Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing最新文献_第7页

Robust segmentation of corneal fibers from noisy images 噪声图像中角膜纤维的鲁棒分割

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010051

Jia Chen, J. Jester, M. Gopi

引用次数: 1

Robust pedestrian tracking using improved tracking-learning-detection algorithm 基于改进跟踪-学习-检测算法的鲁棒行人跟踪

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3009999

Ritika Verma, I. Sreedevi

引用次数: 0

A stratified registration framework for DSA artifact reduction using random walker 一种基于随机漫步器的分层配准框架

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010066

Manivannan Sundarapandian, K. Ramakrishnan

引用次数: 0

Iris recognition using partial sum of second order Taylor series expansion 利用二阶泰勒级数展开的部分和进行虹膜识别

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010065

B. H. Shekar, S. S. Bhat

引用次数: 7

Fast frontier detection in indoor environment for monocular SLAM 单目SLAM在室内环境下的快速边界检测

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010063

Sarthak Upadhyay, K. Krishna, S. Kumar

{"title":"Fast frontier detection in indoor environment for monocular SLAM","authors":"Sarthak Upadhyay, K. Krishna, S. Kumar","doi":"10.1145/3009977.3010063","DOIUrl":"https://doi.org/10.1145/3009977.3010063","url":null,"abstract":"Frontier detection is a critical component in autonomous exploration, wherein the robot decides the next best location to move in order to continue its mapping process. The existing frontier detection methods require dense reconstruction which is difficult to attain in a poorly textured indoor environment using a monocular camera. In this effort, we present an alternate method of detecting frontiers during the course of robot motion that circumvents the requirement of dense mapping. Based on the observation that frontiers typically occur around areas with sudden change in texture (zero-crossings), we propose a novel linear chain Conditional Random Field(CRF) formulation that is able to detect the presence or absence of frontier regions around such areas. We use cues like spread of 3D points and scene change around these areas as an observation to CRF. We demonstrate that this method gives us more relevant frontiers compared to other monocular camera based methods in the literature. Finally, we present results in an indoor environment, wherein frontiers are reliably detected around walls leading to new corridors, doors leading to new rooms or corridors and tables and other objects that open up to a new space in rooms.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"75 1","pages":"39:1-39:8"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83794189","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

An image analysis approach for transcription of music played on keyboard-like instruments 一种图像分析方法，用于在类似键盘的乐器上演奏音乐的转录

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010007

Souvik Deb, Ajit V. Rajwade

引用次数: 4

Mosaicing deep underwater imagery 拼接深水图像

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010029

Kuldeep Purohit, Subeesh Vasu, A. Rajagopalan, V. Jyothi, Ramesh Raju

引用次数: 3

Automatic detection of Malaria infected RBCs from a focus stack of bright field microscope slide images 从亮场显微镜载玻片图像聚焦堆栈中自动检测疟疾感染红细胞

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010024

G. Gopakumar, M. Swetha, G. S. Siva, G. R. S. Subrahmanyam

{"title":"Automatic detection of Malaria infected RBCs from a focus stack of bright field microscope slide images","authors":"G. Gopakumar, M. Swetha, G. S. Siva, G. R. S. Subrahmanyam","doi":"10.1145/3009977.3010024","DOIUrl":"https://doi.org/10.1145/3009977.3010024","url":null,"abstract":"Malaria is a deadly infectious disease affecting red blood cells in humans due to the protozoan of type Plasmodium. In 2015, there is an estimated death toll of 438, 000 patients out of the total 214 million malaria cases reported world-wide. Thus, building an accurate automatic system for detecting the malarial cases is beneficial and has huge medical value. This paper addresses the detection of Plasmodium Falciparum infected RBCs from Leishman's stained microscope slide images. Unlike the traditional way of examining a single focused image to detect the parasite, we make use of a focus stack of images collected using a bright field microscope. Rather than the conventional way of extracting the specific features we opt for using Convolutional Neural Network that can directly operate on images bypassing the need for hand-engineered features. We work with image patches at the suspected parasite location there by avoiding the need for cell segmentation. We experiment, report and compare the detection rate received when only a single focused image is used and when operated on the focus stack of images. Altogether the proposed novel approach results in highly accurate malaria detection.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"94 1","pages":"16:1-16:7"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74408271","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

User guided generation of corroded objects 用户引导生成腐蚀对象

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010031

N. Jain, P. Kalra, R. Ranjan, Subodh Kumar

{"title":"User guided generation of corroded objects","authors":"N. Jain, P. Kalra, R. Ranjan, Subodh Kumar","doi":"10.1145/3009977.3010031","DOIUrl":"https://doi.org/10.1145/3009977.3010031","url":null,"abstract":"Rendering of corrosion often requires pain-staking modeling and texturing. On the other hand, there exist techniques for stochastic modeling of corrosion, which can automatically perform simulation and rendering under control of some user-specified parameters. Unfortunately, these parameters are non-intuitive and have a global impact. It is hard to determine the values of these parameters to obtain a desired look. For example, in real life corrosion gets influenced by both internal object-specific geometric factors, like sharp corners and curvatures, and external interventions like scratches, blemishes etc. Further, a graphics designer may want to selectively corrode areas to obtain a particular scene. We present a technique for user guided spread of corrosion. Our framework encapsulates both structural and aesthetic factors. Given the material properties and the surrounding environmental conditions of an object, we employ a physio-chemically based stochastic model to deduce the decay of different points on that object. Our system equips the user with a platform where the imperfections can be provided by either manual or systematic interference on a rendering of the three dimensional object. We demonstrate several user guided characteristic simulations encompassing varied influences including material, object characteristics and environment conditions. Our results are visually validated to understand the impact of imperfections with elapsed time.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"5 1","pages":"89:1-89:8"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82299752","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

Analyzing object categories via novel category ranking measures defined on visual feature embeddings 基于视觉特征嵌入的分类排序方法分析目标类别

Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing Pub Date : 2016-12-18 DOI: 10.1145/3009977.3010037

Ravi Kiran Sarvadevabhatla, Raviteja Meesala, Manjunath Hegde, R. Venkatesh Babu

{"title":"Analyzing object categories via novel category ranking measures defined on visual feature embeddings","authors":"Ravi Kiran Sarvadevabhatla, Raviteja Meesala, Manjunath Hegde, R. Venkatesh Babu","doi":"10.1145/3009977.3010037","DOIUrl":"https://doi.org/10.1145/3009977.3010037","url":null,"abstract":"Visualizing 2-D/3-D embeddings of image features can help gain an intuitive understanding of the image category landscape. However, popular visualization methods of visualizing such embeddings (e.g. color-coding by category) are impractical when the number of categories is large. To address this and other shortcomings, we propose novel quantitative measures defined on image feature embeddings. Each measure produces a ranked ordering of the categories and provides an intuitive vantage point from which to view the entire set of categories. As an experimental testbed, we use deep features obtained from category-epitomes, a recently introduced minimalist visual representation, across 160 object categories. We embed the features in a visualization-friendly yet similarity-preserving 2-D manifold and analyze the inter/intra-category distributions of these embeddings using the proposed measures. Our analysis demonstrates that the category ordering methods enable new insights for the domain of large-category object representations. Moreover, our ordering measure approach is general in nature and can be applied to any feature-based representation of categories.","PeriodicalId":93806,"journal":{"name":"Proceedings. Indian Conference on Computer Vision, Graphics & Image Processing","volume":"53 1","pages":"79:1-79:6"},"PeriodicalIF":0.0,"publicationDate":"2016-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83263374","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