用

International conference on High Performance Computing

作者： R. Krishnamoorthi P. Bhattacharyya Departmentt. of Computer Science and Engineering Indian Institute of Technology Kharagpur India

The authors propose an orthogonal polynomial based color texture model for color texture segmentation. The proposed model is based on a complete set of difference operators which are easily configurable from a set of orthogonal polynomials. The operators are employed to represent a color image region as a linear combination of the operator's responses towards texture and noise. A simple statistical design of experiments paradigm is used for separating out the responses towards color texture from the responses towards noise. A local descriptor for color texture called cpronum is computed by encoding only the significant responses towards texture. Finally a global descriptor for color texture called cprospectrum is obtained by measuring the frequency of occurrences of the cpronums in the color image. The usage of the proposed color texture representation scheme is highlighted in unsupervised color texture segmentation.

关键词： image segmentation Colored noise Color Polynomials Equations computer science image coding Frequency measurement image processing computer vision

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An incremental topological map building using monocular vision 18

An incremental topological map building using monocular visi...

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Soumabha Bhowmick Jayanta Mukherjee Alok Kanti Deb Indian Institute of Technology Kharagpur India Institute for Clarity in Documentation Kharagpur India

ISBN: (纸本)9781450366151

In this work a memory efficient topological map generation algorithm has been proposed using local descriptors. A topological map is a graphical data structure where each node signifies an area within an environment. These nodes are connected by links which ensure the presence of a physical path between the pair. Experiments have been conducted with feature descriptors using a vocabulary based approach. These approaches take huge memory and time. To deal with these a KD-tree based map generation algorithm has been proposed where each node in the tree stores a descriptor and a table of occurrence. This table stores node ids of the locations, where the corresponding descriptor is present. The map generation algorithm is a two-stage algorithm. In the first stage, the visual similarity based position identification is conducted in order to check for loop-closures. It is followed by a corrective step on validating the decision of loop closure, if any. The table of occurrence keeps track of presence of each descriptor. The least occurring descriptors are pruned at regular intervals, making the algorithm memory-efficient. The approach has been experimented with several benchmark datasets.

关键词： KD-tree

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Single image LDR to HDR Conversion Using Conditional Diffusion

Single Image LDR to HDR Conversion Using Conditional Diffusi...

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IEEE International conference on image processing

作者： Dwip Dalal Gautam Vashishtha Prajwal Singh Shanmuganathan Raman Computer Vision Imaging and Graphics Lab Indian Institute of Technology Gandhinagar India

Digital imaging aims to replicate realistic scenes, but Low Dynamic Range (LDR) cameras cannot represent the wide dynamic range of real scenes, resulting in under-/overexposed images. This paper presents a deep learning-based approach for recovering intricate details from shadows and highlights while reconstructing High Dynamic Range (HDR) images. We formulate the problem as an image-to-image (I2I) translation task and propose a conditional Denoising Diffusion Probabilistic Model (DDPM) based framework using classifier-free guidance. We incorporate a deep CNN-based autoencoder in our proposed framework to enhance the quality of the latent representation of the input LDR image used for conditioning. Moreover, we introduce a new loss function for LDR-HDR translation tasks, termed Exposure Loss. This loss helps direct gradients in the opposite direction of the saturation, further improving the results’ quality. By conducting comprehensive quantitative and qualitative experiments, we have effectively demonstrated the proficiency of our proposed method. The results indicate that a simple conditional diffusion-based method can replace the complex camera pipeline-based architectures.

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proceedings of International conference on Scientific and Natural Computing 1

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丛书名： Algorithms for Intelligent Systems

1000年

作者： Dipti Singh Amit K. Awasthi Ivan Zelinka Kusum Deep

ISBN: (数字)9789811615283

ISBN: (纸本)9789811615276;9789811615306

This book includes high-quality papers presented at International conference on Scientific and Natural Computing (SNC 2021), organized by Department of Applied Mathematics, Gautam Buddha University, Greater Noida in collaboration with IIT Roorkee and Technical University of Ostrava (VSB-TU) and technically sponsored by Soft Computing Research Society of India, held online during 5 – 6 February 2021. The topics include self-organizing migrating algorithm, genetic algorithms, swarm intelligence based techniques, evolutionary computing, fuzzy computing, probabilistic computing, genetic programming, particle swarm optimization, neuro computing, hybrid methods, deep learning, including convolutional neural networks, generative adversarial networks and auto-encoders, bio-inspired systems, data mining, data visualization, intelligent agents, engineering design optimization, multi-objective optimization, fault diagnosis, decision support, robotics, signal or image processing, system identification and modelling, systems integration, time series prediction, virtual reality, vision or pattern recognition, intelligent information retrieval, motion control and power electronics, Internet of Everything (IoE), control systems, and supply chain management.

