Papers by Homayoun Mahdavi
Automatic Persian License Plate Recognition by Edge Detection Using Hopfield Neural Network
DOAJ (DOAJ: Directory of Open Access Journals), 2011
Multi-scale morphological image enhancement of chest radiographs by a hybrid scheme
DOAJ (DOAJ: Directory of Open Access Journals), 2015
Chest radiography is a common diagnostic imaging test, which contains an enormous amount of infor... more Chest radiography is a common diagnostic imaging test, which contains an enormous amount of information about a patient. However, its interpretation is highly challenging. The accuracy of the diagnostic process is greatly influenced by image processing algorithms; hence enhancement of the images is indispensable in order to improve visibility of the details. This paper aims at improving radiograph parameters such as contrast, sharpness, noise level, and brightness to enhance chest radiographs, making use of a triangulation method. Here, contrast limited adaptive histogram equalization technique and noise suppression are simultaneously performed in wavelet domain in a new scheme, followed by morphological top-hat and bottom-hat filtering. A unique implementation of morphological filters allows for adjustment of the image brightness and significant enhancement of the contrast. The proposed method is tested on chest radiographs from Japanese Society of Radiological Technology database. The results are compared with conventional enhancement techniques such as histogram equalization, contrast limited adaptive histogram equalization, Retinex, and some recently proposed methods to show its strengths. The experimental results reveal that the proposed method can remarkably improve the image contrast while keeping the sensitive chest tissue information so that radiologists might have a more precise interpretation.
Sparse representation based intraframe and semi‐intraframe video coding schemes for low bitrates
Iet Image Processing, Dec 27, 2020
Image Registration Using Template Matching and Similarity Measures for Dental Radiograph
2012 Fourth International Conference on Computational Intelligence and Communication Networks, 2012
ABSTRACT
A novel approach in automatic control based on the genetic algorithm in STATCOM for improvement of voltage regulation
2012 11th International Conference on Environment and Electrical Engineering, 2012
Signal and Data Processing
Addiction is a biological, psychological, and social disease. Several factors are involved in eti... more Addiction is a biological, psychological, and social disease. Several factors are involved in etiology, substance abuse, and addiction which interact with each other and lead to the beginning of drug use and then addiction. Heroin is an addictive drug that, by acting on the central nervous system, reduces the density of neurons in the brain and interferes with decision making. This paper examines the effects of
Journal of Intelligent Procedures in Electrical Technology, 2012
Watermarking is used to protect copyright proof. Robustness index is the most important parameter... more Watermarking is used to protect copyright proof. Robustness index is the most important parameter that evaluates watermarking algorithm against different attacks such as noise and compression. In this paper, a novel semi- blind image watermarking algorithm based on joint wavelet transform (WT) and singular value decomposition (SVD) transform is proposed. In this algorithm, a new strategy is used to joint WT and SVD effectively. The most important advantage of this algorithm is robustness against a number of common attacks. Experimental results show that the proposed algorithm improves evaluation parameters more than other methods presented previously in the literature.
This paper aims at applying H.264/AVC in medical video compression applications and improving its... more This paper aims at applying H.264/AVC in medical video compression applications and improving its compression performance with higher perceptual quality and lower coding complexity. We propose a new method that uses lossless compression in the region of interest (ROI) and very high rate lossy compression in other regions. The propose method achieves a fast intra-and interprediction mode decision that is based on encountering coarse MBs for intra-and inter-prediction mode decision of the background region and fine MBs for the ROI region. The MBs of the background region are encoded with the maximum quantization parameter allowed by H.264/AVC in order to maximize the number of null coefficients. Also, in order to further reduce the computational complexity, a two-adaptive search range decision method (proposed previously by the authors) is enhanced using the ROI concept. Experimental results show that the proposed algorithm achieves a higher compression rate on medical videos with a higher quality of ROI with low coding complexity when compared to other standard algorithms reported in the literature and our previous algorithm.
