Using OCR programs is one of the best ways to convert written and printed documents into digital form. The first phase in OCR is segmenting the input image and identifying text and non-text regions. This paper propose...
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Using OCR programs is one of the best ways to convert written and printed documents into digital form. The first phase in OCR is segmenting the input image and identifying text and non-text regions. This paper proposes a new method for segmentation of Persian printed texts which is based on the ink spread effect. Considering that the Persian scripts are very different from the English script, most methods proposed for the English script have not rendered good results for the Persian scripts. The method proposed in this paper has been designed considering the special features of the Persian scripts. In addition, one of the most important characteristics of this method is resistance to skew. Moreover, the proposed approach is directly applicable to Arabic scripts
The development of approaches for understanding the complex dynamics of biological systems is a growing research area in electrical engineering, particularly in the fields of signalprocessing and controls. The focus ...
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ISBN:
(纸本)9781424407033
The development of approaches for understanding the complex dynamics of biological systems is a growing research area in electrical engineering, particularly in the fields of signalprocessing and controls. The focus of our research is the exploitation of the parallels between engineering and biology through the development of optimization and identification methods. Specifically, this research consists of developing methods for the estimation of unmeasured states, the identification of parameters of kinetic models and the validation of biochemical models. This work falls under the general research topic of systems biology. We explore the use of interval analysis in developing numerical algorithms for optimization and validation of systems biology problems. A major attribute of this method is that convergence to global minima is guaranteed. This paper includes a development of an adaptive interval optimization method based on the branch-and-bound method known as Smooth Interval Branch-and-Bound. One potential impact of this research is the development of more accurate models of biological systems. This will aid in the design of drugs for cancer and disease treatment and aid in the study of how they propagate.
This paper examines the feasibility of an approach to image retrieval from a heterogeneous collection based on texture. For each texture of interest (T), a T-vs-other classifier is evolved for small n times n windows ...
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This paper examines the feasibility of an approach to image retrieval from a heterogeneous collection based on texture. For each texture of interest (T), a T-vs-other classifier is evolved for small n times n windows using genetic programming. The classifier is then used to segment the images in the collection. If there is a significant contiguous area of T in an image, it is considered to contain that texture for retrieval purposes. We have experimented with sky and grass textures in the Corel Volume 12 image set. Experiments with a single image indicate that classifiers for the two textures can be learned to a high accuracy. Experiments with a test set of 714 Corel images gave a retrieval accuracy of 84% for both sky and grass textures. These results suggest that the use of texture could enhance retrieval accuracy in content based image retrieval systems
Brain computer Interface (BCI) has gained a lot of attention recently, as a means to detect individuals' intents using brain signals such as electroencephalographic (EEG) for control of machines. In order to achie...
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ISBN:
(纸本)9781424407101
Brain computer Interface (BCI) has gained a lot of attention recently, as a means to detect individuals' intents using brain signals such as electroencephalographic (EEG) for control of machines. In order to achieve the possible use of BCI in stroke rehabilitation, computational intelligent algorithms are important for reliable separation of shoulder versus elbow movement intentions. Efforts have been made on developing data processing and classification algorithm for such task Differently, this paper investigates the optimal use of electrodes and signal channels, which is formulated as a data-driven feature selection problem. 163 EEG electrodes are used to collect scalp recordings to predict shoulder abduction and elbow flexion intentions in healthy and stroke subjects. We combine the support vector channel selection with a time-frequency synthesized classification algorithm and examine the performances of using different subsets of channel inputs. Preliminary results show that 1) a reduced number of electrodes can be used to achieve the same or better performance than using the full set of signal channels;2) besides the fact that the accuracy on able-bodied subjects is expectedly higher than the stroke subject, the stroke subject tends to need more electrodes to achieve the best performance;3) visualization of spatial distribution of channel rankings shows reasonable connection with functional motor cortex areas.
A machine vision based keg inspection system can allow cost effective keg tracking and preventative maintenance programs to be implemented, leading to substantial savings for breweries with large keg fleets. A robust ...
