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A chemometric study on the analgesic activity of cannabinoid compounds using SDA, KNN and SIMCA methods

ARROIO, A.; LIMA, E. F.; HONORIO, K. M.; SILVA, A. B. F. da
Fonte: SPRINGER/PLENUM PUBLISHERS Publicador: SPRINGER/PLENUM PUBLISHERS
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
56.02%
The supervised pattern recognition methods K-Nearest Neighbors (KNN), stepwise discriminant analysis (SDA), and soft independent modelling of class analogy (SIMCA) were employed in this work with the aim to investigate the relationship between the molecular structure of 27 cannabinoid compounds and their analgesic activity. Previous analyses using two unsupervised pattern recognition methods (PCA-principal component analysis and HCA-hierarchical cluster analysis) were performed and five descriptors were selected as the most relevants for the analgesic activity of the compounds studied: R (3) (charge density on substituent at position C(3)), Q (1) (charge on atom C(1)), A (surface area), log P (logarithm of the partition coefficient) and MR (molecular refractivity). The supervised pattern recognition methods (SDA, KNN, and SIMCA) were employed in order to construct a reliable model that can be able to predict the analgesic activity of new cannabinoid compounds and to validate our previous study. The results obtained using the SDA, KNN, and SIMCA methods agree perfectly with our previous model. Comparing the SDA, KNN, and SIMCA results with the PCA and HCA ones we could notice that all multivariate statistical methods classified the cannabinoid compounds studied in three groups exactly in the same way: active...

Técnicas de seleção de características com aplicações em reconhecimento de faces.; Feature selection techniques with applications to face recognition.

Campos, Teófilo Emídio de
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 25/05/2001 PT
Relevância na Pesquisa
45.95%
O reconhecimento de faces é uma área de pesquisa desafiadora que abre portas para a implementação de aplicações muito promissoras. Embora muitos algoritmos eficientes e robustos já tenham sido propostos, ainda restam vários desafios. Dentre os principais obstáculos a serem uperados, está a obtenção de uma representação robusta e compacta de faces que possibilite distinguir os indivíduos rapidamente. Visando abordar esse problema, foi realizado um estudo de técnicas de reconhecimento estatístico de padrões, principalmente na área de redução de dimensionalidade dos dados, além de uma revisão de métodos de reconhecimento de faces. Foi proposto (em colaboração com a pesquisadora Isabelle Bloch) um método de seleção de características que une um algoritmo de busca eficiente (métodos de busca seqüencial flutuante) com uma medida de distância entre conjuntos nebulosos (distância nebulosa baseada em tolerância). Essa medida de distância possui diversas vantagens, sendo possível considerar as diferentes tipicalidades de cada padrão dos conjuntos de modo a permitir a obtenção de bons resultados mesmo com conjuntos com sobreposição. Os resultados preliminares com dados sintéticos mostraram o caráter promissor dessa abordagem. Com o objetivo de verificar a eficiência de tal técnica com dados reais...

Fusion of fingerprint recognition methods for robust human identification

Falguera, Fernanda Pereira Sartori; Marana, Aparecido Nilceu; Falguera, Juan Rogelio
Fonte: Universidade Estadual Paulista Publicador: Universidade Estadual Paulista
Tipo: Conferência ou Objeto de Conferência Formato: 413-420
ENG
Relevância na Pesquisa
45.9%
Biometrics is one of the biggest tendencies in human identification. The fingerprint is the most widely used biometric. However considering the automatic fingerprint recognition a completely solved problem is a common mistake. The most popular and extensively used methods, the minutiae-based, do not perform well on poor-quality images and when just a small area of overlap between the template and the query images exists. The use of multibiometrics is considered one of the keys to overcome the weakness and improve the accuracy of biometrics systems. This paper presents the fusion of a minutiae-based and a ridge-based fingerprint recognition method at rank, decision and score level. The fusion techniques implemented leaded to a reduction of the Equal Error Rate by 31.78% (from 4.09% to 2.79%) and a decreasing of 6 positions in the rank to reach a Correct Retrieval (from rank 8 to 2) when assessed in the FVC2002-DB1A database. © 2008 IEEE.

