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检索条件"任意字段=International Conference on Computer Vision and Image Processing, CVIP 2016"

共 1407 条记录，以下是411-420 订阅

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Intelligent Digital Built Heritage Models: An Approach from image processing and Building Information Modelling Technology 13

Intelligent Digital Built Heritage Models: An Approach from ...

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13th international Joint conference on computer vision, Imaging and computer Graphics Theory and Applications (VISIGRAPP) / international conference on computer vision Theory and Applications (VISAPP)

作者： de Paiva, Pedro V. V. Cogima, Camila K. Dezen-Kempter, Eloisa de Carvalho, Marco A. G. Cerqueira, Lucas R. Univ Estadual Campinas Sch Technol Limeira SP Brazil

ISBN: (纸本)9789897583063

Conservation and maintenance of historic buildings have exceptional requirements and need a detailed diagnosis and an accurate as-is documentation. This paper reports the use of Unmanned Aerial Vehicle (UAV) imagery to create an Intelligent Digital Built HeritageModel (IDBHM) based on Building Information Modeling (BIM) technology. Our work outlines a model-driven approach based on UAV data acquisition, photogrammetry, post-processing and segmentation of point clouds to promote partial automation of BIM modeling process. The methodology proposed was applied to a historical building facade located in Brazil. A qualitative and quantitative assessment of the proposed segmentation method was undertaken through the comparison between segmented clusters and as-designed documents, also as between point clouds and ground control points. An accurate and detailed parametric IDBHM was created from high-resolution Dense Surface Model (DSM). This Model can improve conservation and rehabilitation works. The results demonstrate that the proposed approach yields good results in terms of effectiveness in the clusters segmentation, compared to the as-designed model.

关键词： BIM Built Heritage image Segmentation Region Growing Point Cloud

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Word spotting based on pyramidal histogram of characters code for handwritten text documents 2nd

Word spotting based on pyramidal histogram of characters cod...

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2nd international conference on computer vision and image processing, cvip 2017

作者： Ali, Tofik Roy, Partha Pratim Department of Computer Science and Engineering Indian Institute of Technology Roorkee RoorkeeUttarakhand India

ISBN: (纸本)9789811078972

In this work, we propose a three-phase convolutional neural network-based approach for word spotting in handwritten text documents. The system uses a reconstructive convolutional neural network model for segmentation of text document into words. The segmented words are converted into pyramidal histogram of characters code for text representation using modified PHOCNet model. Finally, edit distance is used as a similarity measure between query word and word from text repository. The system is capable of answering the query by example as well as query by a string. The proposed model is also very robust and flexible for the availability of handwritten document repository as training data. The proposed model is validated on IAM dataset of handwritten documents which have 1539 different handwritten text documents from 657 writers. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Graphic methods

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Gabor filter meanPCA feature extraction for gender recognition 1

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2nd international conference on computer vision and image processing, cvip 2017

作者： Gupta, Sandeep K. Nain, Neeta Department of Computer Science & Engineering Malaviya National Institute of Technology JaipurRajasthan302017 India

ISBN: (数字)9789811078989

ISBN: (纸本)9789811078972

This paper proposed Novel feature extraction techniques as Gabor-meanPCA for automatic gender recognition using faces of person. Feature extraction is the main stage on which accuracy of gender recognition system depended. Male and female have different edge and texture pattern on faces. Gabor filter is able to extract edges and texture pattern of faces but has a problem of huge dimension and high redundancy. In this paper, Gabor filter is used for extraction of edge pattern of faces using different angles. Problem of huge dimension and high redundancy is reduced by proposed two-level feature reduction technique. The proposed technique also provides better accuracy as well as compact feature vector for reducing classification time. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Feature extraction

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Robust ellipse detection via duality principle with a false determination control 1

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2nd international conference on computer vision and image processing, cvip 2017

作者： Dong, Huixu Chen, I-Ming Prasad, Dilip K. Robotics Research Centre Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore School of Computer Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore

ISBN: (数字)9789811078989

ISBN: (纸本)9789811078972

In this paper, we propose a novel ellipse detection approach that eliminates false detection-based parameter space decomposition, principal of symmetric tangents, and a novel geometric constraint utilizing properties of tangents of ellipses. The principle of symmetric tangents provides better computational efficiency through confirmation of the ellipse center in the decomposed parameter space. The geometric constraint is used for alleviating the false detection probability. The experimental results confirm that the approach detects ellipse with an excellent accuracy at a high speed. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Computational efficiency

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A computer vision approach for lung cancer classification using FNAC-based cytological images 1

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2nd international conference on computer vision and image processing, cvip 2017

作者： Dholey, Moumita Sarkar, Atasi Maity, Maitreya Giri, Amita Sadhu, Anup Chaudhury, Koel Das, Soumen Chatterjee, Jyotirmoy School of Medical Science and Technology Indian Institute of Technology Kharagpur KharagpurWest Bengal721302 India Calcutta National Medical College and Hospital KolkataWest Bengal700014 India EKO Diagnostic Centre Medical College KolkataWest Bengal700073 India

ISBN: (数字)9789811078989

ISBN: (纸本)9789811078972

Lung cancer represents malignant tumour having uncontrolled lung cell growth/proliferation. It can be diagnosed by invasive and non-invasive diagnostic approaches. One of the most effective and accurate approach is Papanicolaou (Pap)-stained cell cytology from fine needle aspiration cytology (FNAC). The manual assessment of cytopathology slides under light microscopy is time-consuming and suffers from feature ambiguities including inter-observer variability. To overcome such problems, the automated cytological analysis is the need of time. This study presents an automated computer vision approach to identify and classify cancerous cell present in microscopic images of Pap smear. The proposed methodology follows colour normalization, image filtering, nucleus segmentation and classification of segmented cells. The nucleus is segmented using the Random Walker with K-means clustering method. The post-processing is carried out on the segmented images to delineate joined nucleus and to remove unwanted regions. Subsequently, multiple nuclear features, i.e. colour, texture and geometric attributes are extracted from each segmented nucleus. After that, a comparative study on supervised classifier selection for the extracted features was adopted towards improving classification accuracy for distinguishing nucleus of non-small cell and small cell lung cancer. Artificial neural network performs best with sensitivity of 97.58 %, specificity of 97.6 %, accuracy of 97.46 %. © Springer Nature Singapore Pte Ltd. 2018.

关键词： computer vision

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image encryption using chaotic 3-D Arnold’s cat map and logistic map 2nd

Image encryption using chaotic 3-D Arnold’s cat map and log...

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2nd international conference on computer vision and image processing, cvip 2017

作者： Musanna, Farhan Rani, Asha Kumar, Sanjeev Department of Mathematics IIT Roorkee Roorkee India Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee247667 India

ISBN: (纸本)9789811078972

image encryption is different from that of traditional texts or binary data because of some inherent properties of images such as large data capacity, i.e., enormous size and high redundancy (statistical and psycho-visual), making them difficult to handle by traditional methods. In recent years, chaos theory has been explored to find efficient ways to develop secure image cryptosystems. Due to the desirable properties of mixing and sensitivity to initial conditions and parameters (butterfly effect), chaotic systems have found great deal in the domain of image encryption. In this paper, the 2-D chaotic cat map is generalized to its 3-D map counterpart for designing a real-time secure and reliable symmetric encryption design. This new scheme deploys the 3-D Arnold’s cat map to shuffle the positions of image pixels and uses the other chaotic logistic map to perplex the relationship between the plain-image and the cipher-image, thereby significantly enhancing the robustness to differential and statistical attacks. Experimental tests are carried out with comprehensive analysis, demonstrating the high security of the scheme. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Chaotic systems

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Wild animal detection using deep convolutional neural network 2nd

Wild animal detection using deep convolutional neural networ...