Journal of Intelligent Procedures in Electrical Technology, 2012
The aim of this paper is the introduction of a CMOS OTA basic block that its transconductance gai... more The aim of this paper is the introduction of a CMOS OTA basic block that its transconductance gain can be electronically and linearly tuned. This transconductance is proportional to the square root of the bias current. To achieve the maximum output voltage and create a wide range of linear transconductance the CMOS OTA has been used.Then the variation of the transconductance and its effects on the performance of Continuous-time filters has been considered. The novelty of this paper is to show that how the transconductance of a first-Order filter is transformed to high pass and low pass filters and the transfer function of a second-order filter is transformed into high pass, low pass , band pass and band rejection filters. The performance of the proposed circuit is discussed and confirmed through MATLAB and PSPICE-simulation results.
Journal of Intelligent Procedures in Electrical Technology, 2011
Color and shape are basic characteristics which are used to recognize traffic signs. In this pape... more Color and shape are basic characteristics which are used to recognize traffic signs. In this paper, a new speed limit sign detection method in various conditions is proposed. In this method, color image is segmented based on a thresholding technique in HSI color space. Then, corner features are detected using convolution masks and found the location of sign. The first advantage of this method is high accuracy to detect the location of sign. So, the object can be detected with 30% noise level, 30 meters for distances of signs, and for rotated signs. The second advantage of the proposed method is high speed in sign detection. Utimately, the type of sign can be recognized with eliminate redundant information and match between extracted image and database image. If the illumination conditions be ideal, the recognition rate is obtained to 89%.
Journal of Intelligent Procedures in Electrical Technology, 2012
Active noise control is based on the destructive interference between the primary noise and gener... more Active noise control is based on the destructive interference between the primary noise and generated noise from the secondary source. An antinoise of equal amplitude and opposite phase is generated and combined with the primary noise. In this paper, performance of the neural networks is evaluated in active cancellation of sound noise. For this reason, feedforward and recurrent neural networks are designed and trained. After training, performance of the feedforwrad and recurrent networks in noise attenuation are compared. We use Elman network as a recurrent neural network. For simulations, noise signals from a SPIB database are used. In order to compare the networks appropriately, equal number of layers and neurons are considered for the networks. Moreover, training and test samples are similar. Simulation results show that feedforward and recurrent neural networks present good performance in noise cancellation. As it is seen, the ability of recurrent neural network in noise attenua...
This paper presents a study of hard combination data fusion for cooperative spectrum sensing in C... more This paper presents a study of hard combination data fusion for cooperative spectrum sensing in Cognitive Radio (CR). Fast and accurate spectrum sensing is crucial in realizing a reliable cognitive network. Cooperative spectrum sensing can help reducing the mean detection time and increasing the agility of the sensing process. However, when the number of cognitive users is large, the bandwidth need for the control channel that are used to report the secondary user nodes’ results to the fusion center may become excessively large. This paper presents a hard decision-based cooperative sequential detection scheme to reduce the average sensing time that is required to reach a detection decision. In the scheme, each cognitive radio computes the log likelihood ratio for its every measurement, and quantizes its measurements then sends its hard-decision to base station and the base station sequentially accumulates these log likelihood statistics and determines whether to stop making measurem...
In this paper, a novel watermarking method based on wavelet coefficient quantization using artifi... more In this paper, a novel watermarking method based on wavelet coefficient quantization using artificial neural networks is proposed. Imperceptibility and robustness are known as the main contradictory requirements of every watermarking scheme. In the proposed method, better compromises are achieved applying neural networks to adjust the watermark strength. Every four non-overlapped wavelet coefficients of the host image are grouped into a block and the differences of appropriately selected coefficients are quantized according to the watermark bit. A binary image is used as the watermark and embedded repetitively into the selected wavelet coefficients. The proposed method also improves the tamper detection in the watermarked image. Experimental results demonstrate simultaneous good imperceptibility and high robustness of the method against several types of attacks, such as Gaussian and salt and pepper noise addition, median filtering, and JPEG compression; in addition to capability of ...
Fire Detection Based on Fractal Analysis and Spatio-Temporal Features
Fire Technology, 2021
Fire detection is one of the most important needs of surveillance and security systems in industr... more Fire detection is one of the most important needs of surveillance and security systems in industrial applications. In this paper, a novel fire detection algorithm based on motion analysis using fractal and spatio-temporal features is presented. Initially, in each frame, dynamic textures are detected through three different fractal analysis methods and thresholding techniques. In the first method, Kernel Principal Component Analysis technique is used with fractal analysis and in the next a temporal blanket method is proposed. Finally, the third method is introduced based on temporal local fractal analysis and Laplace method. An RGB probability model is provided to separate the moving regions that have similar colors to the fire regions in each frame. Then, several spatio-temporal features such as correlation coefficient and mutual information are extracted from the candidate regions. Lastly, a two-class SVM classifier is used to classify these candidate regions. Various experimental results show that our proposed algorithm outperforms the relevant state-of-the-art algorithms.