Frontal sinus recognition for human identification

Falguera, Juan Rogelio; Falguera, Fernanda Pereira Sartori; Marana, Aparecido Nilceu
Fonte: Universidade Estadual Paulista Publicador: Universidade Estadual Paulista
Tipo: Conferência ou Objeto de Conferência
ENG
Relevância na Pesquisa
45.9%
Many methods based on biometrics such as fingerprint, face, iris, and retina have been proposed for person identification. However, for deceased individuals, such biometric measurements are not available. In such cases, parts of the human skeleton can be used for identification, such as dental records, thorax, vertebrae, shoulder, and frontal sinus. It has been established in prior investigations that the radiographic pattern of frontal sinus is highly variable and unique for every individual. This has stimulated the proposition of measurements of the frontal sinus pattern, obtained from x-ray films, for skeletal identification. This paper presents a frontal sinus recognition method for human identification based on Image Foresting Transform and shape context. Experimental results (ERR = 5,82%) have shown the effectiveness of the proposed method.

ANN statistical image recognition method for computer vision in agricultural mobile robot navigation

Lulio, Luciano C.; Tronco, Mario L.; Porto, Arthur J. V.
Fonte: Universidade Estadual Paulista Publicador: Universidade Estadual Paulista
Tipo: Conferência ou Objeto de Conferência Formato: 1771-1776
ENG
Relevância na Pesquisa
46%
The main application area in this project, is to deploy image processing and segmentation techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. Thereby, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for image recognition. Hence, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave computational platforms, along with the application of customized Back-propagation Multilayer Perceptron (MLP) algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of segmented images in which reasonably accurate results were obtained. © 2010 IEEE.

Factors limiting the performance of prediction-based fold recognition methods.

de la Cruz, X.; Thornton, J. M.
Fonte: Cold Spring Harbor Laboratory Press Publicador: Cold Spring Harbor Laboratory Press
Tipo: Artigo de Revista Científica
Publicado em /04/1999 EN
Relevância na Pesquisa
45.98%
In the past few years, a new generation of fold recognition methods has been developed, in which the classical sequence information is combined with information obtained from secondary structure and, sometimes, accessibility predictions. The results are promising, indicating that this approach may compete with potential-based methods (Rost B et al., 1997, J Mol Biol 270:471-480). Here we present a systematic study of the different factors contributing to the performance of these methods, in particular when applied to the problem of fold recognition of remote homologues. Our results indicate that secondary structure and accessibility prediction methods have reached an accuracy level where they are not the major factor limiting the accuracy of fold recognition. The pattern degeneracy problem is confirmed as the major source of error of these methods. On the basis of these results, we study three different options to overcome these limitations: normalization schemes, mapping of the coil state into the different zones of the Ramachandran plot, and post-threading graphical analysis.

Evaluation of Face Datasets as Tools for Assessing the Performance of Face Recognition Methods

Shamir, Lior
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em //2008 EN
Relevância na Pesquisa
45.88%
Face datasets are considered a primary tool for evaluating the efficacy of face recognition methods. Here we show that in many of the commonly used face datasets, face images can be recognized accurately at a rate significantly higher than random even when no face, hair or clothes features appear in the image. The experiments were done by cutting a small background area from each face image, so that each face dataset provided a new image dataset which included only seemingly blank images. Then, an image classification method was used in order to check the classification accuracy. Experimental results show that the classification accuracy ranged between 13.5% (color FERET) to 99% (YaleB). These results indicate that the performance of face recognition methods measured using face image datasets may be biased. Compilable source code used for this experiment is freely available for download via the internet.

Applying pattern recognition methods to analyze the molecular properties of a homologous series of nitrogen mustard agents

Bartzatt, Ronald; Donigan, Laura
Fonte: Springer-Verlag Publicador: Springer-Verlag
Tipo: Artigo de Revista Científica
Publicado em 14/04/2006 EN
Relevância na Pesquisa
45.91%
The purpose of this research was to analyze the pharmacological properties of a homologous series of nitrogen mustard (N-mustard) agents formed after inserting 1 to 9 methylene groups (-CH2-) between 2-N(CH2CH2Cl)2 groups. These compounds were shown to have significant correlations and associations in their properties after analysis by pattern recognition methods including hierarchical classification, cluster analysis, nonmetric multi-dimensional scaling (MDS), detrended correspondence analysis, K-means cluster analysis, discriminant analysis, and self-organizing tree algorithm (SOTA) analysis. Detrended correspondence analysis showed a linear-like association of the 9 homologs, and hierarchical classification showed that each homolog had great similarity to at least one other member of the series—as did cluster analysis using paired-group distance measure. Nonmetric multi-dimensional scaling was able to discriminate homologs 2 and 3 (by number of methylene groups) from homologs 4, 5, and 6 as a group, and from homologs 7, 8, and 9 as a group. Discriminant analysis, K-means cluster analysis, and hierarchical classification distinguished the high molecular weight homologs from low molecular weight homologs. As the number of methylene groups increased the aqueous solubility decreased...

