Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing最新文献_第5页

Extended multi-spectral face recognition across two different age groups: an empirical study 扩展多光谱人脸识别在两个不同年龄组:一个实证研究

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010026

N. Vetrekar, Ramachandra Raghavendra, A. Gaonkar, G. Naik, R. Gad

{"title":"Extended multi-spectral face recognition across two different age groups: an empirical study","authors":"N. Vetrekar, Ramachandra Raghavendra, A. Gaonkar, G. Naik, R. Gad","doi":"10.1145/3009977.3010026","DOIUrl":"https://doi.org/10.1145/3009977.3010026","url":null,"abstract":"Face recognition has attained a greater importance in bio-metric authentication due to its non-intrusive property of identifying individuals at varying stand-off distance. Face recognition based on multi-spectral imaging has recently gained prime importance due to its ability to capture spatial and spectral information across the spectrum. Our first contribution in this paper is to use extended multi-spectral face recognition in two different age groups. The second contribution is to show empirically the performance of face recognition for two age groups. Thus, in this paper, we developed a multi-spectral imaging sensor to capture facial database for two different age groups (≤ 15years and ≥ 20years) at nine different spectral bands covering 530nm to 1000nm range. We then collected a new facial images corresponding to two different age groups comprises of 168 individuals. Extensive experimental evaluation is performed independently on two different age group databases using four different state-of-the-art face recognition algorithms. We evaluate the verification and identification rate across individual spectral bands and fused spectral band for two age groups. The obtained evaluation results shows higher recognition rate for age groups ≥ 20years than ≤ 15years, which indicates the variation in face recognition across the different age groups.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"16 1","pages":"78:1-78:8"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86549944","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: 8

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010056

Sunil Kumar, M. Bhuyan, B. Chakraborty

{"title":"Uncorrelated multiview discriminant locality preserving projection analysis for multiview facial expression recognition","authors":"Sunil Kumar, M. Bhuyan, B. Chakraborty","doi":"10.1145/3009977.3010056","DOIUrl":"https://doi.org/10.1145/3009977.3010056","url":null,"abstract":"Recently several multi-view learning-based methods have been proposed, and they are found to be more efficient in many real world applications. However, existing multi-view learning-based methods are not suitable for finding discriminative directions if the data is multi-modal. In such cases, Locality Preserving Projection (LPP) and/or Local Fisher Discriminant Analysis (LFDA) are found to be more appropriate to capture discriminative directions. Furthermore, existing methods show that imposing uncorrelated constraint onto the common space improves classification accuracy of the system. Hence inspired from the above findings, we propose an Un-correlated Multi-view Discriminant Locality Preserving Projection (UMvDLPP)-based approach. The proposed method searches a common uncorrelated discriminative space for multiple observable spaces. Moreover, the proposed method can also handle the multimodal characteristic, which is inherently embedded in multi-view facial expression recognition (FER) data. Hence, the proposed method is effectively more efficient for multi-view FER problem. Experimental results show that the proposed method outperforms state-of-the-art multi-view learning-based methods.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"1 1","pages":"86:1-86:8"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84120810","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: 5

Enhancement of high dynamic range images using variational calculus regularizer with stochastic resonance 利用随机共振变分微积分正则化器增强高动态范围图像

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010039

Sumit Kumar, R. K. Jha

{"title":"Enhancement of high dynamic range images using variational calculus regularizer with stochastic resonance","authors":"Sumit Kumar, R. K. Jha","doi":"10.1145/3009977.3010039","DOIUrl":"https://doi.org/10.1145/3009977.3010039","url":null,"abstract":"While capturing pictures by a simple camera in a scene with the presence of harsh or strong lighting like a full sunny day, we often find loss of highlight detail information (overexposure) in the bright regions and loss of shadow detail information (underexposure) in dark regions. In this manuscript, a classical method for retrieval of minute information from the high dynamic range image has been proposed. Our technique is based on variational calculus and dynamic stochastic resonance (DSR). We use a regularizer function, which has been added in order to optimise the correct estimation of the lost details from the overexposed or underexposed region of the image. We suppress the dynamic range of the luminance image by attenuating large gradient with the large magnitude and low gradient with low magnitude. At the same time, dynamic stochastic resonance (DSR) has been used to improve the underexposed region of the image. The experimental results of our proposed technique are capable of enhancing the quality of images in both overexposed and underexposed regions. The proposed technique is compared with most of the state-of-the-art techniques and it has been observed that the proposed technique is better or at most comparable to the existing techniques.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"6 1","pages":"38:1-38:8"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87256210","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: 10

Fusion-based skin detection using image distribution model 基于图像分布模型的融合皮肤检测

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010002

B. Chakraborty, M. Bhuyan, Sunil Kumar

引用次数: 5

MPMF: multi-part multi-feature based object tracking MPMF:基于多部分多特征的目标跟踪

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010057

Neha Bhargava, S. Chaudhuri

{"title":"MPMF: multi-part multi-feature based object tracking","authors":"Neha Bhargava, S. Chaudhuri","doi":"10.1145/3009977.3010057","DOIUrl":"https://doi.org/10.1145/3009977.3010057","url":null,"abstract":"The objective of tracking is to determine the states of an object in video frames while maintaining appearance and motion consistency. In this paper, we propose a novel multi-part multi-feature (MPMF) based object tracking which falls in the category of part-based trackers. We represent a target by a set of fixed parts (not semantic as body parts such as limbs, face) and each part is represented by a set of features. The multi-part representation of the object aids in partial occlusion handling and the multi-feature based object description increases robustness of the target representation. Instead of considering all the features of the parts, we measure tracker's confidence for a candidate by utilizing only the strong features of the candidate. This ensures that weak features do not interfere in the decision making. We also present an automatic method for selecting this subset of appropriate features for each part. To increase the tracker's speed and to reduce the number of erroneous candidates, we do not search in the whole frame. We keep the size of search area adaptive that depends on the tracker's confidence for the predicted location of the object. Additionally, it is easy to integrate more parts and features to the proposed tracker. The results on various challenging videos from VOT dataset are encouraging. MPMF outperforms state-of-the-art trackers on some of the standard challenging videos.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"246 1","pages":"17:1-17:8"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76968945","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

