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Prediction and filtering for random parameter systems

IRE Transactions on Information Theory, 1958

This work generalizes the Wiener-Kolmogorov theory of optimum linear filtering and prediction of stationary random inputs. It is assumed here that signal and noise have passed through a random device before being available for filtering and prediction. A random device is a unit whose behavior depends on an unknown parameter for which an a priori probability distribution is given. A ...


Border detection of document images scanned from large books

2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP), 2013

Document images usually suffer from some unwanted transformations like skew and warping. When dealing with large books, another problem is also introduced; when we capture a page of a large book using digital camera or scanner, some extra margins appears. The resulting document is often framed by a dark border and noisy text regions from neighboring page. In this paper, ...


Lung tumour detection and classification using EK-Mean clustering

2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2016

In recent years the image processing techniques are used commonly in various medical areas for improving prior detection and treatment stages, in which the time span or elapse is very important to identify the disease in the patient as possible as fast, especially in many tumours such as the lung cancer, breast cancer. This system first segments the region of ...


The Image Reconstruction Algorithm Based on Wiener Filtering

2013 Ninth International Conference on Computational Intelligence and Security, 2013

In the actual scanning process, the projection data is inevitably influenced by various noises, thus it affects the precision of reconstructed image, which brings certain troubles for subsequent image processing. In order to improve the quality of the image reconstruction, this paper proposes a new method of image reconstruction. Firstly, we use the wiener filtering to deal with the iteration ...


The deconvolution technique for spatial resolution enhancement of MMSI on FY-4 satellite

2016 IEEE International Conference on Microwave and Millimeter Wave Technology (ICMMT), 2016

The deconvolution technique can improve the spatial resolution of Millimeter and Sub-millimeter Sounding/Imager (MMSI) of FY-4 satellite. In this paper, the technique is applied to the simulated brightness temperature image of a typhoon scene of MMSI, which indicates that the deconvolution technique is feasible to enhance the spatial resolution of MWSI. The typhoon's brightness temperature in the enhanced image displays ...


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  • Prediction and filtering for random parameter systems

    This work generalizes the Wiener-Kolmogorov theory of optimum linear filtering and prediction of stationary random inputs. It is assumed here that signal and noise have passed through a random device before being available for filtering and prediction. A random device is a unit whose behavior depends on an unknown parameter for which an a priori probability distribution is given. A number of engineering applications are cited. Two of these are worked out in some detail to illustrate the optimization procedure.

  • Border detection of document images scanned from large books

    Document images usually suffer from some unwanted transformations like skew and warping. When dealing with large books, another problem is also introduced; when we capture a page of a large book using digital camera or scanner, some extra margins appears. The resulting document is often framed by a dark border and noisy text regions from neighboring page. In this paper, we introduce a novel technique for enhancing the document images by automatically detecting the document borders and cutting out noisy area. Our methodology is based on projecting profiles combined with an edge detection process. Experimental results on several document images, mainly historical with a small slope, indicate the effectiveness of the proposed technique.

  • Lung tumour detection and classification using EK-Mean clustering

    In recent years the image processing techniques are used commonly in various medical areas for improving prior detection and treatment stages, in which the time span or elapse is very important to identify the disease in the patient as possible as fast, especially in many tumours such as the lung cancer, breast cancer. This system first segments the region of interest (lung) and then analyses the separately obtained area for nodule detection in order to examine the disease. Even with several lung tumour segmentations have been presented, enhancing tumour segmentation methods are still interesting because lung tumour CT images has some complex characteristics, such as large variation in tumour appearance and uncertain tumour boundaries. To address this problem, tumour segmentation method for CT Images which takes apart non- enhancing lung tumours from healthy tissues has been carried out by clustering method. The proposed method uses pre-processing technique that remove unwanted artifacts using median and wiener filters. Initially, the segmentation of the CT images has been carried out by using K- Means clustering method. To the clustered result, EK-Mean clustering is applied . Further the features like entrpy, Contrast, Correlation,Homogenity and the area are extracted from the tumorous part of Fuzzy Ek- Means segmented Image. For feature extraction, statistic method called Gray Level Co-occurrence Matrix (GLCM). Classification is done by using the supervised neural network called the Back Propagation Network (BPN). Results of the classification gives, whether the CT Image is a normal Image or cancerous.

