Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing最新文献_第3页

Detection and segmentation of mirror-like surfaces using structured illumination 使用结构照明的镜面检测和分割

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010020

R. Aggarwal, A. Namboodiri

引用次数: 1

Limitations with measuring performance of techniques for abnormality localization in surveillance video and how to overcome them? 监控视频异常定位技术测量性能的局限性及如何克服?

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010044

M. Sharma, S. Sarcar, D. Sheet, P. Biswas

{"title":"Limitations with measuring performance of techniques for abnormality localization in surveillance video and how to overcome them?","authors":"M. Sharma, S. Sarcar, D. Sheet, P. Biswas","doi":"10.1145/3009977.3010044","DOIUrl":"https://doi.org/10.1145/3009977.3010044","url":null,"abstract":"Now a days video surveillance is becoming more popular due to global security concerns and with the increasing need for effective monitoring of public places. The key goal of video surveillance is to detect suspicious or abnormal behavior. Various efforts have been made to detect an abnormality in the video. Further to these advancements, there is a need for better techniques for evaluation of abnormality localization in video surveillance. Existing technique mainly uses forty percent overlap rule with ground-truth data, and does not considers the extra predicted region into the computation. Existing metrics have been found to be inaccurate when more than one region is present within the frame which may or may not be correctly localized or marked as abnormal. This work attempts to bridge these limitations in existing metrics. In this paper, we investigate three existing metrics and discuss their benefits and limitations for evaluating localization of abnormality in video. We further extend the existing work by introducing penalty functions and substantiate the validity of proposed metrics with a sufficient number of instances. The presented metric are validated on data (35 different situations) for which the overlap has been computed analytically.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"3 1","pages":"75:1-75:8"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82196394","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

Reinforced random forest 强化随机森林

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010003

Angshuman Paul, D. Mukherjee

引用次数: 12

Deep image inpainting with region prediction at hierarchical scales 基于层次尺度区域预测的深度图像绘制

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3009992

Souradeep Chakraborty, Jogendra Nath Kundu, R. Venkatesh Babu

{"title":"Deep image inpainting with region prediction at hierarchical scales","authors":"Souradeep Chakraborty, Jogendra Nath Kundu, R. Venkatesh Babu","doi":"10.1145/3009977.3009992","DOIUrl":"https://doi.org/10.1145/3009977.3009992","url":null,"abstract":"In this paper, we propose a CNN based method for image inpainting, which utilizes the inpaintings generated at different hierarchical resolutions. Firstly, we begin with the prediction of the missing image region with larger contextual information at the lowest resolution using deconv layers. Next, we refine the predicted region at greater hierarchical scales by imposing gradually reduced contextual information surrounding the predicted region by training different CNNs. Thus, our method not only utilizes information from different hierarchical resolutions but also intelligently leverages the context information at different hierarchy to produce better inpainted image. The individual models are trained jointly, using loss functions placed at intermediate layers. Finally, the CNN generated image region is sharpened using the unsharp masking operation, followed by intensity matching with the contextual region, to produce visually consistent and appealing inpaintings with more prominent edges. Comparison of our method with well-known inpainting methods, on the Caltech 101 objects dataset, demonstrates the quantitative and qualitative strengths of our method over the others.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"60 1","pages":"33:1-33:8"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81366487","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

Spectral decomposition and progressive reconstruction of scalar volumes 标量体积的光谱分解与递进重建

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010017

Uddipan Mukherjee

{"title":"Spectral decomposition and progressive reconstruction of scalar volumes","authors":"Uddipan Mukherjee","doi":"10.1145/3009977.3010017","DOIUrl":"https://doi.org/10.1145/3009977.3010017","url":null,"abstract":"Modern 3D imaging technologies often generate large scale volume datasets that may be represented as 3-way tensors. These volume datasets are usually compressed for compact storage, and interactive visual analysis of the data warrants efficient decompression techniques at real time. Using well known tensor decomposition techniques like CP or Tucker decomposition the volume data can be represented by a few basis vectors, the number of such vectors, called the rank of the tensor, determining the visual quality. However, in such methods, the basis vectors used between successive ranks are completely different, thereby requiring a complete recomputation of basis vectors whenever the visual quality needs to be altered. In this work, a new progressive decomposition technique is introduced for scalar volumes wherein new basis vectors are added to the already existing lower rank basis vectors. Large scale datasets are usually divided into bricks of smaller size and each such brick is represented in a compressed form. The bases used for the different bricks are data dependent and are completely different from one another. The decomposition method introduced here uses the same basis vectors for all the bricks at all hierarchical levels of detail. The basis vectors are data independent thereby minimizing storage and allowing fast data reconstruction.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"56 1","pages":"31:1-31:8"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82888717","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

