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This paper presents an Image Filter with noise detector using Fuzzy Logic and Particle Swarm Optimization (PSO), which is called the IFFLPSO filter, for removal and restoration of impulse noises. In IFFLPSO filter, the fuzzy logic is employed to efficiently design the noise detector. The proposed filter effectively judges the input pixel vector whether it is corrupted or not. Meanwhile, the particle...
Noise filtering is a commonly-used methodology to improve the performance of learners built using low-quality data. A common type of noise filtering is a data preprocessing technique called classification filtering. In classification filtering, a classifier is built and evaluated on the training dataset (typically using cross-validation) and any misclassified instances are considered noisy. The strategies...
Two damage anomalous filters which were set up by BP neural network have been used to alarm the damage in structural members. After dealing with eigenparameter extracted from damaged and intact structure, different input data is considered for setting up different damage warning anomalous filters. Filter □: the first eight natural frequencies are chosen as input data of network. Filter □: one mode...
Current texture analysis methods enable good discrimination but are computationally too expensive for applications which require high frame rates. This occurs because they use redundant calculations, failing in capturing the essence of the texture discrimination problem. In this paper we use a learning approach to obtain simple filters for this task. Although others have proposed learning-based methods,...
Tactic analysis is receiving more attention in sports video analysis for its assistance to coaches and players. This paper proposes an efficient key sub-trajectory feature representation of ball trajectory for tactic analysis. Ball trajectories are modeled with the generalized suffix tree where frequent sub-trajectory patterns are searched for. Key sub-trajectory patterns are extracted by further...
Detecting the region of a license plate is the key component of the vehicle license plate recognition system. The spatial frequency characteristic in the license plate region usually varies more than in the background. In this paper, we propose a new approach for vehicle license plate localization using an optimal trade-off maximum average correlation height (TO-MACH) filter in the frequency domain...
The performance of a statistical machine translation (SMT) system heavily depends on the quantity and quality of the bilingual language resource. However, the pervious work mainly focuses on the quantity and tries to collect more bilingual data. In this paper, we aim to optimize the bilingual corpus to improve the performance of the translation system. We propose methods to process the bilingual language...
Based on the analysis of the characteristics of practical noises in a driver's cab, and aiming at the real time signal processing requirements, the high speed digital signal processor is used in the active noise control system. And combined with the nonlinear characteristics of the noise, an adaptive active noise control project based on the radial basis function neural network is proposed, at the...
In this work, we present a new approach for optimum design of nonlinear filters based on support vector machines. Taking advantage on the general concept of binary filters and machine learning theory, this proposed approach, is based on the concept of a new filter structured, called support vector machine filter (SVMF) and statistical data analysis. This proposed filter approach, is used as an impulsive...
RAW tools are PC software tools that develop the RAWs, i.e. the camera sensor data, into full-color photos. In this paper, we propose to study the internal processing characteristics of these RAW tools using 3 heterogeneous sets of demosaicing features. Through feature-level fusion, normalization and an Eigen-space regularization technique, we derive a compact set of discriminant features. Experimentally,...
This paper presents a new approach to image restoration based on ANN, considering the learning of the inverse process using a standard image for training under a multiscale approach. Different models of ANN were tested and compared with the traditional techniques. The standard image was artificially degraded to simulate some types of frequent degradation problems. Due to the huge amount of data generated...
We study the problem of robust pedestrian detection. A new descriptor, Pyramidal Statistics of Oriented Filtering (PSOF), is proposed for shape representation. Unlike one-scale gradient-based methods, the PSOF descriptor constructs an image pyramid and uses a Gabor filter bank to obtain multi-scale pixel-level orientation information. Then, locally normalized pyramidal statistics of these Gabor responses...
We introduce a new methodology to construct a Gaussian mixture approximation to the true filter density in hybrid Markovian switching systems. We relax the assumption that the mode transition process is a Markov chain and allow it to depend on the actual and unobservable state of the system. The main feature of the method is that the Gaussian densities used in the approximation are selected as the...
This paper introduces a class of correlation filters called average of synthetic exact filters (ASEF). For ASEF, the correlation output is completely specified for each training image. This is in marked contrast to prior methods such as synthetic discriminant functions (SDFs) which only specify a single output value per training image. Advantages of ASEF training include: insensitivity to over-fitting,...
In this paper, we propose a new general Quality Assessment method based on the curvelet transform, called Curvelet No-Reference (CNR) model, which can estimate levels of noise, blur and JPEG 2000 compression of natural images. The peak coordinate of the curvelet coefficient histogram occupies distinctive regions depending on how the image was modified from the original. During training, we associate...
As an important image feature, a corner takes significant position in camera calibration, pattern recognition and image matching area. A large amount of image corner points are the intersecting points of the edges of polygons. A corner point extracting method based on support vector for regression (SVR) was proposed aimed at extracting intersecting points. First, a digital image of geometric figures...
This paper presents PWEM, a technique for detecting class label noise in training data. PWEM detects mislabeled examples by assigning to each training example a probability that its label is correct. PWEM calculates this probability by clustering examples from pairs of classes together and analyzing the distribution of labels within each cluster to derive the probability of each label's correctness...
This paper presents a robust and precise eye detection algorithm on gray intensity face images. Our method combines the strength of two existing methods which are a feature based method and an appearance based method to detect and locate a precise pupil center. It includes the following three steps. First, the feature based method is used. The method uses a projection function to detect all possible...
This paper applies a neural networks (NN) multiobjective learning algorithm called the Minimum Gradient Method (MGM) to filter noise in regression problems. This method is based on the concept that the learning is a bi-objective problem aiming at minimizing the empirical risk (training error) and the function complexity. The complexity is modeled as the norm of the network output gradient. After training,...
In this work we investigate the performance of Advanced Correlation Filters (ACFs) in the automatic classification of partial shoeprints for use in forensic science. In particular, the Optimum Trade-off Synthetic Discriminant Function (OTSDF) filter is used to match low quality partial shoeprints. Experiments were conducted on a database of images of 100 different shoes available on the market. For...
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