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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1381-1390 订阅

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Low Resource Degraded Quality Document image Binarization – Domain Adaptation is the Way 22

Low Resource Degraded Quality Document Image Binarization –...

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

作者： Ahana Kundu Ujjwal Bhattacharya Computer Vision and Pattern Recognition Indian Statistical Institute IN Computer Vision and Pattern Recognition ISI Kolkata IN

ISBN: (纸本)9781450398220

Usually, image binarization plays a crucial role in automatic analysis of degraded documents from their captured images. However, this binarization task is often difficult due to a number of reasons including the high similarity between noisy background and faded foreground pixels. The study presented here is particularly focused on binarization of images of low-resource degraded quality documents based on a set of recently collected image samples of several rare, ancient and severely degraded quality printed documents of Bangla, the 2nd and 5th most popular script of India and the world respectively. This new collection of degraded document image samples will henceforth be referred as ’ISIDDI2’ and it consists of 139 images of Bangla old document pages. Samples of ’ISIDDI’, another existing database of degraded Bangla document image samples, have also been used in the present study. A novel deep architecture based on attention UNET++ with dilated convolution operation is proposed for this binarization task. The model is optimized using human vision perceptible distance reciprocal distortion (DRD) loss. Since the binarization ground truth of samples of both ’ISIDDI2’ and ’ISIDDI’ are not available, the proposed network has been trained using samples of DIBCO and H-DIBCO datasets and an unsupervised domain adaptation (DA) module is employed for adaptation of the proposed architecture to the degradation patterns of ’ISIDDI2’ or ’ISIDDI’ samples. The proposed binarization strategy includes certain post-processing operation based on a modified k-neighbourhood based approach for recovery of broken characters. Results of our extensive experimentation show that the proposed binarization strategy has improved the binarization output of state-of-the-art methods on both ISIDDI2 and ISIDDI datasets. Also, its performance on well-known DIBCO samples is satisfactory.

关键词： UNET++ degraded document image domain adaptation image binarization attention

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A Dataset and Model for Crossing indian Roads 22

A Dataset and Model for Crossing Indian Roads

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

作者： Siddhi Brahmbhatt G H Patel College of Engineering and Technology IN

ISBN: (纸本)9781450398220

Roads in medium-sized indian towns often have lots of traffic but no (or disregarded) traffic stops. This makes it hard for the blind to cross roads safely, because vision is crucial to determine when crossing is safe. Automatic and reliable image-based safety classifiers thus have the potential to help the blind to cross indian roads. Yet, we currently lack datasets collected on indian roads from the pedestrian point-of-view, labelled with road crossing safety information. Existing classifiers from other countries are often intended for crossroads, and hence rely on the detection and presence of traffic lights, which is not applicable in indian conditions. We introduce INDRA (indian Dataset for RoAd crossing), the first dataset capturing videos of indian roads from the pedestrian point-of-view. INDRA contains 104 videos comprising of 26k 1080p frames, each annotated with a binary road crossing safety label and vehicle bounding boxes. We train various classifiers to predict road crossing safety on this data, ranging from SVMs to convolutional neural networks (CNNs). The best performing model DilatedRoadCrossNet is a novel single-image architecture tailored for deployment on the Nvidia Jetson Nano. It achieves 79% recall at 90% precision on unseen images. Lastly, we present a wearable road crossing assistant running DilatedRoadCrossNet, which can help the blind cross indian roads in real-time. The project webpage is https://***/Website/***

关键词： assistive technology convolutional neural networks road-crossing safety

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Real Time Incremental Foveal Texture Mapping for Autonomous Vehicles

Real Time Incremental Foveal Texture Mapping for Autonomous ...

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25th IEEE/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Kumar, Ashish McBride, James R. Pandey, Gaurav Indian Inst Technol Dept Elect Engn Kanpur Uttar Pradesh India Ford Motor Co Res & Innovat Ctr Dearborn MI 48121 USA

ISBN: (纸本)9781538680940

We propose an end-to-end real time framework to generate high resolution graphics grade textured 3D map of urban environment. The generated detailed map finds its application in the precise localization and navigation of autonomous vehicles. It can also serve as a virtual test bed for various vision and planning algorithms as well as a background map in the computer games. In this paper, we focus on two important issues: (i) incrementally generating a map with coherent 3D surface, in real time and (ii) preserving the quality of color texture. To handle the above issues, firstly, we perform a pose-refinement procedure which leverages camera image information, Delaunay triangulation and existing scan matching techniques to produce high resolution 3D map from the sparse input LIDAR scan. This 3D map is then texturized and accumulated by using a novel technique of ray-filtering which handles occlusion and inconsistencies in pose-refinement. Further, inspired by human fovea, we introduce foveal-processing which significantly reduces the computation time and also assists ray-filtering to maintain consistency in color texture and coherency in 3D surface of the output map. Moreover, we also introduce texture error (TE) and mean texture mapping error (MTME), which provides quantitative measure of texturing and overall quality of the textured maps.

