Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing最新文献_第2页

Crowd motion analysis for group detection 群体检测的人群运动分析

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010071

Neha Bhargava, S. Chaudhuri

引用次数: 0

TraCount: a deep convolutional neural network for highly overlapping vehicle counting TraCount:用于高度重叠车辆计数的深度卷积神经网络

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010060

Shiv Surya, R. Venkatesh Babu

引用次数: 9

Generic TV advertisement detection using progressively balanced perceptron trees 基于渐进式平衡感知器树的通用电视广告检测

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3009995

Raghvendra Kannao, P. Guha

{"title":"Generic TV advertisement detection using progressively balanced perceptron trees","authors":"Raghvendra Kannao, P. Guha","doi":"10.1145/3009977.3009995","DOIUrl":"https://doi.org/10.1145/3009977.3009995","url":null,"abstract":"Automatic detection of TV advertisements is of paramount importance for various media monitoring agencies. Existing works in this domain have mostly focused on news channels using news specific features. Most commercial products use near copy detection algorithms instead of generic advertisement classification. A generic detector needs to handle inter-class and intra-class imbalances present in data due to variability in content aired across channels and frequent repetition of advertisements. Imbalances present in data make classifiers biased towards one of the classes and thus require special treatment. We propose to use tree of perceptrons to solve this problem. The training data available for each perceptron node is balanced using cluster based over-sampling and TOMEK link cleaning as we traverse the tree downwards. The trained perceptron node then passes the original unbalanced data to its children. This process is repeated recursively till we reach the leaf nodes. We call this new algorithm as \"Progressively Balanced Perceptron Tree\". We have also contributed a TV advertisements dataset consisting of 250 hours of videos recorded from five non-news TV channels of different genres. Experimentations on this dataset have shown that the proposed approach has comparatively superior and balanced performance with respect to six baseline methods. Our proposal generalizes well across channels, with varying training data sizes and achieved a top F1-score of 97% in detecting advertisements.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"15 1","pages":"8:1-8:8"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74600618","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

On improved CS-SS image watermark detection over radio mobile channel 无线移动信道上改进的CS-SS图像水印检测

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010049

A. Bose, S. Maity

{"title":"On improved CS-SS image watermark detection over radio mobile channel","authors":"A. Bose, S. Maity","doi":"10.1145/3009977.3010049","DOIUrl":"https://doi.org/10.1145/3009977.3010049","url":null,"abstract":"Recently compressed sensing or compressive sampling (CS), apart from its intrinsic applications of sub-sample signal reconstruction, is explored a lot in the design of bandwidth preserving-energy efficient wireless networks. At the same time, due to open nature of wireless channel, digital data (media) transmission needs their protection from unauthorized access and digital watermarking has been devised as one form of potential solution over the years. Among the various methods, spread spectrum (SS) watermarking is found to be efficient due to its improved robustness and imperceptibility. SS watermarking on digital images in presence of additive and multiplicative noise is studied a lot. To the best of knowledge, CS-SS watermarking in presence of both multiplicative (fading channel) and additive noise is not explored much in the existing literature. To address this problem, a wireless communication theoretic model is suggested here to develop an improved detection scheme on additive SS image watermark framework. System model considers sub-sample (CS) transmission of the watermarked image over both non-fading and fading channel. Then a diversity assisted weighted combining scheme for the improved watermark detection is developed. An optimization problem is formulated where the weight for the individual link is calculated through eigen filter approach to maximize the watermark detection probability for a fixed false alarm rate under the constraint of an embedding power (strength). A large set of simulation results validate the mathematical model of the diversity assisted compressive watermark detector.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"6 1","pages":"60:1-60:8"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83817376","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

DeepFly: towards complete autonomous navigation of MAVs with monocular camera DeepFly:用单目相机实现无人机的完全自主导航

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010047

Utsav Shah, Rishabh Khawad, K. Krishna

{"title":"DeepFly: towards complete autonomous navigation of MAVs with monocular camera","authors":"Utsav Shah, Rishabh Khawad, K. Krishna","doi":"10.1145/3009977.3010047","DOIUrl":"https://doi.org/10.1145/3009977.3010047","url":null,"abstract":"Recently, the interest in Micro Aerial Vehicles (MAVs) and their autonomous flights has increased tremendously and significant advances have been made. The monocular camera has turned out to be most popular sensing modality for MAVs as it is light-weight, does not consume more power, and encodes rich information about the environment around. In this paper, we present DeepFly, our framework for autonomous navigation of a quadcopter equipped with monocular camera. The navigable space detection and waypoint selection are fundamental components of autonomous navigation system. They have broader meaning than just detecting and avoiding immediate obstacles. Finding the navigable space emphasizes equally on avoiding obstacles and detecting ideal regions to move next to. The ideal region can be defined by two properties: 1) All the points in the region have approximately same high depth value and 2) The area covered by the points of the region in the disparity map is considerably large. The waypoints selected from these navigable spaces assure collision-free path which is safer than path obtained from other waypoint selection methods which do not consider neighboring information.\u0000 In our approach, we obtain a dense disparity map by performing a translation maneuver. This disparity map is input to a deep neural network which predicts bounding boxes for multiple navigable regions. Our deep convolutional neural network with shortcut connections regresses variable number of outputs without any complex architectural add on. Our autonomous navigation approach has been successfully tested in both indoors and outdoors environment and in range of lighting conditions.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"27 1","pages":"59:1-59:8"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83063301","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: 20

