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共 2008 条记录，以下是281-290 订阅

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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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2-D Dual Tree Complex Wavelet Transform (DT-CWT) Based Fingerprint Ridge Structure Quality Enhancement 5

2-D Dual Tree Complex Wavelet Transform (DT-CWT) Based Finge...

引用

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

作者： Kundargi, Jayshree M. Karandikar, R. G. KJ Somaiya Coll Engn Dept Elect Engn Bombay 77 Maharashtra India KJ Somaiya Coll Engn Dept Elect & Tele Engn Bombay 77 Maharashtra India KJ Somaiya Coll Engn Bombay 77 Maharashtra India

ISBN: (纸本)9781467385640

Nowadays fingerprint is widely used to ascertain identity of an individual. the performance of an automated fingerprint verification system depends on the quality of the fingerprint image captured by the sensor. In this paper we propose fingerprint ridge structure quality enhancement using 2D Dual Tree Complex Wavelet Transform (DT-CWT). the DT-CWT provides multiscale description of fingerprint images with good directional selectivity. the DT-CWT transforms fingerprint images into six directionally selective subbands at multiple scales. the wavelet coefficients in all subbands are compensated adaptively based on the compensation coefficients obtained from the singular values of subbands of fingerprint image and a referred Gaussian template. the original image is then reconstructed using inverse DT-CWT. the experimental results show that the DT-CWT is more effective than the Discrete Wavelet Transform in terms of improvement in singular point detection accuracy.

关键词： Fingerprint verification system quality of fingerprint image 2-D Dual Tree Complex Wavelet Transform Discrete Wavelet Transform Gaussian template singular point detection

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Depth estimation from single image using defocus and texture cues 5

Depth estimation from single image using defocus and texture...

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

作者： Srikakulapu, Vivek Kumar, Himanshu Gupta, Sumana Venkatesh, K. S.

ISBN: (纸本)9781467385640

As imaging is a process of 2D projection of a 3D scene, the depth information is lost at the time of image capture from conventional camera. this depth information can be inferred back from a set of visual cues present in the image. In this work, we present a model that combines two monocular depth cues namely Texture and Defocus. Depth is related to the spatial extent of the defocus blur by assuming that more an object is blurred, the farther it is from the camera. At first, we estimate the amount of defocus blur present at edge pixels of an image. this is referred as the Sparse Defocus map. Using the sparse defocus map we generate the full defocus map. However such defocus maps always contain hole regions and ambiguity in depth. To handle this problem an additional depth cue, in our case texture has been integrated to generate better defocus map. this integration mainly focuses on modifying the erroneous regions in defocus map by using the texture energy present at that region. the sparse defocus map is corrected using texture based rules. the hole regions, where there are no significant edges and texture are detected and corrected in sparse defocus map. We have used region wise propagation for better defocus map generation. the accuracy of full defocus map is increased with the region wise propagation.

关键词： Cameras

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Pose and Illumination Invariant Face Recognition using Binocular Stereo 3D Reconstruction 5

Pose and Illumination Invariant Face Recognition using Binoc...

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

作者： Nigam, Aditya Chhalotre, Gitesh Gupta, Phalguni Indian Inst Technol Mandi Sch Comp & Elect Engn Suran 175001 India Indian Inst Technol Kanpur Dept CSE Kanpur 208016 Uttar Pradesh India NITTTR CSE Dept Kolkata 700106 India

ISBN: (纸本)9781467385640

Traditional 2D face recognition systems drastically fails with pose variance and poor illuminations. Many techniques but with limited success has been introduced. Expensive 3D setup can be used to deal with this problem. In this work a low cost, low computation and quick good quality 3D reconstruction helping 2D face recognition systems is proposed. the proposed system is a fast automatic 3D face reconstruction approach from rectified stereo images. An automatic synthesis of training images of various face poses is proposed. three enhancements adaptive histogram equalization (AHE) to improve contrast of face images, horizontal gradient ordinal relationship pattern(HGORP) to handle poor illumination and steerable filter(SF) for noise reduction and illumination invariance are used to improve the system performance. Later SURF based matching is done with score level fusion of all three enhancements. A database of 107 subjects has been collected to evaluate the system performance. It is observed that the proposed system can handle large pose variations and poor illumination very well.