关键词： Three-dimensional displays Laser radar Cameras Real-time systems image color analysis Global Positioning System

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A Novel Multi-Scale Residual Dense Dehazing Network (MSRDNet) for Single image Dehazing✱ 22

A Novel Multi-Scale Residual Dense Dehazing Network (MSRDNet...

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

作者： Chippy M Manu Sreeni K G College of engineering trivandrum Kerala India IN College of Engineering Trivandrum India

ISBN: (纸本)9781450398220

Dehazing is a difficult process because of the damage caused by the non-uniform fog and haze distribution in images. To address these issues, a Multi-Scale Residual dense Dehazing Network (MSRDNet) is proposed in this paper. A Contextual feature extraction module (CFM) for extracting multi-scale features and an Adaptive Residual Dense Module (ARDN) are used as sub-modules of MSRDNet. Moreover, all the hierarchical features extracted by each ARDN are fused, which helps to detect hazy maps of varying lengths with multi-scale features. This framework outperforms the state-of-the-art dehazing methods in removing haze while maintaining and restoring image detail in real-world and synthetic images captured under various scenarios.

关键词： Dehazing Dilated Convolution image restoration

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An Efficient Motor imagery Classification Framework using Sparse Brain Connectivity and Class-consistent Dictionary Learning from Electroencephalogram Signals 23

An Efficient Motor Imagery Classification Framework using Sp...

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

作者： Subrata Pain Debasis Samanta Monalisa Sarma Advanced Technology Development Center Indian Institute of Technology Kharagpur India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Subir Chowdhury School of Quality and Reliability Indian Institute of Technology Kharagpur India

ISBN: (纸本)9798400716256

Identifying patterns in high-dimensional and noisy neuro-imaging data such as Electroencephalogram (EEG) has always been challenging. This fact hampers the performance of the real-time Brain-computer Interfaces (BCI) controlled by EEG recordings of brain signals, such as imagined motor activity (MI-EEG). Traditionally, the MI-EEG signals are classified by mainly two approaches, firstly, using the Common Spatial Patterns (CSPs) extracted from MI-EEG as features for Machine Learning (ML) classifiers, and secondly, using various Deep Learning (DL)-based approaches. But these methods are computationally heavy and ignore the dynamic topological interactions between the brain regions. Recently, due to high performance and low testing time, sparse representation-based classifiers have been utilized for MI-EEG classifications with higher real-life applicability. But these methods consider a fixed training dictionary consisting of inherently noisy training features. This study proposes an efficient MI-EEG classification framework utilizing a sparse representation of brain connectivity features and a dictionary learned from the training features. Here, the dynamic synchrony of co-activating brain regions is captured by suitable connectivity measures, which are further used as features. Further, sparse representations of the connectivity features are obtained through sparsity and dictionary learning. As this dictionary learning method preserves the class information during dictionary updation, this method is best suited for classification tasks. The dictionary learning method simultaneously learns a reduced dictionary and a linear classifier further used for classification. The proposed framework is validated using a publicly available MI dataset, and corresponding classification performances are observed. The proposed framework outperforms the other sparsity-based classifiers in mixed-, intra-, and cross-subject performances, making it suitable for real-life BCI applications.

关键词： Brain Connectivity Dictionary Learning Electroencephalogram Patterns Motor Imagination Sparse Representations

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A Fast Method for Very Low Bit-Rate Coding of Hyperspectral images 2018

A Fast Method for Very Low Bit-Rate Coding of Hyperspectral ...

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

作者： Saurabh Kumar Subhasis Chaudhuri Indian Institute of Technology Bombay Mumbai India

ISBN: (纸本)9781450366151

In this work, we propose a computationally efficient compressive sensing based approach for very low bit rate lossy coding of hyperspectral (HS) image data by exploiting the redundancy inherent in this imaging modality. We divide the HS datacube into subsets of adjacent bands, each of which is encoded into a coded snapshot using a random code matrix. These coded snapshot images are encoded using the wavelet-based SPIHT compression technique. The decompression from the coded snapshots at the receiver is done using the orthogonal matching pursuit with the help of an overcomplete dictionary learned on a general purpose training dataset. We provide ample experimental results and performance comparisons to substantiate the usefulness of the proposed method. In the proposed technique the encoder is free from any decoder and it offers a significant saving in computation and yet yields a much higher compression quality.

