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共 2018 条记录，以下是1421-1430 订阅

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Shape Recognition based on Shape-Signature Identification and Condensibility: Application to Underwater imagery

Shape Recognition based on Shape-Signature Identification an...

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

作者： Banerjee, Jeet Ray, Ranjit Vadali, Siva Ram Krishna Layek, Ritwik Kumar Shome, Sankar Nath Indian Inst Technol Elect & Elect Commun Dept Kharagpur 721302 W Bengal India CSIR Cent Mech Engn Res Inst Robot & Automat Div Durgapur 713209 W Bengal India

ISBN: (纸本)9781479915880

In this paper, a shape recognition method is proposed for a few common geometrical shapes including straight line, circle, ellipse, triangle, quadrilateral, pentagon and hexagon. In the present work, two indices namely Unique Shape Signature (USS) and Condensibility (C) are employed for shape recognition of an object. Using the USS index, all the above mentioned non-circular shapes are neatly recognized, whereas, the C index recognized the circular objects. An added advantage of the proposed method is that it can further differentiate triangles, quadrilaterals and both symmetric and non-symmetric shapes of pentagon and hexagon using distance variance (Var(d(si))) parameter calculated from USS. Applying the proposed method on above mentioned shapes, an overall recognition rate of 98.80% is achieved on several simulated and real objects of different shapes. Proposed method has also been compared with two existing methods, presents better result. Performance of the proposed method is illustrated by applying it on underwater images and it is observed to perform satisfactory on all the images under test.

关键词： C (programming language)

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Adaptive BPSO based Feature Selection and Skin Detection based Background Removal for Enhanced Face Recognition

Adaptive BPSO based Feature Selection and Skin Detection bas...

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

作者： Sattiraju, Mayukh Vikram, Manikandan M. Manikantan, K. Ramachandran, S. MS Ramaiah Inst Tech Dept Elect & Commun Engn Bangalore 560054 Karnataka India SJB Inst Tech Dept Elect & Commun Engn Bangalore 560060 Karnataka India

ISBN: (纸本)9781479915880

Face recognition under varying background and pose is challenging, and extracting background and pose invariant features is an effective approach to solve this problem. this paper proposes a skin detection-based approach for enhancing the performance of a Face Recognition (FR) system, employing a unique combination of Skin based background removal, Discrete Wavelet Transform (DWT), Adaptive Multi-Level threshold Binary Particle Swarm Optimization (ABPSO) and an Error Control Feedback (ECF) loop. Skin based background removal is used for efficient background removal and ABPSO-based feature selection algorithm is used to search the feature space for the optimal feature subset. the ECF loop is used to neutralize pose variations. Experimental results, obtained by applying the proposed algorithm on Color FERET and CMUPIE face databases, show that the proposed system outperforms other FR systems. A significant increase in the recognition rate and substantial reduction in the number of features are observed.

关键词： Face Recognition image Preprocessing Feature Selection Particle Swarm Optimization

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Real-Time Approximate and Exact CSG of Implicit Surfaces on the GPU

Real-Time Approximate and Exact CSG of Implicit Surfaces on ...

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

作者： Singh, Jag Mohan Qualcomm Res India Hyderabad Andhra Pradesh India

ISBN: (纸本)9781479915880

We present a simple and powerful scheme to allow CSG of implicit surfaces on the GPU. We decompose the boolean expression of surfaces into sum-of-products form. Our algorithm presented in this paper then renders each product term, sum of products can be automatically by enabling depth test. Our Approximate CSG uses adaptive marching points algorithm for finding ray-surface intersection. Once we find an interval where root exists after root-isolation, this is used for presence of intersection. We perform root-refinement only for the uncomplemented terms in the product. Exact CSG is done by using the discriminant of the ray-surface intersection for the presence of the root. Now we can simply evaluate the product expression by checking all uncomplemented terms should be true and all complemented terms should be false. If our condition is met, we find the maximum of all the roots among uncomplemented terms to be the solution. Our algorithm is linear in the number of terms O(n). We achieve real-time rates for 4-5 terms in the product for approximate CSG. We achieve more than real-time rates for Exact CSG. Our primitives are implicit surfaces so we can achieve fairly complex results with less terms.

关键词： Implicit GRAPPER PICK UP graphics processing Unit Root Approximate Intersection Crossing Exact Real time Discriminant Boolean expression

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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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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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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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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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Pan-sharpening Based On Non-subsampled Contourlet Transform Detail Extraction

Pan-sharpening Based On Non-subsampled Contourlet Transform ...

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

作者： Upla, Kishor P. Gajjar, Prakash P. Joshi, Manjunath V. SV Natl Inst Technol Surat 395007 Gujarat India Govt Polytech Ahmadabad 380015 Gujarat India Dhirubhai Ambani Inst Infor & Commun Tech Gandhinagar 382007 Gujarat India

ISBN: (纸本)9781479915880

In this paper, we propose a new pan-sharpening method using Non-subsampled Contourlet Transform. the panchromatic (Pan) and multi-spectral (MS) images provided by many satellites have high spatial and high spectral resolutions, respectively. the pan-sharpened image which has high spatial and spectral resolutions is obtained by using these images. Since the NSCT is shift invariant and it has better directional decomposition capability compared to contourlet transform, we use it to extract high frequency information from the available Pan image. First, two level NSCT decomposition is performed on the Pan image which has high spatial resolution. the required high frequency details are obtained by using the coarser subband available after the two level NSCT decomposition of the Pan image. the coarser sub-band is subtracted from the original Pan image to obtain these details. these extracted details are then added to MS image such that the original spectral signature is preserved in the final fused image. the experiments have been conducted on images captured from different satellite sensors such as IKonos-2, Worlview-2 and Quickbird. the traditional quantitative measures along with quality with no reference (QNR) index are evaluated to check the potential of the proposed method. the proposed approach performs better compared to the recently proposed state of the art methods such as additive wavelet luminance proportional (AWLP) method and context based decision (CBD) method.

关键词： images profile transformation peroxyacetyl nitrate Spectral resolution state of the art image imagery (Psychotherapy) subband

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Multi-sensor image Fusion Based on Statistical Features and Wavelet Transform

Multi-sensor Image Fusion Based on Statistical Features and ...

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

作者： Pramanik, Sourav Bhattacharjee, Debotosh Prusty, Swagatika Natl Inst Sci & Technol Dept Comp Sci & Engn Berhampur Orissa India Jadavpur Univ Dept Comp Sci & Engn Kolkata India

ISBN: (纸本)9781479915880

In this paper, we have proposed a novel feature based fusion algorithm which integrates the fusion technique of low pass and high pass wavelet coefficients. First we have decomposed source images into discrete wavelet decomposition coefficients. thereafter, approximation coefficients are partition into n number of blocks and features are extracted from each block in the coefficients. Two same correspondence blocks features are then combined to make a feature matrix M. Finally, the weight is computed by applying the principle component analysis (PCA) on the feature matrix M to fuse the same corresponding blocks. After fusion of n blocks, they are combined to get final fused low pass coefficients. In the next phase, all the details or high pass coefficients are fused by computing the relative information of the coefficients and activity measure of each pixel in the neighborhood. Finally, the new coefficient matrix is obtained by concatenating fused approximation and details coefficients and the fused image is reconstructed using inverse wavelet transform. To verify the superiority of our proposed algorithm, we have tested it on 100 pair different sensor images collected from Manchester University UK databases. Finally, the proposed algorithm is compared with one existing algorithm and it performs better in terms of subjective and objective perception.

关键词： relative information block-based features activity measure moment wavelet transform

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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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