Relevant bibliographies by topics / Feature Recognition Methods

Academic literature on the topic 'Feature Recognition Methods'

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Published: 10 March 2023

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Journal articles on the topic "Feature Recognition Methods"

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Chatterji, B. N. "Feature Extraction Methods for Character Recognition." IETE Technical Review 3, no. 1 (January 1986): 9–22. http://dx.doi.org/10.1080/02564602.1986.11437879.

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Chaudhary, Gopal, Smriti Srivastava, and Saurabh Bhardwaj. "Feature Extraction Methods for Speaker Recognition: A Review." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 12 (September 17, 2017): 1750041. http://dx.doi.org/10.1142/s0218001417500410.

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This paper presents main paradigms of research for feature extraction methods to further augment the state of art in speaker recognition (SR) which has been recognized extensively in person identification for security and protection applications. Speaker recognition system (SRS) has become a widely researched topic for the last many decades. The basic concept of feature extraction methods is derived from the biological model of human auditory/vocal tract system. This work provides a classification-oriented review of feature extraction methods for SR over the last 55 years that are proven to be successful and have become the new stone to further research. Broadly, the review work is dichotomized into feature extraction methods with and without noise compensation techniques. Feature extraction methods without noise compensation techniques are divided into following categories: On the basis of high/low level of feature extraction; type of transform; speech production/auditory system; type of feature extraction technique; time variability; speech processing techniques. Further, feature extraction methods with noise compensation techniques are classified into noise-screened features, feature normalization methods, feature compensation methods. This classification-oriented review would endow the clear vision of readers to choose among different techniques and will be helpful in future research in this field.
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Long, Yi, Fu Rong Liu, and Guo Qing Qiu. "Research of Face Recognition Methods Based on Binding Feature Extraction." Applied Mechanics and Materials 568-570 (June 2014): 668–71. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.668.

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To address the problem that the dimension of the feature vector extracted by Local Binary Pattern (LBP) for face recognition is too high and Principal Component Analysis (PCA) extract features are not the best classification features, an efficient feature extraction method using LBP, PCA and Maximum scatter difference (MSD) has been introduced in this paper. The original face image is firstly divided into sub-images, then the LBP operator is applied to extract the histogram feature. and the feature dimensions are further reduced by using PCA. Finally,MSD is performed on the reduced PCA-based feature.The experimental results on ORL and Yale database demonstrate that the proposed method can classify more effectively and can get higher recognition rate than the traditional recognition methods.
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Jiefei Zhang, Jiefei Zhang. "MASFF: Multiscale Adaptive Spatial Feature Fusion Method for Vehicle Recognition." 電腦學刊 33, no. 1 (February 2022): 001–11. http://dx.doi.org/10.53106/199115992022023301001.

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<p>Traditional vehicle recognition methods have the disadvantages such as low efficiency and time-consuming due to the complex background and overlapping situation. In this paper, we propose a multiscale adptive spatial feature fusion (ASFF) method for vehicle recognition. First, it calculates the difference hash values of images. Then the hash value is used to judge the similarity between the current frame and the previous frame. When the similarity is less than the threshold value, it is input to ResNet18 model for detection. Using ResNet18 as the base network can reduce network parameters. Then, aiming at the problem that the detection effect of ASFF for vehicle recognition is not ideal, the offset loss and width-height loss are replaced by the intersection ratio loss. Meanwhile, multi-scale adaptive spatial feature fusion method is adopted to fuse the multi-level features of the network. The experimental results show that the average accuracy with proposed methed is increased by 2.1%. For BDD100K and Pascal VOC datasets, the average accuracy of predicted borders is increased by 5.5%, when the IoU is greater than 0.5. With the GTX1060Ti, the recognition speed can reach 149 frames per second. The multiscale ASFF in this paper can significantly improve the vehicle recognition accuracy.</p> <p>&nbsp;</p>
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Taha, Mohammed A., Hanaa M. Ahmed, and Saif O. Husain. "Iris Features Extraction and Recognition based on the Scale Invariant Feature Transform (SIFT)." Webology 19, no. 1 (January 20, 2022): 171–84. http://dx.doi.org/10.14704/web/v19i1/web19013.

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Iris Biometric authentication is considered to be one of the most dependable biometric characteristics for identifying persons. In actuality, iris patterns have invariant, stable, and distinguishing properties for personal identification. Due to its excellent dependability in personal identification, iris recognition has received more attention. Current iris recognition methods give good results especially when NIR and specific capture conditions are used in collaboration with the user. On the other hand, values related to images captured using VW are affected by noise such as blurry images, eye skin, occlusion, and reflection, which negatively affects the overall performance of the recognition systems. In both NIR and visible spectrum iris images, this article presents an effective iris feature extraction strategy based on the scale-invariant feature transform algorithm (SIFT). The proposed method was tested on different databases such as CASIA v1 and ITTD v1, as NIR images, as well as UBIRIS v1 as visible-light color images. The proposed system gave good accuracy rates compared to existing systems, as it gave an accuracy rate of (96.2%) when using CASIA v1 and (96.4%) in ITTD v1, while the system accuracy dropped to (84.0 %) when using UBIRIS v1.
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Hu, Gang, Kejun Wang, Yuan Peng, Mengran Qiu, Jianfei Shi, and Liangliang Liu. "Deep Learning Methods for Underwater Target Feature Extraction and Recognition." Computational Intelligence and Neuroscience 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/1214301.

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The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM) was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved.
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Prasad, Binod Kumar, and Rajdeep Kundu. "SEVERAL METHODS OF FEATURE EXTRACTION TO HELP IN OPTICAL CHARACTER RECOGNITION." International Journal of Students' Research in Technology & Management 5, no. 4 (November 27, 2017): 52–57. http://dx.doi.org/10.18510/ijsrtm.2017.547.