Automated motion detection and tracking is a challenging task in traffic surveillance. In this pa... more Automated motion detection and tracking is a challenging task in traffic surveillance. In this paper, a system is developed to gather useful information from stationary cameras for detecting moving objects in digital videos. The moving detection and tracking system is developed based on optical flow estimation together with application and combination of various relevant computer vision and image processing techniques to enhance the process. To remove noises, median filter is used and the unwanted objects are removed by applying thresholding algorithms in morphological operations. Also the object type restrictions are set using blob analysis. The results show that the proposed system successfully detects and tracks moving objects in urban videos.
Many classes of images contain spatial regions which are more important than other regions. Compr... more Many classes of images contain spatial regions which are more important than other regions. Compression methods capable of delivering higher reconstruction quality for important parts are attractive in this situation. For medical images, only a small portion of the image might be diagnostically useful, but the cost of a wrong interpretation is high. Lossless compression schemes with secure transmission play a key role in telemedicine applications that help in accurate diagnosis and research. In this paper, we propose lossless compression method for Digital Imaging and Communications in Medicine images. The method includes the compression of region of interest using lossless image compression technique i.e. Huffman coding while the remaining irrelevant area of image (background) is compressed using the near lossless image compression techniques i.e. SPIHT. The main objective of this work is to reject the noisy background and reconstruct the image portions in a lossless manner. The im...
In this paper, a blind watermarking method based on neural networks in discrete wavelet transform... more In this paper, a blind watermarking method based on neural networks in discrete wavelet transform domain is proposed. Robustness and imperceptibility are main contradictory requirements of a watermark. In the proposed method, better compromises are achieved using artificial neural networks to adjust the watermark strength. A binary image is used as the watermark and embedded repetitively into the selected wavelet coefficients, which also improves the watermark robustness. Experimental results demonstrate that the proposed scheme has a simultaneous good imperceptibility and high robustness against several types of attacks, such as Gaussian and salt and pepper noise addition, cropping, mean and median filtering and JPEG compression.
Active noise control (ANC) is based on the destructive interference between the primary noise and... more Active noise control (ANC) is based on the destructive interference between the primary noise and generated noise from the secondary source. An antinoise signal with equal amplitude and opposite phase is generated and combined with the primary noise. In this paper, the performance of two kinds of feedforward neural networks in active noise cancellation is evaluated. For this reason, multilayer perceptron (MLP) and generalized regression neural networks (GRNN) are designed and trained with acoustic noise signals. After training, performance of these networks in noise attenuation is investigated and compared. In order to compare the two networks, training and test samples are similar. Sound noise signals are selected from SPIB database. The results of simulation show the ability of MLP network and GRNN in active cancellation of sound noise. As it is seen, multilayer perceptron network has better performance in noise attenuation than the generalized regression neural network. 1
The effective utilization of wind energy conversion system )WECS( is one of the most crucial conc... more The effective utilization of wind energy conversion system )WECS( is one of the most crucial concerns for the development of renewable energy systems. In order to achieve appropriate wind power, different pitch angle methods are used. Recurrent Adaptive Neuro-Fuzzy Inference System (RANFIS) is utilized in this paper in a new effective design to improve the performance of classical and adaptive Proportional Integral (PI) controllers applied for the pitch control purposes. Adaptive-online performance and high robustness coverage are the main advantages of the suggested controller. The effectiveness of the proposed method is verified by a simplified two-mass wind turbine model and a detailed aero-elastic wind turbine simulator (FAST7). At any given wind speed, the proposed controller has outperformed PI, Adaptive Neuro-Fuzzy Inference System (ANFIS), and RANFIS based controllers, reducing the mechanical stress of drive train while presenting suitable aerodynamic power tracking and maint...
Uploads
Papers by Homayoun Mahdavi