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检索条件"任意字段=15th Indian Conference on Computer Vision Graphics and Image Processing"

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Low Complexity Encoder for Feedback-Channel-Free Distributed Video Coding using Deep Convolutional Neural Networks at the Decoder 16

Low Complexity Encoder for Feedback-Channel-Free Distributed...

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Bhagath, P. Raj Mukherjee, Jayanta Mukopadhayay, Sudipta Indian Inst Technol Dept Elect & Elect Commun Engn Kharagpur W Bengal India Indian Inst Technol Dept Comp Sci & Engn Kharagpur W Bengal India

ISBN: (纸本)9781450347532

We propose a very low complexity encoder for feedback channel-free distributed video coding (DVC) applications using deep convolutional neural network (CNN) at the decoder side. Deep CNN on super resolution uses low resolution (LR) images with 25% pixels information of high resolution (HR) image to super resolve it by the factor 2. Instead we train the network with 50% of noisy Wyner-Ziv (WZ) pixels to get full original WZ frame. So at the decoder, deep CNN reconstructs the original WZ image from 50% noisy WZ pixels. these noisy samples are obtained from the iterative algorithm called DLRTex. At the encoder side we compute local rank transform (LRT) of WZ frames for alternate pixels instead of all to reduce bit rate and complexity. these local rank transformed values are merged and their rank positions in the WZ frame are entropy coded using MQ-coder. In addition, average intensity values of each block of WZ frame are also transmitted to assist motion estimation. At the decoder, side information (SI) is generated by implementing motion estimation and compensation in LRT domain. the DLRTex algorithm is executed on SI using LRT to get the 50% noisy WZ pixels which are used in reconstructing full WZ frame. We compare our results with pixel domain DVC approaches and show that the coding efficiency of our codec is better than pixel domain distributed video coders based on low-density parity check and accumulate (LDPCA) or turbo codes. We also derive the complexity of our encoder interms of number of operations and prove that its complexity is very less compared to the LDPCA based methods.

关键词： Deep convolutional neural network (CNN) Local rank transform (LRT) delta-LRT with extended neighborhood (DLRTex)

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Semantic Combined Lightweight Transformer Low Light image Enhancement Method 15

Semantic Combined Lightweight Transformer Low Light Image En...

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15th International conference on graphics and image processing, ICGIP 2023

作者： Zhang, Hao Jin, Hongmei Li, Zhanli College of Computer Science and Technology Xi'an University of Science and Technology Xi'an China

ISBN: (纸本)9781510675018

In this paper, we propose a lightweight approach to address the problems of noise amplification, detail loss, and edge blurring encountered in low-light image enhancement, which typically hinder subsequent computer vision tasks. Our proposed method utilizes transformers and depth-separated convolution, integrates the ISP image processing pipeline, and incorporates semantic information. First, we introduce an enhancement compensation extraction module that utilizes a transformer-based two-branch structure. Notably, depth-separated convolution replaces the original multi-attention transformer to achieve a lightweight design. the local branch estimates pixel-level illumination defects in low-light images, and the global branch enhances global structural information. Subsequently, we design a progressive enhancement module to receive enhancement compensation and reconstruct the enhanced image using the ISP pipeline. Together, these two modules form an enhancement network. Finally, we design a VGG16-based semantic segmentation module to preserve semantic information during the enhancement process and complete the image enhancement. Evaluations on benchmark datasets and extensive experiments with other algorithms demonstrate the effectiveness of our proposed low-light image enhancement method. the reconstructed enhanced images are improved in terms of brightness, contrast and detail sharpness, while effectively mitigating the problems associated with noise amplification and edge blurring. © 2024 SPIE. All rights reserved.