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An Optical Character Recognition (OCR) consists of three bold steps namely Preprocessing, Feature extraction, Classification. Methods of Feature extraction yield feature vectors based on which the classification of a testing pattern is executed. The paper aims at proposing some methods of feature extraction that may go a long way to recognize a Bengali numeral or character. Pixel Ex-OR Method presents a digital gating (Ex-OR) technique to extract the information in an image. Two successive elements of a row in image matrix have been Ex-ORed and the output is again Ex-ORed with the next element. Alphabetical coding codes a binary character image by means of letters of English alphabet. Directional features find gradient information using Sobel Masks to make position of stroke clear in an image. The features have been derived in eight standard directions and then these eight feature vectors are merged into four sets of features to reduce the system complexity and hence processing time is saved considerably. These features will help develop a Bengali numeral recognition system.
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Swiniarski, Roman W., and Andrzej Skowron. "Rough set methods in feature selection and recognition." Pattern Recognition Letters 24, no. 6 (March 2003): 833–49. http://dx.doi.org/10.1016/s0167-8655(02)00196-4.

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Due Trier, Øivind, Anil K. Jain, and Torfinn Taxt. "Feature extraction methods for character recognition-A survey." Pattern Recognition 29, no. 4 (April 1996): 641–62. http://dx.doi.org/10.1016/0031-3203(95)00118-2.

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Ear, Mong Heng, Cheng Cheng, Salem Mostafa Hamdy, and Alhazmi Marwah. "Feature Recognition for Virtual Environments." Applied Mechanics and Materials 610 (August 2014): 642–46. http://dx.doi.org/10.4028/www.scientific.net/amm.610.642.

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This paper demonstrates methods to recognize 3D designed features for virtual environments and apply them to Virtual assembly. STEP is a standard of Product data Exchange for interfacing different design systems, but it cannot be used as input for virtual environments. In order to use feature data in virtual assembly environments, main data source from a STEP file should be recognized and features should be re-built. First, Attributed Adjacency Graph (AAG) is used to analyze and express the boundary representation; second, a feature-tree of a part is constructed; third, using the AAG and feature-tree as inputs, we analyze and extract of features with a feature recognition algorithm; finally, various levels of detail of object geometric shapes is built and expressed in XML for virtual assembly applications.
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Dissertations / Theses on the topic "Feature Recognition Methods"

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Wu, Zhili. "Kernel based learning methods for pattern and feature analysis." HKBU Institutional Repository, 2004. http://repository.hkbu.edu.hk/etd_ra/619.

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Cohen, Gregory Kevin. "Event-Based Feature Detection, Recognition and Classification." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066204/document.

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La detection, le suivi de cible et la reconnaissance de primitives visuelles constituent des problèmes fondamentaux de la vision robotique. Ces problématiques sont réputés difficiles et sources de défis. Malgré les progrès en puissance de calcul des machines, le gain en résolution et en fréquence des capteurs, l’état-de-l’art de la vision robotique peine à atteindre des performances en coût d’énergie et en robustesse qu’offre la vision biologique. L’apparition des nouveaux capteurs, appelés "rétines de silicium” tel que le DVS (Dynamic Vision Sensor) et l’ATIS (Asynchronous Time-based Imaging Sensor) reproduisant certaines fonctionnalités des rétines biologiques, ouvre la voie à de nouveaux paradigmes pour décrire et modéliser la perception visuelle, ainsi que pour traiter l’information visuelle qui en résulte. Les tâches de suivi et de reconnaissance de formes requièrent toujours la caractérisation et la mise en correspondance de primitives visuelles. La détection de ces dernières et leur description nécessitent des approches fondamentalement différentes de celles employées en vision robotique traditionnelle. Cette thèse développe et formalise de nouvelles méthodes de détection et de caractérisation de primitives spatio-temporel des signaux acquis par les rétines de silicium (plus communément appelés capteurs “event-based”). Une structure théorique pour les tâches de détection, de suivi, de reconnaissance et de classification de primitives est proposée. Elle est ensuite validée par des données issues de ces capteurs “event-based”,ainsi que par des bases données standard du domaine de la reconnaissance de formes, convertit au préalable à un format compatible avec la representation “événement”. Les résultats présentés dans cette thèse démontrent les potentiels et l’efficacité des systèmes "event-based”. Ce travail fournit une analyse approfondie de différentes méthodes de reconnaissance de forme et de classification “event-based". Cette thèse propose ensuite deux solutions basées sur les primitives. Deux mécanismes d’apprentissage, un purement événementiel et un autre, itératif, sont développés puis évalués pour leur capacité de classification et de robustesse. Les résultats démontrent la validité de la classification “event-based” et souligne l’importance de la dynamique de la scène dans les tâches primordiales de définitions des primitives et de leur détection et caractétisation
One of the fundamental tasks underlying much of computer vision is the detection, tracking and recognition of visual features. It is an inherently difficult and challenging problem, and despite the advances in computational power, pixel resolution, and frame rates, even the state-of-the-art methods fall far short of the robustness, reliability and energy consumption of biological vision systems. Silicon retinas, such as the Dynamic Vision Sensor (DVS) and Asynchronous Time-based Imaging Sensor (ATIS), attempt to replicate some of the benefits of biological retinas and provide a vastly different paradigm in which to sense and process the visual world. Tasks such as tracking and object recognition still require the identification and matching of local visual features, but the detection, extraction and recognition of features requires a fundamentally different approach, and the methods that are commonly applied to conventional imaging are not directly applicable. This thesis explores methods to detect features in the spatio-temporal information from event-based vision sensors. The nature of features in such data is explored, and methods to determine and detect features are demonstrated. A framework for detecting, tracking, recognising and classifying features is developed and validated using real-world data and event-based variations of existing computer vision datasets and benchmarks. The results presented in this thesis demonstrate the potential and efficacy of event-based systems. This work provides an in-depth analysis of different event-based methods for object recognition and classification and introduces two feature-based methods. Two learning systems, one event-based and the other iterative, were used to explore the nature and classification ability of these methods. The results demonstrate the viability of event-based classification and the importance and role of motion in event-based feature detection
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Nelson, Jonas. "Methods for Locating Distinct Features in Fingerprint Images." Thesis, Linköping University, Department of Science and Technology, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1147.