  • The Image Reconstruction Algorithm Based on Wiener Filtering

    In the actual scanning process, the projection data is inevitably influenced by various noises, thus it affects the precision of reconstructed image, which brings certain troubles for subsequent image processing. In order to improve the quality of the image reconstruction, this paper proposes a new method of image reconstruction. Firstly, we use the wiener filtering to deal with the iteration result after each iteration by SART algorithm, and adopt the steepest gradient descent method to adjust the total variation of the filtered results. The experimental results show that this method is very effective, and the quality of image is improved obviously compared with the traditional reconstruction methods.

  • The deconvolution technique for spatial resolution enhancement of MMSI on FY-4 satellite

    The deconvolution technique can improve the spatial resolution of Millimeter and Sub-millimeter Sounding/Imager (MMSI) of FY-4 satellite. In this paper, the technique is applied to the simulated brightness temperature image of a typhoon scene of MMSI, which indicates that the deconvolution technique is feasible to enhance the spatial resolution of MWSI. The typhoon's brightness temperature in the enhanced image displays more details. Outline of the typhoon is closer to initial image than non-enhanced image. The correlation coefficient is improved and spatial resolution is enhanced twice, which indicates the effectiveness of this technique.

  • A comparative study on noise models an d filtering techniques using HMIS based microalgae images

    In biological building, presence of microalgae are for the most part used as a piece of monitoring of water pollution. Most recently, innovative advancement has fundamentally enhanced in examining computerized pictures. his work proposes the separating procedures for expulsion of different filtering from computerized pictures. In any case, this noises worldview depends on kind of disturbances, which can be diminished using linear and non linear filtering frameworks. Since, different sort of filtering exhibit in a picture, noises can be expelled utilizing filtering expulsion calculation. Additionally, this paper produces consequences of applying diverse filter sorts to a picture and explored. Since, the originality of noise diminishing in pictures is measured by the real sum measures, for instance, Root Mean Square Error (RMSE) and Peak Signal-to-Noise Ratio (PSNR). The execution of these filters on pictures corrupted with different noise of various filtering levels is differentiated and Wiener isolating framework.

  • Variable block wavelet wiener estimator in image denoising

    This paper improves the Wiener filter in the wavelet domain without the usual zero mean assumption. An improved LESE (local expected square error) formula is derived. For each block size, the LESE and LESEimproved are computed. The block size that gave the minimum LESE is chosen. For that block if LESE is less than LESEimproved then the zero-mean filter is used else the non-zero mean filter is used. The improved filter gave a higher PSNR for all the test images and all the noise variances that we have used.

  • Comparative analysis of non linear filtering techniques for denoising of X-ray images

    Image denoising is a vital problem in medical image processing. During image acquisition process images are often degraded. The degradation may involve blurring, information loss due to noises, sampling and quantization effect. Disturbances in medical images occurs due to flow of blood in body, use of interior devices, and etc. which results in altering the image with unknown parameters. In this paper the performance of nonlinear filtering methods to obtain improved medical image from distorted image is compared and carefully examined on the basis of various performance parameters viz. PSNR, MSE, SNR, MAE. Investigational outcomes on images will illustrate the competencies of all the studied approaches.

  • Hybrid Technique for Denoising Multi Environment Noise in Speech Processing

    In general noise control is critical issue in signal processing. Almost every signal that we are receiving at the receiver side of any communication system is somehow affected by noise. Noise is the unwanted part of the signal. In any communication system filtering is needed for rejecting all other unwanted frequencies present in the received signal and gives the desired signal. To denoising the different types noise signals requires different noise removing methods. This paper introducing a hybrid technique for noise reduction in speech signals those are corrupted by noise in multi environments i.e. street, airport, car and train noises are processed by Gaussian window and kalman filter. In order to access the accuracy of this combination of filters, the performance of this hybrid technique gives evaluation of both Mean Square Error and Peak Signal to Noise Ratio at the input to the corresponding values at the output of the system. The PSNR value of the proposed system for noise level of 10dB is affected by street noise is achieved 35.457339 as output. The results obtained by using this hybrid technique are better than the other techniques.

  • Rudolf E. Kalman: The Father of Mathematical Systems Theory [Historical Perspectives]

    Prof. Rudolf Kalman was a friend and mentor to both of us, and his profound insights deeply influenced our research. His contributions are far too numerous to describe in such a short note, so we will just outline some of the key highlights.



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