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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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Color image compression using a Hilbert scan

Color image compression using a Hilbert scan

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International conference on Pattern Recognition

作者： S.-I. Kamata T.N. Nishi Y. Bandoh Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka Japan

The Hilbert curve is one of the space-filling curves published by G. Peano. There are several applications using this curve such as image processing, computer graphics, etc. We concentrate on a compression technique for color images using the Hilbert curve. The merit of this curve is to pass through all points in a quadrant, and always to move to the neighboring quadrant. Our method is based on this neighborhood property by a simple segmentation of the scanned one dimensional data using a zero order interpolation. From our experiments, we have confirmed that inspite of the simple computation in comparison to JPEG, acceptable quality images can be obtained at the same bit-rates.

关键词： Color image coding

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Selective mixing and voting network for semi-supervised domain generalization 21

Selective mixing and voting network for semi-supervised doma...

引用

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

作者： Arfeen, Ahmad Dutta, Titir Biswas, Soma Indian Institute of Science Karnataka Bangalore India

ISBN: (纸本)9781450391276

Domain generalization (DG) addresses the problem of generalizing classification performance across any unknown domain, by leveraging training samples from multiple source domains. Currently, the training process of the state-of-the-art DG-methods is dependent on a large amount of labeled data. This restricts the application of the models in many real-world scenarios, where collecting and annotating a large dataset is an expensive and difficult task. Thus, in this paper, we address the problem of Semi-supervised Domain Generalization (SSDG), where the training set contains only a few labeled data, in addition to a large number of unlabeled data from multiple domains. This is relatively unexplored in literature and poses a considerable challenge to the state-of-the-art DG models, since their performance degrades under such condition. To address this scenario, we propose a novel Selective Mixing and Voting Network (SMV-Net), which effectively extracts useful knowledge from the set of unlabeled training data, available to the model. Specifically, we propose a mixing strategy on selected unlabeled samples on which the model is confident about their predicted class labels to achieve a domain-invariant representation of the data, which generalizes effectively across any unseen domain. Secondly, we also propose a voting module, which not only improves the generalization capability of the classifier, but can also comment on the prediction of the test samples, using references from a few labeled training examples, despite of their domain gap. Finally, we introduce a test-time mixing strategy to re-look at the top class-predictions and re-order them if required to further boost the classification performance. Extensive experiments on two popular DG-datasets demonstrate the usefulness of the proposed framework. © 2021 ACM.

关键词： Large dataset

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HSADML: Hyper-Sphere Angular Deep Metric Based Learning for Brain Tumor Classification 12th

HSADML: Hyper-Sphere Angular Deep Metric Based Learning for ...

引用

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

作者： Verma, Aman Singh, Vibhav Prakash Department of Electronics and Communications National Institute of Technology Raipur Raipur India Department of Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad Prayagraj India

ISBN: (纸本)9789811941351

Brain Tumors are an abnormal mass of clustered cells penetrating regions of the brain. Their timely identification and classification help doctors to provide appropriate treatment. However, Classification of Brain Tumors is quite intricate because of high-intra-class similarity and low-inter- class variability. Due to morphological similarity amongst various MRI-Slices of different classes the challenge deepens more. This all leads to hampering the generalizability of classification models. To this end, this paper proposes HSADML, a novel framework which enables Deep Metric Learning (DML) using SphereFace Loss. SphereFace loss embeds the features into a hyperspheric-manifold and then imposes margin on the embedding to enhance differentiability between the classes. With the utilization of SphereFace loss-based deep metric learning it is ensured that samples from class are clustered together while the different ones are pushed apart. Results reflect the prominence of the approach, the proposed framework achieved state-of-the-art 98.69% validation accuracy using k-NN (k = 1) and this is significantly higher than normal Soft Max Loss training which though obtains 98.47% validation accuracy but that too with limited inter-class reparability and intra-class closeness. Experimental analysis is done over various classifiers and loss function settings suggest potential in the approach. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Brain

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Transfer learning by finetuning pretrained CNNs entirely with synthetic images 6th

Transfer learning by finetuning pretrained CNNs entirely wit...