Robust registration of Mouse brain slices with severe histological artifacts 具有严重组织伪影的小鼠脑切片的鲁棒配准

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010053

Nitin Agarwal, Xiangmin Xu, M. Gopi

{"title":"Robust registration of Mouse brain slices with severe histological artifacts","authors":"Nitin Agarwal, Xiangmin Xu, M. Gopi","doi":"10.1145/3009977.3010053","DOIUrl":"https://doi.org/10.1145/3009977.3010053","url":null,"abstract":"Brain mapping research is facilitated by first aligning digital images of mouse brain slices to standardized atlas framework such as the Allen Reference Atlas (ARA). However, conventional processing of these brain slices introduces many histological artifacts such as tears and missing regions in the tissue, which make the automatic alignment process extremely challenging. We present an end-to-end fully automatic registration pipeline for alignment of digital images of mouse brain slices that may have histological artifacts, to a standardized atlas space. We use a geometric approach where we first align the bounding box of convex hulls of brain slice contours and atlas template contours, which are extracted using a variant of Canny edge detector. We then detect the artifacts using Constrained Delaunay Triangulation (CDT) and remove them from the contours before performing global alignment of points using iterative closest point (ICP). This is followed by a final non-linear registration by solving the Laplace's equation with Dirichlet boundary conditions. We tested our algorithm on 200 mouse brain slice images including slices acquired from conventional processing techniques having major histological artifacts, and from serial two-photon tomography (STPT) with no major artifacts. We show significant improvement over other registration techniques, both qualitatively and quantitatively, in all slices especially on slices with significant histological artifacts.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"36 1","pages":"10:1-10:8"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83158190","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

First quantization matrix estimation for double compressed JPEG images utilizing novel DCT histogram selection strategy 首先利用新的DCT直方图选择策略对双压缩JPEG图像进行量化矩阵估计

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010067

N. Dalmia, M. Okade

{"title":"First quantization matrix estimation for double compressed JPEG images utilizing novel DCT histogram selection strategy","authors":"N. Dalmia, M. Okade","doi":"10.1145/3009977.3010067","DOIUrl":"https://doi.org/10.1145/3009977.3010067","url":null,"abstract":"The Double JPEG problem in image forensics has been gaining importance since it involves two compression cycles and there is a possibility of tampering having taken place after the first cycle thereby calling for accurate methods to detect and localize the introduced tamper. First quantization matrix estimation which basically retrieves the missing quantization table of the first cycle is one of the ways of image authentication for Double JPEG images. This paper presents a robust method for first quantization matrix estimation in case of double compressed JPEG images by improving the selection strategy which chooses the quantization estimate from the filtered DCT histograms. The selection strategy is made robust by increasing the available statistics utilizing the DCT coefficients from the double compressed image under investigation coupled with performing relative comparison between the obtained histograms followed by a novel priority assignment and selection step, which accurately estimates the first quantization value. Experimental testing and comparative analysis with two state-of-art methods show the robustness of the proposed method for accurate first quantization estimation. The proposed method finds its application in image forensics as well as in steganalysis.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"1 1","pages":"27:1-27:8"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90108629","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

Hierarchical spectral clustering based large margin classification of visually correlated categories 基于层次光谱聚类的视觉相关类目大余量分类

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010064

Digbalay Bose, S. Chaudhuri

引用次数: 1

Overlapping cell nuclei segmentation in microscopic images using deep belief networks 基于深度信念网络的显微图像重叠细胞核分割

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010043

Rahul Duggal, Anubha Gupta, Ritu Gupta, Manya Wadhwa, Chirag Ahuja

引用次数: 56

Event recognition in egocentric videos using a novel trajectory based feature 基于轨迹特征的自我中心视频事件识别

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010011

Vinodh Buddubariki, Sunitha Gowd Tulluri, Snehasis Mukherjee

{"title":"Event recognition in egocentric videos using a novel trajectory based feature","authors":"Vinodh Buddubariki, Sunitha Gowd Tulluri, Snehasis Mukherjee","doi":"10.1145/3009977.3010011","DOIUrl":"https://doi.org/10.1145/3009977.3010011","url":null,"abstract":"This paper proposes an approach for event recognition in Egocentric videos using dense trajectories over Gradient Flow - Space Time Interest Point (GF-STIP) feature. We focus on recognizing events of diverse categories (including indoor and outdoor activities, sports and social activities and adventures) in egocentric videos. We introduce a dataset with diverse egocentric events, as all the existing egocentric activity recognition datasets consist of indoor videos only. The dataset introduced in this paper contains 102 videos with 9 different events (containing indoor and outdoor videos with varying lighting conditions). We extract Space Time Interest Points (STIP) from each frame of the video. The interest points are taken as the lead pixels and Gradient-Weighted Optical Flow (GWOF) features are calculated on the lead pixels by multiplying the optical flow measure and the magnitude of gradient at the pixel, to obtain the GF-STIP feature. We construct pose descriptors with the GF-STIP feature. We use the GF-STIP descriptors for recognizing events in egocentric videos with three different approaches: following a Bag of Words (BoW) model, implementing Fisher Vectors and obtaining dense trajectories for the videos. We show that the dense trajectory features based on the proposed GF-STIP descriptors enhance the efficacy of the event recognition system in egocentric videos.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"82 1","pages":"76:1-76:8"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83921090","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