The Experts below are selected from a list of 307515 Experts worldwide ranked by ideXlab platform
Chun Wei Yap - One of the best experts on this subject based on the ideXlab platform.
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padel descriptor an open source software to calculate molecular Descriptors and fingerprints
Journal of Computational Chemistry, 2011Co-Authors: Chun Wei YapAbstract:Introduction PaDEL-Descriptor is a software for calculating molecular Descriptors and fingerprints. The software currently calculates 797 Descriptors (663 1D, 2D Descriptors, and 134 3D Descriptors) and 10 types of fingerprints. These Descriptors and fingerprints are calculated mainly using The Chemistry Development Kit. Some additional Descriptors and fingerprints were added, which include atom type electrotopological state Descriptors, McGowan volume, molecular linear free energy relation Descriptors, ring counts, count of chemical substructures identified by Laggner, and binary fingerprints and count of chemical substructures identified by Klekota and Roth. Methods PaDEL-Descriptor was developed using the Java language and consists of a library component and an interface component. The library component allows it to be easily integrated into quantitative structure activity relationship software to provide the descriptor calculation feature while the interface component allows it to be used as a standalone software. The software uses a Master/Worker pattern to take advantage of the multiple CPU cores that are present in most modern computers to speed up calculations of molecular Descriptors. Results The software has several advantages over existing standalone molecular descriptor calculation software. It is free and open source, has both graphical user interface and command line interfaces, can work on all major platforms (Windows, Linux, MacOS), supports more than 90 different molecular file formats, and is multithreaded. Conclusion PaDEL-Descriptor is a useful addition to the currently available molecular descriptor calculation software. The software can be downloaded at http://padel.nus.edu.sg/software/padeldescriptor. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2011
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PaDEL‐descriptor: An open source software to calculate molecular Descriptors and fingerprints
Journal of computational chemistry, 2010Co-Authors: Chun Wei YapAbstract:Introduction PaDEL-Descriptor is a software for calculating molecular Descriptors and fingerprints. The software currently calculates 797 Descriptors (663 1D, 2D Descriptors, and 134 3D Descriptors) and 10 types of fingerprints. These Descriptors and fingerprints are calculated mainly using The Chemistry Development Kit. Some additional Descriptors and fingerprints were added, which include atom type electrotopological state Descriptors, McGowan volume, molecular linear free energy relation Descriptors, ring counts, count of chemical substructures identified by Laggner, and binary fingerprints and count of chemical substructures identified by Klekota and Roth. Methods PaDEL-Descriptor was developed using the Java language and consists of a library component and an interface component. The library component allows it to be easily integrated into quantitative structure activity relationship software to provide the descriptor calculation feature while the interface component allows it to be used as a standalone software. The software uses a Master/Worker pattern to take advantage of the multiple CPU cores that are present in most modern computers to speed up calculations of molecular Descriptors. Results The software has several advantages over existing standalone molecular descriptor calculation software. It is free and open source, has both graphical user interface and command line interfaces, can work on all major platforms (Windows, Linux, MacOS), supports more than 90 different molecular file formats, and is multithreaded. Conclusion PaDEL-Descriptor is a useful addition to the currently available molecular descriptor calculation software. The software can be downloaded at http://padel.nus.edu.sg/software/padeldescriptor. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2011
Dengsheng Zhang - One of the best experts on this subject based on the ideXlab platform.
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A novel fusion approach in the extraction of kernel descriptor with improved effectiveness and efficiency
Multimedia Tools and Applications, 2021Co-Authors: Priyabrata Karmakar, Shyh Wei Teng, Dengsheng ZhangAbstract:Image representation using feature Descriptors is crucial. A number of histogram-based Descriptors are widely used for this purpose. However, histogram-based Descriptors have certain limitations and kernel Descriptors (KDES) are proven to overcome them. Moreover, the combination of more than one KDES performs better than an individual KDES. Conventionally, KDES fusion is performed by concatenating them after the gradient, colour and shape Descriptors have been extracted. This approach has limitations in regard to the efficiency as well as the effectiveness. In this paper, we propose a novel approach to fuse different image features before the descriptor extraction, resulting in a compact descriptor which is efficient and effective. In addition, we have investigated the effect on the proposed descriptor when texture-based features are fused along with the conventionally used features. Our proposed descriptor is examined on two publicly available image databases and shown to provide outstanding performances.
