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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"

共 1497 条记录，以下是761-770 订阅

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Integrated Algorithm for Different Tracking Challenges 5

Integrated Algorithm for Different Tracking Challenges

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Shah, Jay Sai Subrahmanyam, G. R. K. Mishra, Deepak Indian Inst Space Sci & Technol Dept Avion Trivandrum 695547 Kerala India Indian Inst Space Sci & Technol Dept Earth & Space Sci Trivandrum 695547 Kerala India

ISBN: (纸本)9781467385640

This paper presents an efficient combination of two well-known tracking algorithms, Tracking-Learning-Detection (TLD) and Compressive Tracking (CT) to devise an algorithm which takes advantages of both and outperforms them on their short-ends by virtue of other. TLD fails in cases including full out-of-plane rotation, fast motion and articulated object tracking. While CT fails in resuming tracking once the object leaves the frame and comes back. We propose a combining algorithm mentioned as Algorithm 1, which robustly handles all the tracking challenges. Different thresholds are set which can be varied to weigh each component as required. The proposed algorithm is tested on different test sequences involving challenging tracking scenarios such as fast motion and their success rates are calculated in Table I. The proposed algorithm works favourably against both algorithms in terms of robustness and success rate.

关键词： Tracking (position)

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A Hierarchical Class-Grouping Approach, and a Study of Classification Strategies for Leaf Classification 5

A Hierarchical Class-Grouping Approach, and a Study of Class...

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Prajapati, Ravinder Bhavsar, Arnav Sao, Anil Indian Inst Technol Sch Comp & Elect Engn Mandi India

ISBN: (纸本)9781467385640

Fine-grained visual classification has been considered for image data in various domains of environmental importance such as birds, animals and plants. This work considers the classification problem of the latter, based on the leaf shape. Traditional works in such areas typically propose better features, or sophisticated classification frameworks. In this work, we ask a different question: Given simple and efficient features, and a well-known binary classifier such as support vector machine (SVM), among various strategies, what may be a good way to pose the multi-class classification problem as multiple binary classifications ? In this respect, we compare three different strategies, all of which use the same set of features. From our results, we conclude that, one of these three approaches, based on hierarchical class-grouping, clearly outperforms the others, with high classification accuracy. This suggest that classification strategy is an important aspect for the given features and classifiers. To our knowledge, such a study in the fine-grained classification area (and particularly for the nascent area of leafclassification), has not yet been explored.

关键词： Classification (of information)

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An Optimal Underwater image Enhancement based on Fuzzy Gray World Algorithm and Bacterial Foraging Algorithm 5

An Optimal Underwater Image Enhancement based on Fuzzy Gray ...

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Sethi, Rajni Sreedevi, Indu Verma, O. P. Jain, Veni Delhi Technol Univ CSE Dept Delhi India Delhi Technol Univ ECE Dept Delhi India CPA Global Noida Uttar Pradesh India

ISBN: (纸本)9781467385640

Underwater images suffer from non uniform contrast and poor visibility due to bad illumination and color cast in deep water. Such images have a hazy and color diminished appearance making underwater studies a difficult task. Researches in last decades performed color correction, assuming that underwater images have bluish color cast which is not always true. In this paper, a new image enhancement approach is proposed which modifies the gray world algorithm by finding the color cast using fuzzy logic and then removing the color cast by optimizing the correction method using Bacterial Foraging Optimisation (BFO). Proposed approach is adaptive in nature as it finds the intensity of color cast instead of assuming it which improves the quality of underwater images. Computed results have enhanced visual details, contrast and color performance.

关键词： image Enhancement Fuzzy logic Gray World Assumption Multiobjective BFO Underwater images Color correction

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A Framework For Dynamic Hand Gesture Recognition Using Key Frames Extraction 5

A Framework For Dynamic Hand Gesture Recognition Using Key F...

