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2012 19th International Conference on Systems, Signals and image processing, IWSSIP 2012

作者： Shalunts, Gayane Haxhimusa, Yll Sablatnig, Robert Institute of Computer Aided Automation Computer Vision Lab. Vienna University of Technology Austria Institute of Computer Graphics and Algorithms Pattern Recognition and Image Processing Lab. Vienna University of Technology Austria

ISBN: (纸本)9783200023284

Architectural elements are the components and details of buildings. Their unique set, combination, design, construction technique form the architectural style of buildings. Building facade classification by architectural styles is viewed as a task of classifying separate architectural structural elements. In the scope of building facade architectural style classification the current paper targets the problem of classification of Gothic and Baroque architectural elements called tracery, pediment and balustrade. Since certain gradient directions dominate on the shape of each architectural element, discrimination between dominating gradients means classification of architectural elements and thus architectural styles. We use local features to describe gradient directions. Our approach is based on clustering and learning of local features and yields a high classification rate. © 2012 Institute of Telecommunica.

关键词： Classification (of information)

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Large Pose Invariant Face Recognition Using Feature-based Recurrent Neural Network

Large Pose Invariant Face Recognition Using Feature-based Re...

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International Joint Conference on Neural Networks

作者： Teddy Salan Khan M. Iftekharuddin Intelligent Systems and Image Processing Lab Department of Electrical and Computer Engineering University of Memphis Vision Lab Department of Electrical and Computer Engineering Old Dominion University

ISBN: (纸本)9781467314886

Cellular Simultaneous Recurrent Network (CSRN) is a novel bio-inspired recurrent neural network that mimics reinforcement learning in the brain. CSRN has been proven to be a powerful tool for learning and predicting temporal information in face image sequences. In this work, we propose a novel implementation of feature-based CSRN for large-scale pose invariant face recognition. We also report systematic evaluation and performance comparison of our feature-based CSRN method with other well-known standard algorithms (PCA, LDA, Bayesian Classifier and EBGM) using face recognition technology standards for large-scale pose invariant face recognition.

关键词： Large pose invariant face recognition Cellular Simultaneous Neural Network Feature extraction Motion unit Face Recognition Vendor Test Face Recognition Technology (FERET) program

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Classification of gothic and baroque architectural elements

Classification of gothic and baroque architectural elements

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International Conference on Systems, Signals and image processing, IWSSIP

作者： Gayane Shalunts Yll Haxhimusa Robert Sablatnig Institute of Computer Aided Automation Computer Vision Laboratory University of Technology Vienna Austria Institute of Computer Graphics and Algorithms Pattern Recognition and Image Processing Lab University of Technology Vienna Austria

关键词： Shape Three dimensional displays Visualization computer vision Histograms Windows

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Automated region growing for segmentation of brain lesion in diffusion-weighted MRI

Automated region growing for segmentation of brain lesion in...

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2012 International MultiConference of Engineers and computer Scientists, IMECS 2012

作者： Mohd Saad, N. Abu-Bakar, S.A.R. Muda, Sobri Mokji, M. Abdullah, A.R. Electronics and Computer Engineering Department Universiti Teknikal Malaysia Melaka 76100 Durian Tunggal Melaka Malaysia Computer Vision Video and Image Processing Research Lab. Faculty of Electrical Engineering Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia Radiology Department Medical Faculty Universiti Kebangsaan Malaysia Jalan Yaacob Latif 56000 Cheras Kuala Lumpur Malaysia Electrical Engineering Department Universiti Teknikal Malaysia Melaka 76100 Durian Tunggal Melaka Malaysia

ISBN: (纸本)9789881925114

This paper presents an automatic segmentation of brain lesions from diffusion-weighted magnetic resonance imaging (DW-MRI or DWI) using region growing approach. The lesions are acute infarction, haemorrhage, tumour and abscess. Region splitting and merging is used to detect the lesion region. Then, histogram thresholding technique is applied to automate the seeds selection. The region is iteratively grown by comparing all unallocated neighbour pixels to the seeds. The difference between pixel's intensity value and the region's mean is used as the similarity measure. Evaluation is made for performance comparison between automatic and manual seeds selection. Overall, automated region growing algorithm provides comparable results with the semi-automatic segmentation.