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2nd international conference on computer vision and image processing, cvip 2017

作者： Verma, Gyanendra K. Gupta, Pragya Department of Computer Engineering National Institute of Technology Kurukshetra Haryana India

ISBN: (纸本)9789811078972

Wildlife monitoring and analysis are an active research field since last many decades. In this paper, we focus on wildlife monitoring and analysis through animal detection from natural scenes acquired by camera-trap networks. The image sequences obtained from camera-trap consist of highly cluttered images that hinder the detection of animal resulting in low-detection rates and high false discovery rates. To handle this problem, we have used a camera-trap database that has candidate animal proposals using multilevel graph cut in the spatiotemporal domain. These proposals are used to create a verification phase that identifies whether a given patch is animal or background. We have designed animal detection model using self-learned Deep Convolutional Neural Network (DCNN) features. This efficient feature set is then used for classification using state-of-the-art machine learning algorithms, namely support vector machine, k-nearest neighbor, and ensemble tree. Our intensive results show that our detection model using DCNN features provides accuracy of 91.4% on standard camera-trap dataset. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Animals

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2nd international conference on computer vision and image processing, cvip 2017

作者： Baheti, Bhakti Kutty, Krishnan Gajre, Suhas Talbar, Sanjay Center of Excellence in Signal and Image Processing Shri Guru Gobind Singhji Institute of Engineering and Technology NandedMaharashtra India Tata Consultancy Services PuneMaharashtra India

ISBN: (纸本)9789811078972

We present a generic vehicle detection approach using a single query image of vehicle to find similar objects in the test image. The proposed method is without any prior training or segmentation of the test image. The approach is based on computing local self-similarity (LSS) descriptors from query and test images that capture local internal geometric layout within the image. Descriptors from query and test image are matched as two-stage process in sliding window framework. In order to exploit usefulness of LSS for generic object detection, we make following contributions: (i) we present few novel ideas to discard the non-informative descriptors to reduce computational expense in feature matching. (ii) We propose a deformation tolerant version of sliding window-based matching framework rather than point-to-point matching. (iii) We also show that selection of landmark points from the query not only makes the algorithm faster but also improves the performance of detection. We evaluate our results on UIUC car dataset, and results clearly outperform earlier training free methods with 91% accuracy. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Object detection

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Multi-scale directional mask pattern for medical image classification and retrieval 2nd

Multi-scale directional mask pattern for medical image class...

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2nd international conference on computer vision and image processing, cvip 2017

作者： Dudhane, Akshay A. Talbar, Sanjay N. Department of Electronics & Telecommunication Engineering SGGSIE&T Nanded India

ISBN: (纸本)9789811078941

This paper presents a classification scheme for interstitial lung disease (ILD) pattern using patch-based approach and artificial neural network (ANN) classifier. A new feature descriptor, Multi-Scale Directional Mask Pattern (MSDMP), is proposed for feature extraction. Proposed MSDMP extracts microstructure information from a (31 × 31) size patches of the region of interest (ROI) which were marked by the radiologists. A two-layer feed-forward neural network is used for classification of ILD patterns. Also, proposed MSDMP feature descriptor has been tested on medical image retrieval system to check its robustness. Two benchmark medical datasets are used to evaluate the proposed descriptor. Performance analysis shows that the proposed feature descriptor outperforms the other existing state-of-the-art methods in terms of average recognition rate (ARR) and F-score. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Multilayer neural networks

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Fast single image learning-based super resolution of medical images using a new analytical solution for reconstruction problem 2nd

Fast single image learning-based super resolution of medical...

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2nd international conference on computer vision and image processing, cvip 2017

作者： Mariyambi, K. Saritha, E. Baburaj, M. KannurKerala670563 India

ISBN: (纸本)9789811078941

The process of retrieving images with high resolution using its low-resolution version is refereed to as super resolution. This paper proposes a fast and efficient algorithm that performs resolution enhancement and denoising of medical images. By using the patch pairs of high- and low-resolution images as database, the super-resolved image is recovered from their decimated, blurred and noise-added version. In this paper, the high-resolution patch to be estimated can be expressed as a sparse linear combination of HR patches over the database. Such linear combination of patches can be modelled as nonnegative quadratic problem. The computational cost of proposed method is reduced by finding closed form solution to the associated image reconstruction problem. Instead of traditional splitting strategy of decimation and convolution process, we decided to use the decimation and blurring operator’s frequency domain properties simultaneously. Simulation result conducted on several images with various noise level shows the potency of our SR approach compared with existing super-resolution techniques. © 2018, Springer Nature Singapore Pte Ltd.

关键词： image reconstruction

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