The Experts below are selected from a list of 18561 Experts worldwide ranked by ideXlab platform
Francisco Escolano - One of the best experts on this subject based on the ideXlab platform.
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active stereo based Compact Mapping
Intelligent Robots and Systems, 2005Co-Authors: Diego Viejo, Juan Manuel Saez, Miguel Cazorla, Francisco EscolanoAbstract:In this paper we propose a method for extracting the planes from a 3D dense map. Three-dimensional data is acquired using active stereo in order to fill texture gaps which are typical in indoor environments. Then, a randomized SLAM algorithm recently proposed by the authors is applied to compute the 3D map by teleoperating a mobile robot. A 3D mesh generation algorithm specially designed to triangulate not solids but open objects, is the basis for computing the main planes in the map after clustering the normals of the vertices in the mesh. Finally, we present our experimental results in indoor environments.
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Compact Mapping in plane parallel environments using stereo vision
Iberoamerican Congress on Pattern Recognition, 2003Co-Authors: Juan Manuel Saez, Antonio Penalver, Francisco EscolanoAbstract:In this paper we propose a method for transforming a 3D map of the environment, composed by a cloud of millions of points, into a Compact representation in terms of basic geometric primitives, 3D planes in this case. These planes, with their texture, yield a very useful representation in robot navigation tasks like localization and motion control. Our method estimates the main planes in the environment (walls, floor and ceiling) using point classification, based on the orientation of their normal and its relative position. Once we have inferred the 3D planes we map their textures using the appearance information of the observations, obtaining a realistic model of the scene.
Juan Manuel Saez - One of the best experts on this subject based on the ideXlab platform.
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active stereo based Compact Mapping
Intelligent Robots and Systems, 2005Co-Authors: Diego Viejo, Juan Manuel Saez, Miguel Cazorla, Francisco EscolanoAbstract:In this paper we propose a method for extracting the planes from a 3D dense map. Three-dimensional data is acquired using active stereo in order to fill texture gaps which are typical in indoor environments. Then, a randomized SLAM algorithm recently proposed by the authors is applied to compute the 3D map by teleoperating a mobile robot. A 3D mesh generation algorithm specially designed to triangulate not solids but open objects, is the basis for computing the main planes in the map after clustering the normals of the vertices in the mesh. Finally, we present our experimental results in indoor environments.
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Compact Mapping in plane parallel environments using stereo vision
Iberoamerican Congress on Pattern Recognition, 2003Co-Authors: Juan Manuel Saez, Antonio Penalver, Francisco EscolanoAbstract:In this paper we propose a method for transforming a 3D map of the environment, composed by a cloud of millions of points, into a Compact representation in terms of basic geometric primitives, 3D planes in this case. These planes, with their texture, yield a very useful representation in robot navigation tasks like localization and motion control. Our method estimates the main planes in the environment (walls, floor and ceiling) using point classification, based on the orientation of their normal and its relative position. Once we have inferred the 3D planes we map their textures using the appearance information of the observations, obtaining a realistic model of the scene.
Patrick D Anderson - One of the best experts on this subject based on the ideXlab platform.