关键词： Magnetic resonance imaging

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Small-size lung nodule modeling and detection with clinical evaluation

Small-size lung nodule modeling and detection with clinical ...

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Cairo International Biomedical Engineering Conference, CIBEC

作者： Amal Farag James Graham Hossam Abdelmunim Salwa Elshazly Mohamed Ei-Mogy Sabry Ei-Mogy Robert Falk Aly A. Farag Computer Vision and Image Processing Laboratory (CVIP Lab) University of Louisville Louisville KY Computer & Systems Engineering Department Ain Shams University Cairo Mogy-Scan Center Mansoura Egypt Jewish Hospital and 3DR Louisville Kentucky

In this paper examination of the template modeling process using the Active Appearance Modeling (AAM) approach for automatic detection of lung nodules is investigated. A template matching approach is formulated to compute a similarity score between the AAM templates and the input lung CT slice, where the goal is to maximize the similarity measure at different image pixels to increase nodule detection. The template matching approach is implemented using nine similarity measures. Performance validation for the robustness of the generated models is tested on three clinical databases.

关键词： Lungs Databases Computed tomography Principal component analysis Computational modeling Solid modeling

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An AAM-based detection approach of lung nodules from LDCT scans

An AAM-based detection approach of lung nodules from LDCT sc...

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IEEE International Symposium on Biomedical Imaging

作者： Amal Farag Hossam Abdelmunim James Graham Aly A. Farag Cambron Carter Salwa Elshazly Mohamed S. El-Mogy Sabry El-Mogy Robert Falk Computer Vision and Image Processing Laboratory (CVIP Lab) University of Louisville Louisville KY 40292 University of Louisville Louisville KY US Mogy Scan Center Mansoura Egypt Jewish Hospital and 3DR Louisville Kentucky

ISBN: (纸本)9781457718571

In this, paper a new approach for lung nodules detection from LDCT scans is proposed. Intensity models of the nodules are generated using an active appearance model formulation. Template matching is used to compute a similarity score between the AAM template and the input image. The goal is to maximize the similarity measure at different image pixels to increase nodule detection. Conventional template matching does not account for rotation variations. Our proposed template matching approach is formulated as an energy optimization problem that computes a transformation that includes rotation(s) parameters as well as the AAM weighting coefficients. The approach is flexible to different scans and different nodule locations because of the ability to handle the variations in the rotation between the template and the input images. The approach can employ different similarity measures. Experimental results will be shown using three similarity measures from the literature: NCC, ZNCC and ZSSD; which illustrate the efficiency of the proposed approach. ROC curves for various nodule types are constructed on a clinical study with known ground truth, showing significant enhancements over conventional parametric nodule models and traditional template matching criterion.

关键词： Lungs Computational modeling Active appearance model Solid modeling Correlation Computed tomography Shape

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Performance evaluation on segmentation methods for medical images 11

Performance evaluation on segmentation methods for medical i...

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Proceedings of the International Conference on Advances in Computing and Artificial Intelligence

作者： Khare, Manish Srivastava, Rajneesh Kumar Khare, Ashish Image Processing and Computer Vision Lab. Department of Electronics and Communication University of Allahabad India

ISBN: (纸本)9781450306355

This paper presents the performance evaluation of different segmentation algorithms for medical images. Accuracy and clarity are very important issues for medical imaging and same in the case with segmentation. In this paper, we have proposed a level set method without reinitialization with some specific shapes for segmentation and compared our proposed approach for segmentation with three other approaches where the first approach is based on region splitting based region growing category, second is based on region merging based region growing category and third is based on level set. This comparison is based on six different performance parameters namely Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Maximum Difference (MD), Normalized Cross Correlation (NCC), Normalized Absolute Error (NAE) and Structural Content (SC). We have compared the proposed approach with three above mentioned approaches for several images, out of which results for four images are provided in this paper, and we find that the approach is better than the other one. © 2011 ACM.