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With the advance of the modern information society, the importance of reliable identity authentication has increased dramatically. Using biometrics as a means for verifying the identity of a person increases both the security and the convenience of the systems. By using yourself to verify your identity such risks as lost keys and misplaced passwords are removed and by virtue of this, convenience is also increased. The most mature and well-developed biometric technique is fingerprint recognition. Fingerprints are unique for each individual and they do not change over time, which is very desirable in this application. There are multitudes of approaches to fingerprint recognition, most of which work by identifying so called minutiae and match fingerprints based on these.

In this diploma work, two alternative methods for locating distinct features in fingerprint images have been evaluated. The Template Correlation Method is based on the correlation between the image and templates created to approximate the homogenous ridge/valley areas in the fingerprint. The high-dimension of the feature vectors from correlation is reduced through principal component analysis. By visualising the dimension reduced data by ordinary plotting and observing the result classification is performed by locating anomalies in feature space, where distinct features are located away from the non-distinct.

The Circular Sampling Method works by sampling in concentric circles around selected points in the image and evaluating the frequency content of the resulting functions. Each images used here contains 30400 pixels which leads to sampling in many points that are of no interest. By selecting the sampling points this number can be reduced. Two approaches to sampling points selection has been evaluated. The first restricts sampling to occur only along valley bottoms of the image, whereas the second uses orientation histograms to select regions where there is no single dominant direction as sampling positions. For each sampling position an intensity function is achieved by circular sampling and a frequency spectrum of this function is achieved through the Fast Fourier Transform. Applying criteria to the relationships of the frequency components classifies each sampling location as either distinct or non-distinct.

Using a cyclic approach to evaluate the methods and their potential makes selection at various stages possible. Only the Circular Sampling Method survived the first cycle, and therefore all tests from that point on are performed on thismethod alone. Two main errors arise from the tests, where the most prominent being the number of spurious points located by the method. The second, which is equally serious but not as common, is when the method misclassifies visually distinct features as non-distinct. Regardless of the problems, these tests indicate that the method holds potential but that it needs to be subject to further testing and optimisation. These tests should focus on the three main properties of the method: noise sensitivity, radial dependency and translation sensitivity.

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Hassan, Wael. "Comparing Geomorphometric Pattern Recognition Methods for Semi-Automated Landform Mapping." Ohio University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou160690391009081.

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Brennan, Michael. "Comparison of automated feature extraction methods for image based screening of cancer cells." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-167602.

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Image based screening is an important tool used in research for development of drugs to fight cancer. Phase contrast video microscopy - a cheap and fast image screening technology - enables a rapid generation of large amounts of data, which requires a fast method for analysis of this data. As videos contain a lot of redundant information, the difficulty is to extract usable information in form of features from the videos, by compressing available information, or filter out redundant data. In this thesis, the problem is approached in an experimental fashion where three different methods have been devised and tested, to evaluate different ways to automatically extract features from phase contrast microscopy videos containing cultured cancer cells. The three methods considered are, in order: an adaptive linear filter, an on-line clustering algorithm, and an artificial neural network. The ambition is that outputs from these methods can create time-varying histograms of features that can be used in further mathematical modeling of cell dynamics. It is concluded that, while the results of the first method is not impressive and can be dismissed, the remaining two are more promising and are able to successfully extract features automatically and aggregate them into time-varying histograms.
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Le, Faucheur Xavier Jean Maurice. "Statistical methods for feature extraction in shape analysis and bioinformatics." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33911.

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The presented research explores two different problems of statistical data analysis. In the first part of this thesis, a method for 3D shape representation, compression and smoothing is presented. First, a technique for encoding non-spherical surfaces using second generation wavelet decomposition is described. Second, a novel model is proposed for wavelet-based surface enhancement. This part of the work aims to develop an efficient algorithm for removing irrelevant and noise-like variations from 3D shapes. Surfaces are encoded using second generation wavelets, and the proposed methodology consists of separating noise-like wavelet coefficients from those contributing to the relevant part of the signal. The empirical-based Bayesian models developed in this thesis threshold wavelet coefficients in an adaptive and robust manner. Once thresholding is performed, irrelevant coefficients are removed and the inverse wavelet transform is applied to the clean set of wavelet coefficients. Experimental results show the efficiency of the proposed technique for surface smoothing and compression. The second part of this thesis proposes using a non-parametric clustering method for studying RNA (RiboNucleic Acid) conformations. The local conformation of RNA molecules is an important factor in determining their catalytic and binding properties. RNA conformations can be characterized by a finite set of parameters that define the local arrangement of the molecule in space. Their analysis is particularly difficult due to the large number of degrees of freedom, such as torsion angles and inter-atomic distances among interacting residues. In order to understand and analyze the structural variability of RNA molecules, this work proposes a methodology for detecting repetitive conformational sub-structures along RNA strands. Clusters of similar structures in the conformational space are obtained using a nearest-neighbor search method based on the statistical mechanical Potts model. The proposed technique is a mostly automatic clustering algorithm and may be applied to problems where there is no prior knowledge on the structure of the data space, in contrast to many other clustering techniques. First, results are reported for both single residue conformations- where the parameter set of the data space includes four to seven torsional angles-, and base pair geometries. For both types of data sets, a very good match is observed between the results of the proposed clustering method and other known classifications, with only few exceptions. Second, new results are reported for base stacking geometries. In this case, the proposed classification is validated with respect to specific geometrical constraints, while the content and geometry of the new clusters are fully analyzed.
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Huang, X. (Xiaohua). "Methods for facial expression recognition with applications in challenging situations." Doctoral thesis, Oulun yliopisto, 2014. http://urn.fi/urn:isbn:9789526206561.

