KEYWORDS: Cameras, Imaging systems, Databases, Roads, Control systems, Neural networks, Detection and tracking algorithms, Optical character recognition, Video, Computing systems
Improvement of the accuracy of an automatic car license plate recognition system is studied. The recognition system has been installed in several ports, factories and national borders around Europe and Middle East. The paper describes the anatomy of a portable version of the recognition system, in which the requirements are different from a fixed installation. For example, the portable system recognizes the license plates with no external triggers (such as inductive loops) and the installation is left to the end-user. The placement of the recognition camera in such systems is often a compromise and cannot be fully controlled. The system learns the characteristics of the setup and tries to correct the imperfections that are due to the installation by a non-expert. One of the main problems is the angle in which the camera is looking at the road, which causes the plate and the characters appear skewed. This paper proposes an algorithm for cancelling the skewness effect. The skewness parameters can be estimated from the earlier
recognition results, and the proposed system learns to correct the skewness resulting in improved recognition results. The improvement of the recognition accuracy is illustrated by experiments with
an annotated database of license plate images.
The best basis paradigm is a lower cost alternative to the principal component analysis (PCA) for feature extraction in pattern recognition applications. Its main idea is to build a collection of bases and search for the best one in terms of e.g. best class separation. Recently, fast best basis search algorithms have been generalized for anisotropic wavelet packet bases. Anisotropy is preferable for 2-D objects since it helps capturing local image features in a better way. In this contribution, the best anisotropic basis search framework is applied to the problem of recognition of characters captured from gray-scale pictures of car license plates. The goals are to simplify the classifier and to avoid a preliminary binarization stage by extracting features directly from the gray-scale images. The collection of bases is formed by anisotropic wavelet packets. The search algorithm seeks for a basis providing the lowest-dimensional data representation preserving the inter-class separability for given training data set, measured as Euclidean distance between class centroids. The relationship between the feature extractor and classifier complexity is clarified by training neural networks for different local bases. The proposed methodology shows its superiority to PCA as it yields equal and even lower classification error rate with considerably reduced computational costs.
An approach for estimating the distribution of soot particle dimensions from electron microscope images is studied. We have implemented simple image-analytical methods that produce an equivalent diameter distribution which can be compared with the corresponding distribution acquired via physical measurements. In comparison with manual object detection with conventional image processing software our method is time-saving and efficient. The shape of the particles emitted from the motor under different loads is affected by phenomena in exhaust dilution or release to air. Particle shape has a significant effect on its harmfulness to health. The researchers are also interested in knowing the actual particle size distribution to be able to improve catalyzer functionality. Engine exhaust particle emissions are often analyzed by methods based on the physical properties of soot particles, and assumptions about their size and shape. Our method provides data for refining these results. The implemented graphical user interface is semi-automatic and allows the user to remove erroneous results from the resulting thresholded image before the analysis. Then the task is to calculate the properties of interest over the particle population. We have written a toolbox with simple functions that realize the semi-automated analysis and the user interface for easy operation.
A novel method of quantifying the level of detail preservation ability of digital filters is proposed. The method assumes only the input distribution of the filter and estimates how much the filter changes the signal. The change is measured by the expectation of the absolute difference between the input and output signal. The method is applicable for many filters and input distributions. As an example case, the formulas for the expectation of the absolute difference for weighted order statistic filters with the uniform and Laplacian (biexponential) input distributions are derived. Finally, the design of weighted order statistic filters using supervised learning is studied. The learning method uses the detail preservation measure as a design criterion to obtain filters with different levels of detail preservation.
Local entropy estimates can be useful in segmentation of Particle Image Velocimeter (PIV) images. Image intensity combined with local entropy estimates forms basis for bubble detection. The acquired images are corrupted by additive noise with fixed density function. Local entropy estimate of the original image can be extracted from the noisy image if noise distribution is known a priori. A new approach to the problem of local entropy estimation in noisy images is presented. We presume that our original image is corrupted by additive i.i.d. noise from an ergodic source. The noise thus comes from a source that has a fixed density function, which can be approximated by taking histogram over the noise image. Now the histogram of the observed image, can be approximated by convolving the histogram of the original with the noise density (or its approximation). Now, in principle, it is possible extract the histogram of the original image by a blind deconvolution and removing the effect of noise. In many cases, it is also possible to utilize a priori information of the noise process. Local histogram deconvolutions with different window sizes and histogram bin numbers are performed. It is found that with careful implementation the resulting entropy estimates improve the estimates based on noisy image. We expect that the proposed method will prove to be useful with higher dimensional input data. With multidimensional data the number samples grows rapidly with the window size which improves significantly the density estimates.
In addition to suppressing noise, smoothers also cause an unwanted deformation of the image. In this paper we show how such deformation can be compensated. We consider the spectral behavior of noise removal by standard median filters. Observing the power spectrum, we notice that the deformation caused by median filter obeys the general form of the corresponding moving average filter, at least for i.i.d. input. We show that it is possible to cancel this deformation by using an approximation of the linear inverse filter, which can be easily implemented for images as well as 1D data.