关键词： image enhancement

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Unsupervised Detection of Surface Mine Sites using Sentinel Multi-spectral imagery and Dual-polarimetric SAR Data 11

Unsupervised Detection of Surface Mine Sites using Sentinel ...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Aswatha, Shashaank M. Saini, Vishnu Mukherjee, Jayanta Biswas, Prabir K. Aikat, Subhas Misra, Arundhati Indian Inst Technol Kharagpur Kharagpur W Bengal India Indian Space Res Org Space Applicat Ctr Ahmadabad Gujarat India

ISBN: (纸本)9781450366151

Detecting surface mine activities has been one of the major challenges of remote sensing community to monitor ecological balance in the vicinity of mine area. Towards addressing this issue, we propose an unsupervised mine-site detection technique using multi-modal remotely sensed data from Sentinel missions. We use Sentinel-1's dual polarized SAR data, and Sentinel-2's multi-spectral data for this purpose. An initial set of seed pixels is sampled using spectral slopes based rules on multi-spectral images, which are tailored to capture mining area pixels that exhibit common characteristics. then, we generate features using multi-spectral image indices and dual polarized SAR data Stokes' parameters. these features over locations sampled by slope based rules are used to train a one-class support vector machine to detect the land anomalies (mine activities). As observed from the experiments, multi-spectral features are efficient in separating mine region and built-up region from the rest of the land surface, and SAR image features discriminate built-up region from the rest of the image. We fuse both of these imagery features to encash their advantage in segregating mine sites in the considered study area. We demonstrate the efficiency of the proposed technique in detecting active coal mines over two study sites, Asansol region (in West bengal, India) and Jharia region (in Jharkhand, India), both of which exhibit common geographical characteristics. the average detection accuracy in our results is found to be around 94.8%.

关键词： Multi-spectral imagery synthetic aperture radar surface mining area detection multi-modal analysis spectral slopes

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FloorNet: A Unified Framework for Floorplan Reconstruction from 3D Scans 15th

FloorNet: A Unified Framework for Floorplan Reconstruction f...

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15th European conference on computer vision (ECCV)

作者： Liu, Chen Wu, Jiaye Furukawa, Yasutaka Washington Univ St Louis MO 63130 USA Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9783030012311;9783030012304

this paper proposes a novel deep neural architecture that automatically reconstructs a floorplan by walking through a house with a smartphone, an ultimate goal of indoor mapping research. the challenge lies in the processing of RGBD streams spanning a large 3D space. the proposed neural architecture, dubbed FloorNet, effectively processes the data through three neural network branches: (1) PointNet with 3D points, exploiting 3D information;(2) CNN with a 2D point density image in a top-down view, enhancing local spatial reasoning;and (3) CNN with RGB images, utilizing full image information. FloorNet exchanges intermediate features across the branches to exploit all the architectures. We have created a benchmark for floorplan reconstruction by acquiring RGBD video streams for 155 residential houses or apartments with Google Tango phones and annotating complete floorplan information. Our qualitative and quantitative evaluations demonstrate that the fusion of three branches effectively improves the reconstruction quality. We hope that the paper together with the benchmark will be an important step towards solving a challenging vector-graphics floorplan reconstruction problem.

关键词： Floorplan reconstruction 3D computer vision 3D CNN

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Reconstruction of Sparse-View Tomography via Banded Matrices 10

Reconstruction of Sparse-View Tomography via Banded Matrices

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10th indian conference on vision, graphics and image processing (ICVGIP)

作者： Prasad, T. Kumar, P. U. Praveen Sastry, C. S. Jampana, P. V. Indian Inst Technol Hyderabad Dept Math Hyderabad Telangana India Indian Inst Technol Hyderabad Dept Chem Engn Hyderabad Telangana India

ISBN: (纸本)9783319681245;9783319681238

Computed Tomography (CT) is one of the significant research areas in medical image analysis. One of the main aspects of CT that researchers remain focused, is on reducing the dosage as Xrays are generally harmful to human bodies. In order to reduce radiation dosage, compressed sensing (CS) based methodologies appear to be promising. the basic premise is that medical images have inherent sparsity in some transformation domain. As a result, CS provides the possibility of recovering a high quality image from fewer projection data. In general, the sensing matrix in CT is generated from Radon projections by appropriately sampling the radial and angular parameters. In our work, by restricting the number of such parameters, we generate an under-determined linear system involving projection (Radon) data and a sparse sensing matrix, bringing thereby the problem into CS framework. Among various recent solvers, the Split-Bregman iterative scheme has of late become popular due to its suitability for solving a wide variety of optimization problems. Intending to exploit the underlying structure of sensing matrix, the present work analyzes its properties and finds a banded structure for an associated intermediate matrix. Using this observation, we simplify the Split-Bregman solver, proposing thereby a CT-specific solver of low complexity. We also provide the efficacy of proposed method empirically.