A comparative study of thermal face recognition methods in unconstrained environments

Ruiz del Solar, Javier; Verschae, Rodrigo; Correa, Mauricio; Hermosilla, Gabriel
Fonte: Elsevier Publicador: Elsevier
Tipo: Artículo de revista
EN
Relevância na Pesquisa
56.08%
Artículo de publicación ISI; The recognition of faces in unconstrained environments is a challenging problem. The aim of this work is to carry out a comparative study of face recognition methods working in the thermal spectrum (8-12 mu m) that are suitable for working properly in these environments. The analyzed methods were selected by considering their performance in former comparative studies, in addition to being real-time, to requiring just one image per person, and to being fully online (no requirements of offline enrollment). Thus, in this study three local-matching methods based on histograms of Local Binary Pattern (LBP) features, on histograms of Weber Linear Descriptors (WLD), and on Gabor Jet Descriptors (GJD), as well as two global image-matching method based on Scale-Invariant Feature Transform (SIFT) Descriptors, and Speeded Up Robust Features (SURF) Descriptors, are analyzed. The methods are compared using the Equinox and UCHThermalFace databases. The use of these databases allows evaluating the methods in real-world conditions that include natural variations in illumination, indoor/outdoor setup, facial expression, pose, accessories, occlusions, and background. The UCHThermalFace database is described for the first time in this article and WLD is used for the first time in face recognition. The results of this comparative study are intended to be a guide for developers of face recognition systems. The main conclusions of this study are: (i) all analyzed methods perform very well under the conditions in which they were evaluated...

Face Recognition in Unconstrained Environments: A Comparative Study

Ruiz del Solar, Javier; Correa, Mauricio; Verschae, Rodrigo
Fonte: Universidade do Chile Publicador: Universidade do Chile
Tipo: Artículo de revista
EN
Relevância na Pesquisa
46.01%
The development of face recognition methods for unconstrained environments is a challenging problem. The aim of this work is to carry out a comparative study of existing face recognition methods that are suitable to work properly in these environments. The analyzed methods are selected by considering their performance in former comparative studies, in addition to be real-time, to require just one image per person, and to be fully online (no requirements of offline enrollment). The methods are compared using the LFW database, which was built to evaluate face recognition methods in real-world conditions. The results of this comparative study are intended to be a guide for developers of face recognition systems.

Generic Image Classification Approaches Excel on Face Recognition

Shen, Fumin; Shen, Chunhua
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
46%
The main finding of this work is that the standard image classification pipeline, which consists of dictionary learning, feature encoding, spatial pyramid pooling and linear classification, outperforms all state-of-the-art face recognition methods on the tested benchmark datasets (we have tested on AR, Extended Yale B, the challenging FERET, and LFW-a datasets). This surprising and prominent result suggests that those advances in generic image classification can be directly applied to improve face recognition systems. In other words, face recognition may not need to be viewed as a separate object classification problem. While recently a large body of residual based face recognition methods focus on developing complex dictionary learning algorithms, in this work we show that a dictionary of randomly extracted patches (even from non-face images) can achieve very promising results using the image classification pipeline. That means, the choice of dictionary learning methods may not be important. Instead, we find that learning multiple dictionaries using different low-level image features often improve the final classification accuracy. Our proposed face recognition approach offers the best reported results on the widely-used face recognition benchmark datasets. In particular...

Nuclear Norm based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes

Yang, Jian; Qian, Jianjun; Luo, Lei; Zhang, Fanlong; Gao, Yicheng
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 06/05/2014
Relevância na Pesquisa
45.92%
Recently regression analysis becomes a popular tool for face recognition. The existing regression methods all use the one-dimensional pixel-based error model, which characterizes the representation error pixel by pixel individually and thus neglects the whole structure of the error image. We observe that occlusion and illumination changes generally lead to a low-rank error image. To make use of this low-rank structural information, this paper presents a two-dimensional image matrix based error model, i.e. matrix regression, for face representation and classification. Our model uses the minimal nuclear norm of representation error image as a criterion, and the alternating direction method of multipliers method to calculate the regression coefficients. Compared with the current regression methods, the proposed Nuclear Norm based Matrix Regression (NMR) model is more robust for alleviating the effect of illumination, and more intuitive and powerful for removing the structural noise caused by occlusion. We experiment using four popular face image databases, the Extended Yale B database, the AR database, the Multi-PIE and the FRGC database. Experimental results demonstrate the performance advantage of NMR over the state-of-the-art regression based face recognition methods.; Comment: 30 pages

