The Experts below are selected from a list of 16683 Experts worldwide ranked by ideXlab platform
Franco Pedreschi - One of the best experts on this subject based on the ideXlab platform.
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segmentation of colour food images using a Robust Algorithm
Journal of Food Engineering, 2005Co-Authors: Domingo Mery, Franco PedreschiAbstract:In this paper, a Robust Algorithm to segmenting food image from a background is presented using colour images. The proposed method has three steps: (i) computation of a high contrast grey value image from an optimal linear combination of the RGB colour components; (ii) estimation of a global threshold using a statistical approach; and (iii) morphological operation in order to fill the possible holes presented in the segmented binary image. Although the suggested threshold separates the food image from the background very well, the user can modify it in order to achieve better results. The Algorithm was implemented in Matlab and tested on 45 images taken in very different conditions. The segmentation performance was assessed by computing the area Az under the receiver operation characteristic (ROC) curve. The achieved performance was Az ¼ 0:9982. � 2004 Elsevier Ltd. All rights reserved.
Caidong Gu - One of the best experts on this subject based on the ideXlab platform.
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ICYCS - Real-time Robust Algorithm for Circle Object Detection
2008 The 9th International Conference for Young Computer Scientists, 2008Co-Authors: Jianping Wu, Jinxiang Li, Changshui Xiao, Caidong GuAbstract:This paper presents a real-time Robust Algorithm to detect and accurately locate the circular objects in digital images. The Algorithm consists of four steps.First the edge pixels are extracted using Canny edge detection Algorithm followed by a noise removal process to remove the non-circle edge points.Afterwards, a direct least square fitting Algorithm is developed to calculate radius and circle center information for each edge pixel cluster (a potential arc or a segment of a circle). In third step, a Robust criterion is developed to distinguish the valid arcs from invalid arcs. Finally, those valid arcs belonging to the same circle are reassembled and fitting Algorithm is run again to obtain the accurate information of that circle. The Algorithm is implemented in Visual C++and tested on a laptop powered by an Intel Centrino Duo CPU at 1.66GHz. The experiment shows the Algorithmpsilas three advantages. Its speed is fast, about 7 images/second for image size of 640X480. It is able toreliably detect full as well as partially-occluded circle objects even in a noisy environment, specifically 92%correct detection among 174 circles; the achievedaccuracy for radius and center location has reachedsub-pixel level on average.
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Real-time Robust Algorithm for Circle Object Detection
2008 The 9th International Conference for Young Computer Scientists, 2008Co-Authors: Jianping Wu, Jinxiang Li, Changshui Xiao, Caidong GuAbstract:This paper presents a real-time Robust Algorithm to detect and accurately locate the circular objects in digital images. The Algorithm consists of four steps.First the edge pixels are extracted using Canny edge detection Algorithm followed by a noise removal process to remove the non-circle edge points.Afterwards, a direct least square fitting Algorithm is developed to calculate radius and circle center information for each edge pixel cluster (a potential arc or a segment of a circle). In third step, a Robust criterion is developed to distinguish the valid arcs from invalid arcs. Finally, those valid arcs belonging to the same circle are reassembled and fitting Algorithm is run again to obtain the accurate information of that circle. The Algorithm is implemented in Visual C++and tested on a laptop powered by an Intel Centrino Duo CPU at 1.66GHz. The experiment shows the Algorithmpsilas three advantages. Its speed is fast, about 7 images/second for image size of 640X480. It is able toreliably detect full as well as partially-occluded circle objects even in a noisy environment, specifically 92%correct detection among 174 circles; the achievedaccuracy for radius and center location has reachedsub-pixel level on average.
Qi-jun Zhang - One of the best experts on this subject based on the ideXlab platform.
