Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing最新文献

A Novel Multi-Scale Residual Dense Dehazing Network (MSRDNet) for Single Image Dehazing✱ 一种新的多尺度残差密集去雾网络(MSRDNet)，用于单幅图像的译者去雾

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2022-12-08 DOI: 10.1145/3571600.3571601

Chippy M. Manu, G. SreeniK.

引用次数: 0

Robust Brain State Decoding using Bidirectional Long Short Term Memory Networks in functional MRI. 利用功能性核磁共振成像中的双向长短期记忆网络进行稳健的大脑状态解码

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2021-12-01 Epub Date: 2021-12-19 DOI: 10.1145/3490035.3490269

Anant Mittal, Priya Aggarwal, Luiz Pessoa, Anubha Gupta

{"title":"Robust Brain State Decoding using Bidirectional Long Short Term Memory Networks in functional MRI.","authors":"Anant Mittal, Priya Aggarwal, Luiz Pessoa, Anubha Gupta","doi":"10.1145/3490035.3490269","DOIUrl":"10.1145/3490035.3490269","url":null,"abstract":"<p><p>Decoding brain states of the underlying cognitive processes via learning discriminative feature representations has recently gained a lot of interest in brain imaging studies. Particularly, there has been an impetus to encode the dynamics of brain functioning by analyzing temporal information available in the fMRI data. Long-short term memory (LSTM), a class of machine learning model possessing a \"memory\" component, to retain previously seen temporal information, is increasingly being observed to perform well in various applications with dynamic temporal behavior, including brain state decoding. Because of the dynamics and inherent latency in fMRI BOLD responses, future temporal context is crucial. However, it is neither encoded nor captured by the conventional LSTM model. This paper performs robust brain state decoding via information encapsulation from both the past and future instances of fMRI data via bi-directional LSTM. This allows for explicitly modeling the dynamics of BOLD response without any delay adjustment. To this end, we utilize a bidirectional LSTM, wherein, the input sequence is fed in normal time-order for one LSTM network, and in the reverse time-order, for another. The two hidden activations of forward and reverse directions in bi-LSTM are collated to build the \"memory\" of the model and are used to robustly predict the brain states at every time instance. Working memory data from the Human Connectome Project (HCP) is utilized for validation and was observed to perform 18% better than it's unidirectional counterpart in terms of accuracy in predicting the brain states.</p>","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"2021 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9639335/pdf/nihms-1789845.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40673961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ICVGIP 2018: 11th Indian Conference on Computer Vision, Graphics and Image Processing, Hyderabad, India, 18-22 December, 2018 ICVGIP 2018:第11届印度计算机视觉，图形和图像处理会议，印度海得拉巴，2018年12月18日至22日

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2018-01-01 DOI: 10.1145/3293353

引用次数: 0

Voxel-based morphometry and minimum redundancy maximum relevance method for classification of Parkinson's disease and controls from T1-weighted MRI 基于体素的形态测量和最小冗余最大关联方法在t1加权MRI中对帕金森病和对照进行分类

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3009998

Bharti, A. Juneja, M. Saxena, S. Gudwani, S. Kumaran, R. Agrawal, M. Behari

{"title":"Voxel-based morphometry and minimum redundancy maximum relevance method for classification of Parkinson's disease and controls from T1-weighted MRI","authors":"Bharti, A. Juneja, M. Saxena, S. Gudwani, S. Kumaran, R. Agrawal, M. Behari","doi":"10.1145/3009977.3009998","DOIUrl":"https://doi.org/10.1145/3009977.3009998","url":null,"abstract":"Parkinson's disease (PD) is a neurodegenerative disorder, which needs to be accurately diagnosed in early stage. Voxel-based morphometry (VBM) has been extensively utilized to determine focal changes between PD patients and controls. However, it is not much utilized in differential diagnosis of an individual subject. Thus, in this study, VBM findings in conjunction with minimum redundancy maximum relevance (mRMR) method are utilized to obtain a set of relevant and non-redundant features for computer-aided diagnosis (CAD) of PD using T1-weighted MRI. In the proposed method, firstly, statistical features are extracted from the clusters obtained from statistical maps, generated using VBM, of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) independently and their different combinations. Then mRMR, a multivariate feature selection method, is utilized to find a minimal set of relevant and non-redundant features. Finally, support vector machine is utilized to learn a decision model using the selected features. Experiments are performed on newly acquired T1-weighted MRI of 30 PD patients and 30 age & gender matched controls. The performance is evaluated using leave-one out cross-validation scheme in terms of sensitivity, specificity and classification accuracy. The maximum accuracy of 88.33% is achieved for GM+WM and GM+WM+CSF. In addition, the proposed method outperforms the existing methods. It is also observed that the selected clusters belong to regions namely middle and superior frontal gyrus for GM, inferior, middle frontal gyrus and insula for WM and lateral ventricle for CSF. Further, correlation of UPDRS/H&Y staging scale with GM/WM/CSF volume is observed to be not significant. Appreciable classification performance of the proposed method highlights the potential of the proposed method in CAD support system for the clinicians in PD diagnosis.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"4 1","pages":"22:1-22:6"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73493078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A distribution-independent risk estimator for image denoising 一种与分布无关的图像去噪风险估计方法