Structure Inference Machines: Recurrent Neural Networks for Analyzing Relations in Group Activity Recognition

Deng, Zhiwei; Vahdat, Arash; Hu, Hexiang; Mori, Greg
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 13/11/2015
Relevância na Pesquisa
45.93%
Rich semantic relations are important in a variety of visual recognition problems. As a concrete example, group activity recognition involves the interactions and relative spatial relations of a set of people in a scene. State of the art recognition methods center on deep learning approaches for training highly effective, complex classifiers for interpreting images. However, bridging the relatively low-level concepts output by these methods to interpret higher-level compositional scenes remains a challenge. Graphical models are a standard tool for this task. In this paper, we propose a method to integrate graphical models and deep neural networks into a joint framework. Instead of using a traditional inference method, we instead use a sequential prediction approximation, modeled by a recurrent neural network. Beyond this, the appropriate structure for inference can be learned by imposing gates on edges between connections of nodes. Empirical results on group activity recognition demonstrate the potential of this model to handle highly structured learning tasks.

Manipulated Object Proposal: A Discriminative Object Extraction and Feature Fusion Framework for First-Person Daily Activity Recognition

Luo, Changzhi; Ni, Bingbing; Yuan, Jun; Wang, Jianfeng; Yan, Shuicheng; Wang, Meng
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 02/09/2015
Relevância na Pesquisa
45.93%
Detecting and recognizing objects interacting with humans lie in the center of first-person (egocentric) daily activity recognition. However, due to noisy camera motion and frequent changes in viewpoint and scale, most of the previous egocentric action recognition methods fail to capture and model highly discriminative object features. In this work, we propose a novel pipeline for first-person daily activity recognition, aiming at more discriminative object feature representation and object-motion feature fusion. Our object feature extraction and representation pipeline is inspired by the recent success of object hypotheses and deep convolutional neural network based detection frameworks. Our key contribution is a simple yet effective manipulated object proposal generation scheme. This scheme leverages motion cues such as motion boundary and motion magnitude (in contrast, camera motion is usually considered as "noise" for most previous methods) to generate a more compact and discriminative set of object proposals, which are more closely related to the objects which are being manipulated. Then, we learn more discriminative object detectors from these manipulated object proposals based on region-based convolutional neural network (R-CNN). Meanwhile...

Text-Independent Speaker Recognition for Low SNR Environments with Encryption

Chadha, Aman; Jyoti, Divya; Roja, M. Mani
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 31/10/2011
Relevância na Pesquisa
45.96%
Recognition systems are commonly designed to authenticate users at the access control levels of a system. A number of voice recognition methods have been developed using a pitch estimation process which are very vulnerable in low Signal to Noise Ratio (SNR) environments thus, these programs fail to provide the desired level of accuracy and robustness. Also, most text independent speaker recognition programs are incapable of coping with unauthorized attempts to gain access by tampering with the samples or reference database. The proposed text-independent voice recognition system makes use of multilevel cryptography to preserve data integrity while in transit or storage. Encryption and decryption follow a transform based approach layered with pseudorandom noise addition whereas for pitch detection, a modified version of the autocorrelation pitch extraction algorithm is used. The experimental results show that the proposed algorithm can decrypt the signal under test with exponentially reducing Mean Square Error over an increasing range of SNR. Further, it outperforms the conventional algorithms in actual identification tasks even in noisy environments. The recognition rate thus obtained using the proposed method is compared with other conventional methods used for speaker identification.; Comment: Biometrics...

Face Recognition Methods & Applications

Parmar, Divyarajsinh N.; Mehta, Brijesh B.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 03/03/2014
Relevância na Pesquisa
45.98%
Face recognition presents a challenging problem in the field of image analysis and computer vision. The security of information is becoming very significant and difficult. Security cameras are presently common in airports, Offices, University, ATM, Bank and in any locations with a security system. Face recognition is a biometric system used to identify or verify a person from a digital image. Face Recognition system is used in security. Face recognition system should be able to automatically detect a face in an image. This involves extracts its features and then recognize it, regardless of lighting, expression, illumination, ageing, transformations (translate, rotate and scale image) and pose, which is a difficult task. This paper contains three sections. The first section describes the common methods like holistic matching method, feature extraction method and hybrid methods. The second section describes applications with examples and finally third section describes the future research directions of face recognition.; Comment: 3 pages, 1 figure