引用

6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Rajpura, Param Aggarwal, Alakh Goyal, Manik Gupta, Sanchit Talukdar, Jonti Bojinov, Hristo Hegde, Ravi Indian Institute of Technology Gandhinagar Gandhinagar382355 India Varanasi Varanasi221005 India BITS Hyderabad Hyderabad500078 India Nirma Institute of Technology Ahmedabad382481 India Innit Inc. Redwood City94063 United States

ISBN: (纸本)9789811300196

We show that finetuning pretrained CNNs entirely on synthetic images is an effective strategy to achieve transfer learning. We apply this strategy for detecting packaged food products clustered in refrigerator scenes. A CNN pretrained on the COCO dataset and fine-tuned with our 4000 synthetic images achieves mean average precision (mAP @ 0.5-IOU) of 52.59 on a test set of real images (150 distinct products as objects of interest and 25 distractor objects) in comparison to a value of 24.15 achieved without such finetuning. The synthetic images were rendered with freely available 3D models with variations in parameters like color, texture and viewpoint without a high emphasis on photorealism. We analyze factors like training data set size, cue variances, 3D model dictionary size and network architecture for their influence on the transfer learning performance. Additionally, training strategies like fine-tuning with selected layers and early stopping which affect transfer learning from synthetic scenes to real scenes were explored. This approach is promising in scenarios where limited training data is available. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Network architecture

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Automatic identification of text in digital video key frames

Automatic identification of text in digital video key frames

引用

International conference on Pattern Recognition

作者： Huiping Li D. Doermann Language and Media Processing Laboratory Institute for Advanced Computer Studies University of Maryland College Park MD USA

ISBN: (纸本)0818685123

Scene and graphic text can provide important supplemental index information in video sequences. In this paper we address the problem automatically identifying text regions in digital video key frames. The text contained in video frames is typically very noisy because it is aliased and/or digitized at a much lower resolution than typical document images, making identification, extraction and recognition difficult. The proposed method is based on the use of a hybrid wavelet/neural network segmenter on a series of overlapping small windows to classify regions which contain text. To detect text over a wide range of font sizes, the method is applied to a pyramid of images and the regions identified at each level are integrated.

关键词： Layout image segmentation Neural networks image resolution Data mining Motion pictures Feature extraction Laboratories computer graphics image recognition

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Enhanced Fetal Development Assessment via Contour Detection and CRL Estimation 3

Enhanced Fetal Development Assessment via Contour Detection ...

引用

3rd IEEE International conference on Control, Instrumentation, Energy and Communication, CIEC 2024

作者： Sriraam, Natarajan Chinta, Babu Suresh, Seshadhri Sudharshan, Suresh Ramaiah Institute of Technology Center for Medical Electronics and Computing Department of Medical Electronics Engineering Bangalore India Center for Medical Electronics and Computing Ramaiah Institute of Technology Bangalore India Mediscans Pvt Ltd Chennai India

ISBN: (纸本)9798350313703

Accurate fetal growth evaluation is critical for monitoring pregnancy health and delivering appropriate prenatal treatment. We provide a unique strategy for improving the precision of fetal growth assessment by merging sophisticated contour recognition algorithms with Crown-Rump Length (CRL) estimate in this work. We establish a more complete and reliable approach of measuring fetal growth and development by integrating cutting-edge computer vision and deep learning methods. Our technique uses powerful image processing methods to determine the shape of the fetal anatomy in ultrasound images, overcoming hurdles such as noise, artifacts, and fetal positional fluctuations. Following contour identification, exact CRL length estimate is performed, which is a critical predictor of gestational age and fetal well-being. The combination of these two components considerably improves accuracy of fetal growth assessment. © 2024 IEEE.

关键词： image enhancement

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Multimodal Egocentric Activity Recognition Using Multi-stream CNN 2018

Multimodal Egocentric Activity Recognition Using Multi-strea...

引用

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

作者： Javed Imran Balasubramanian Raman Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee India

ISBN: (纸本)9781450366151

Egocentric activity recognition (EAR) is an emerging area in the field of computer vision research. Motivated by the current success of Convolutional Neural Network (CNN), we propose a multi-stream CNN for multimodal egocentric activity recognition using visual (RGB videos) and sensor stream (accelerometer, gyroscope, etc.). In order to effectively capture the spatio-temporal information contained in RGB videos, two types of modalities are extracted from visual data: Approximate Dynamic image (ADI) and Stacked Difference image (SDI). These image-based representations are generated both at clip level as well as entire video level, and are then utilized to finetune a pretrained 2D-CNN called MobileNet, which is specifically designed for mobile vision applications. Similarly for sensor data, each training sample is divided into three segments, and a deep 1D-CNN network is trained (corresponding to each type of sensor stream) from scratch. During testing, the softmax scores of all the streams (visual + sensor) are combined by late fusion. The experiments performed on multimodal egocentric activity dataset demonstrates that our proposed approach can achieve state-of-the-art results, outperforming the current best handcrafted and deep learning based techniques.