关键词： image segmentation

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Fully automated region growing segmentation of brain lesion in diffusion-weighted MRI

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2012年第2期39卷 155-164页

作者： Mohd Saad, N. Abu-Bakar, S.A.R. Muda, Sobri Mokji, M. Abdullah, A.R. Electronics and Computer Engineering Department Universiti Teknikal Malaysia Melaka 76100 Durian Tunggal Melaka Malaysia Faculty of Electrical Engineering Computer Vision Video and Image Processing Research Lab Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia Radiology Department Medical Faculty Universiti Kebangsaan Malaysia 56000 Cheras Kuala Lumpur Malaysia Electrical Engineering Department Universiti Teknikal Malaysia Melaka 76100 Durian Tunggal Melaka Malaysia

This paper presents a fully automatic segmentation of brain lesions from diffusion-weighted magnetic resonance imaging (DW-MRI or DWI). The lesions are infarction, hemorrhage, tumor and abscess. Pre-processing stage is performed for intensity normalization, background removal and intensity enhancement. Then, split and merge algorithm is designed. Several statistical features are discussed and evaluated to select the best feature as homogeneity criteria. Lesions are segmented by merging the homogenous regions according to the selected criteria. This process produces blocky lesion region. Then, histogram thresholding is acquired to automate the seeds selection for region growing process. The region is iteratively grown by comparing all unallocated neighboring pixels to the seeds. The difference between pixel's intensity value and the region's mean is used as the similarity measure. The proposed segmentation technique has been validated by using misclassified area (MA), false positive rate (FPR), false negative rate (FNR), mean absolute percentage error (MAPE) and pixel absolute error ratio (rerr), and compared with previous methods. The result shows that automatic region growing method can successfully segment the lesions and is suitable for analysis and classification of DWI.

关键词： Magnetic resonance imaging

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Car plate segmentation based on morphological and labeling approach

Car plate segmentation based on morphological and labeling a...

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2011 Computation and Communication Technologies: 3rd International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2011

作者： Mousavi, Seyed Mojtaba Shahdi, Seyed Omid Abu-Bakar, S.A.R. Computer Vision Video and Image Processing Research Lab Faculty of Electrical Engineering Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia

ISBN: (纸本)9781629934563

Automatic car plate extraction is one of the interesting research areas in surveillance applications such as traffic control, parking lot and detection of stolen vehicles. This topic is divided in two main parts, firstly license plate localization, and secondly, license plate characters recognition. In this paper merely the first part is taken into consideration by means of morphological operations and labeling algorithm. Moreover, performance of proposed method is investigated to unveil the ability in extracting of the license plate. Space between characters and also height and width of the license plate are fundamental issues in morphological operations along with dimension of characters in labeling algorithm. Results for this method are promising even in tough conditions.

关键词： Edge detection

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Width distributions for shape description

Width distributions for shape description

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2011 International Conference on Digital image Computing: Techniques and Applications, DICTA 2011

作者： Zhang, Xiaozheng Gao, Yongsheng Biosecurity Group Queensland Research Lab. National ICT Australia Australia Computer Vision and Image Processing Lab. Griffith University Australia

ISBN: (纸本)9780769545882

Measuring the similarity between articulated shapes is a fundamental yet challenging problem. This paper proposes a novel shape descriptor based on Width Distributions (WD), which is robust to articulations. We show that the width distributions are articulation insensitive yet descriptive to distinguish different shapes with varied part structures. With measurements on distributions only, the proposed method does not require any alignment between two objects and thus is more robust than correspondence-based measurements. First, the medial axes of the objects are extracted and fitted to B-Splines to remove outliners. The width of the object shape perpendicular to the medial axis can be calculated for each position on the medial axis. The histograms of those widths are compared using chi-square method for similarity. Experiments on standard 2D shape database show that the proposed method performed better than standard shape distribution algorithms and similarly to other articulation-robust shape descriptors, such as inner-distance. The speed of the proposed method is much faster than the more sophisticated inner-distance, as the proposed method is much simpler in nature and requires limited image processing and pattern classification. These results suggested it could be an efficient and effective method to describe and to match articulated shapes. © 2011 IEEE.

关键词： image processing

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