The images formed by coherent imaging systems are characterized by presence of multiplicative noise with non- symmetrical p.d.f.s. The examples are the Rayleigh and one- side (negative) exponential distributions. For these cases the optimal L-filters are derived for different coefficient censoring by minimizing MSE of residual fluctuations. Some sub-optimal L-filters are considered as well. They are the Lpq-filters that use only two order statistics and the trimmed filters with symmetrical and nonsymmetrical coefficient censoring. Those filters are parametrically optimized according to the same criterion. The robust features of the considered filters are analyzed both theoretically using empirical influence functions and numerically with application of contamination noise model. As contaminating factor we exploited the salt-and-pepper noise with different probabilities and amplitudes of positive and negative spikes. Output estimate bias and variance were calculated and examined. It is shown that the use of sub-optimal filters is well motivated from different points of view.
An adaptive approach to restoration of images corrupted by blurring, combined with additive, impulsive and multiplicative noise is proposed. It is based on the combination of nonlinear filters, iterative filtering procedures, and the principles of local adaptation. Numerical simulations and test images illustrating the efficiency of the approach are presented.
The filtering performance of the soft morphological filters in decomposition schemes is studied. Optimal soft morphological filters for the filtering of the decomposition bands are sought and their properties are analyzed. The performance and properties of the optimal filters found are compared to those of the corresponding optimal composite soft morphological filters. Also, the applicability of different decomposition methods, especially those related to soft morphological filters, is studied.
An adaptive approach to restoration of images corrupted by blurring, additive, impulsive and multiplicative noise is proposed. It is based on the combination of nonlinear filters, iterative filtering procedures, and the principles of local adaptation. Finally, numerical simulations and test images illustrating the efficiency of the approach are presented.
Soft morphological filters form a large class of nonlinear filters with many desirable properties. However, few design methods exist for these filters. This paper demonstrates how optimization schemes, simulated annealing and genetic algorithms, can be employed in the search for soft morphological filters having optimal performance in a given signal processing task. Furthermore, the properties of the achieved optimal soft morphological filters in different situations are analyzed.
The recursive approaching signal filter (RASF) calculates the weights for each filtering window position from the difference of the original signal and a prefiltered signal. The original definition suggests the use of an exponential function for calculating the weights, but any nonincreasing function may be used as well. This paper addresses the problem of selecting the optimal one among them via empirical simulations applying the programming paradigm of genetic algorithms for the optimization problem. Furthermore, another modification to the RASF filter class taking advantage of a larger number of observations with smaller time complexity is proposed and thus a novel filter class is presented. The designed optimization scheme for finding the optimal weighting function is applied also to these filters and comparisons with the RASF filter are presented.
KEYWORDS: Image filtering, Optimal filtering, Linear filtering, Digital filtering, Electronic filtering, Filtering (signal processing), Quantization, Signal processing, Interference (communication), Digital signal processing
Filters can be designed using a pair of training signals, a desired one and a corrupted one, by finding from a filter class the filter that maps the corrupted training signal closest to the desired signal. This paper addresses the effect that the wordlength used in the training signals has on the applicability of the found optimal filters to other signals with different wordlength. The study is done by concentrating on three filter classes that are shown to reveal different aspects of the topic in hand.
Soft morphological filters form a large class of nonlinear filters with many desirable properties. However, few design methods exist for these filters and in the existing methods the selection of the filter composition tends to be ad-hoc and application specific. This paper demonstrates how optimization schemes, simulated annealing and genetic algorithms, can be employed in the search for optimal soft morphological filter sequences realizing optimal performance in a given signal processing task. This paper describes also the modifications in the optimization schemes required to obtain sufficient convergence.
In this work we present a new approach to robust image modeling. the proposed method is based on M-estimation algorithms. However, unlike in other M-estimator based image processing algorithms, the new algorithm takes into consideration spatial relations between picture elements. The contribution of the sample to the model depends not only on the current residual of that sample, but also on the neighboring residuals. In order to test the proposed algorithm we apply it to an image filtering problem, where images are modeled as piecewise polynomials. We show that the filter based on our algorithm has excellent detail preserving properties while suppressing additive Gaussian and impulsive noise very efficiently.
Meyer watershed algorithm, which creates watershed lines to the output image, and its extended split-and-merge version are further analyzed in this paper based on our previous work. Specifically, it is shown that no permanently isolated areas exist in the output of the Meyer watershed algorithm when applied to an eight-connected square grid. The scanning order dependency of watershed algorithms is also studied. First, the different scanning order operations which are implementation dependent are identified; then their effects on the output are analyzed. Finally, an alternative implementation of the split-and-merge watershed algorithm is presented which detects isolated areas using a new technique; the goal is to illustrate and analyze the operations of the split-and-merge watershed algorithm.
Soft morphological filters are robustly behaving extensions of standard flat morphological filters which include, as an extreme case, the weighted median filter. Soft morphological filters can take into account the geometrical shape of the processed objects and, at the same time, they are robust to additive noise and small variations in the shapes of the objects to be filtered. For suitable parameters they also have a good ability for preserving details. Thus, they provide a robust method for text enhancement. In this paper, an enhancement method based on soft morphological filters is demonstrated.
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