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检索条件"任意字段=Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing"

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A Color Based image Segmentation and its Application to Text Segmentation

A Color Based Image Segmentation and its Application to Text...

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： Anandarup Roy Swapan Kumar Parui Amitav Paul Utpal Roy CVPR Unit Indian Statistical Institute Kolkata India DCSS Visva Bharati University India

The goal of this article is twofold. First, it deals with color image segmentation in hue-saturation space. A model for circular data is provided by the vM-Gauss distribution, which is a joint distribution of von-Mises and Gaussian distributions. The mixture of vM-Gauss distributions is used to model hue-saturation data. After segmentation, a post processing based on both spectral and spatial similarity of clusters is applied to separate such identifiable objects in the image. The results and comparisons are shown on Berkeley segmentation dataset. The problem of text extraction from a color image is taken as an application of the proposed method. We use a laboratory made text image dataset to test the method.

关键词： image segmentation Virtual manufacturing Clustering algorithms Parameter estimation Gaussian distribution Data mining image processing Testing image color analysis computer vision

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A Framework for Analysis of Surveillance Videos

A Framework for Analysis of Surveillance Videos

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： Ayesha Choudhary Santanu Chaudhury Subhashis Banerjee Department of Computer Science and Engineering Indian Institute of Technology New Delhi India Department of Electrical Engneering Indian Institute of Technology New Delhi India

In this paper, we propose a novel framework for automated analysis of surveillance videos. By analysis, we imply summarizing and mining of the information in the video for learning usual patterns and discovering unusual ones. We approach this video analysis problem by acknowledging that a video contains information at multiple levels and in multiple attributes. Each such component and co-occurrences of these component values play an important role in characterizing an event as usual or unusual. Therefore, we cluster the video data at multiple levels of abstraction and in multiple attributes and view these clusters as a summary of the information in the video. We apply cluster algebra to mine this summary from multiple perspectives and to adapt association learning for automated selection of components because of which the event is unusual. We also propose a novel incremental clustering algorithm.

关键词： Surveillance Videos Information analysis image analysis Pattern analysis Algebra Humans computer vision computer graphics image processing

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Color Harmonization for Videos

Color Harmonization for Videos

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： Nikhil Sawant Niloy J. Mitra Department of CSE Indian Institute of Technology New Delhi India

Color harmonization is an artistic technique to adjust the colors of a given image in order to enhance their visual harmony. In this paper, we present a method to automatically improve the color harmony of images. Harmonization is performed using a carefully designed optimization in the hue space, while keeping the saturation and intensity components unchanged. Finally, for videos, we pose the problem as an efficient joint optimization in space and time, thus minimizing flickering or visual artifacts in the harmonized output video. We report the performance of our algorithm on a variety of test images and video sequences.

关键词： Videos

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Multiple Objects Tracking via Topology Adaptive Active Membrane

Multiple Objects Tracking via Topology Adaptive Active Membr...

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： Sitansu Kumar Das Dipti Prasad Mukherjee Electronics and Communication Sciences Unit Indian Statistical Institute Kolkata India

Tracking multiple deformable objects simultaneously in a video is a challenging problem. The tracking is more difficult when the objects are touching each other while they are moving or suddenly appear in or disappear from a frame of the video. In this paper we propose a parametric active membrane, which can change its topology to detect and track multiple objects present in the video. The membrane evolves in image space and also along the image intensity surface and if requires, splits into multiple membranes to track multiple objects. The methodology is tested on real video segments that demonstrates the efficacy of the proposed scheme.

关键词： Topology Biomembranes Layout Shape TV Object detection image segmentation Object recognition Finite element methods computer vision

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Implementation of the "Local Rank Differences" image Feature Using SIMD Instructions of CPU

Implementation of the "Local Rank Differences" Image Feature...

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： Adam Herout Pavel Zemcik Roman Juranek Michal Hradis Graph FIT Brno University of Technology Czech Republic

Usage of statistical classifiers, namely AdaBoost and its modifications, in object detection and pattern recognition is a contemporary and popular trend. The computatiponal performance of these classifiers largely depends on low level image features they are using: both from the point of view of the amount of information the feature provides and the executional time of its evaluation. Local rank difference is an image feature that is alternative to commonly used Haar features. It is suitable for implementation in programmable (FPGA) or specialized (ASIC) hardware as well as graphics hardware (GPU). Additionally, as shown in this paper, it performs very well on common CPUpsilas. The paper discusses the LRD features and their properties, describes an experimental implementation of LRD using the multimedia instruction set of current general-purpose processors, presents its empirical performance measures compared to alternative approaches, and suggests several notes on practical usage of LRD and proposes directions for future work.

关键词： Detectors Face detection computer vision Hardware computer graphics image processing Object detection Pattern recognition Application software Humans

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Incorporating Cohort Information for Reliable Palmprint Authentication

Incorporating Cohort Information for Reliable Palmprint Auth...