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Abstract In recent years, facial expression recognition has become a useful scheme for computers to affectively understand the emotional state of human beings. Facial representation and facial expression recognition under unconstrained environments have been two critical issues for facial expression recognition systems. This thesis contributes to the research and development of facial expression recognition systems from two aspects: first, feature extraction for facial expression recognition, and second, applications to challenging conditions. Spatial and temporal feature extraction methods are introduced to provide effective and discriminative features for facial expression recognition. The thesis begins with a spatial feature extraction method. This descriptor exploits magnitude while it improves local quantized pattern using improved vector quantization. It also makes the statistical patterns domain-adaptive and compact. Then, the thesis discusses two spatiotemporal feature extraction methods. The first method uses monogenic signal analysis as a preprocessing stage and extracts spatiotemporal features using local binary pattern. The second method extracts sparse spatiotemporal features using sparse cuboids and spatiotemporal local binary pattern. Both methods increase the discriminative capability of local binary pattern in the temporal domain. Based on feature extraction methods, three practical conditions, including illumination variations, facial occlusion and pose changes, are studied for the applications of facial expression recognition. First, with near-infrared imaging technique, a discriminative component-based single feature descriptor is proposed to achieve a high degree of robustness and stability to illumination variations. Second, occlusion detection is proposed to dynamically detect the occluded face regions. A novel system is further designed for handling effectively facial occlusion. Lastly, multi-view discriminative neighbor preserving embedding is developed to deal with pose change, which formulates multi-view facial expression recognition as a generalized eigenvalue problem. Experimental results on publicly available databases show that the effectiveness of the proposed approaches for the applications of facial expression recognition
Tiivistelmä Kasvonilmeiden tunnistamisesta on viime vuosina tullut tietokoneille hyödyllinen tapa ymmärtää affektiivisesti ihmisen tunnetilaa. Kasvojen esittäminen ja kasvonilmeiden tunnistaminen rajoittamattomissa ympäristöissä ovat olleet kaksi kriittistä ongelmaa kasvonilmeitä tunnistavien järjestelmien kannalta. Tämä väitöskirjatutkimus myötävaikuttaa kasvonilmeitä tunnistavien järjestelmien tutkimukseen ja kehittymiseen kahdesta näkökulmasta: piirteiden irrottamisesta kasvonilmeiden tunnistamista varten ja kasvonilmeiden tunnistamisesta haastavissa olosuhteissa. Työssä esitellään spatiaalisia ja temporaalisia piirteenirrotusmenetelmiä, jotka tuottavat tehokkaita ja erottelukykyisiä piirteitä kasvonilmeiden tunnistamiseen. Ensimmäisenä työssä esitellään spatiaalinen piirteenirrotusmenetelmä, joka parantaa paikallisia kvantisoituja piirteitä käyttämällä parannettua vektorikvantisointia. Menetelmä tekee myös tilastollisista malleista monikäyttöisiä ja tiiviitä. Seuraavaksi työssä esitellään kaksi spatiotemporaalista piirteenirrotusmenetelmää. Ensimmäinen näistä käyttää esikäsittelynä monogeenistä signaalianalyysiä ja irrottaa spatiotemporaaliset piirteet paikallisia binäärikuvioita käyttäen. Toinen menetelmä irrottaa harvoja spatiotemporaalisia piirteitä käyttäen harvoja kuusitahokkaita ja spatiotemporaalisia paikallisia binäärikuvioita. Molemmat menetelmät parantavat paikallisten binärikuvioiden erottelukykyä ajallisessa ulottuvuudessa. Piirteenirrotusmenetelmien pohjalta työssä tutkitaan kasvonilmeiden tunnistusta kolmessa käytännön olosuhteessa, joissa esiintyy vaihtelua valaistuksessa, okkluusiossa ja pään asennossa. Ensiksi ehdotetaan lähi-infrapuna kuvantamista hyödyntävää diskriminatiivistä komponenttipohjaista yhden piirteen kuvausta, jolla saavutetaan korkea suoritusvarmuus valaistuksen vaihtelun suhteen. Toiseksi ehdotetaan menetelmä okkluusion havainnointiin, jolla dynaamisesti havaitaan peittyneet kasvon alueet. Uudenlainen menetelmä on kehitetty käsittelemään kasvojen okkluusio tehokkaasti. Viimeiseksi työssä on kehitetty moninäkymäinen diskriminatiivisen naapuruston säilyttävään upottamiseen pohjautuva menetelmä käsittelemään pään asennon vaihtelut. Menetelmä kuvaa moninäkymäisen kasvonilmeiden tunnistamisen yleistettynä ominaisarvohajotelmana. Kokeelliset tulokset julkisilla tietokannoilla osoittavat tässä työssä ehdotetut menetelmät suorituskykyisiksi kasvonilmeiden tunnistamisessa
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Oliveira, e. Cruz Rafael Menelau. "Methods for dynamic selection and fusion of ensemble of classifiers." Universidade Federal de Pernambuco, 2011. https://repositorio.ufpe.br/handle/123456789/2436.

