PDF superior ICA applied to image feature extraction

ICA applied to image feature extraction

ICA applied to image feature extraction

Aunque la transformación ICA encuentra direcciones independientes o un conjunto de características lo mayor decorrelacionado posible, es necesario hacer una selección de las características a usar en la construcción del clasificador de forma que no permanezcan características aun correlacionadas que no aportan información. Como trabajo futuro se plantea utilizar ICA como técnica primaria de caracterización acompañada de métodos de selección de características que permitan que aquellas componentes con más alto grado de separación emerjan más fácilmente que las otras, lo cual incrementaría la probabilidad de que las componentes seleccionadas sean más significantes en la clasificación. Como en los distintos algoritmos ICA, los vectores independientes garantizan una transformación ortogonal de los datos [19], es decir la transformación ICA realiza una rotación de los datos originales es conveniente utilizar clasificadores invariantes a rotación (p.ej Naive- Bayes) en donde existen ventajas al utilizar la rotación que hace ICA. Mientras si se utilizan clasificadores no invariantes a la rotación (aquellos basados en distancias), no se aprovecharían las ventajas de ICA y bajo estas circunstancias puede no superarse los resultados de PCA.
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6 Lee mas

Analysis of image processing, machine learning and information fusion techniques for contact-less hand biometrics

Analysis of image processing, machine learning and information fusion techniques for contact-less hand biometrics

It has been decided to evaluate the multibiometric solutions under realistic condi- tions due to it is more challenging than the evaluation under controlled conditions, which already presents very good accuracy rates. To this end, gb 2 s ID database have been employed keeping the same division of samples than monomodal evaluation under realistic conditions in order to make equitable comparisons. Each method in- volved in the fusion has been configured with the parameters that provides better results during the monomodal evaluation. In those cases that more than one parame- ter configuration provide the best accuracy, faster parameter combinations have been chosen. Particularly, images have been resized to 32 × 32 px. and threshold has been established into 0 . 1 for Sobel filter. In the case of Gabor filter, the values for filter size, frequency and σ parameters are 17 × 17 , 0 . 0916 and 5 . 6179 respectively. Unary pat- terns, 8 -neighbourhood and radio equal to 1 are employed for LBP feature extraction, while region size parameter takes value 8 × 8 when LBP is combined with distance- based matching and value 16 × 16 when SVMs are used for comparison. In the case of LDP, region size parameter is set to 16 × 16 and directions 0 , 45 and 90 are involved in the feature extraction process. When Curvelets are combined with Euclidean distance just band 1 information is included in the feature vector, while band 3 information is
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335 Lee mas

A new feature extraction method for signal classification applied to cat spinal cord signals

A new feature extraction method for signal classification applied to cat spinal cord signals

classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Th[r]

11 Lee mas

A transfer-learning approach to feature extraction from cancer transcriptomes with deep autoencoders

A transfer-learning approach to feature extraction from cancer transcriptomes with deep autoencoders

Abstract. The diagnosis and prognosis of cancer are among the more challeng- ing tasks that oncology medicine deals with. With the main aim of fitting the more appropriate treatments, current personalized medicine focuses on using data from heterogeneous sources to estimate the evolution of a given disease for the partic- ular case of a certain patient. In recent years, next-generation sequencing data have boosted cancer prediction by supplying gene-expression information that has allowed diverse machine learning algorithms to supply valuable solutions to the problem of cancer subtype classification, which has surely contributed to bet- ter estimation of patient’s response to diverse treatments. However, the efficacy of these models is seriously affected by the existing imbalance between the high dimensionality of the gene expression feature sets and the number of samples available for a particular cancer type, To counteract what is known as the curse of dimensionality, feature selection and extraction methods have been tradition- ally applied to reduce the number of input variables present in gene expression datasets. Although these techniques work by scaling down the input feature space, the prediction performance of traditional machine learning pipelines using these feature reduction strategies remains moderate. In this work, we propose the use of the Pan-Cancer dataset to pre-train deep autoencoder architectures on a subset composed of thousands of gene expression samples of very diverse tumor types. The resulting architectures are subsequently fine-tuned on a collection of specific breast cancer samples. This transfer-learning approach aims at combining super- vised and unsupervised deep learning models with traditional machine learning classification algorithms to tackle the problem of breast tumor intrinsic-subtype classification. Our main goal is to investigate whether leveraging the information extracted from a large collection of gene expression data of diverse tumor types contributes to the extraction of useful latent features that ease solving a complex prediction task on a specific neoplasia.
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12 Lee mas

