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AguilarGonzález, Pablo M.; Kober, Vitaly
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1 Citations
Correlation filters for object detection and location estimation are commonly designed assuming the shape and graylevel structure of the object of interest are explicitly available. In this work we propose the design of correlation filters when the appearance of the target is given in a single training image. The target is assumed to be embedded in a cluttered background and the image is assumed to be corrupted by additive sensor noise. The designed filters are used to detect the target in an input scene modeled by the nonoverlapping signal model. An optimal correlation filter, with respect to the peaktooutput energy ratio criterion, is proposed for object detection and location estimation. We also present estimation techniques for the required parameters. Computer simulation results obtained with the proposed filters are presented and compared with those of common correlation filters.
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Batyrshin, Ildar; Sheremetov, Leonid
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3 Citations
The methods of pattern recognition in time series based on moving approximation (MAP) transform and MAP image of patterns are proposed. We discuss main properties of MAP transform, introduce a concept of a MAP image of time series and distance between time series patterns based on this concept which were used for recognition of small patterns in noisy time series. To illustrate the application of this technique to recognition of perception based patterns given by sequence of slopes, an example of recognition of water production patterns in petroleum wells used in expert system for diagnosis of water production problems is considered.
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SanchezDiaz, Guillermo; PizaDavila, Ivan; LazoCortes, Manuel; MoraGonzalez, Miguel; SalinasLuna, Javier
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3 Citations
Typical testors are a useful tool for both feature selection and for determining feature relevance in supervised classification problems. Nowadays, generating all typical testors of a training matrix is computationally expensive; all reported algorithms have exponential complexity, depending mainly on the number of columns in the training matrix. For this reason, different approaches such as sequential and parallel algorithms, genetic algorithms and hardware implementations techniques have been developed. In this paper, we introduce a fast implementation of the algorithm CT_EXT (which is one of the fastest algorithms reported) based on an accumulative binary tuple, developed for generating all typical testors of a training matrix. The accumulative binary tuple implemented in the CT_EXT algorithm, is a useful way to simplifies the search of feature combinations which fulfill the testor property, because its implementation decreases the number of operations involved in the process of generating all typical testors. In addition, experimental results using the proposed fast implementation of the CT_EXT algorithm and the comparison with other state of the art algorithms that generated typical testors are presented.
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MartínezDíaz, Saúl; CarmonaTroyo, Javier A.
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Fingerprint recognition has been used from many years for identification of persons. However, conventional fingerprint recognition systems might fail with poor quality, noisy or rotated images. Recently, novel nonlinear composite filters for correlationbased pattern recognition have been introduced. The filters are designed with information from distorted versions of reference object to achieve distortioninvariant recognition. Besides, a nonlinear correlation operation is applied among the filter and the test image. These kinds of filters are robust to nonGaussian noise. In this paper we apply nonlinear composite filters for fingerprint verification. Computer simulations show performance of proposed filters with distorted fingerprints. In addition, in order to illustrate robustness to noise, filters were tested with noisy images.
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Pogrebnyak, Oleksiy; HernándezBautista, Ignacio
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A method for adjustment of lifting scheme wavelet filters to achieve a higher image lossless compression is presented. The proposed method analyzes the image spectral characteristics and output the suboptimal coefficients to obtain a higher compression ratio in comparison to the standard lifting filters. The analysis follows by spectral pattern recognition with 1NN classifier. Spectral patterns are of a small fixed length for the entire image permitting thus the optimization of the filter coefficients for different imager sizes. The proposed method was applied to a set of test images obtaining better image compression results in comparison to the standard wavelet lifting filters.