Complementary tracker's fusion for robust visual tracking 互补跟踪融合的鲁棒视觉跟踪

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010006

S. Kakanuru, Madan Kumar Rapuru, Deepak Mishra, R. S. S. Gorthi

{"title":"Complementary tracker's fusion for robust visual tracking","authors":"S. Kakanuru, Madan Kumar Rapuru, Deepak Mishra, R. S. S. Gorthi","doi":"10.1145/3009977.3010006","DOIUrl":"https://doi.org/10.1145/3009977.3010006","url":null,"abstract":"Though visual object tracking algorithms are capable of handling various challenging scenarios individually, still none of them is robust enough to handle all the challenges simultaneously. This paper aims at proposing a novel robust tracking algorithm by elegantly fusing the frame level detection strategy of Tracking Learning & Detection (TLD) with systematic model update strategy of Kernelized Correlation Filter tracker (KCF). The motivation behind the selection of trackers is their complementary nature in handling tracking challenges. The proposed algorithm efficiently combines the two tracking algorithms based on conservative correspondence measure with strategic model updates, which takes advantages of both and outperforms them on their short-ends by the virtue of other. The proposed fusion approach is quite general and any complimentary tracker (not just KCF) can be fused with TLD to leverage the best performance. Extensive evaluation of the proposed method based on different metrics is carried out on the datasets ALOV300++, Online Tracking Benchmark (OTB) and Visual Object Tracking (VOT2015) and demonstrated its superiority in terms of robustness and success rate by comparing with state-of-the-art trackers.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"7 41 1","pages":"51:1-51:8"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79628659","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: 2

Qualitative spatial and temporal reasoning over diagrams for activity recognition 定性的空间和时间推理图的活动识别

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010015

Chayanika Deka Nath, S. Hazarika

引用次数: 3

Event recognition in broadcast soccer videos 足球转播视频中的事件识别

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010074

Himangi Saraogi, R. Sharma, Vijay Kumar

引用次数: 10

Immersive augmented reality system for assisting needle positioning during ultrasound guided intervention 沉浸式增强现实系统在超声引导干预期间协助针头定位

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010023

P. Kanithi, J. Chatterjee, D. Sheet

{"title":"Immersive augmented reality system for assisting needle positioning during ultrasound guided intervention","authors":"P. Kanithi, J. Chatterjee, D. Sheet","doi":"10.1145/3009977.3010023","DOIUrl":"https://doi.org/10.1145/3009977.3010023","url":null,"abstract":"Ultrasound (US) guided intervention is a surgical procedure where the clinician makes use of imaging in realtime, to track the position of the needle, and correct its trajectory for accurately steering it to the lesion of interest. However, the needle is visible in the US image, only when aligned in-plane with the scanning plane of the US probe. In practice, clinicians often use a mechanical needle guide, thus restricting their available degrees of freedom in the US probe movement. Alternatively, during free-hand procedure, they use multiple needle punctures to achieve this in-plane positioning. Our present work details an augmented reality (AR) system for patient comfort centric aid to needle intervention through an overlaid visualization of the needle trajectory on the US frame prior to its insertion. This is implemented by continuous visual tracking of the US probe and the needle in 3D world coordinate system using fiducial markers. The tracked marker positions are used to draw the needle trajectory and tip visualized in realtime to augment on the US feed. Subsequently, the continuously tracked US probe and needle, and the navigation assistance information, would be overlaid with the visual feed from a head mounted display (HMD) for generating totally immersive AR experience for the clinician.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"72 1","pages":"65:1-65:8"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81876073","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: 9

How much can a Gaussian smoother denoise? 高斯平滑算法能降噪多少?

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010027

S. Gubbi, Ashutosh Gupta, C. Seelamantula

{"title":"How much can a Gaussian smoother denoise?","authors":"S. Gubbi, Ashutosh Gupta, C. Seelamantula","doi":"10.1145/3009977.3010027","DOIUrl":"https://doi.org/10.1145/3009977.3010027","url":null,"abstract":"Recently, a suite of increasingly sophisticated methods have been developed to suppress additive noise from images. Most of these methods take advantage of sparsity of the underlying signal in a specific transform domain to achieve good visual or quantitative results. These methods apply relatively complex statistical modelling techniques to bifurcate the noise from the signal. In this paper, we demonstrate that a spatially adaptive Gaussian smoother could be a very effective solution to the image denoising problem. To derive the optimal parameter estimates for the Gaussian smoothening kernel, we derive and deploy a surrogate of the mean-squared error (MSE) risk similar to the Stein's estimator for Gaussian distributed noise. However, unlike the Stein's estimator or its counterparts for other noise distributions, the proposed generic risk estimator (GenRE) uses only first- and second-order moments of the noise distribution and is agnostic to the exact form of the noise distribution. By locally adapting the parameters of the Gaussian smoother, we obtain a denoising function that has a denoising performance (quantified by the peak signal-to-noise ratio (PSNR)) that is competitive to far more sophisticated methods reported in the literature. To avail the parallelism offered by the proposed method, we also provide a graphics processing unit (GPU) based implementation.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"8 1","pages":"7:1-7:8"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78627775","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: 0