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

共 1494 条记录，以下是161-170 订阅

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A novel floor segmentation algorithm for mobile robot navigation 5

A novel floor segmentation algorithm for mobile robot naviga...

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

作者： Bhowmick, Soumabha Pant, Abhishek Mukherjee, Jayanta Deb, Alok Kanti IIT Kharagpur Adv Technol Dev Ctr Kharagpur W Bengal India IIT Kharagpur Comp Sci & Engn Kharagpur W Bengal India IIT Kharagpur Elect Engn Kharagpur W Bengal India

ISBN: (纸本)9781467385640

The task of detection of floor area for mobile robot navigation has received immense importance over the years. The main challenging problem as long as the path planning of robots is concerned, is the obstacle avoidance. Obstacle detection and avoidance in real time is a complex and computationally expensive process as a result of which the robotics researchers opted to segment out floors, which is comparatively easier process and at the same time very much feasible in real time applications. The proposed floor segmentation algorithm detects floor from a scene irrespective of the change of illumination as well as shadows. A conventional breadth first search based region growing technique has been used with histogram based features in YCbCr color space and floor junction masking to detect the floor pixels from the scene. The algorithm has been compared with various other existing techniques to showcase the improved results of the proposed one.

关键词： Histogram floor junction masking breadth first search

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Greedy Partitioning based Tree Structured Multiclass SVM for Odia OCR 5

Greedy Partitioning based Tree Structured Multiclass SVM for...

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

作者： Sahu, Sandeep Kumar Pujari, Arun K. Kumar, Vikas Kagita, Venkateswara Rao Padmanabhan, Vineet Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 Andhra Pradesh India

ISBN: (纸本)9781467385640

There have been many proposals to extend the basic two-class SVM classifier for multiclass classification and it is established that among these extensions binary-structured hierarchical SVMs is the most efficient computationally. However, determining an effective binary structure by recursively dividing the classes is a major research issue. We describe a new classifier, GP-SVM, based on greedy partitioning of classes and demonstrate that GP-SVM gives better classification accuracy than all major combinational techniques besides having the computational advantages. The advantages of GP-SVM is better realized when the number of classes is large. We demonstrate this advantage in recognition of printed Odia character. We built a corpus of 10025 tagged Odia aksharas collected over multiple printed documents of different fonts. We used a very modest number of features. GP-SVM with 133 classes yielded 95% accuracy of recognition. During the learning process of GP-SVM, the proposed system could learn the taxonomy of character-shapes of Odia script.

关键词： Optical character recognition

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Single image dehazing using image boundary constraint and nearest neighborhood optimization 11

Single image dehazing using image boundary constraint and ne...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Gautam, Sidharth Gandhi, Tapan Kumar Panigrahi, B. K. Indian Inst Technol New Delhi India

ISBN: (纸本)9781450366151

Single image dehazing is an ill-posed problem that requires assumptions, priors and constraints to solve. In this paper, boundary constraint utilizing median filter has been proposed on the image radiance for the rough estimation of transmission-map in haze images. Furthermore, for the refinement of estimated transmission-map, the proposed method use a multi-dimensional feature space that uses nearest neighborhood optimization under the framework of non-local principle. Experimental results manifest that proposed method is effective and results in visually appealing dehaze images that can be useful for subsequent user or computer based application.

关键词： Median filters

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Kernelization of Locality Preserving Discriminant Projection 5

Kernelization of Locality Preserving Discriminant Projection

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

作者： Shikkenawis, Gitam Mitra, Suman K. Dhirubhai Ambani Inst Informat & Commun Technol Gandhinagar 382007 India

ISBN: (纸本)9781467385640

In recent years, non-linear dimensionality reduction approaches have become popular due to their suitability to capture the non-linearities present in the data. However, these are not applicable for recognition related applications as representation of new data points is not defined in the reduced subspace. Hence, to explore the non-linearities of the data, local structure preserving approaches received considerable attention. These approaches keep the local information of the data points intact in the lower dimensional space as well. Locality Preserving Discriminant Projection (LPDP) not only preserves the neighborhood information for each data point, but also tries to discriminate data points from different classes using their class labels. The performance of such neighborhood information preserving dimensionality reduction techniques do not guarantee to capture complex non-linearities present in the data. To address this issue, kernel functions that map the data in a non-linear feature space before applying the dimensionality reduction approach are widely used. This article discusses Kernelization of LPDP which explores complex non-linear changes of face images due to facial expression, illumination, pose and environmental changes. The proposed approach is applied for face recognition on some of the benchmark databases with such variability.