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A machine vision based keg inspection system can allow cost effective keg tracking and preventative maintenance programs to be implemented, leading to substantial savings for breweries with large keg fleets. A robust keg serial number recognition and keg condition assessment process is required to cater for different keg brands and a range of keg ages in the fleet. It has been demonstrated that the proposed imageprocessing methodology, and neural network based number recognition system, successfully located and identified keg serial numbers with a 92% digit accuracy. Furthermore, the vision system allowed the concurrent assessment of the keg condition by assessing deformity of the keg rim, and that of the filler valve. A correlation coefficient, generated using a template matching process, proved to be a suitable metric which adequately indicated rims within and outside acceptable deformity bounds
In this paper, we present a progressive image reconstruction scheme based on the semantically scalable multi-scale edge representation of images, with the resolution and visual quality scalable to various bitrate requ...
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In this paper, we present a progressive image reconstruction scheme based on the semantically scalable multi-scale edge representation of images, with the resolution and visual quality scalable to various bitrate requirements. In the multi-scale edge representation an image is decomposed into its multi-scale primal sketch and the background where the multi-scale primal sketch preserves the structural semantics of images, and the background represents the smooth locale. Edge compensation is performed to smoothly remove edges at each scale. The multi-scale edges are then embedded encoded using the GFA modeling. The image reconstruction is progressively achieved by synthesizing multi-scale edges on the reconstructed image obtained from previous scale. As edge synthesis is performed at consecutive scales, the visual quality of the reconstructed image is progressively enhanced. Experiment shows that the proposed scheme performs well at low bit-rate multiresolution representation and progressive reconstruction
Silhouette is an important research issue in the field of non-photorealistic rendering (NPR) and it is also a popular drawing feature in illustrations and line-drawing artworks. In this paper, we present a real-time i...
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Silhouette is an important research issue in the field of non-photorealistic rendering (NPR) and it is also a popular drawing feature in illustrations and line-drawing artworks. In this paper, we present a real-time image-based stylized rendering system. First, we project a 3D model to image-space. Then we extract edges in the image-space data. We perform edge-detection algorithms on GPU (graphics processing units) for speedup. GPU is good at floating-points calculating and processing with parallelism. Both features match the property of most imageprocessing tasks. Our system can run at an interactive frame rate when combining our edge-detection algorithms with graphic hardware architecture. We demonstrate that this system performance can reach real-time and render images in good NPR style
The segmentation of MR images has been playing an important role to improve the detection and diagnosis of breast cancer. Main problem in breast images is the identification of the boundary between chest wall and brea...
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The segmentation of MR images has been playing an important role to improve the detection and diagnosis of breast cancer. Main problem in breast images is the identification of the boundary between chest wall and breast tissue. Minimizing the effects of patient motion is also important step in segmentation process. In imageprocessing, there are many different segmentation algorithms. The most common used method among them is thresholding. However, classic thresholding methods are not effective for axial MR breast images completely because of the fact that the sequence artifacts in axial MR breast images are very high. For this reason, we have proposed a regional thresholding algorithm to segment MR images successfully. The outstanding problem is how to obtain an automatic procedure for detecting boundary between breast tissue and chest wall
Excessive background noise is one of the most common complaints from hearing aid users. Background noise classification systems can be used in hearing aids to adjust the response based on the noise environment. This p...
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Excessive background noise is one of the most common complaints from hearing aid users. Background noise classification systems can be used in hearing aids to adjust the response based on the noise environment. This paper examines and compares two promising classification techniques, non-windowed artificial neural networks (ANN) and hidden Markov models (HMM), with an artificial neural network using windowed input. Results obtained show that an ANN with a windowed input gives an accuracy of up to 97.9%, which is more accurate than both the non-windowed ANN and the HMM. Overall, a windowed ANN is able to give excellent accuracy and reliability and is considered to be a good model for background noise classification in hearing aids
The channel assignment problem is the problem of determining an optimal allocation of channels to mobile users that minimizes call-blocking and call-dropping probabilities. The power control problem is the problem of ...
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The channel assignment problem is the problem of determining an optimal allocation of channels to mobile users that minimizes call-blocking and call-dropping probabilities. The power control problem is the problem of determining an optimal allocation of power levels to transmitters such that minimizes power consumption. In wireless mobile networks, channels and transmitter powers are limited resources and an efficient use of both these resources can greatly increase the network's capacity. However, very few papers have attempted to concurrently optimize both resource with significant success. We propose a multi-objective evolution strategy that combines the optimizations of channel assignment and power control. Preliminary results show substantial increase in network's capacity when compared with some current channel assignment method
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