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Pathak, Bhumika Jalal, Anand Singh Agrawal, Subhash Chand Bhatnagar, Charul GLA Univ Mathura India

ISBN: (纸本)9781467385640

Hand Gesture Recognition is one of the natural ways of human computer interaction (HCI) which has wide range of technological as well as social applications. A dynamic hand gesture can be characterized by its shape, position and movement. This paper presents a user independent framework for dynamic hand gesture recognition in which a novel algorithm for extraction of key frames is proposed. This algorithm is based on the change in hand shape and position, to find out the most important and distinguishing frames from the video of the hand gesture, using certain parameters and dynamic threshold. For classification, Multiclass Support Vector Machine (MSVM) is used. Experiments using the videos of hand gestures of indian Sign Language show the effectiveness of the proposed system for various dynamic hand gestures. The use of key frame extraction algorithm speeds up the system by selecting essential frames and therefore eliminating extra computation on redundant frames.

关键词： Hand gesture recognition key frame extraction sign language recognition muliclass support vector machine skin color segmentation

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A Robust Watermarking Scheme Against Re-compression Attack for H.265/HEVC 5

A Robust Watermarking Scheme Against Re-compression Attack f...

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Gaj, Sibaji Sur, Arijit Bora, Prabin Kumar Indian Inst Technol Dept CSE Multimedia Lab Gauhati 781039 Assam India Indian Inst Technol Dept EEE Gauhati 781039 Assam India

ISBN: (纸本)9781467385640

In this paper, a compressed domain blind watermarking scheme is proposed which embeds the watermark by altering the number of nonzero transform co-efficients (NNZ) of 4 x 4 transform blocks of the HEVC video sequence. To embed the watermark, firstly, temporally homogeneous blocks having relatively less motion are selected. In this work, watermark is inserted in the Intra (I) frame and the motion characteristics of the I frame has been determined using the motion information of the Inter (P or B) predicted frames of its close neighborhood. The watermark is embedded by altering the NNZ difference of 4 x 4 transform blocks in the consecutive intra predicted frames. A comprehensive set of experiments is carried out to show that the scheme is robust against re-compression attacks while maintaining a descent visual quality (PSNR), the bit increase rate (BIR) of the watermarked video.

关键词： Watermarking

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Gaussian Mixture Model for Summarization of Surveillance Videos 5

Gaussian Mixture Model for Summarization of Surveillance Vid...

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Sujatha, C. Mudenagudi, Uma BVB Coll Engn & Tech Dept CSE Hubli 580031 India BVB Coll Engn & Tech Deparment ECE Hubli 580031 India

ISBN: (纸本)9781467385640

We propose a method to address the problem of Video Summarization, which aims to generate a summarized video by preserving the salient activities of the input video for a user specified time. We model the motion of a feature points as Gaussian Mixture Model (GMM) to select the key feature points, which in-turn estimate the salient frames. The saliency of feature points depends on the contribution of motion in entire video and user specified time duration of summary. We generate a summarized video keeping chronology of salient frames to avoid the viewing ambiguity for the viewers. We demonstrate the proposed method for different stored surveillance videos and achieve retention ratio as 1 for the closest condensation ratio obtained for stroboscopic approach and also demonstrate the proposed GMM method with interactively selected region of interest (ROI) based results.

关键词： Motion of feature point Optical flow GMM Chronology of activities ROI

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Automated Detection and Classification of Mass from Breast Ultrasound images 5

Automated Detection and Classification of Mass from Breast U...

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Menon, Radhika V. Raha, Poulami Kothari, Shweta Chakraborty, Sumit Chakrabarti, Indrajit Karim, Rezaul IIT Kharagpur Dept E & ECE Kharagpur W Bengal India IPGMER SSKM Kolkata India CMSDH Kolkata India

ISBN: (纸本)9781467385640

This paper introduces a computer aided diagnosis (CAD) technique for segmentation of mass in breast ultrasound (BUS) images followed by an efficient classification of the image into benign or malignant one. The presence of speckle noise, low contrast and blurred boundary of mass in a BUS image makes it challenging to determine the mass, which is the region of interest (ROI) in the current work. Detecting an accurate ROI in turn results in efficient feature extraction and classification. In current work, image enhancement and speckle noise reduction are implemented for preprocessing in a simple but efficient way through filtering techniques. The results of the preprocessing stage are as effective as those obtained using traditional speckle reduction anisotropic diffusion (SRAD) algorithm. ROI is then accurately determined on preprocessed image by employing local region based active contour method. BUS images are classified through textural, morphological and histogram oriented feature metrics in this work. The obtained features are dimensionally reduced using principal component analysis (PCA) and classified through support vector machine (SVM) method. The proposed method is tested on several images and found to be very effective having an accuracy of 95.7% with very high specificity and positive predictive value (PPV).