关键词： Face recognition

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Singer Based Classification of Song Dataset Using Vocal Signature Inherent in Signal 5

Singer Based Classification of Song Dataset Using Vocal Sign...

引用

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

作者： Sarkar, Rajib Saha, Sanjoy Kumar Jadavpur Univ Dept Comp Sci & Engn Kolkata India

ISBN: (纸本)9781467385640

Singer based classification of song data is important in the applications like, organized archival and indexing of music data, music retrieval. In a song, singing voice is mixed with accompanying instrument signal. To extract the vocal characteristics of the singer, the effect of non-voiced part is to be minimized. In this work a simple methodology is proposed to remove the non-voiced segments and to reduce the impression of instruments from the voice-dominating signal. To extract the vocal signature, proposed features extract the variation pattern of zero crossing rate and short term energy. In broad sense, the features try to capture the range of pitch and energy over which a singer mostly operates. This is motivated by the way a human being tries to identify a singer. Finally, singer based classification is done using multi-layer perceptron network. Experiment is carried out with artist 20 dataset and 63% classification accuracy is achieved. Comparison with reported works on the same dataset shows that the performance of the proposed simple methodology is better than the majority and very close to others.

关键词： singer identification time domain filtering frequency domain filtering STE and ZCR based feature MFCC

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Multi-resolution image Fusion Using Multistage Guided Filter

Multi-resolution Image Fusion Using Multistage Guided Filter

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4th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Joshi, Sharad Upla, Kishor P. Joshi, Manjunath V. SV Natl Inst Technol Surat 395007 Gujarat India Dhirubhai Ambani Inst Informat & Commun Technol Gandhinagar 382007 Gujarat India

ISBN: (纸本)9781479915880

In this paper, we propose a multi-resolution image fusion approach based on multistage guided filter (MGF). Given the high spatial resolution panchromatic (Pan) and high spectral resolution multi-spectral (MS) images, the multi-resolution image fusion algorithm obtains a single fused image having both the high spectral and the high spatial resolutions. Here, we extract the missing high frequency details of MS image by using multistage guided filter. The detail extraction process exploits the relationship between the Pan and MS images by utilizing one of them as a guidance image and extracting details from the other. This way the spatial distortion of MS image is reduced by consistently combining the details obtained using both types of images. The final fused image is obtained by adding the extracted high frequency details to corresponding MS image. The results of the proposed algorithm are compared with the commonly used traditional methods as well as with a recently proposed method using Quickbird, Ikonos-2 and Worldview-2 satellite images. The quantitative assessment is evaluated using the conventional measures as well as using a relatively new index i. e., quality with no reference (QNR) which does not require a reference image. The results and measures clearly show that there is significant improvement in the quality of the fused image using the proposed approach.

关键词： MULTISTAGE image fusion images Multi-resolution imagery (Psychotherapy) image Filters peroxyacetyl nitrate Mass spectroscopy Multiple Sclerosis

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CHILD: A Robust Computationally-Efficient Histogram-based image Local Descriptor

CHILD: A Robust Computationally-Efficient Histogram-based Im...

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4th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Anamandra, Sai Hareesh Chandrasekaran, V. Sri Sathya Sai Inst Higher Learning Dept Math & Comp Sci Puttaparthi 515134 India

ISBN: (纸本)9781479915880

Designing a robust image local descriptor for the purpose of pattern recognition and classification has been an active area of research. Towards this end, a number of local descriptors based on Weber's law have been proposed recently. Notable among them are Weber Local Descriptor (WLD), Weber Local Binary Pattern (WLBP) and Gabor Weber Local Descriptor (GWLD). Experiments reveal their inability to classify patterns under noisy environments. Our analysis indicates that the components of the WLD: differential excitation and orientation are to be redesigned for robustness and computational efficiency. In this paper, we propose a local Descriptor, called Computationally-Efficient Histogram-based image Local Descriptor (CHILD), which implements the differential excitation component based on LoG as in WLBP and the orientation component based on fractional order derivatives. The novelty of CHILD stems from the fact that both the LoG and the fractional order derivatives are robust against noise. Further, we have used Wasserstein distance metric to compute the distance between the two histograms and a nearest neighbour classifier which are computationally efficient. We have demonstrated that on the benchmark texture database KTH-TIPS2-a, under both noiseless and noisy conditions, the average classification accuracies of the CHILD consistently outperform the popular local descriptors for texture classification quoted in the literature until 2013.

关键词： useful area Descriptors Frontotemporal Dementia Computational efficiency Pattern recognition Noisy environment Robustness imagery (Psychotherapy) image images

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Extraction of Line-Word-Character Segments Directly from Run-Length Compressed Printed Text-Documents

Extraction of Line-Word-Character Segments Directly from Run...