关键词： Compressed sensing Split-Bregman Computed tomography Least squares

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4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, ICVGIP 2018

4th Workshop on Document Analysis and Recognition, DAR 2018,...

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4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, ICVGIP 2018

ISBN: (纸本)9789811393600

the proceedings contain 14 papers. the special focus in this conference is on Document Analysis and Recognition. the topics include: Word-Wise Handwriting Based Gender Identification Using Multi-Gabor Response Fusion;a Secure and Light Weight User Authentication System Based on Online Signature Verification for Resource Constrained Mobile Networks;benchmark Datasets for Offline Handwritten Gurmukhi Script Recognition;benchmark Dataset: Offline Handwritten Gurmukhi City Names for Postal Automation;attributed Paths for Layout-Based Document Retrieval;textual Content Retrieval from Filled-in Form images;A Study on the Effect of CNN-Based Transfer Learning on Handwritten Indic and Mixed Numeral Recognition;symbol Spotting in Offline Handwritten Mathematical Expressions;online Handwritten Bangla Character Recognition Using Frechet Distance and Distance Based Features;an Efficient Multi Lingual Optical Character Recognition System for indian Languages through Use of Bharati Script;telugu Word Segmentation Using Fringe Maps;an Efficient Character Segmentation Algorithm for Connected Handwritten Documents.

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COMA-Boost: Co-Operative Multi Agent AdaBoost 16

COMA-Boost: Co-Operative Multi Agent AdaBoost

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Lahiri, Avisek Paria, Biswajit Biswas, Prabir Kumar IIT Kharagpur Dept E&ECE Kharagpur W Bengal India IIT Kharagpur Dept CSE Kharagpur W Bengal India

ISBN: (纸本)9781450347532

A Abstract : Multi feature space representation is a common practise in computer vision applications. Traditional features such as HOG, SIFT, SURF etc., individually encapsulates certain discriminative cues for visual classification. On the other hand, each layer of a deep neural network generates multi ordered representations. In this paper we present a novel approach for such multi feature representation learning using Adaptive Boosting (AdaBoost). General practise in AdaBoost [8] is to concatenate components of feature spaces and train base learners to classify examples as correctly/incorrectly classified. We posit that multi feature space learning should be viewed as a derivative of cooperative multi agent learning. To this end, we propose a mathematical framework to leverage performance of base learners over each feature space, gauge a measure of "difficulty" of training space and finally make soft weight updates rather than strict binary weight updates prevalent in regular AdaBoost. this is made possible by periodically sharing of response states by our learner agents in the boosting framework. theoretically, such soft weight update policy allows in finite combinations of weight updates on training space compared to only two possibilities in AdaBoost. this opens up the opportunity to identify 'more difficult' examples compared to 'less difficult' examples. We test our model on traditional multi feature representation of MNIST handwritten character dataset and 100-Leaves classification challenge. We consistently outperform traditional and variants of multi view boosting in terms of accuracy while margin analysis reveals that proposed method fosters formation of more con fident ensemble of learner agents. As an application of using our model in conjecture with deep neural network, we test our model on the challenging task of retinal blood vessel segmentation from fundus images of DRIVE dataset by using kernel dictionaries from layers of unsupervised trained stac

关键词： AdaBoost multi view learning deep learning

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Effect of Particle image Velocimetry processing on CT Dicom images in a deep learning based pipeline for lung nodule segmentationg± 13

Effect of Particle Image Velocimetry processing on CT Dicom ...

