Search Results - "Caltech"

Object and Scene Category Recognition using a Combination of Dense Color SIFT Descriptors by Rassem, Taha Hussein Alaaldeen, Khoo, Bee Ee, Bayuaji, Luhur, Makbol, Nasrin M., Suryanti, Awang

“…The performances of these descriptors and all their possible combinations were evaluated using challenging data sets. Caltech-04, Caltech-101, Caltech-256, Graz-02 are examples of object data sets used, whereas Oliva and Torralba data set (OT) and SUN-398 are examples of scene data sets. …”
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Resfeats: Residual network based features for image classification by Mahmood, A., Bennamoun, M., An, Senjian, Sohel, F.

“…Experimental results are provided to show the effectiveness of ResFeats with state-of-the-art classification accuracies on Caltech-101, Caltech-256 and MLC datasets and a significant performance improvement on MIT-67 dataset compared to the widely used CNN features.…”
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Vulnerable Road Users Detection using Convolutional Deep Feedforward Network by Lau, Mian Mian

“…A new convolutional deep feedforward network (C-DFN) is proposed to detect vulnerable road users at 57.9% misclassification rate using Caltech Dataset. Instead of going deeper, three C-DFN is stacked to achieve 43.4% misclassification rate. …”
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Joint learning and dictionary construction for pattern recognition by Pham, DucSon, Venkatesh, Svetha

“…The method is then evaluated over two important classification problems in computer vision: object categorization of natural images using the Caltech 101 database and face recognition using the Extended Yale B face database. …”
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Big data and a smarter university: a literature review by Hayikader, Sameer, Mohamad Niyaz Khan, Mohd. Toriq Khan, Ahmad Dahlan, Abdul Rahman

“…Research has proven that smart universities such as Caltech University and Northwestern University which are among the top universities in the world has also endeavored into the big data arena.…”
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Dynamic fast local Laplacian completed local ternary pattern (dynamic FLapCLTP) for face recognition by Sam, Yin Yee

“…Finally, the proposed FLapCLTP and dynamic FLapCLTP are evaluated for facial recognition systems using ORL Faces, Sheffield Face, Collection Facial Images, Georgia Tech Face, Caltech Pedestrian Faces 1999, JAFFE, FEI Face and YALE datasets. …”
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The mass of a millisecond pulsar by Jacoby, B., Hotan, Aidan, Bailes, M., Ord, Stephen, Kulkarni, S.

“…We report on nearly two years of timing observations of the low-mass binary millisecond pulsar, PSR J1909-3744 with the Caltech-Parkes-Swinburne Recorder II (CPSR2), a new instrument that gives unprecedented timing precision. …”
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Sub-micron scale distributions of trace elements in zircon by Hofmann, A., Valley, J., Watson, E., Cavosie, Aaron, Eiler, J.

“…Ion images were made using the Caltech Microanalysis Center's CAMECA NanoSIMS 50L with an O- primary beam focused to ~300 nm on the sample surface. …”
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Performance Evaluation of Completed Local Ternary Pattern (CLTP) for Face Image Recognition by Sam, Yin Yee, Rassem, Taha H., Mohammed, Mohammed Falah, Makbol, Nasrin M.

“…Several face datasets are used in this paper, such as Georgia Tech Face, Collection Facial Images, Caltech Pedestrian Faces, JAFFE, FEI, YALE, ORL, UMIST datasets.…”
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kNN Classification of Epilepsy Brainwaves by Mahfuzah, Mustafa, Nor Anis Aneza, Lokman

“…The Epilepsy brainwaves were downloaded from http://www.vis.caltech.edu/~rodri/data.htm of Fp1 and Fp2 channels which is from rats. …”
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A new fast local laplacian completed local ternary count (FLL-CLTC) for facial image classification by Alalayah, Khaled M., Reyazur Rashid, Irshad, Rassem, Taha H., Mohammed, Badiea Abdulkarem

“…The FLL-CLTC achieved 99.1%, 86.93%, 93.21%, 84.92% and 99.15% with JAFFE, YALE, Georgia Tech, Caltech and ORL face image datasets, respectively.…”
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Redshift determination and CO line excitation modeling for the multiply lensed galaxy HLSW-01 by Scott, K., Lupu, R., Aguirre, J., Auld, R., Aussel, H., Baker, A., Beelen, A., Bock, J., Bradford, C., Brisbin, D., Burgarella, D., Carpenter, J., Chanial, P., Chapman, S., Clements, D., Conley, A., Cooray, A., Cox, P., Dowell, C., Eales, S., Farrah, D., Franceschini, A., Frayer, D., Gavazzi, R., Glenn, J., Griffin, M., Harris, A., Ibar, E., Ivison, R., Kamenetzky, J., Kim, S., Krips, M., Maloney, P., Matsuhara, H., Mortier, A., Murphy, E., Naylor, B., Neri, R., Nguyen, H., Oliver, S., Omont, A., Page, M., Papageorgiou, A., Pearson, C., Pérez-Fournon, I., Pohlen, M., Rawlings, J., Raymond, G., Riechers, D., Rodighiero, G., Roseboom, I., Rowan-Robinson, M., Scott, D., Seymour, Nick, Smith, A., Symeonidis, M., Tugwell, K., Vaccari, M., Vieira, J., Vigroux, L., Wang, L., Wright, G., Zmuidzinas, J.

“…With the 100 GHz instantaneous bandwidth of the Z-Spec instrument on the Caltech Submillimeter Observatory, we robustly identify a redshift of z = 2.958 ± 0.007 for this source, using the simultaneous detection of four CO emission lines (J = 7 → 6, J = 8 → 7, J = 9 → 8, and J = 10 → 9). …”
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