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： Ajay Kumar Biometrics Research Laboratory Department of Electrical Engineering Indian Institute of Technology Delhi New Delhi India

This paper presents a new approach to achieve the performance improvement for the traditional palmprint authentication approaches. The cohort information is used in the matching stage but only when the matching scores are inadequate to generate reliable decisions. The cohort information can also be utilized to achieve the significant performance improvement for the combination of modalities and this is demonstrated from the experimental results in this paper. The rigorous palmprint authentication results presented in this paper are the best in the literature and confirm the utility of significant information that can be extracted from the imposter scores. The statistical estimation of confidence level for the palmprint matching requires an excellent match between the theoretical distribution and the real score distribution. The performance analysis presented in this paper, from over 29.96 million imposter matching scores, suggests that Beta-Binomial function can more accurately model the distribution of real palmprint matching scores.

关键词： Authentication Spatial databases Data mining Biometrics image databases Feature extraction Iris computer vision computer graphics image processing

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JPEG2000 Compatible Watermarking of Text in images

JPEG2000 Compatible Watermarking of Text in Images

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： K.A. Navas M.L. Aravind D.M. Sasikumar College of Engineering Trivandrum India Marian Engineering College Trivandrum India

In most of the image processing applications there is a maximum limit up to which an image can be compressed without adversely affecting the quality of the image to a great deal. In this paper, we propose a new watermarking scheme to embed text in images using an algorithm for finding the optimum bit plane that can be substituted from the image by the character bit plane. The maximum embedding capacity is kept as a function of minimum expected quality. This watermarking method is experimentally found to be highly robust against many image processing attacks including JPEG 2000 as it takes in to consideration both the lossy stages of JPEG 2000 during its implementation.

关键词： Watermarking image coding Transform coding Discrete wavelet transforms image processing Educational institutions Streaming media Robustness Entropy coding Bit rate

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A Novel Key-Frame Detection Technique Using Statistical Run Test and Majority Voting

A Novel Key-Frame Detection Technique Using Statistical Run ...

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： Partha Pratim Mohanta Sanjoy Kumar Saha Bhabatosh Chanda Indian Statistical Institute ECS Unit Kolkata India CSE Department Jadavpur University Kolkata India

Detection of representative frames, also called key-frames, is essential for efficient indexing, browsing and retrieval of video data and also for video summarization. Once a video stream is segmented into shots, the representative frames or key-frames for the shot are selected. The number of such frames in a shot may vary depending on the variation in the content. Thus, for a wide variety of shots automatic selection of suitable number of representative frames still remains a challenge. In this work, we propose a novel scheme for key-frame detection by dividing an available shot into subshots using hypothesis testing and majority voting. Each subshot is supposed to be uniform in terms of visual content. Then for each subshot, the frame rendering the highest fidelity is extracted as the key-frame. Experimental result shows that the scheme works satisfactorily for a wide variety of shots.

关键词： Testing Voting Indexing Gunshot detection systems Information retrieval Streaming media Cameras computer vision computer graphics image processing

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Integrated Detect-Track Framework for Multi-view Face Detection in Video

Integrated Detect-Track Framework for Multi-view Face Detect...

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： Anoop Kolar Rajagopal Paresh Anandathirtha K.R. Ramakrishnan Mohan S. Kankanhalli Dept of Electr. Eng. Indian Inst. of Sci. Bangalore Dept of Electrical Engineering Indian Institute of Science Bangalore India School of Computing National Univ. of Singapore Singapore

An Experiential sampling and Meanshift tracker based Multi-view face detection in video is proposed in this paper. In this framework, instead of performing face detection at every position in a frame, we determine certain key positions to run the multi-view face detectors. These key positions are statistical samples drawn from a density function that is estimated based on color cues, past detection results, Meanshift tracker results and a temporal continuity model. These samples are then propogated using a Particle filter framework. We use a Meanshift tracker to track faces that are missed by the multiview face detectors. Our framework results in a significant reduction in computation time and accounts for the detection of complete 180 degree pose of the face. We also come up with a novel likelihood measure for track termination, which becomes important when used for detection purposes.

关键词： Face detection Detectors computer vision Robustness computer graphics image processing Videoconference image sampling Density functional theory Particle filters

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Object Category Detection by Statistical Test of Hypothesis

Object Category Detection by Statistical Test of Hypothesis

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： Gaurav Sharma Santanu Chaudhury J.B. Srivastava Department of Mathematics Indian Institute of Technology Delhi India Department of Electrical Engineering Indian Institute of Technology Delhi India

We propose a novel framework for object detection and localization in images containing appreciable clutter and occlusions. The problem is cast in a statistical hypothesis testing framework. The image under test is converted into a set of local features using affine invariant local region detectors, described using the popular SIFT descriptor. Due to clutter and occlusions, this set is expected to contain features which do not belong to the object. We sample subsets of local features from this set and test for the alternate hypothesis of object present against the null hypothesis of object absent. Further, we use a method similar to the recently proposed spatial scan statistic to refine the object localization estimates obtained from the sampling process. We demonstrate the results of our method on the two datasets TUD Motorbikes and TUD Cars. TUD Cars database has background clutter. TUD Motorbikes dataset is recognized to have substantial variation in terms of scale, background, illumination, viewpoint and occlusions.

关键词： Object detection Testing Statistics computer vision Motorcycles Lighting Application software Layout computer graphics image processing

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