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

作者： Suji, R Jenkin Godfrey, W Wilfred Dhar, Joydip ABV-IIITM Gwalior Madhya Pradesh Gwalior India

ISBN: (纸本)9781450398237

Segmentation of lung nodules is critical to computer-aided diagnosis systems for lung cancer diagnosis. In recent times, with the application of deep learning in medical image processing, novel architectures have been proposed for segmentation. However, most of these architectures work on a single modality and work on a single image or whole image volume. In this work, we present Particle image Velocimetry approach as a preprocessing technique by processing sequence of two input images. We used with and without PIV processed images on a deep learning-based pipeline to extract features from these images for candidate lung nodule detection. the results show that the use of particle image velocimetry processing significantly improves the performance of the deep learning pipeline. © 2022 ACM.

关键词： Pipelines

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Fast and Generalisable License Plate Re-identification using Neural Embedding of Fisher Vectors 11

Fast and Generalisable License Plate Re-identification using...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Kumar, Abhinav Gupta, Shantanu Kozitsky, Vladimir Madhvanath, Sriganesh Univ Utah Salt Lake City UT 84112 USA Univ Wisconsin Madison WI USA MKS Instruments Andover MA USA eBay New York NY USA Conduent Labs Res Triangle Pk NC USA

ISBN: (纸本)9781450366151

We consider the license plate re-identification task, treated here as a one-shot image retrieval problem. Our objective is to learn a feature representation for license plate images, such that a single training image of a given license plate (referred to as a template image) is sufficient to perform nearest-neighbour retrieval with high accuracy at test time. Also, the feature representation should ideally be generalisable across datasets and should be extractable in real-time on resource-constrained embedded hardware or a moderately powerful cellphone. We evaluate representations from person re-identification (re-id) literature, learned from a trained deep convolutional network as well with those derived from a trained Fisher vector. While the convolutional network features perform better than the Fisher vector, we obtain comparable results from a hybrid model projecting the Fisher vector into a lower-dimensional space via two fully connected layers called f2nn using the triplet loss. the proposed hybrid model f2nn generates features which outperform and generalise better than convolutional features on datasets dissimilar to the training corpus. the model can be trained in stages and takes significantly less time to extract features. Further, it uses much smaller feature dimensions for license plate images resulting in faster re-identification, and is therefore well-suited for resource-constrained platforms such as mobile devices.

关键词： image Retrieval Feature Generation Fisher Vectors Neural Networks Triplet Loss Signature Matching Generalisation Dimensionality Reduction License Plate Re-identification Optical Character Recognition

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Hierarchical Deep Network for Group Discovery and Multi-level Activity Recognition 11

Hierarchical Deep Network for Group Discovery and Multi-leve...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Goyal, Ashish Bhargava, Neha Chaudhuri, Subhasis Velmurugan, Rajbabu Indian Inst Technol Mumbai Maharashtra India

ISBN: (纸本)9781450366151

We present a deep network based hierarchical framework to recognize activities performed by people collectively from videos at various levels of granularity - individual person, group and overall (or scene level). Individual person analysis, which includes person detection, tracking, pose estimation and individual activity recognition, has been studied extensively. Most of the existing work on collective activity recognition has focused on overall scene activity estimation. However, in scenarios where multiple groups perform different group activities, overall scene activity recognition in isolation paints an incomplete picture of various activities. Identifying groups and recognizing their activities is therefore important to understand a scene in it's completeness. To this end, we add an extra layer in existing methods that finds the groups (or clusters) of people present in a scene and their activities. We then utilize these group activities along with the scene context to recognize the scene activity. To discover these groups, we propose a min-max criteria within the framework to train a sub-network which learns pairwise similarity between any two individuals, used by a clustering algorithm for identification of groups. the group activity is captured by an LSTM module whereas the individual and scene activities are captured by CNN-LSTM based modules. these modules along with the grouping layer form the proposed network. We evaluate the network on a publicly available dataset to indicate the usefulness of our approach.

关键词： Clustering algorithms

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