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eigenmode analysis of advective diffusive transport by the Compact Mapping method
European Journal of Mechanics B-fluids, 2015Co-Authors: Oleksandr O Gorodetskyi, Mfm Michel Speetjens, Patrick D AndersonAbstract:Abstract The present study concerns an efficient spectral analysis of advective–diffusive transport in periodic flows by the way of a Compact version of the diffusive Mapping method. Key to the Compact approach is the representation of the scalar evolution by only a small subset of the eigenmodes of the Mapping matrix, and capturing the relevant features of the transient towards the homogeneous state. This has been demonstrated for purely advective transport in an earlier study by Gorodetskyi et al. (2012). Here this ansatz is extended to advective–diffusive transport and more complex 3D flow fields, motivated primarily by the importance of molecular diffusion in many mixing processes. The study exposed an even greater potential for such transport problems due to the progressive widening of the spectral gaps in the eigenvalue spectrum of the Mapping matrix with increasing diffusion. This facilitates substantially larger reductions of the eigenmode basis compared to the purely advective limit for a given approximation tolerance. The Compact diffusive Mapping method is demonstrated for a representative three-dimensional prototype micro-mixer. This revealed a reliable prediction of (transient) scalar evolutions and mixing patterns with reductions of the eigenmode basis by up to a factor 2000. The accurate estimation of the truncation error from the eigenvalue spectrum enables the systematic determination of the spectral cut-off for a desired degree of approximation. The validity and universality of the presumed correlation between spectral cut-off and truncation error have been established. This has the important practical consequence that the cut-off can a priori be chosen such that the truncation error remains within a preset tolerance. This offers a way to systematically (and reliably) employ the Compact Mapping method for an in-depth analysis of advective–diffusive transport.
Oleksandr O Gorodetskyi - One of the best experts on this subject based on the ideXlab platform.
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eigenmode analysis of advective diffusive transport by the Compact Mapping method
European Journal of Mechanics B-fluids, 2015Co-Authors: Oleksandr O Gorodetskyi, Mfm Michel Speetjens, Patrick D AndersonAbstract:Abstract The present study concerns an efficient spectral analysis of advective–diffusive transport in periodic flows by the way of a Compact version of the diffusive Mapping method. Key to the Compact approach is the representation of the scalar evolution by only a small subset of the eigenmodes of the Mapping matrix, and capturing the relevant features of the transient towards the homogeneous state. This has been demonstrated for purely advective transport in an earlier study by Gorodetskyi et al. (2012). Here this ansatz is extended to advective–diffusive transport and more complex 3D flow fields, motivated primarily by the importance of molecular diffusion in many mixing processes. The study exposed an even greater potential for such transport problems due to the progressive widening of the spectral gaps in the eigenvalue spectrum of the Mapping matrix with increasing diffusion. This facilitates substantially larger reductions of the eigenmode basis compared to the purely advective limit for a given approximation tolerance. The Compact diffusive Mapping method is demonstrated for a representative three-dimensional prototype micro-mixer. This revealed a reliable prediction of (transient) scalar evolutions and mixing patterns with reductions of the eigenmode basis by up to a factor 2000. The accurate estimation of the truncation error from the eigenvalue spectrum enables the systematic determination of the spectral cut-off for a desired degree of approximation. The validity and universality of the presumed correlation between spectral cut-off and truncation error have been established. This has the important practical consequence that the cut-off can a priori be chosen such that the truncation error remains within a preset tolerance. This offers a way to systematically (and reliably) employ the Compact Mapping method for an in-depth analysis of advective–diffusive transport.
Sholik Mohammad - One of the best experts on this subject based on the ideXlab platform.