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Complementing a region descriptor for shape retrieval
2008Co-Authors: Atul Sajjanhar, Dengsheng Zhang, Wanlei ZhouAbstract:In this paper, we propose to complement a region descriptor for shape retrieval with a contour descriptor; the descriptor thus obtained is a composite descriptor. We use a combination of a region descriptor and contour descriptor to obtain the proposed descriptor. A contour extraction technique is also proposed; it is required for extracting the contour of the image prior to applying a contour descriptor. Normalization of Descriptors is required before disparate Descriptors can be combined into a single descriptor. Hence, we test the performance of two common methods for normalization of Descriptors and adopt the better performing method. Experiments are performed to test the effectiveness of the proposed descriptor for retrieval of 2d images. Sets of composite Descriptors, obtained by assigning different weights to the region component and the contour component, are also evaluated. Item S8 within the MPEG-7 Still Images Content Set is used for performing experiments; this dataset consists of 3621 still images. Experimental results show that the proposed descriptor is effective.
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Evaluation of MPEG-7 shape Descriptors against other shape Descriptors
Multimedia Systems, 2003Co-Authors: Dengsheng ZhangAbstract:Shape is an important image feature-it is one of the primary low level image features exploited in content-based image retrieval (CBIR). There are generally two types of shape Descriptors in the literature: contour-based and region-based. In MPEG-7, the curvature scale space descriptor (CSSD) and Zernike moment descriptor (ZMD) have been adopted as the contour-based shape descriptor and region-based shape descriptor, respectively. In this paper, the two shape Descriptors are evaluated against other shape Descriptors, and the two shape Descriptors are also evaluated against each other. Standard methodology is used in the evaluation. Specifically, we use standard databases, large data sets and query sets, commonly used performance measurement and guided principles. A Java-based client-server retrieval framework has been implemented to facilitate the evaluation. Results show that Fourier descriptor (FD) outperforms CSSD, and that CSSD can be replaced by either FD or ZMD.
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shape based image retrieval using generic fourier descriptor
Signal Processing-image Communication, 2002Co-Authors: Dengsheng ZhangAbstract:Shape description is one of the key parts of image content description for image retrieval. Most of the existing shape Descriptors are usually either application dependent or non-robust, making them undesirable for generic shape description. In this paper, a generic Fourier descriptor (GFD) is proposed to overcome the drawbacks of existing shape representation techniques. The proposed shape descriptor is derived by applying two-dimensional Fourier transform on a polar-raster sampled shape image. The acquired shape descriptor is application independent and robust. Experimental results show that the proposed GFD outperforms common contour-based and region-based shape Descriptors.
Irene Hassinger - One of the best experts on this subject based on the ideXlab platform.
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Toward the development of a quantitative tool for predicting dispersion of nanocomposites under non-equilibrium processing conditions
Journal of Materials Science, 2016Co-Authors: Irene Hassinger, Xiaolin Li, He Zhao, Hongyi Xu, Yanhui Huang, Aditya Prasad, Linda S. Schadler, Wei Chen, L. Catherine BrinsonAbstract:Developing process-structure relationships that predict the impact of the filler-matrix interfacial thermodynamics is crucial to nanocomposite design. This work focuses on developing quantitative relationships between the filler-matrix interfacial energy, the processing conditions, and the nanoparticle dispersion in polymer nanocomposites. We use a database of nanocomposites made of polypropylene, polystyrene, and poly(methyl methacrylate) with three different surface-modified silica nanoparticles under controlled processing conditions. The silica surface was modified with three different monofunctional silanes: octyldimethylmethoxysilane, chloropropyldimethylethoxysilane, and aminopropyldimethylethoxysilane. Three Descriptors were used to establish the relationship between interfacial energy, processing conditions, and final nanoparticle dispersion. The ratio of the work of adhesion between filler and polymer to the work of adhesion between filler to filler (descriptor: $$ W_{\text{PF}} /W_{\text{FF}} $$ W PF / W FF ) and the mixing energy for the production of the nanocomposites (descriptor: E _ γ ) are used to determine the final dispersion state of the nanoparticles. The dispersion state is described using a descriptor that characterizes the amount of interfacial area from TEM images (descriptor: $$ \bar{I}_{\text{filler}} $$ I ¯ filler ). In order to capture the Descriptors accurately, the TEM images of the nanocomposites are binarized using a pixel-wise neighbor-dependent Niblack thresholding algorithm. The significance of the microstructural Descriptors was ranked using supervised learning and the interfacial area emerged as the most significant descriptor for describing the nanoparticle dispersion. Our results show a stronger dependence of the final dispersion on the interfacial energy than the processing conditions. Nevertheless, for the final dispersion state, both Descriptors have to be taken into account. We also introduce a matrix-dependent term to establish a quantitatively non-linear relationship between the processing and microstructure Descriptors.