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Made available in DSpace on 2014-06-12T15:58:13Z (GMT). No. of bitstreams: 2 arquivo3310_1.pdf: 8155353 bytes, checksum: 2f4dcd5adb2b0b1a23c40bf343b36b34 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2011
Faculdade de Amparo à Ciência e Tecnologia do Estado de Pernambuco
Ensemble of Classifiers (EoC) é uma nova alternative para alcançar altas taxas de reconhecimento em sistemas de reconhecimento de padrões. O uso de ensemble é motivado pelo fato de que classificadores diferentes conseguem reconhecer padrões diferentes, portanto, eles são complementares. Neste trabalho, as metodologias de EoC são exploradas com o intuito de melhorar a taxa de reconhecimento em diferentes problemas. Primeiramente o problema do reconhecimento de caracteres é abordado. Este trabalho propõe uma nova metodologia que utiliza múltiplas técnicas de extração de características, cada uma utilizando uma abordagem diferente (bordas, gradiente, projeções). Cada técnica é vista como um sub-problema possuindo seu próprio classificador. As saídas deste classificador são utilizadas como entrada para um novo classificador que é treinado para fazer a combinação (fusão) dos resultados. Experimentos realizados demonstram que a proposta apresentou o melhor resultado na literatura pra problemas tanto de reconhecimento de dígitos como para o reconhecimento de letras. A segunda parte da dissertação trata da seleção dinâmica de classificadores (DCS). Esta estratégia é motivada pelo fato que nem todo classificador pertencente ao ensemble é um especialista para todo padrão de teste. A seleção dinâmica tenta selecionar apenas os classificadores que possuem melhor desempenho em uma dada região próxima ao padrão de entrada para classificar o padrão de entrada. É feito um estudo sobre o comportamento das técnicas de DCS demonstrando que elas são limitadas pela qualidade da região em volta do padrão de entrada. Baseada nesta análise, duas técnicas para seleção dinâmica de classificadores são propostas. A primeira utiliza filtros para redução de ruídos próximos do padrão de testes. A segunda é uma nova proposta que visa extrair diferentes tipos de informação, a partir do comportamento dos classificadores, e utiliza estas informações para decidir se um classificador deve ser selecionado ou não. Experimentos conduzidos em diversos problemas de reconhecimento de padrões demonstram que as técnicas propostas apresentam um aumento de performance significante
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Křístek, Jakub. "Rozpoznávání ručně kreslených objektů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221329.

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This work deals with recognition of hand-drawn objects traced by children with mental disorders. The aim is to classify object’s geometrical primitives into classes so then can be plotted along with the idealized shape of the input object. Level of mental retardation is determined by the variance of the input (drawn) object from idealized shape of the object (artwork).
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Radermacher, Matthew Jeffery. "Pattern Recognition and Feature Extraction Using Liar-Derived Elevation Models in GIS: A Comparison Between Visualization Techniques and Automated Methods for Identifying Prehistoric Ditch-Fortified Sites in North Dakota." Thesis, North Dakota State University, 2016. https://hdl.handle.net/10365/28010.

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As technologies advance in the fields of geology and computer science, new methods in remote sensing, including data acquisition and analyses, make it possible to accurately model diverse landscapes. Archaeological applications of these systems are becoming increasingly popular, especially in regards to site prospection and the geospatial analysis of cultural features. Different methodologies were used to identify fortified ditch features of anthropogenic origin using aerial lidar from known prehistoric sites in North Dakota. The results were compared in an attempt to develop a system aimed at detecting similar, unrecorded morphological features on the landscape. The successful development of this program will allow archaeological investigators to review topography and locate specific features on the surface that otherwise could be difficult to identify as a result of poor visibility in the field.
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North Dakota State University
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Books on the topic "Feature Recognition Methods"

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Shishkin, Aleksey. Methods of digital processing and speech recognition. ru: INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1904325.

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The monograph discusses the theory, algorithms and practical methods of implementing digital processing and recognition of speech signals. The basics of mathematical analysis of digital signals necessary for speech processing are presented. The acoustic theory of speech formation with the construction of a general discrete model is briefly described. The main characteristic features of speech signals, as well as methods of their isolation are considered. Hidden Markov models and the architecture of traditional recognition systems based on them are described in detail. Weighted finite converters used to increase the efficiency and speed up the process of decoding acoustic signals are considered. The main architectures of artificial neural networks and examples of integrated (end-to-end) speech recognition systems based on them are presented. It is intended for students, postgraduates, researchers and specialists dealing with speech signal processing, pattern recognition and artificial intelligence.
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Anthropological atlas of male facial features. 2nd ed. Frankfurt: Verlag fur Polizeiwissenschaft, 2008.

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Jain, L. C. Modeling machine emotions for realizing intelligence: Foundations and applications. Berlin: Springer-Verlag, 2010.

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Lamel, Lori, and Jean-Luc Gauvain. Speech Recognition. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0016.

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Speech recognition is concerned with converting the speech waveform, an acoustic signal, into a sequence of words. Today's approaches are based on a statistical modellization of the speech signal. This article provides an overview of the main topics addressed in speech recognition, which are, acoustic-phonetic modelling, lexical representation, language modelling, decoding, and model adaptation. Language models are used in speech recognition to estimate the probability of word sequences. The main components of a generic speech recognition system are, main knowledge sources, feature analysis, and acoustic and language models, which are estimated in a training phase, and the decoder. The focus of this article is on methods used in state-of-the-art speaker-independent, large-vocabulary continuous speech recognition (LVCSR). Primary application areas for such technology are dictation, spoken language dialogue, and transcription for information archival and retrieval systems. Finally, this article discusses issues and directions of future research.
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Müller, Christian. Speaker Classification I: Fundamentals, Features, and Methods. Springer London, Limited, 2007.

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Hilgurt, S. Ya, and O. A. Chemerys. Reconfigurable signature-based information security tools of computer systems. PH “Akademperiodyka”, 2022. http://dx.doi.org/10.15407/akademperiodyka.458.297.