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Klasifikasi Sel Serviks Pada Citra Pap Smear Berdasarkan Fitur Bentuk Deskriptor Regional Dan Fitur Tekstur Uniform Rotated Local Binary Pattern
2017Co-Authors: Sholik MohammadAbstract:Kanker serviks merupakan salah satu penyebab utama kematian kanker pada wanita di dunia. Hal ini dapat dicegah jika diperiksa pada tahap pre-cancerous. Papanicolaou test adalah pemeriksaan kanker serviks secara manual yang membutuhkan waktu lama dalam mengklasifikasi sel, sehingga dibutuhkan sistem klasifikasi sel berbasis komputer. Perubahan orientasi objek pada saat akuisisi memerlukan metode ekstraksi fitur yang invariant terhadap rotasi. Area dan Compactness merupakan deskriptor regional bentuk yang tidak berpengaruh terhadap orientasi objek dan deskriptor tekstur merupakan deskriptor penting untuk mendeteksi setiap tahapan kanker. Ekstraksi fitur tekstur yang telah digunakan dalam kombinasi fitur sebelumnya untuk klasifikasi sel serviks pada dataset Herlev antara lain Homogenitas GLCM, Uniform Rotation Invariant Local Binary Pattern (LBPriu), dan Local Binary Pattern Histogram Fourier (LBP-HF). Namun perhitungan GLCM sensitif terhadap rotasi, LBPriu mengabaikan beberapa informasi orientasi lokal dan kehilangan beberapa informasi diskriminatif citra karena pemetaan yang padat, dan transformasi fourier LBP-HF mengabaikan penataan struktur histogram dengan hanya mempertimbangkan magnitude spektrum transformasi, sehingga kehilangan beberapa informasi diskriminatif dan informasi frekuensi citra. Uniform Rotated Local Binary Pattern (uRLBP) merupakan metode ekstraksi fitur yang dapat mengatasi kelemahan metode tekstur sebelumnya dengan mengatur arah referensi lokal mengikuti orientasi objek yang dapat mempertahankan informasi orientasi lokal dan informasi diskriminatif citra sehingga mencapai invariant terhadap rotasi. Penelitian sebelumnya menunjukkan peningkatan akurasi ketika fitur bentuk dan fitur tekstur dikombinasikan yang menjadi dasar dalam mengombinasikan fitur bentuk dan fitur tekstur untuk membedakan ciri antar kelas sel agar lebih spesifik. Penelitian ini mengusulkan kombinasi fitur bentuk deskriptor regional dan fitur tekstur uRLBP yang invariant terhadap rotasi untuk mengklasifikasikan sel serviks pada citra pap smear. Dari evaluasi diperoleh bahwa kombinasi fitur bentuk dan fitur tekstur untuk klasifikasi berdasarkan dua kategori sel dan tujuh kelas sel untuk klasifikasi sel serviks pada citra pap smear menggunakan Fuzzy k-NN, yaitu dengan akurasi tertinggi 91.59% dan 67.89% ketika parameter (P=8,R=3) pada uRLBP dan k=14 pada Fuzzy k-NN. ================================================================= Cervical cancer is one of the leading causes of cancer death in women in the world. This can be prevented if examined at a pre-cancerous stage. Papanicolaou test is a manual cervical cancer examination that takes a long time in classifying the cell, so it takes a computer-based classification system. Changes in object orientation at the time of acquisition require feature extraction methods that produces a rotation invariant. Area and Compactness are regional descriptor shapes that have no effect on object orientation and texture descriptor is an important to detect every stage of cancer. Extraction of texture features that have been used in previous feature combinations for cervical cell classification in Herlev dataset including homogeneity of GLCM, Uniform Rotation Invariant Local Binary Pattern (LBPriu), and Local Binary Pattern Histogram Fourier (LBP-HF). But the GLCM calculation is sensitive to rotation, LBPriu ignores some local orientation information and loses some discriminative information of image due to the Compact Mapping, and the fourier transform of LBP-HF completely ignores the structure arrangement of histogram by only considering the magnitude of the transformation spectrum, thereby losing some discriminative information and the information present in the frequency from image. Uniform Rotated Local Binary Pattern (uRLBP) is a feature extraction method that able to overcome the limitation of previous texture methods by setting the local reference direction according to object orientation that able to maintain local orientation information and discriminative information so as to achieve rotation invariant. Previous studies have proven that classification accuracy will be increased when the shape and texture features were combine. This become the motivation in combining shape and texture feature to discriminate the between-class characteristics of the cell to be more specific. This study proposes the combination of regional descriptor shape and uRLBP texture features that produces a rotation invariant feature to classify cervical cells in pap smear images. The evaluation result shows that the combination of shape and texture features is able to produce a rotation invariant feature and used to classify cervical cells in pap smear images based on two cell categories and seven cell classes using Fuzzy k-NN, with highest accuracy is 91.59% and 67.89% respectively when parameters (P=8,R=3) on uRLBP and k=14 on Fuzzy k-NN