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Toward the development of a quantitative tool for predicting dispersion of nanocomposites under non-equilibrium processing conditions
Journal of Materials Science, 2016Co-Authors: Irene Hassinger, Xiaolin Li, He Zhao, Hongyi Xu, Yanhui Huang, Aditya Prasad, Linda S. Schadler, Wei Chen, L. Catherine BrinsonAbstract:Developing process-structure relationships that predict the impact of the filler-matrix interfacial thermodynamics is crucial to nanocomposite design. This work focuses on developing quantitative relationships between the filler-matrix interfacial energy, the processing conditions, and the nanoparticle dispersion in polymer nanocomposites. We use a database of nanocomposites made of polypropylene, polystyrene, and poly(methyl methacrylate) with three different surface-modified silica nanoparticles under controlled processing conditions. The silica surface was modified with three different monofunctional silanes: octyldimethylmethoxysilane, chloropropyldimethylethoxysilane, and aminopropyldimethylethoxysilane. Three Descriptors were used to establish the relationship between interfacial energy, processing conditions, and final nanoparticle dispersion. The ratio of the work of adhesion between filler and polymer to the work of adhesion between filler to filler (descriptor: \( W_{\text{PF}} /W_{\text{FF}} \)) and the mixing energy for the production of the nanocomposites (descriptor: Eγ) are used to determine the final dispersion state of the nanoparticles. The dispersion state is described using a descriptor that characterizes the amount of interfacial area from TEM images (descriptor: \( \bar{I}_{\text{filler}} \)). In order to capture the Descriptors accurately, the TEM images of the nanocomposites are binarized using a pixel-wise neighbor-dependent Niblack thresholding algorithm. The significance of the microstructural Descriptors was ranked using supervised learning and the interfacial area emerged as the most significant descriptor for describing the nanoparticle dispersion. Our results show a stronger dependence of the final dispersion on the interfacial energy than the processing conditions. Nevertheless, for the final dispersion state, both Descriptors have to be taken into account. We also introduce a matrix-dependent term to establish a quantitatively non-linear relationship between the processing and microstructure Descriptors.
Subrata Pramanik - One of the best experts on this subject based on the ideXlab platform.
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modeling bioconcentration factor bcf using mechanistically interpretable Descriptors computed from open source tool padel descriptor
Environmental Science and Pollution Research, 2014Co-Authors: Subrata PramanikAbstract:Predictive regression-based models for bioconcentration factor (BCF) have been developed using mechanistically interpretable Descriptors computed from open source tool PaDEL-Descriptor (http://padel.nus.edu.sg/software/padeldescriptor/). A data set of 522 diverse chemicals has been used for this modeling study, and extended topochemical atom (ETA) indices developed by the present authors’ group were chosen as the Descriptors. Due to the importance of lipohilicity in modeling BCF, XLogP (computed partition coefficient) was also tried as an additional descriptor. Genetic function approximation followed by multiple linear regression algorithm was applied to select Descriptors, and subsequent partial least squares analyses were performed to establish mathematical equations for BCF prediction. The model generated from only ETA indices shows importance of seven Descriptors in model development, while the model generated from ETA Descriptors along with XlogP shows importance of four Descriptors in model development. In general, BCF depends on lipophilicity, presence of heteroatoms, presence of halogens, fused ring system, hydrogen bonding groups, etc. The developed models show excellent statistical qualities and predictive ability. The developed models were used also for prediction of an external data set available from the literature, and good quality of predictions (R2pred = 0.812 and 0.826) was demonstrated. Thus, BCF can be predicted using ETA and XlogP Descriptors calculated from open source PaDEL-Descriptor software in the context of aquatic chemical toxicity management.
Jitendra Malik - One of the best experts on this subject based on the ideXlab platform.
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recognizing objects in range data using regional point Descriptors
European Conference on Computer Vision, 2004Co-Authors: Andrea Frome, Daniel Huber, Ravi Krishna Kolluri, Thomas Bulow, Jitendra MalikAbstract:Recognition of three dimensional (3D) objects in noisy and cluttered scenes is a challenging problem in 3D computer vision. One approach that has been successful in past research is the regional shape descriptor. In this paper, we introduce two new regional shape Descriptors: 3D shape contexts and harmonic shape contexts. We evaluate the performance of these Descriptors on the task of recognizing vehicles in range scans of scenes using a database of 56 cars. We compare the two novel Descriptors to an existing descriptor, the spin image, showing that the shape context based Descriptors have a higher recognition rate on noisy scenes and that 3D shape contexts outperform the others on cluttered scenes.