关键词： Face recognition

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A Spectral Filtering Based Deep Learning for Detection of Logo and Stamp 5

A Spectral Filtering Based Deep Learning for Detection of Lo...

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

作者： Nandedkar, Amit V. Mukherjee, Jayanta Sural, Shamik IIT Kharagpur Sch Informat Technol Kharagpur W Bengal India IIT Kharagpur Dept Comp Sci & Engn Kharagpur W Bengal India

ISBN: (纸本)9781467385640

this paper presents a novel spectral filtering based deep learning algorithm (SFDL) for detecting logos and stamps in a scanned document image. In a document image, textual contents are main source of high spatial frequency components. Accordingly, the high frequency filtering is used to suppress the text symbols. In the next step, segmentation process is used for localizing the candidate regions of interests such as logos and stamps. Preprocessing of these candidate regions is essential before classification. the proposed preprocessing includes steps such as region fusion, resizing and key point based pooling. Finally, the preprocessed candidate regions are classified using deep convolutional neural network. the main advantage of the SFDL is its capability to detect logos without prior information or assumption about their locations in a document. the performance of the proposed SFDL algorithm is evaluated using publicly accessible document image database StaVer. It is observed that SFDL performs satisfactorily for detecting logo and stamp. the precision and recall measures of the proposed SFDL are compared with existing techniques. Experimental results show that recall and precision of logo detection are 86.8%, 97.2%, respectively. Similarly, recall and precision for stamp detection are 85.3% and 94.8%.

关键词： Learning algorithms

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Medical image analysis an attempt for mammogram classification using texture based association rule mining

Medical image analysis an attempt for mammogram classificati...

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

作者： Deshpande, Deepa S. Rajurkar, Archana M. Manthalkar, Ramchandra M. MGMs Jawaharlal Nehru Engn Coll Dept Comp Sci & Engn Aurangabad Maharashtra India MGMs Coll Engn Dept Comp Sci & Engn Nanded India SGGS Inst Engn & Technol Dept Elect & Telecommun Engn Nanded India

ISBN: (纸本)9781479915880

Breast cancer, the most common type of cancer in women is one of the leading causes of cancer deaths. Due to this, early detection of cancer is the major concern for cancer treatment. the most common screening test called mammography is useful for early detection of cancer. It has been proven that there is potential raise in the cancers detected due to consecutive reading of mammograms. But this approach is not monetarily viable. therefore there is a significant need of computer aided detection systems which can produce intended results and assist medical staff for accurate diagnosis. In this research we made an attempt to build classification system for mammograms using association rule mining based on texture features. the proposed system uses most relevant GLCM based texture features of mammograms. New method is proposed to form associations among different texture features by judging the importance of different features. Resultant associations can be used for classification of mammograms. Experiments are carried out using MIAS image Database. the performance of the proposed method is compared with standard Apriori algorithm. It is found that performance of proposed method is better due to reduction in multiple times scanning of database which results in less computation time. We also investigated the use of association rules in the field of medical image analysis for the problem of mammogram classification.

关键词： Data mining Minimum support Association Rule Mining(ARM) Frequent feature set

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Efficient Adaptive Weighted Minimization for Compressed Sensing Magnetic Resonance image Reconstruction 16

Efficient Adaptive Weighted Minimization for Compressed Sens...

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Datta, Sumit Deka, Bhabesh Tezpur Univ Dept ECE CVIP Lab Tezpur 784028 Assam India

ISBN: (纸本)9781450347532

Compressed sensing magnetic resonance imaging (CSMRI) have demonstrated that it is possible to accelerate MRI scan time by reducing the number of measurements in the k-space without significant loss of anatomical details. the number of k-space measurements is roughly proportional to the sparsity of the MR signal under consideration. Recently, a few works on CSMRI have revealed that the sparsity of the MR signal can be enhanced by suitable weighting of different regularization priors. In this paper, we have proposed an efficient adaptive weighted reconstruction algorithm for the enhancement of sparsity of the MR image. Experimental results show that the proposed algorithm gives better reconstructions with less number of measurements without significant increase of the computational time compared to existing algorithms in this line.