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4th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Javed, Mohammed Nagabhushan, P. Chaudhuri, B. B. Univ Mysore Dept Studies Comp Sci Mysore 570006 Karnataka India Indian Stat Inst Comp Vis & Pattern Recognit Unit Kolkata 700108 India

ISBN: (纸本)9781479915880

Segmentation of a text-document into lines, words and characters, which is considered to be the crucial preprocessing stage in Optical Character Recognition (OCR) is traditionally carried out on uncompressed documents, although most of the documents in real life are available in compressed form, for the reasons such as transmission and storage efficiency. However, this implies that the compressed image should be decompressed, which indents additional computing resources. This limitation has motivated us to take up research in document image analysis using compressed documents. In this paper, we think in a new way to carry out segmentation at line, word and character level in run-length compressed printed-text-documents. We extract the horizontal projection profile curve from the compressed file and using the local minima points perform line segmentation. However, tracing vertical information which leads to tracking words-characters in a run-length compressed file is not very straight forward. Therefore, we propose a novel technique for carrying out simultaneous word and character segmentation by popping out column runs from each row in an intelligent sequence. The proposed algorithms have been validated with 1101 text-lines, 1409 words and 7582 characters from a data-set of 3 5 noise and skew free compressed documents of Bengali, Kannada and English Scripts.

关键词： Compressed document segmentation run-length compression Line word and character segmentation

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Psoriatic Plaque Segmentation in Skin images 5

Psoriatic Plaque Segmentation in Skin Images

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

作者： Pal, Anabik Garain, Utpal Chatterjee, Raghunath Senapati, Swapan ISI CVPRU Kolkata 108 W Bengal India ISI HGU Kolkata 108 W Bengal India

ISBN: (纸本)9781467385640

This paper concerns about automatic psoriatic plaque segmentation in skin images. A novel pixel clustering based image segmentation approach is proposed. The clustering approach works with the circular-linear color spaces which remained unexplored because of the unavailability of appropriate mixture models and hence, the traditional clustering algorithms are incapable of handling such color spaces. This paper makes use of a novel Semi-Wrapped Gaussian Mixture Model (SWGMM) based approach for clustering image pixels. The experiment considers CIE Lch color space which is essentially a circularlinear space. The performance of SWGMM-based segmentation is compared to commonly used Fuzzy C-Means(FCM) and Fuzzy Kernel C-Means (FKCM)-based segmentation methods in linear color space. Results on a newly generated dataset show that SWGMM-based segmentation achieves accuracy of 82.84% in CIE Lch;whereas experiments with three linear color spaces namely, RGB, CIE L*u*v* and CIE L*a*b* show that FKCM and FCM achieve best accuracies of 78.80% and 77.79%, respectively in Lu*v* space. For comparing the clustering-based diseased detection result to a supervised pixel classification based approach, a support vector machines (SVM) based classifier is trained with labelled diseased and normal healthy skin pixels. As the number of images is limited for the present problem, SVM based diseased detection achieves 63.56% accuracy which is significantly lower than the accuracies given by the clustering based approaches.

关键词： Support vector machines

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Large-scale Virtual Texturing on a Distributed Rendering System 5

Large-scale Virtual Texturing on a Distributed Rendering Sys...

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

作者： Revanth, N. R. Narayanan, P. J. Int Inst Informat Technol Ctr Visual Informat Technol Hyderabad 500032 Andhra Pradesh India

ISBN: (纸本)9781467385640

There has been a profound interest of late in the digitization and reconstruction of historical monuments. Rendering massive monument models requires a cluster of workstations because of the computational infeasibility of rendering over a single machine. Moreover, interactive rendering of these massive models in an immersive environment is only possible over a cluster of machines. In this paper, we present a design of distributed rendering system to efficiently handle models with massive textures. A server holds the skeleton of the whole model and divides the screen space balancing the rendering load among multiple clients. Each client loads only the required geometry and textures to render its sub-scene. We present a virtual texturing method for handling massive textures over the distributed rendering system. These textures are combined into a texture atlas which is split into equally sized tiles. A virtual texture is built over this atlas with each pixel representing a tile in the atlas. An efficient caching module loads only the required tiles into the memory, that are identified using the virtual texture. A fragment shader uses the virtual texture as a mapping to the physical texture in memory to generate the fragments. We demonstrate the performance of our system over a 350M triangles and 500 gigapixel textured model of Vittala temple.

关键词： Rendering (computer graphics)

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