A fuzzy approach for feature extraction of brain tissues in Non-Contrast CT

A fuzzy approach for feature extraction of brain tissues in Non-Contrast CT

A Hounsfiel Unit is the number assigned to each pixel in the CT image and is the expression of the density of the irradiated object. It represents the absorption char- acteristics or the linear attenuation coefficient of a par- ticular volume of tissue. The HU scale has a range from -1000 to +1000, each constituting a different level of optical density. This scale of relative densities is based on air (-1000), water (0) and dense bone (+1000). The CT cases from DICOM files are in gray levels, therefore they were converted to HU following next equation:

9 Lee mas

FPGA and SoC devices applied to new trends in image/video an signal processing fiels

FPGA and SoC devices applied to new trends in image/video an signal processing fiels

In reference to traffic sign recognition (TSR) systems, they are generally comprised of two parts: sign detection and sign recognition/classification. Many approaches for sign detection transform the image to be processed on an alternate color space information such as normalized RGB, hue saturation, hue saturation enhancement, etc. For sign recognition, several feature extraction methods have been applied: canny edge detection, scale invariance feature (SIFT), speeded-up robust feature (SURF) and histogram of oriented gradients (HOG). Typically, image features are extracted for the subsequent machine learning stage, which is used for sign classification. Support vector machine (SVM) and neural networks (NNs) are popular for use as classifiers. In [2], a new TSR algorithm flow is proposed, which performs exceptionally robustly against environmental challenges, such as partially obscured, rotated and skewed traffic signs. Another critical component is the embedded system implementation of the algorithm on a programmable logic device that can enable real-time operation. The proposed work is based on previous research of the same authors which introduced a programmable hardware platform for TSR. This study shares the sign detection steps, but introduces a new sign recognition algorithm based on feature extraction and classification steps and its corresponding hardware implementation. The system shows a robust detection performance even for rotated or skewed signs. False classification rates can be reduced down to less than 1%, which is very promising. The proposed TSR system that combines a hardware and software co-design is implemented on Xilinx’s ZedBoard. An ARM CPU framework based on the AXI interconnect is developed for custom IP design and testing. Overall, the system throughput is eight times faster compared to the authors’ previous design based on the Virtex 5 FPGA, when considering both IP hardware execution and algorithms implemented in the software. The current execution time of 992 ms may not be sufficient for real-time operation, but the FPGA resource usage is very low, and may be used to implement more complex algorithms to improve their computation times.
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6 Lee mas

Comprehensive retinal image analysis: image processing and feature extraction techniques oriented to the clinical task

Comprehensive retinal image analysis: image processing and feature extraction techniques oriented to the clinical task

In this section we describe the different stages of the proposed restoration method depicted in Fig. 6.1. This work follows from Chapter 5 and addresses a more general problem: restoration of retinal images in the presence of a SV PSF. Blind deconvolution of blur from a single image is a difficult problem, but it can be made easier if multiple images are available. In § 5.5.1 we showed that single image blind deconvolution for blurred retinal images does not provide a suitable restoration. Moreover, in images with SV blur the restoration is even worse. Alternatively, by taking two images of the same retina we can use a multi-channel blind deconvolution strategy that is mathematically better-posed (Sroubek & Flusser, 2005). In fact, in order to properly estimate the SV PSF we use the degraded original image z (Fig. 6.2(a)) and a second auxiliary image g of the same retina, shown in Fig. 6.3(b). Unlike the previous chapter, the images used here were acquired in the same session. It is important to note that because the eye is a dynamical system the two images are not blurred in exactly the same way, which is a requirement in the multi-channel approach. The second image is used only for the purpose of PSF estimation. We make clear that the method is proposed so that there is no preference over which of the two images is restored; meaning that in ideal conditions restoring one or the other would produce the similar results. A practical approach, nevertheless, would be to restore the image that is less degraded, thereby obtaining the best restoration possible. Once the PSF is properly estimated, we restore the image as described in the last part of this section.
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159 Lee mas