关键词： Computational Intelligence Artificial Intelligence Machine Learning Professional Computing

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Generalized Hermite polynomials for image reconstruction from zero crossing contours

引用

IEEE International conference on image processing

作者： Y.V. Venkatesh Computer Vision and Artificial Intelligence Laboratory Department of Electrical Engineering Indian Institute of Science Bangalore India

ISBN: (纸本)0818688211

Generalized Hermite polynomials in two variables are employed for the reconstruction of images from a knowledge of their zero crossing contours. The problem of reconstruction of signals as functions of two variables is not a mere extension of that of a single variable. This is a consequence of the fact that the spatial and spectral characteristics of two-variable functions are quite distinct from what one can expect from their separate projections on to the coordinate axes. One of the results of the paper is that we cannot guarantee uniqueness in reconstruction unless we impose certain constraints on, for instance, space-bandwidth products/ratios in the x/spl omega//sub x/, y/spl omega//sub y/ directions, of the unknown image. Further, a distinguishing feature of the proposed approach is that the standard assumption of bandlimitedness is not invoked. The proposed framework is believed to provide a more unified procedure for signal reconstruction (of uni- and multi-dimensional signals) from partial information than most of the results of the literature. For lack of space, only the main analytical and computational results are presented.

关键词： Polynomials image reconstruction Filtering theory image analysis Signal processing algorithms Geometry computer vision Artificial intelligence Laboratories Signal reconstruction

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Fast and high-quality bilateral filtering using Gauss-Chebyshev approximation

Fast and high-quality bilateral filtering using Gauss-Chebys...

引用

International conference on Signal processing and Communications, SPCOM

作者： Sanjay Ghosh Kunal N. Chaudhury Department of Electrical Engineering Indian Institute of Science Bangalore India

ISBN: (纸本)9781509017478

The bilateral filter is an edge-preserving smoother that has diverse applications in image processing, computer vision, computer graphics, and computational photography. The filter uses a spatial kernel along with a range kernel to perform edge-preserving smoothing. In this paper, we consider the Gaussian bilateral filter where both the kernels are Gaussian. A direct implementation of the Gaussian bilateral filter requires O(σ s 2 ) operations per pixel, where σ s is the standard deviation of the spatial Gaussian. In fact, it is well-known that the direct implementation is slow in practice. We present an approximation of the Gaussian bilateral filter, whereby we can cut down the number of operations to O(1) per pixel for any arbitrary σ s , and yet achieve very high-quality filtering that is almost indistinguishable from the output of the original filter. We demonstrate that the proposed approximation is few orders faster in practice compared to the direct implementation. We also demonstrate that the approximation is competitive with existing fast algorithms in terms of speed and accuracy.

关键词： Chebyshev approximation Kernel Approximation algorithms image edge detection Filtering Complexity theory Hardware

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7th International conference on Information and Communication Technology for Intelligent Systems, ICTIS 2023

7th International Conference on Information and Communicatio...

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7th International conference on Information and Communication Technology for Intelligent Systems, ICTIS 2023

ISBN: (纸本)9789819940394

The proceedings contain 45 papers. The special focus in this conference is on . The topics include: Skin Cancer Recognition Using CNN, VGG16 and VGG19;diagnosis of Cardiovascular Disease Using Machine Learning Algorithms and Feature Selection Method for Class Imbalance Problem;similarity Based Answer Evaluation in Academic Questions Using Natural Language processing Techniques;fake News Detection Using Machine Learning and Deep Learning Classifiers;survey on Pre-Owned Car Price Prediction Using Random Forest Algorithm;sentiment Analysis of Youtube Comment Section in indian News Channels;deep Learning Framework for Speaker Verification Under Multi Sensor, Multi Lingual and Multi Session Conditions;DLLACC: Design of an Efficient Deep Learning Model for Identification of Lung Air Capacity in COPD Affected Patients;Content Based Document image Retrieval Using computer vision and AI Techniques;drowsiness Detection System;Monitor the Effectiveness of Cardiovascular Disease Illness Diagnostics Utilizing AI and Supervised Machine Learning Classifiers;architecture Based Classification for Intrusion Detection System Using Artificial Intelligence and Machine Learning Classifiers;a Novel Privacy-Centric Training Routine for Maintaining Accuracy in Traditional Machine Learning Systems;outside the Closed World: On Using Machine Learning for Network Intrusion Detection;data Collection for a Machine Learning Model to Suggest Gujarati Recipes to Cardiac Patients Using Gujarati Food and Fruit with Nutritive Values;plant and Weed Seedlings Classification Using Deep Learning Techniques;a Comprehensive Review on Various Artificial Intelligence Based Techniques and Approaches for Cyber Security;applicability of Machine Learning for Personalized Medicine;I-LAA: An Education Chabot;A Comparison of Machine Learning Approaches for Forecasting Heart Disease with PCA Dimensionality Reduction.

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