关键词： Magnetic resonance imaging compressed sensing l(1)-norm total variation regularization image reconstruction

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A scalable projective bundle adjustment algorithm using the L norm

A scalable projective bundle adjustment algorithm using the ...

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6th indian conference on computer vision, graphics and image processing, ICVGIP 2008

作者： Mitra, Kaushik Chellappa, Rama Dept. of Electrical and Computer Engineering College Park MD United States

ISBN: (纸本)9780769534763

the traditional bundle adjustment algorithm for structure from motion problem has a computational complexity of O((m + n)3) per iteration and memory requirement of O(mn(m+n)), wheremis the number of cameras and n is the number of structure points. the sparse version of bundle adjustment has a computational complexity of O(m3+mn) per iteration and memory requirement of O(mn). Here we propose an algorithm that has a computational complexity of O(mn(vm + vn)) per iteration and memory requirement of O(max(m, n)). the proposed algorithm is based on minimizing the L norm of reprojection error. It alternately estimates the camera and structure parameters, thus reducing the potentially large scale optimization problem to many small scale subproblems each of which is a quasiconvex optimization problem and hence can be solved globally. Experiments using synthetic and real data show that the proposed algorithm gives good performance in terms of minimizing the reprojection error and also has a good convergence rate.© 2008 IEEE.

关键词： Computational complexity

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TraCount: A Deep Convolutional Neural Network for Highly Overlapping Vehicle Counting 16

TraCount: A Deep Convolutional Neural Network for Highly Ove...

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Surya, Shiv Babu, Venkatesh R. Indian Inst Sci Bangalore Karnataka India

ISBN: (纸本)9781450347532

We propose a novel deep framework, TraCount, for highly overlapping vehicle counting in congested traffic scenes. TraCount uses multiple fully convolutional(FC) sub-networks to predict the density map for a given static image of a traffic scene. the different FC sub-networks provide a range in size of receptive fields that enable us to count vehicles whose perspective effect varies significantly in a scene due to the large visual field of surveillance cameras. the predictions of different FC sub-networks are fused by weighted averaging to obtain a final density map. We show that TraCount outperforms the state of the art methods on the challenging TRANCOS dataset that has a total of 46796 vehicles annotated across 1244 images.

关键词： Convolutional neural networks deep learning vehicle counting

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A Combined Fuzzy and Level Sets' Based Approach for Brain MRI image Segmentation

A Combined Fuzzy and Level Sets' Based Approach for Brain MR...

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

作者： Anami, Basavaraj S. Unki, Prakash H. KLE Inst Technol Hubli Karnataka India BLDEAs Dr PGH Coll Engn & Technol Bijapur Karnataka India

ISBN: (纸本)9781479915880

the different tissues namely gray matter (GM) white matter (WM), and cerebrospinal fluid (CSF) are spread over the entire brain. It is difficult to demarcate them individually when a brain image is considered. the boundaries are not well defined. Modified fuzzy C means (MFCM) and level sets segmentation based methodology is proposed in this paper for automated brain MRI image segmentation into WM, GM and CSF. the initial segmentation is done by MFCM approach and the results thus obtained are input to the level set methodology. We have tested the methodology on 100 different brain MRI images. the results are compared by using individual MFCM and level set segmentation methods. We took the opinion of 10 expert radiologists to corroborate our results. the results are validated by radiologists as 'Accurate', 'Satisfactory', 'Adequate' and 'Not acceptable'. the results obtained using only level set are 'not acceptable'. Most of the results obtained using MFCM are 'Adequate'. the results obtained using combined method are 'Satisfactory'. Hence, the results obtained using combined MFCM and level sets based segmentation are considered better than using individual MFCM and level set segmentation methods. the manual intervention is avoided in the combined approach. the time required to segment using combined approach is also less compared to level set method. the segmentation using proposed methodology is helpful for radiologists in hospitals for brain MRI image analysis.

关键词： brain MRI level set FCM segmentation

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