Efficient adaptive weighted minimization for compressed sensing magnetic resonance image reconstruction 压缩感知磁共振图像重构的有效自适应加权最小化

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3009991

S. Datta, B. Deka

引用次数: 5

Prototypes based discriminative appearance model for object tracking 基于原型的判别外观模型用于目标跟踪

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3009994

Ajoy Mondal, Ashish Ghosh, Susmita K. Ghosh

{"title":"Prototypes based discriminative appearance model for object tracking","authors":"Ajoy Mondal, Ashish Ghosh, Susmita K. Ghosh","doi":"10.1145/3009977.3009994","DOIUrl":"https://doi.org/10.1145/3009977.3009994","url":null,"abstract":"Occlusion is one of the major challenges for object tracking in real life scenario. Various techniques in particle filter framework have been developed to solve this problem. This framework depends on two issues: motion model and observation (likelihood) model. Due to the lack of effective observation model and efficient motion model, problem of occlusion still remains unsolvable in the tracking task. In this article, an effective observation model is proposed based on confidence (classification) score provided by the developing online prototypes based discriminative appearance model. This appearance model is constructed with the prior knowledge of two classes (object and background) and tries to discriminate between three classes such as object, background and occluded part of the object. The considered composite motion model can handle both the object motion as well as scale change of the object. The proposed update mechanism is able to adapt the appearance changes during tracking. We show a realization of the proposed method and demonstrate its performance (both quantitatively and qualitatively) with respect to state-of-the-art techniques on several challenging sequences. Analysis of the results concludes that the proposed technique can track (fully or partially) occluded objects as well as objects in various complex environments in a better way as compared to the existing ones.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"71 1","pages":"2:1-2:8"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91150084","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: 3

Low complexity encoder for feedback-channel-free distributed video coding using deep convolutional neural networks at the decoder 采用深度卷积神经网络解码器的无反馈信道分布式视频编码低复杂度编码器

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3009986

Pudi Raj Bhagath, J. Mukherjee, Sudipta Mukopadhayay

{"title":"Low complexity encoder for feedback-channel-free distributed video coding using deep convolutional neural networks at the decoder","authors":"Pudi Raj Bhagath, J. Mukherjee, Sudipta Mukopadhayay","doi":"10.1145/3009977.3009986","DOIUrl":"https://doi.org/10.1145/3009977.3009986","url":null,"abstract":"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.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"15 4 1","pages":"44:1-44:7"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91263863","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: 3

Automated brain tractography segmentation using curvature points 使用曲率点的自动脑束图分割

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010013

Vedang Patel, Anand Parmar, A. Bhavsar, A. Nigam

引用次数: 5

A context dependent fragile watermarking scheme for tamper detection from demosaicked color images 基于上下文的彩色图像篡改检测脆弱水印方案

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3009987

V. Manikandan, V. Masilamani

{"title":"A context dependent fragile watermarking scheme for tamper detection from demosaicked color images","authors":"V. Manikandan, V. Masilamani","doi":"10.1145/3009977.3009987","DOIUrl":"https://doi.org/10.1145/3009977.3009987","url":null,"abstract":"Fragile watermarking schemes are very common in practice for tamper detection and image authentication. In this paper, we propose a new fragile watermarking scheme to detect common image tampering operations such as copy and paste attack and image splicing attack from color images. The proposed scheme is intended to do the watermarking with an image dependent authentication code during color image demosaicking procedure. We propose a new method to generate almost unique authentication code from an image by comparing RGB color components of the pixels. In the proposed scheme, every pixel will be watermarked with an encrypted authentication code derived from the same pixel. To embed the watermark a new method has been proposed in this paper, where the color filter array sampled components will not be considered during watermarking, and only the rebuilt color component values of every pixel will be modified according to the authentication code. The experimental study shows that the proposed watermarking scheme produces images with better visual quality as compared to the state-of-the-art, and is capable of detecting both copy and paste attacks and image splicing attacks more accurately than the existing schemes.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"22 1","pages":"61:1-61:8"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73108587","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: 6