关键词： Stacked Difference image Dynamic image Egocentric Activity Recognition Convolutional Neural Network Multimodal Fusion

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Validation of stimulus for EEG signal based cognitive load analysis

Validation of stimulus for EEG signal based cognitive load a...

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

作者： Aniruddha Sinha Debatri Chatterjee Sanjoy Kumar Saha Anupam Basu Innovation Lab Tata Consultancy Services Ltd. Kolkata India Computer Science & Engineering Jadavpur University Kolkata India Computer Science & Engineering Indian Institute of Technology Kharagpur India

Cognitive load is defined as the mental workload imparted on brain while doing a task. The amount of cognitive load experienced depends on individual's ability of perception, assimilation and response to a task. Real-time measurement of the level of cognitive load using low cost Electroencephalogram (EEG) signal enables understanding of personal cognitive skills. In this paper, we propose a methodology of selecting a reference task whose bio-markers closely match with a given task while probing different cognitive abilities. The benefit of this approach is to have a limited set of training models for the reference tasks related to various cognitive categories and use the same for a variety of unknown tasks. Experiment is performed for two levels of cognitive load with three different tasks namely Stroop color task, logical reasoning task and usage of on-screen keyboards. The training models of the reference tasks, selected by cluster analysis of low and high cognitive levels are used to evaluate an unknown task. Experimental results indicate that the Stroop is a better reference for On-Screen keyboard test compared to the Logical reasoning test. Support vector machine (SVM) and principal component analysis (PCA) followed by SVM (PCA-SVM) are used as the classifiers for the testing.

关键词： Electroencephalography Keyboards Layout Training Cognition Brain models

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Classification of hardwood species using ANN classifier

Classification of hardwood species using ANN classifier

引用

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

作者： Arvind R. Yadav M. L. Dewal R. S. Anand Sangeeta Gupta Department of Electrical Engineering Indian Institute of Technology Roorkee Roorkee India Botany Division Forest Research Institute Dehradun Dehradun India

In this paper, an approach for the classification of different hardwood species of open access database, using texture feature extraction and supervised machine learning technique has been implemented. Edges of complex cellular structure of microscopic images of hardwood are enhanced with the application of Gabor filter, and Gray Level Co-occurrence Matrix (GLCM) as an effective texture feature extraction technique is being revalidated. About, 44 features have been extracted from GLCM; these features have been further normalized in the range [0.1, 1]. Multilayer Perceptron Backpropagation Artificial Neural Network have been used for classification. Experiments conducted on 25 wood species have resulted in recognition accuracy of about 88.60% and 92.60% using Levenberg-Marquardt backpropagation training function with two different datasets for training, validation and testing ratio (70%, 15%, 15% and 80%, 10%, 10%) respectively. Proposed methodology can be extended with optimized machine learning techniques for online identification of wood.

关键词： Feature extraction Training Accuracy Microscopy Gabor filters Backpropagation Artificial neural networks

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A low complexity block matching algorithm for fast motion estimation in High Efficiency Video Coding

A low complexity block matching algorithm for fast motion es...

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

作者： K.C. Ravi Chandra Varma Venkata Phani Kumar M. Sudipta Mahapatra Electronics and Electrical Communication Engineering Indian Institute of Technology Kharagpur Kharagpur West Bengal IN Electronics and Electrical Communication Engineering IIT Kharagpur West Bengal India

This paper proposes a search mechanism, namely accelerated internal stop search (AISS), to reduce the complexity of the block matching process used for motion estimation in High Efficiency Video Coding (HEVC). The proposed mechanism reduces the number of operations required to find the best matching reference block. Moreover, it uses the correlation with the previous reference frames to further reduce the complexity of motion estimation for the current reference frame. Results demonstrate that the proposed mechanism is able to reduce the complexity of motion estimation in the fast search mode of the HM-14.0 reference software for HEVC, i.e., the TZ Search motion estimation. The AISS mechanism outperforms the dynamic internal stop search algorithm (DISS) proposed by Ismail et al. in the year 2012 in terms of motion estimation time saving. Moreover, when AISS is integrated with the dynamic model based fast motion estimation proposed by Ismail et al. [1], the complexity of motion estimation is further reduced.

关键词： Complexity theory Motion estimation Mathematical model Heuristic algorithms Bit rate Video recording Quality assessment

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