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The book is devoted to the research and development of methods for combining computational structures for reconfigurable signature-based information protection tools for computer systems and networks in order to increase their efficiency. Network security tools based, among others, on such AI-based approaches as deep neural networking, despite the great progress shown in recent years, still suffer from nonzero recognition error probability. Even a low probability of such an error in a critical infrastructure can be disastrous. Therefore, signature-based recognition methods with their theoretically exact matching feature are still relevant when creating information security systems such as network intrusion detection systems, antivirus, anti-spam, and wormcontainment systems. The real time multi-pattern string matching task has been a major performance bottleneck in such systems. To speed up the recognition process, developers use a reconfigurable hardware platform based on FPGA devices. Such platform provides almost software flexibility and near-ASIC performance. The most important component of a signature-based information security system in terms of efficiency is the recognition module, in which the multipattern matching task is directly solved. It must not only check each byte of input data at speeds of tens and hundreds of gigabits/sec against hundreds of thousand or even millions patterns of signature database, but also change its structure every time a new signature appears or the operating conditions of the protected system change. As a result of the analysis of numerous examples of the development of reconfigurable information security systems, three most promising approaches to the construction of hardware circuits of recognition modules were identified, namely, content-addressable memory based on digital comparators, Bloom filter and Aho–Corasick finite automata. A method for fast quantification of components of recognition module and the entire system was proposed. The method makes it possible to exclude resource-intensive procedures for synthesizing digital circuits on FPGAs when building complex reconfigurable information security systems and their components. To improve the efficiency of the systems under study, structural-level combinational methods are proposed, which allow combining into single recognition device several matching schemes built on different approaches and their modifications, in such a way that their advantages are enhanced and disadvantages are eliminated. In order to achieve the maximum efficiency of combining methods, optimization methods are used. The methods of: parallel combining, sequential cascading and vertical junction have been formulated and investigated. The principle of multi-level combining of combining methods is also considered and researched. Algorithms for the implementation of the proposed combining methods have been developed. Software has been created that allows to conduct experiments with the developed methods and tools. Quantitative estimates are obtained for increasing the efficiency of constructing recognition modules as a result of using combination methods. The issue of optimization of reconfigurable devices presented in hardware description languages is considered. A modification of the method of affine transformations, which allows parallelizing such cycles that cannot be optimized by other methods, was presented. In order to facilitate the practical application of the developed methods and tools, a web service using high-performance computer technologies of grid and cloud computing was considered. The proposed methods to increase efficiency of matching procedure can also be used to solve important problems in other fields of science as data mining, analysis of DNA molecules, etc. Keywords: information security, signature, multi-pattern matching, FPGA, structural combining, efficiency, optimization, hardware description language.
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Shah, Minal A., and Rabih O. Darouiche. Spinal Epidural Abscess. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199937837.003.0152.

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Spinal epidural abscess is a rare and debilitating illness that requires prompt recognition to prevent unfavorable outcomes. Despite increased awareness of the disease and improved imaging methods, spinal epidural abscess sometimes remains a diagnostic and therapeutic challenge; as a result, morbidity and mortality can be high. Optimal management of spinal epidural abscess requires early intervention and coordination with a multidisciplinary team, including emergency medicine physicians, infectious disease specialists, radiologists, neurosurgeons, orthopedists, internists, and hospitalists. This chapter reviews the epidemiology, microbiology, pathogenesis, clinical features, diagnosis, treatment, and outcome of spinal epidural abscess.
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Mehta, Vaishali, Dolly Sharma, Monika Mangla, Anita Gehlot, Rajesh Singh, and Sergio Márquez Sánchez, eds. Challenges and Opportunities for Deep Learning Applications in Industry 4.0. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150360601220101.

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The competence of deep learning for the automation and manufacturing sector has received astonishing attention in recent times. The manufacturing industry has recently experienced a revolutionary advancement despite several issues. One of the limitations for technical progress is the bottleneck encountered due to the enormous increase in data volume for processing, comprising various formats, semantics, qualities and features. Deep learning enables detection of meaningful features that are difficult to perform using traditional methods. The book takes the reader on a technological voyage of the industry 4.0 space. Chapters highlight recent applications of deep learning and the associated challenges and opportunities it presents for automating industrial processes and smart applications. Chapters introduce the reader to a broad range of topics in deep learning and machine learning. Several deep learning techniques used by industrial professionals are covered, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical project methodology. Readers will find information on the value of deep learning in applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. The book also discusses prospective research directions that focus on the theory and practical applications of deep learning in industrial automation. Therefore, the book aims to serve as a comprehensive reference guide for industrial consultants interested in industry 4.0, and as a handbook for beginners in data science and advanced computer science courses.
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Maynard, Douglas W., and John Heritage, eds. The Ethnomethodology Program. Oxford University PressNew York, 2022. http://dx.doi.org/10.1093/oso/9780190854409.001.0001.

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Abstract Harold Garfinkel's Studies in Ethnomethodology (1967) was published a little more than 50 years ago. Since then, there has been a substantial—although often subterranean—growth in ethnomethodological work and influence. Studies in and appreciation of ethnomethodological work continue to grow, but the breadth and penetration of his insights and inspiration for ongoing research have yet to secure their full measure of recognition. The origins of Garfinkel’s ethnomethodology include both the theorizing of Parsonian sociology and the phenomenology of Alfred Schütz, whose analysis of the trust conditions making for a stable society informed Garfinkel’s analysis of the “taken-for-granted” aspects of the ordinary social world. Further theoretical contributions include the development of analyses related to the “documentary method of interpretation,” highly innovative analyses of rules and rule usage, and a radical treatment of such phenomena as language use and accountability. Separate chapters highlight contributions to such areas or subdisciplines as conversation analysis, ethnomethodology’s distinctive forms of ethnographic inquiry, and its influences on a host of substantive domains, including legal environments, science and technology, workplace and organizational inquiries, survey research, social problems and deviance, disability and atypical interaction, and others. Ethnomethodology especially helped to set the agenda for gender studies, while also developing insights for inquiries into racial and ethnic features of everyday life and experience. Still, there is much of what Garfinkel called “unfinished business,” which means that ethnomethodological inquiries are continuing to intensify and develop.
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Book chapters on the topic "Feature Recognition Methods"

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Zbiciak, Rafał, and Cezary Grabowik. "Feature Recognition Methods Review." In Proceedings of the 13th International Scientific Conference, 605–15. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50938-9_63.

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Rudnicki, Witold R., Mariusz Wrzesień, and Wiesław Paja. "All Relevant Feature Selection Methods and Applications." In Feature Selection for Data and Pattern Recognition, 11–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45620-0_2.

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Yang, Zhao, Lianwen Jin, and Dapeng Tao. "A Comparative Study of Several Feature Extraction Methods for Person Re-identification." In Biometric Recognition, 268–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35136-5_33.