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A Robust Algorithm for automatic development of neural network models for microwave applications
2001 IEEE MTT-S International Microwave Sympsoium Digest (Cat. No.01CH37157), 2001Co-Authors: V. Devabhaktuni, M.c.e. Yagoub, Qi-jun ZhangAbstract:In this paper, we propose a Robust Algorithm for automating the neural network based RF/Microwave model development process. The Algorithm can build a neural model starting with zero amount of training/test data, and then proceeding with neural network training in a stage-wise manner. In each stage, the Algorithm utilizes neural network error criteria to determine additional training/test samples required and their location in model input space. The Algorithm dynamically generates these new data samples during training, by automatic driving of simulation tools, e.g., OSA90, Ansoft-HFSS. Initially, fewer hidden neurons are used, and the Algorithm adjusts the neural network size whenever it detects under-learning. Our technique integrates all the sub-tasks involved in neural modeling, thereby facilitating a more efficient and automated model building process. It significantly reduces the intensive human effort demanded by the conventional step-by-step neural modeling approach. The Algorithm is demonstrated through MESFET and Embedded Capacitor examples.
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a Robust Algorithm for automatic development of neural network models for microwave applications
International Microwave Symposium, 2001Co-Authors: V. Devabhaktuni, M.c.e. Yagoub, Qi-jun ZhangAbstract:For the first time, we propose a Robust Algorithm for automating the neural-network-based RF/microwave model development process. Starting with zero amount of training data and then proceeding with neural-network training in a stage-wise manner, the Algorithm can automatically produce a neural model that meets the user-desired accuracy. In each stage, the Algorithm utilizes neural-network error criteria to determine additional training/validation samples required and their location in model input space. The Algorithm dynamically generates these new data samples during training, by automatic driving of simulation tools (e.g., OSA90, Ansoft-HFSS, Agilent-ADS). Initially, fewer hidden neurons are used, and the Algorithm adjusts the neural-network size whenever it detects under-learning. Our technique integrates all the subtasks involved in neural modeling, thereby facilitating a more efficient and automated model development framework. It significantly reduces the intensive human effort demanded by the conventional step-by-step neural modeling approach. The Algorithm inherently distinguishes nonlinear and smooth regions of model behavior and uses relatively fewer samples in smooth subregions. It automatically deals with large data errors that can occur during dynamic sampling by using a Huber quasi-Newton technique. The Algorithm is demonstrated through practical microwave device and circuit examples.
Fei Xing - One of the best experts on this subject based on the ideXlab platform.
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CAD/Graphics - Robust Algorithm for Detecting the Maximum Inscribed Circle
2007 10th IEEE International Conference on Computer-Aided Design and Computer Graphics, 2007Co-Authors: Jibing Zhao, Hongyou Bian, Fei XingAbstract:In this paper, we propose a new Robust Algorithm for the detection of the maximum inscribed circles (MIC) in images. We first use a vector distance transformation (VDT) strategy to create a distance field. Then, we globally search the maximal value in distance field to extract the medial axes. Finally, we give a procedure to determine the center and radius of MIC. Our experimental results show indeed that new Algorithm is capable of detecting the MIC with excellent accuracy and high efficiency under various image conditions.
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Robust Algorithm for Detecting the Maximum Inscribed Circle
2007 10th IEEE International Conference on Computer-Aided Design and Computer Graphics, 2007Co-Authors: Jibing Zhao, Hongyou Bian, Fei XingAbstract:In this paper, we propose a new Robust Algorithm for the detection of the maximum inscribed circles (MIC) in images. We first use a vector distance transformation (VDT) strategy to create a distance field. Then, we globally search the maximal value in distance field to extract the medial axes. Finally, we give a procedure to determine the center and radius of MIC. Our experimental results show indeed that new Algorithm is capable of detecting the MIC with excellent accuracy and high efficiency under various image conditions.
Domingo Mery - One of the best experts on this subject based on the ideXlab platform.
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segmentation of colour food images using a Robust Algorithm
Journal of Food Engineering, 2005Co-Authors: Domingo Mery, Franco PedreschiAbstract:In this paper, a Robust Algorithm to segmenting food image from a background is presented using colour images. The proposed method has three steps: (i) computation of a high contrast grey value image from an optimal linear combination of the RGB colour components; (ii) estimation of a global threshold using a statistical approach; and (iii) morphological operation in order to fill the possible holes presented in the segmented binary image. Although the suggested threshold separates the food image from the background very well, the user can modify it in order to achieve better results. The Algorithm was implemented in Matlab and tested on 45 images taken in very different conditions. The segmentation performance was assessed by computing the area Az under the receiver operation characteristic (ROC) curve. The achieved performance was Az ¼ 0:9982. � 2004 Elsevier Ltd. All rights reserved.