Medical 3D image processing applied to computed tomography and magnetic resonance imaging

Medical 3D image processing applied to computed tomography and magnetic resonance imaging

cantly improved the extraction of information beyond BMD without overfitting, as indicated by AICc’s, robR 2 and robRMSE. This highlights the different aspects of bone quality captured by the set of fractal parameters. The results also showed that the same fractal parameters obtained with either of the both fractal meth- ods were nearly redundant as they did not add independent information in any linear regression model and also showed high Spearman’s rank correlations (FD: ρ = 0.96 ∗ , FD.SD: ρ = 0.94 ∗ , fRV/BV: ρ = 0.97 ∗ ). Thus, for the prediction of failure load, one could simply implement the first fractal method rather than both methods, explaining 84% (adjR 2 ) with the model of three and 89% with the model of four predictors. Robustness against image degradation is particularly important for in-vivo microstructural parameters. Precision or reproducibility (STP) is im- portant to derive longitudinal skeletal changes. Accuracy or trueness (LTP), on the other hand, reflects robustness against inhomogeneous settings (different pro- tocols, scanners) and defines, in particular, the ability to translate results from phantom- to patient-studies[40]. In this study, both the accuracy and precision of the structural information improved if computed with fractal methods. In partic- ular, fTb.Th improved the accuracy of Tb.Th though still significantly correlated: ρ(fTb.Th, Tb.Th) = 0.75 ∗ . The parameters fRV/BV and FD improved the accu- racy of SMI. However, in contrast to fRV/BV and FD, SMI generally measures the convexity of the trabecular bone rather than its structural model [115]. This might explain the weak correlations between SMI and the related fractal parameters (FD 1 : ρ = −0.48 ∗ , fRV 1 /BV : ρ = 0.51 ∗ , FD 2 : ρ = −0.29, fRV 2 /BV : ρ = 0.35).
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166 Lee mas

Measuring external face appearance for face classification

Measuring external face appearance for face classification

The information source of a facial image can divided in two sets, depending on the zone of the face. The internal information is composed by the eyes, nose and mouth, while the external features are the regions of the hair, forehead, both laterals, ears, jaw line and chin. Traditionally, face recognition algorithms have used only the internal information of face images for classification purposes since these features can be easily extracted. In fact, most of these algorithms use the aligned thumbnails as an input for some feature extraction process that yields a final feature set used to train the classifier. Classic examples of this approach are the eigenfaces technique (Turk & Pentland, 1991), or the use of Fisher Linear Discriminant Analysis (Hespanha Belhumeur & Kriegman, 1997). Moreover, in the face classification field, there are a lot of security related applications where the reliability obtained by the internal features is essential: notice that the external information is more variable and easier to imitate. For this reason, the use of external features for these security- related tasks has often been ignored, given their changing nature. However, with the advances of technology in chip integration, small embedded computers are more integrated in our everyday life, favouring the appearance of new applications not directly related to security dealing with face classification, where the users do not make specific efforts to mislead the classifier. Typical examples are embedded camera-devices for human user- friendly interfaces, user profiling, or reactive marketing. In these cases we consider the external features as an extra source of information for improving the accuracies obtained using only internal features. Furthermore, notice that this consideration can be specially beneficial in natural and uncontrolled environments, where usually artefacts such as strong local illumination changes or partial occlusions difficult the classification task.
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18 Lee mas

A comparative study on unconstrained hand biometrics

A comparative study on unconstrained hand biometrics

This acquisition procedure implies no severe constraints on neither illumination nor distance to mobile camera, being every acquisition carried out under natural light. In addition, it is a database with a huge variability in terms of size, skin color, orientation, hand openness and illumination conditions. In order to ensure a proper feature extraction, independently on segmentation, acquisitions were taken on a defined blue- coloured background, so that segmentation can be easily performed, focusing on hands. Both hands were taken, in a total of two sessions: During the first session, 10 acquisitions from both hands are collected; second session is carried out after 10-15 minutes, collecting again 10 images per hand. The image size provided by the device is 640x340 pixels. This first database is publicly available at www.gb2s.es. This database will be referred in this paper as GB2S database.
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6 Lee mas