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010025

Babu Kishore Subramanian, Ashutosh Gupta, C. Seelamantula

{"title":"A distribution-independent risk estimator for image denoising","authors":"Babu Kishore Subramanian, Ashutosh Gupta, C. Seelamantula","doi":"10.1145/3009977.3010025","DOIUrl":"https://doi.org/10.1145/3009977.3010025","url":null,"abstract":"We address the problem of image denoising for an additive white noise model without placing any restrictions on the statistical distribution of noise. We assume knowledge of only the first- and second-order noise statistics. In the recent mean-square error (MSE) minimization approaches for image denoising, one considers a particular noise distribution and derives an expression for the unbiased risk estimate of the MSE. For additive white Gaussian noise, an unbiased estimate of the MSE is Stein's unbiased risk estimate (SURE), which relies on Stein's lemma. We derive an unbiased risk estimate without using Stein's lemma or its counterparts for additive white noise model irrespective of the noise distribution. We refer to the MSE estimate as the generic risk estimate (GenRE). We demonstrate the effectiveness of GenRE using shrinkage in the undecimated Haar wavelet transform domain as the denoising function. The estimated peak-signal-to-noise-ratio (PSNR) using GenRE is typically within 1% of the PSNR obtained when optimizing with the oracle MSE. The performance of the proposed method is on par with SURE for Gaussian noise distribution, and better than SURE-based methods for other noise distributions such as uniform and Laplacian distribution in terms of both PSNR and structural similarity (SSIM).","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"220 1","pages":"52:1-52:8"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75624899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Informed multimodal latent subspace learning via supervised matrix factorization 基于监督矩阵分解的多模态潜在子空间学习

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010012

Ramashish Gaurav, Mridula Verma, K. K. Shukla

引用次数: 2

Color restoration in turbid medium 在浑浊介质中恢复颜色

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010028

M. NimishaT., K. Seemakurthy, A. Rajagopalan, N. Vedachalam, Ramesh Raju

引用次数: 5

A framework to assess Sun salutation videos 一个评估敬礼视频的框架

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010045

Hiteshi Jain, Gaurav Harit

{"title":"A framework to assess Sun salutation videos","authors":"Hiteshi Jain, Gaurav Harit","doi":"10.1145/3009977.3010045","DOIUrl":"https://doi.org/10.1145/3009977.3010045","url":null,"abstract":"There are many exercises which are repetitive in nature and are required to be done with perfection to derive maximum benefits. Sun Salutation or Surya Namaskar is one of the oldest yoga practice known. It is a sequence of ten actions or 'asanas' where the actions are synchronized with breathing and each action and its transition should be performed with minimal jerks. Essentially, it is important that this yoga practice be performed with Grace and Consistency. In this context, Grace is the ability of a person to perform an exercise with smoothness i.e. without sudden movements or jerks during the posture transition and Consistency measures the repeatability of an exercise in every cycle. We propose an algorithm that assesses how well a person practices Sun Salutation in terms of grace and consistency. Our approach works by training individual HMMs for each asana using STIP features[11] followed by automatic segmentation and labeling of the entire Sun Salutation sequence using a concatenated-HMM. The metric of grace and consistency are then laid down in terms of posture transition times. The assessments made by our system are compared with the assessments of the yoga trainer to derive the accuracy of the system. We introduce a dataset for Sun Salutation videos comprising 30 sequences of perfect Sun Salutation performed by seven experts and used this dataset to train our system. While Sun Salutation can be judged on multiple parameters, we focus mainly on judging Grace and Consistency.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"43 1","pages":"29:1-29:8"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86528823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Towards quantifying the amount of uncollected garbage through image analysis 通过图像分析来量化未收集垃圾的数量

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010061

Susheel Suresh, Tarun Sharma, K. PrashanthT., V. Subramaniam, D. Sitaram, M. Nirupama

引用次数: 5

Generating synthetic handwriting using n-gram letter glyphs 生成合成手写使用n-gram字母字形

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010042

Arka Ujjal Dey, Gaurav Harit

引用次数: 2