Object-based Place Recognition for Mobile Robots Using Panoramas

Ribes, Arturo; Ramisa, Arnau; Lopez de Mantaras, Ramon; Toledo, Ricardo
Fonte: IOS Press Publicador: IOS Press
Tipo: Artículo Formato: 1506236 bytes; application/pdf
ENG
Relevância na Pesquisa
45.98%
The original publication is available at http://www.booksonline.iospress.nl/Content/View.aspx?piid=10600; Object recognition has been widely researched for several decades and in the recent years new methods capable of general object classification have appeared. However very few work has been done to adapt these methods to the challenges raised by mobile robotics. In this article we discuss the data sources (appearence information, temporal context, etc.) that such methods could use and we review several state of the art object recognition methods that build in one or more of these sources. Finally we run an object based robot localization experiment using an state of the art object recognition method and we show that good results are obtained even with a naïve place descriptor.; This work has been partially funded by the FI grant and the BE grant from the AGAUR, the 2005/SGR/00093 project, supported by the Generalitat de Catalunya , the MIDCBR project grant TIN 200615140C0301, TIN 200615308C0202 and FEDER funds.; Peer reviewed

A Tale of Two Object Recognition Methods for Mobile Robots

Ramisa, Arnau; Vasudevan, Shrihari; Scharamuzza, Davide; Lopez de Mantaras, Ramon; Siegwart, Roland
Fonte: Springer Publicador: Springer
Tipo: Artículo Formato: 262807 bytes; application/pdf
ENG
Relevância na Pesquisa
55.9%
This original publication is available at www.springerlink.com; Object recognition is a key feature for building robots capable of moving and performing tasks in human environments. However, current object recognition research largely ignores the problems that the mobile robots context introduces. This work addresses the problem of applying these techniques to mobile robotics in a typical household scenario. We select two state-of-the-art object recognition methods, which are suitable to be adapted to mobile robots, and we evaluate them on a challenging dataset of typical household objects that caters to these requirements. The different advantages and drawbacks found for each method are highlighted, and some ideas for extending them are proposed. Evaluation is done comparing the number of detected objects and false positives for both approaches.; This work has been partially funded by the FI grant and the BE grant from the AGAUR, the European Social Fund, the 2005/SGR/00093 project, supported by the Generalitat de Catalunya , the MIDCBR project grant TIN 200615140C0301, TIN 200615308C0202 and FEDER funds.; Peer reviewed

Frontal sinus recognition for human identification

Falguera, Juan Rogelio; Pereira, Sérgio; Marana, Aparecido Nilceu
Fonte: Spie - Int Soc Optical Engineering Publicador: Spie - Int Soc Optical Engineering
Tipo: Conferência ou Objeto de Conferência Formato: S9440-S9440
ENG
Relevância na Pesquisa
45.9%
Many methods based on biometrics such as fingerprint, face, iris, and retina have been proposed for person identification. However, for deceased individuals, such biometric measurements are not available. In such cases, parts of the human skeleton can be used for identification, such as dental records, thorax, vertebrae, shoulder, and frontal sinus. It has been established in prior investigations that the radiographic pattern of frontal sinus is highly variable and unique for every individual. This has stimulated the proposition of measurements of the frontal sinus pattern, obtained from x-ray films, for skeletal identification. This paper presents a frontal sinus recognition method for human identification based on Image Foresting Transform and shape context. Experimental results (ERR = 5,82%) have shown the effectiveness of the proposed method.

Fuzzy Logic-Based Scenario Recognition from Video Sequences

Elbaşi,E.
Fonte: UNAM, Centro de Ciencias Aplicadas y Desarrollo Tecnológico Publicador: UNAM, Centro de Ciencias Aplicadas y Desarrollo Tecnológico
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/10/2013 EN
Relevância na Pesquisa
45.97%
In recent years, video surveillance and monitoring have gained importance because of security and safety concerns. Banks, borders, airports, stores, and parking areas are the important application areas. There are two main parts in scenario recognition: Low level processing, including moving object detection and object tracking, and feature extraction. We have developed new features through this work which are RUD (relative upper density), RMD (relative middle density) and RLD (relative lower density), and we have used other features such as aspect ratio, width, height, and color of the object. High level processing, including event start-end point detection, activity detection for each frame and scenario recognition for sequence of images. This part is the focus of our research, and different pattern recognition and classification methods are implemented and experimental results are analyzed. We looked into several methods of classification which are decision tree, frequency domain classification, neural network-based classification, Bayes classifier, and pattern recognition methods, which are control charts, and hidden Markov models. The control chart approach, which is a decision methodology, gives more promising results than other methodologies. Overlapping between events is one of the problems...