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SpoofNET: Resolving Facial Makeup based Spoofs 2018

SpoofNET: Resolving Facial Makeup based Spoofs

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

作者： Avishek Bhattacharjee Samik Banerjee Sukhendu Das Indian Institute of Technology Madras Chennai Tamil Nadu India

ISBN: (纸本)9781450366151

Face Recognition (FR) under adversarial conditions has been a big challenge for researchers in the computer vision and Machine Learning communities in the recent past. Most of state-of-the-art face recognition systems have been designed to overcome degradations in a face due to variations in pose, illumination, contrast, resolution, along with blur. However, interestingly none have addressed the fascinating issue of makeup as a spoof attack, which drastically changes the appearance of a face, making it difficult for even humans to detect and identify the impostor. In this paper, we propose a novel multi-component deep convolutional neural network (CNN) based architecture which performs the complex task of makeup removal from a disguised face, to reveal the original mugshot image of the impostor (i.e. without makeup). The proposed network also performs the hard tasks of FR on a disguised face in addition to recognition of identity and generation of the face of the spoofed target, by minimizing a novel multi-component objective function. Comparison of performance with a few recent state-of-the-art methods of FR over three benchmark datasets reveals the superiority of our proposed method for both synthesis as well as recognition (FR) tasks.

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Controllable image Synthesis via Feature Mask Coupling using Implicit Neural Representation✱ 22

Controllable Image Synthesis via Feature Mask Coupling using...

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

作者： Sonam Gupta Arti Keshari Sukhendu Das Indian Institute of Technology Madras India

ISBN: (纸本)9781450398220

Implicit neural representation (INR) has emanated as a powerful paradigm for 2D image representation. Recent works like INR-GAN have successfully adopted INR for 2D image synthesis. However, these lack explicit control on the generated images as achieved by their 3D-aware image synthesis counterparts like GIRAFFE. Our work investigates INRs for the task of controllable image synthesis. We propose a novel framework that allows for manipulation of foreground, background and their shape and appearance in the latent space. To achieve effective control over these attributes, we introduce a novel feature mask coupling technique that leverages the foreground and background masks for mutual learning. Extensive quantitative and qualitative analysis shows that our model can disentangle the latent space successfully and allows to change the foreground and/or background’s shape and appearance. We further demonstrate that our network takes lesser training time than other INR-based image synthesis methods.

关键词： Generative adversarial networks

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An approach to a rough set decision system for classification of different heart diseases

An approach to a rough set decision system for classificatio...

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AMSE International conference on Modelling and Simulation, MS'2004

作者： Mitra, S. Mitra, M. Chattopadhya, S. Chaudhuri, B.B. Department of Applied Physics Faculty of Technology University of Calcutta India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India JIS College of Engineering Kalyani India

In this paper a rule based rough set decision system for development of a disease inference engine is described. For this purpose an off-line data acquisition system of paper electrocardiogram (ECG) records are developed using image processing techniques. A QRS detector is developed for detection of R-R interval from ECG waves. After detection of this R-R interval the P and T waves are detected based on syntactic approaches and different time-plane features are extracted from every ECG signals. From a knowledgebase which is developed from the feedback of different reputed cardiologists and consultation of different medical books the essential time plane features for ECG interpretation have been selected. Finally, a rule-based roughest decision system is generated for the development of an inference engine for disease identification from these time-plane features.

关键词： Inference engines

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1st International conference on Artificial Intelligence and Cognitive Computing, AICC 2018

1st International Conference on Artificial Intelligence and ...

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1st International conference on Artificial Intelligence and Cognitive Computing, AICC 2018

ISBN: (纸本)9789811315794

The proceedings contain 68 papers. The special focus in this conference is on Artificial Intelligence and Cognitive Computing. The topics include: Cognitive-based adaptive path planning for mobile robot in dynamic environment;prediction for indian road network images dataset using feature extraction method;Design and simulation of capacitive MEMS accelerometer;bioinformatics and image processing—Detection of plant diseases;hybrid approach for pixel-wise semantic segmentation using SegNet and squeezenet for embedded platforms;software modernization through model transformations;Semi-automatic annotation of images using eye gaze data (SAIGA);optimizing regression test suite reduction;application monitoring—Active learning approach;acquiring best rules that represent datasets;an automated computer vision system for extraction of retail food product metadata;attacks on two-key elliptic curve digital signature algorithm;bioinformatics: An application in information science;leveraging deep learning for anomaly detection in video surveillance;speaker diarization system using hidden Markov toolkit;Survey: Enhanced trust management for improving QoS in MANETs;a generic survey on medical big data analysis using internet of things;mean estimation under post-stratified cluster sampling scheme;privacy-preserving Naive Bayesian classifier for continuous data and discrete data;learning style recognition: A neural network approach;morphological-based localization of an Iris image;an enhanced efficiency of key management method for wireless sensor networks using mobile agents;a survey on emotion’s recognition using internet of things;KAZE feature based passive image forgery detection;efficiency-based analysis of homomorphic encryption implications;Performance and analysis of human attention using single-channel wireless EEG sensor for medical application;smart heartbeat monitoring system using machine learning.

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