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AguilarGonzález, Pablo M.; Kober, Vitaly
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Correlation filters for object detection use information about the appearance and shape of the object of interest. Therefore, detection performance degrades when the appearance of the object in the scene differs from the appearance used in the filter design process. This problem has been approached by utilizing composite filters designed from a training set containing known views of the object of interest. However, common composite filter design is usually carried out under the assumption that the ideal appearance and shape of the target are known. In this work we propose an algorithm for composite filter design using noisy training images. The algorithm is a modification of the class synthetic discriminant function technique that uses arbitrary filter impulse responses. Furthermore, filters can be adapted to achieve a prescribed discrimination capability for a class of backgrounds if a representative sample is known. Computer simulation results obtained with the proposed algorithm are presented and compared with those of common composite correlation filters.
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Pogrebnyak, Oleksiy; HernándezBautista, Ignacio; Camacho Nieto, Oscar; Manrique Ramírez, Pablo
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A method for image lossless compression using lifting scheme wavelet transform is presented. The proposed method adjusts wavelet filter coefficients analyzing signal spectral characteristics to obtain a higher compression ratio in comparison to the standard CDF(2,2) and CDF(4,4) filters. The proposal is based on spectral pattern recognition with 1NN classifier. Spectral patterns of a small fixed length are formed for the entire image permitting thus the global optimization of the filter coefficients, equal for all decompositions. The proposed method was applied to a set of test images obtaining better results in entropy values in comparison to the standard wavelet lifting filters.
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AguilarGonzález, Pablo M.; Kober, Vitaly
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1 Citations
Correlation filters for recognition of a target in nonoverlapping background noise are proposed. The object to be recognized is given implicitly; that is, it is placed in a noisy reference image at unknown coordinates. For the filters design two performance criteria are used: signaltonoise ratio and peaktooutput energy. Computer simulations results obtained with the proposed filters are discussed and compared with those of classical correlation filters in terms of discrimination capability.
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Cano, Sergio; Suaste, Israel; Escobedo, Daniel; ReyesGarcía, Carlos A.; Ekkel, Taco
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4 Citations
The present work proposes a combined classifier of infant cry units that links in a single structure two focuses: a thresholdbased classification and ANNbased classification. The thresholdbased classifier considers 4 new acoustic features:stridor, melody, voicedness, shifts, that show properly their robustness in front of alterations of the acoustics of infant cry concerned with the presence of some diseases. In order to satisfy the automatic estimation their practical implementations are also considered. The ANNbased classifier consists in a feedforward network using the method of Scale Gradient Conjugate (MSGC) as learning algorithm and the MFCCs as input vectors to the net. Each focus or classification stage gives in the exit one indicator (FN1 and FN2) that generates to the output a decision on two classes with gradation (normal, moderatelypathologic and pathologic). The results demonstrate the potentiality of these types of combined classifiers when the advantages of each focus in particular are properly emphasized
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Vázquez, Miguel A.; Cuevas, Francisco J.
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In this paper, a facial recognition system is described, which carry out the classification process by analyzing 3D information of the face. The process begins with the acquisition of the 3D face using light structured projection and the phase shifting technique. The faces are aligned respect a face profile and the region of front, eyes and nose is segmented. The descriptors are obtained using the eigenfaces approach and the classification is performed by linear discriminant analysis. The main contributions of this work are: a) the application of techniques of structured light projection for the calculation of the cloud of points related to the face, b) the use of the phase of the signal to perform recognition with 97% reliability, c) the use of the profile of the face in the alignment process and d) the robustness in the recognition process in the presence of gestures and facial expressions.
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AguilarGonzález, Pablo M.; Kober, Vitaly
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4 Citations
Correlation filters for recognition of a target in overlapping background noise are proposed. The object to be recognized is given implicitly; that is, it is placed in a noisy reference image at unknown coordinates. For the filters design two performance criteria are used: signaltonoise ratio and peaktooutput energy. Computer simulations results obtained with the proposed filters are discussed and compared with those of classical correlation filters in terms of discrimination capability.