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Pudil, Pavel, Jana Novovičová, and Petr Somol. "Recent Feature Selection Methods in Statistical Pattern Recognition." In Pattern Recognition and String Matching, 565–615. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4613-0231-5_23.

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Revathy, K. "Feature Recognition and Classification Using Spectral Methods." In Artificial Intelligence in Recognition and Classification of Astrophysical and Medical Images, 339–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-47518-7_5.

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Su, Chang, Jiefang Deng, Yong Yang, and Guoyin Wang. "Expression Recognition Methods Based on Feature Fusion." In Brain Informatics, 346–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15314-3_33.

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Maldonado, Sebastían, and Gaston L’Huillier. "SVM-Based Feature Selection and Classification for Email Filtering." In Pattern Recognition - Applications and Methods, 135–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36530-0_11.

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Paja, Wiesław, Krzysztof Pancerz, and Piotr Grochowalski. "Generational Feature Elimination and Some Other Ranking Feature Selection Methods." In Advances in Feature Selection for Data and Pattern Recognition, 97–112. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67588-6_6.

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Llobet, Rafael, Roberto Paredes, and Juan C. Pérez-Cortés. "Comparison of Feature Extraction Methods for Breast Cancer Detection." In Pattern Recognition and Image Analysis, 495–502. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11492542_61.

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Doquire, Gauthier, and Michel Verleysen. "A Performance Evaluation of Mutual Information Estimators for Multivariate Feature Selection." In Pattern Recognition - Applications and Methods, 51–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36530-0_5.

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Conference papers on the topic "Feature Recognition Methods"

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"IMPROVING FEATURE LEVEL LIKELIHOODS USING CLOUD FEATURES." In International Conference on Pattern Recognition Applications and Methods. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003777904310437.

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Talab, Mohammed Ahmed, Neven Ali Qahraman, Mais Muneam Aftan, Alaa Hamid Mohammed, and Mohd Dilshad Ansari. "Local Feature Methods Based Facial Recognition." In 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). IEEE, 2022. http://dx.doi.org/10.1109/hora55278.2022.9799910.

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"SCHOG Feature for Pedestrian Detection." In International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004813000600066.

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Wang, Eric, Yong Se Kim, and Yoonhwan Woo. "Feature Recognition Using Combined Convex and Maximal Volume Decompositions." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85522.

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Next generation process planning systems should be capable of dealing with industrial demands of versatility, flexibility, and agility for product manufacturing. Development of process planning system is heavily dependent on feature recognition, but presently there is no satisfactory feature recognition system relying on a single method. In this paper, we describe a hybrid feature recognition method for machining features that combines three feature recognition technologies: graph-based, convex volume decomposition, and maximal volume decomposition. Based on an evaluation of the strengths and weaknesses of these methods, we integrate them in a sequential workflow, such that each method recognizes features according to its strengths, and successively simplifies the part model for the following methods. We identify two anomalous cases arising from the application of maximal volume decomposition, and discuss their cure by introducing limiting halfspaces. All recognized features are combined into a unified hierarchical feature representation, which captures feature interaction information, including geometry-based machining precedence relations.
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Cai, Le, Sam Ferguson, Haiyan Lu, and Gengfa Fang. "Feature Selection Approaches for Optimising Music Emotion Recognition Methods." In 12th International Conference on Artificial Intelligence, Soft Computing and Applications. Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.122302.

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The high feature dimensionality is a challenge in music emotion recognition. There is no common consensus on a relation between audio features and emotion. The MER system uses all available features to recognize emotion; however, this is not an optimal solution since it contains irrelevant data acting as noise. In this paper, we introduce a feature selection approach to eliminate redundant features for MER. We created a Selected Feature Set (SFS) based on the feature selection algorithm (FSA) and benchmarked it by training with two models, Support Vector Regression (SVR) and Random Forest (RF) and comparing them against with using the Complete Feature Set (CFS). The result indicates that the performance of MER has improved for both Random Forest (RF) and Support Vector Regression (SVR) models by using SFS. We found using FSA can improve performance in all scenarios, and it has potential benefits for model efficiency and stability for MER task.
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"Accelerated Nonlinear Gaussianization for Feature Extraction." In International Conference on Pattern Recognition Applications and Methods. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004204701210126.

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Krämer, Marc Steven, Simon Hardt, and Klaus-Dieter Kuhnert. "Image Features in Space - Evaluation of Feature Algorithms for Motion Estimation in Space Scenarios." In 7th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006555303000308.

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Arai, Masayuki. "Feature extraction methods for cartoon character recognition." In 2012 5th International Congress on Image and Signal Processing (CISP). IEEE, 2012. http://dx.doi.org/10.1109/cisp.2012.6469644.

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Regli, William C., Satyandra K. Gupta, and Dana S. Nau. "Interactive Feature Recognition Using Multi-Processor Methods." In ASME 1995 Design Engineering Technical Conferences collocated with the ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/detc1995-0232.

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Abstract The availability of low-cost computational power is enabling development of increasingly sophisticated CAD software. Automation of design and manufacturing activities poses many difficult computational problems. Design is an interactive process and speed is a critical factor in systems that enable designers to explore and experiment with alternative ideas. As more downstream manufacturing activities are considered during the design phase, computational costs become problematic. Achieving interactivity requires a sophisticated allocation of computational resources in order to perform realistic design analyses and generate feedback in real time. This paper presents our initial efforts to use distributed algorithms to recognize machining features from solid models of parts with large numbers of features and many geometric and topological entities. Our goal is to outline how significant improvements in computation time can be obtained using existing hardware and software tools. An implementation of our approach is discussed.
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Gil, Fabian, and Stanislaw Osowski. "Feature Selection Methods in Gene Recognition Problem." In 2020 IEEE 21st International Conference on Computational Problems of Electrical Engineering (CPEE). IEEE, 2020. http://dx.doi.org/10.1109/cpee50798.2020.9238726.