Appling parallelism in image mining

Appling parallelism in image mining

Figure 1 shows a general structure model for image mining System. The system considers a specified sample of images as an input, whose image features are extracted to represent concisely the image content. Besides the relevance of this mining task, it is essential to consider in- variance problem to some geometric transformations and robustness with respect to noise and other distortions in designing a feature extraction op- erator. After representing the image content, the model description of a given image - the correct semantic image interpretation - is obtained. Mining results are obtained after matching the model description with its complementary symbolic description. The symbolic description might be just a feature or a set of features, a verbal description or phrase in order to
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5 Lee mas

A Comparison of Fuzzy Clustering Algorithms Applied to Feature Extraction on Vineyard

A Comparison of Fuzzy Clustering Algorithms Applied to Feature Extraction on Vineyard

Several segmentation techniques have been developed to object identification in industrial applications, (e.g. quality control in the size or volume of mechani- cal parts) and agricultural applications like, identification of defects and size of various horticultural products [2], [4]. For the identification of a product these techniques are based mainly on the identification by color and shape. However most of the applications are performed under controlled conditions (structured environment) of lighting, speed and distance to the product, making the al- gorithms valid only under those conditions. Thus, classical techniques are not applicable for unstructured environments [12], so recently, techniques from the area of artificial intelligence are being tested to increase the degree of general- ization to identify objects [15].
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10 Lee mas

Retinal Image Analysis: Image Processing and Feature Extraction Oriented to the Clinical Task

Retinal Image Analysis: Image Processing and Feature Extraction Oriented to the Clinical Task

To test the robustness of the proposed FM, a set of 140 artificially degraded images were produced. The dataset consisted in blurring the images and then adding noise. We tested for Gaussian noise, Speckle noise, and Impulse noise. From these tests we concluded that S1, S4, and Sa performed the best. We then tested on real images. One of the experiments we carried out was to analyze the performance of the FMs in subjects of different ages. In Fig. 4 we show the focusing curves obtained from subjects with the ages: 27, 40, 68, and 70. In general, from the comparison against S1 and S4 it is clear that the proposed FM Sa outperforms them in the considered cases. From the four cases shown only in one (Fig. 4(c)) the Sa measure peak did not coincide precisely with the optimal focus position. However, the error is no more than a single position. The FMs curves of S1 and S4 are generally flatter than those of Sa which in a focus search strategy is not wanted because of the difficulty to properly distinguish the optimum position in a coarse or initial search. In the other experiments the proposed measure outperformed the considered measures in robustness and accuracy. The code is available in [14].
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14 Lee mas

Reduction of the size of datasets by using evolutionary feature selection: the case of noise in a modern city

Reduction of the size of datasets by using evolutionary feature selection: the case of noise in a modern city

In the context of the Noise Feature Selection (NFS) optimization problem, we deal with the daily noise curve C, divided in an arbitrary daily rate DR (number of time blocks/samples/trapezoids per day). Thus, we define T the set of trapezoids or features that defines the original dataset of C (kTk=DR), and ST ⊆ T to be a subset of the original set of features. The main target of this work is to find the most efficient (smallest) subset of trapezoids/features (ST ) that allows us to maximize the accuracy (ACC(ST )) [4], i.e., minimize the error of a given machine learning classification approach (see Equation 2). In this work, we have evaluated our feature selection method over K-Means Clustering.
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10 Lee mas

Machine printed character recognition system using feature point extraction and neural network classifier

Machine printed character recognition system using feature point extraction and neural network classifier

We have found that many applications do not extract their entities, like neurons, layers of neurons, or a network of layers, instead they implement the entire neuron network architecture in a single class. In some cases, it is arguable what is better, but in most cases, it is favorable to split all these entities into distinct classes, what leads not only to easier understanding, but also allows reusing of all these components and building new neural networks architectures from smaller generic pieces. And, the present system follows this method, in this way it fulfills the Object Oriented Model (OOP) [ROGERS, 1996].
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18 Lee mas