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Rodríguez, Alejandro; Jimenez, Enrique; Radzimski, Mateusz; Gómez, Juan Miguel; Alor, Giner; PosadaGomez, Rubén; Gayo, Jose E. Labra
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Nowadays there is a large number of Expert Systems available to users requiring the extraction of data relevant to specific domains, many of which are based on reasoning and inference. However, many of these tools offer slow execution time, resulting in delayed response times to the queries made by users. The strategy of caching to define specific patterns of results enables such systems to eliminate the requirement to repeat the same queries, speeding up the response time and eliminating redundancy. This paper proposes a caching strategy for an Expert System based on Semantic Web and reasoning and inference techniques. Caching strategies have previously been applied to simple XML queries. Performance has been evaluated using an existing system for medical diagnosis, which demonstrates the increased efficiency of the system.
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MoralesManilla, Luis Roberto; SanchezDiaz, Guillermo
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In this paper a novel exterior escale algorithm for the calculation of FStypical testor set of a learning matrix is proposed. This algorithm allows to use any given similarity function between objects. Besides, results of experiments done, shows the performance obtained by proposed algorithm. A comparison between proposed algorithm and an exhaustive searching algorithm, that is the only one reported on literature that can calculate the complete FStypical testor set, is also included.
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Kober, V.; Ovseevich, I. A.
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A correlation filter for reliable recognition of a target embedded into a cluttered background blurred by relative motion between a camera and an input scene is derived. With a help of computer simulations the performance of the proposed filter in terms of discrimination capability and location errors is analyzed.
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SanchezDíaz, Guillermo; LazoCortés, Manuel
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6 Citations
Typical testors are a useful tool for feature selection and for determining feature relevance in supervised classification problems, especially when quantitative and qualitative features are mixed. Nowadays, computing all typical testors is a highly costly procedure; all described algorithms have exponential complexity. Existing algorithms are not acceptable methods owing to several problems (particularly run time) which are dependent on matrix size. Because of this, different approaches, such as sequential algorithms, parallel processing, genetic algorithms, heuristics and others have been developed. This paper describes a novel external type algorithm that improves the run time of all other reported algorithms. We analyze the behaviour of the algorithm in some experiments, whose results are presented here.
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AguilarGonzàlez, P. M.; Kober, V.; Ovseyevich, I. A.
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In the design of conventional correlation filters for pattern recognition, the appearance and the shape of a target are assumed to be known. In this work, the target is assumed to have unknown coordinates in a noisecorrupted reference image. We obtain a filter th at is optimal in terms of the ratio of the correlation peak to the energy of the correlation plane. Computer simulation is used to compare the performance of the conventional and the developed filters with regard to the detection of targets.
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Kober, V.; Mozerov, M.; Ovseevich, I. A.
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4 Citations
Adaptive correlation filters based on synthetic discriminant functions (SDFs) for reliable pattern recognition are proposed. A given value of discrimination capability can be achieved by adapting a SDF filter to the input scene. This can be done by iterative training. Computer simulation results obtained with the proposed filters are compared with those of various correlation filters in terms of recognition performance.
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Sossa, Humberto; Barrón, Ricardo; Cuevas, Francisco; Aguilar, Carlos
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In this note we show how a binary memory can be used to recall graylevel patterns. We take as example the α β associative memories recently proposed in Yáñez, Associative Memories based on order Relations and Binary Operators(In Spanish), PhD Thesis, Center for computing Research, February of 2002, only useful in the binary case. Basically, the idea consists on that given a set of graylevel patterns to be first memorized: (1) Decompose them into their corresponding binary patterns, and (2) Build the corresponding binary associative memory (one memory for each binary layer) with each training pattern set (by layers). A given pattern or a distorted version of it, it is recalled in three steps: (1) Decomposition of the pattern by layers into its binary patterns, (2) Recalling of each one of its binary components, layer by layer also, and (3) Reconstruction of the pattern from the binary patterns already recalled in step 2. The proposed methodology operates at two phases: training and recalling. Conditions for perfect recall of a pattern either from the fundamental set or from a distorted version of one them are also given. Experiments where the efficiency of the proposal is tested are also given.
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