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Reports on the topic "Feature Recognition Methods"

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Varastehpour, Soheil, Hamid Sharifzadeh, Iman Ardekani, and Abdolhossein Sarrafzadeh. Human Biometric Traits: A Systematic Review Focusing on Vascular Patterns. Unitec ePress, December 2020. http://dx.doi.org/10.34074/ocds.086.

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Authentication methods based on human traits, including fingerprint, face, iris, and palm print, have developed significantly, and currently they are mature enough to be reliably considered for human identification purposes. Recently, as a new research area, a few methods based on non-facial skin features such as vein patterns have been developed. This literature review paper explores some key biometric systems such as face recognition, iris recognition, fingerprint, and palm print, and discusses their respective advantages and disadvantages; then by providing a comprehensive analysis of these traits, and their applications, vein pattern recognition is reviewed.
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Solovyanenko, N. I. LEGAL REGULATION OF THE USE OF ELECTRONIC SIGNATURES IN ELECTRONIC COMMERCE. DOI CODE, 2021. http://dx.doi.org/10.18411/0131-5226-2021-70002.

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The article is devoted to the legal problems of using documents signed with electronic signatures in electronic commerce. The article considers the different legal regime of electronic documents depending on the type of electronic signature. Legal features of a qualified electronic signature are analyzed. The legal status of a certification service provider and its legal functions in e-commerce are examined. The conclusion is made about the recognition of electronic documents as a priority method of legal interaction in the field of electronic commerce and the complication of the legal construction of an electronic signature.
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Markova, Oksana, Serhiy Semerikov, and Maiia Popel. СoCalc as a Learning Tool for Neural Network Simulation in the Special Course “Foundations of Mathematic Informatics”. Sun SITE Central Europe, May 2018. http://dx.doi.org/10.31812/0564/2250.

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The role of neural network modeling in the learning сontent of special course “Foundations of Mathematic Informatics” was discussed. The course was developed for the students of technical universities – future IT-specialists and directed to breaking the gap between theoretic computer science and it’s applied applications: software, system and computing engineering. CoCalc was justified as a learning tool of mathematical informatics in general and neural network modeling in particular. The elements of technique of using CoCalc at studying topic “Neural network and pattern recognition” of the special course “Foundations of Mathematic Informatics” are shown. The program code was presented in a CofeeScript language, which implements the basic components of artificial neural network: neurons, synaptic connections, functions of activations (tangential, sigmoid, stepped) and their derivatives, methods of calculating the network`s weights, etc. The features of the Kolmogorov–Arnold representation theorem application were discussed for determination the architecture of multilayer neural networks. The implementation of the disjunctive logical element and approximation of an arbitrary function using a three-layer neural network were given as an examples. According to the simulation results, a conclusion was made as for the limits of the use of constructed networks, in which they retain their adequacy. The framework topics of individual research of the artificial neural networks is proposed.
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Saldanha, Ian J., Andrea C. Skelly, Kelly Vander Ley, Zhen Wang, Elise Berliner, Eric B. Bass, Beth Devine, et al. Inclusion of Nonrandomized Studies of Interventions in Systematic Reviews of Intervention Effectiveness: An Update. Agency for Healthcare Research and Quality (AHRQ), September 2022. http://dx.doi.org/10.23970/ahrqepcmethodsguidenrsi.

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Introduction: Nonrandomized studies of interventions (NRSIs) are observational or experimental studies of the effectiveness and/or harms of interventions, in which participants are not randomized to intervention groups. There is increasingly widespread recognition that advancements in the design and analysis of NRSIs allow NRSI evidence to have a much more prominent role in decision making, and not just as ancillary evidence to randomized controlled trials (RCTs). Objective: To guide decisions about inclusion of NRSIs for addressing the effects of interventions in systematic reviews (SRs), this chapter updates the 2010 guidance on inclusion of NRSIs in Agency for Healthcare Research and Quality (AHRQ) Evidence-based Practice Center (EPC) SRs. The chapter focuses on considerations for decisions to include or exclude NRSIs in SRs. Methods: In November 2020, AHRQ convened a 20-member workgroup that comprised 13 members representing 8 of 9 AHRQ-appointed EPCs, 3 AHRQ representatives, 1 independent consultant with expertise in SRs, and 3 representatives of the AHRQ-appointed Scientific Resource Center. The workgroup received input from the full EPC Program regarding the process and specific issues through discussions at a virtual meeting and two online surveys regarding challenges with NRSI inclusion in SRs. One survey focused on current practices by EPCs regarding NRSI inclusion in ongoing and recently completed SRs. The other survey focused on the appropriateness, completeness, and usefulness of existing EPC Program methods guidance. The workgroup considered the virtual meeting and survey input when identifying aspects of the guidance that needed updating. The workgroup used an informal method for generating consensus about guidance. Disagreements were resolved through discussion. Results: We outline considerations for the inclusion of NRSIs in SRs of intervention effectiveness. We describe the strengths and limitations of RCTs, study design features and types of NRSIs, and key considerations for making decisions about inclusion of NRSIs (during the stages of topic scoping and refinement, SR team formation, protocol development, SR conduct, and SR reporting). We discuss how NRSIs may be applicable for the decisional dilemma being addressed in the SR, threats to the internal validity of NRSIs, as well as various data sources and advanced analytic methods that may be used in NRSIs. Finally, we outline an approach to incorporating NRSIs within an SR and key considerations for reporting. Conclusion: The main change from the previous guidance is the overall approach to decisions about inclusion of NRSIs in EPC SRs. Instead of recommending NRSI inclusion only if RCTs are insufficient to address the Key Question, this updated guidance handles NRSI evidence as a valuable source of information and lays out important considerations for decisions about the inclusion of NRSIs in SRs of intervention effectiveness. Different topics may require different decisions regarding NRSI inclusion. This guidance is intended to improve the utility of the final product to end-users. Inclusion of NRSIs will increase the scope, time, and resources needed to complete SRs, and NRSIs pose potential threats to validity, such as selection bias, confounding, and misclassification of interventions. Careful consideration must be given to both concerns.
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