Visual 3-D SLAM from UAVs

Visual 3-D SLAM from UAVs

Interest points' detection and features to be tracked have been found based on two different approaches, Harris corner detection and SURF invariant feature extraction.. The approach ba[r]

23 Lee mas

TítuloClassification of signals by means of genetic programming

TítuloClassification of signals by means of genetic programming

The next step is feature extraction, where different features are extracted from a signal in order to characterize it for the following classification. It is very important that the extracted features allow the separation of the classes in two (or more) regions of the problems’ search space. In general, there are a lot of techniques that allow the feature extraction from different types of signals, after carrying out various types of analysis, such as entropy, frequency or wavelet analysis (Torrence and Compo 1998), Lyapunov exponents (Rosenblum et al. 1996), etc. From all these techniques, features that characterize the original signals more or less accurately can be extracted. The main problem of this step is that, usually, the features extracted from a signal depend on previous knowledge of the signals. Therefore, lacks in the knowledge can lead to having a worse set of features, and, at the end, to have lower classification accuracy. This problem can be treated by using an automatic feature extraction technique, which does not use previous knowledge in the extraction. The main objective of this work is to present a technique which is an alternative for this automatic feature extraction. It may be highlighted that automatic extraction can have as side effect the discovery of new knowledge, which is not dependent of the previous one.
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15 Lee mas

GENERAL ARCHITECTURE OF A MIR SYSTEM

GENERAL ARCHITECTURE OF A MIR SYSTEM

Content-based music information retrieval and associated data-mining opens a number of perspectives for music industry and related multimedia commercial activities. Due to the great variability of musical audio, its non-verbal basis, and its interconnected levels of description, musical audio-mining is a very complex research domain that involves efforts from musicology, signal processing, and statistical modeling. This paper gives a general critical overview of the state-of-the- art followed by a discussion of musical audio-mining issues which are related to bottom-up processing (feature extraction), top-down processing (taxonomies and knowledge-driven processing), similarity matching, and user analysis and profiling.
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6 Lee mas

Speech Signals Feature Extraction Model for a Speaker’s Gender and Age Identification System

Speech Signals Feature Extraction Model for a Speaker’s Gender and Age Identification System

Voice is a biometric source of information: it stores particular speaker’s information that allows us to differentiate their voice among others. Speaker gender and age are part of that information which, besides, is inherent to the voice. The auditory human system is able to capture those features from the frequencies of the acoustic wave and send them to the brain where they are interpreted. The voice can be classified into groups of similar voices. The voice will be classified as female or male voice (based on gender groups), or as childish, adult or aged voice (based on age groups). So, which features does the brain select to classify the voice? Would it be possible to isolate those features and use them in ASR systems? If those features could be isolated and used before the speaker identification/verification, the error probabilities owing to the population number would be reduced since the comparison of one individual would be limited to a group instead of the whole database.
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321 Lee mas

Design & implementation of a powered wheelchair control system using EOG signals

Design & implementation of a powered wheelchair control system using EOG signals

is known as Electrooculography (EOG) and was introduced for diagnostic purposes by R. Jung in the 1930s [9]. The potential difference in EOG comes from the large presence of electrically active nerves in the posterior part of the eyeball, where the retina is placed, and the front part, where we can find the cornea [1]. This creates an electrical dipole between the cornea and the retina and its movements generates the potential differences that we can record in an EOG. The voltage values in EOG varies from 50 to 3500 µV with a frequency of 100 Hz. It has a practically linear behaviour for gaze angles of ± 30 ◦ and it changes approximately 20 µV per degree [10]. A waveform corresponding to up and down movements could be seen in Figure 1.1. For acquiring this signals some electrodes need to be placed near the eyes in contact with the skin. The electrode position should be adequate to capture horizontal and vertical components on the eye movements. An example of the electrode position could be seen in Figure 1.2. In this figure, electrodes B and C are used to calculate the vertical component, electrodes D and E for the horizontal component and electrode A is used to establish the ground or reference for the signal.
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42 Lee mas

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