关键词： breast ultrasound CAD active contours geometric features PCA classification SVM

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Framework for 3D Object Hole Filling 5

Framework for 3D Object Hole Filling

引用

5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Setty, Shankar Ganihar, Syed Altaf Mudenagudi, Uma BV Bhoomaraddi Coll Engn & Technol Hubli India

ISBN: (纸本)9781467385640

In this paper we address the problem of hole filling in a point cloud of 3D object. Even with most popular 3D scanning devices like Microsoft Kinect and Time of Flight (ToF) cameras, occlusions during the scanning process result in occurrence of missing regions or holes in 3D data. We propose a framework for hole filling in a point cloud of 3D object using Riemannian metric tensor and Christoffel symbols as a set of geometric features, which capture the inherent geometry of the 3D object. The framework involves detection and extraction of the boundary points surrounding the hole, decomposition of boundary points into basic shapes and selective surface interpolation to fill the hole. We demonstrate the performance of the proposed method on point clouds with different complexities and sizes for both synthetically generated holes and real missing regions during the capturing process on 3D models of heritage sites.

关键词： Filling

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OHCS: A Database for Handwritten Atomic Odia Character Recognition 5

OHCS: A Database for Handwritten Atomic Odia Character Recog...

引用

5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Mohapatra, Ramesh Kumar Mishra, Tusar Kanti Panda, Sandeep Majhi, Banshidhar Natl Inst Technol Comp Sci & Engn Rourkela 769008 India

ISBN: (纸本)9781467385640

In this paper, a complete database of handwritten atomic Odia characters is suggested. The first version of the database has been modeled and named OHCSv1.0 (Odia handwritten character set). The database comprises of 17,100 transcribed characters, each collected twice from 150 unique people at different point of time. Each character has 300 number of occurrences. The character images are standardized to a size of 6 4 x 6 4 pixels. A novel framework for perceiving transcribed Odia characters from this database has also been proposed. The character images are gathered into various groups in view of their shape components utilizing an incremental spectral clustering algorithm. During testing, affinity of probe character to a cluster is first decided. Subsequently, the trained classifier recognizes the character inside the cluster. Suitable simulation has been carried out to validate the scheme.

关键词： Character recognition

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Classification of Brain MR images using Discrete Wavelet Transform and Random Forests 5

Classification of Brain MR Images using Discrete Wavelet Tra...

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Nayak, Deepak Ranjan Dash, Ratnakar Majhi, Banshidhar Natl Inst Technol Dept Comp Sci & Engn Rourkela 769008 India

ISBN: (纸本)9781467385640

Development of computer-aided diagnosis (CAD) systems for early detection of the pathological brain is essential to save medical resources. In recent years, a variety of techniques have been proposed to upgrade the system's performance. In this paper, a new automatic CAD system for brain magnetic resonance (MR) image classification is proposed. The method utilizes two-dimensional discrete wavelet transform to extract features from the MR images. The dimension of the features have been reduced using principal component analysis (PCA) and linear discriminant analysis (LDA), to obtain the more significant features. Finally, the reduced set of features are applied to the random forests classifier to determine the normal or pathological brain. A standard dataset, Dataset-255 of 255 images (35 normal and 220 pathological) is used for the validation of the proposed scheme. To improve the generalization capability of the scheme, 5-fold stratified cross-validation procedure is utilized. The results of the experiments reveal that the proposed scheme is superior to other state-of-the-art techniques in terms of classification accuracy with substantially reduced number of features.

关键词： Discrete wavelet transforms

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