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5 results on '"Kämäräinen, J.-K."'

1. The Eighth Visual Object Tracking VOT2020 Challenge Results

Author

Kristan, M., Leonardis, A., Matas, J., Felsberg, Michael, Pflugfelder, R., Kämäräinen, J.-K., Danelljan, M., Zajc, L.C., Lukežic, A., Drbohlav, O., He, Linbo, Zhang, Yushan, Yan, S., Yang, J., Fernández, G., Hauptmann, A., Memarmoghadam, A., García-Martín, Á., Robinson, Andreas, Varfolomieiev, A., Gebrehiwot, A.H., Uzun, B., Yan, B., Li, B., Qian, C., Tsai, C.-Y., Micheloni, C., Wang, D., Wang, F., Xie, F., Järemo-Lawin, Felix, Gustafsson, F., Foresti, G.L., Bhat, G., Chen, G., Ling, H., Zhang, H., Cevikalp, H., Zhao, H., Bai, H., Kuchibhotla, H.C., Saribas, H., Fan, H., Ghanei-Yakhdan, H., Li, H., Peng, H., Lu, H., Khaghani, J., Bescos, J., Li, J., Fu, J., Yu, J., Xu, J., Kittler, J., Yin, J., Lee, J., Yu, K., Liu, K., Yang, K., Dai, K., Cheng, L., Zhang, L., Wang, L., Van, Gool L., Bertinetto, L., Dunnhofer, M., Cheng, M., Dasari, M.M., Wang, N., Zhang, P., Torr, P.H.S., Wang, Q., Timofte, R., Gorthi, R.K.S., Choi, S., Marvasti-Zadeh, S.M., Zhao, S., Kasaei, S., Qiu, S., Chen, S., Schön, T.B., Xu, T., Lu, W., Hu, W., Zhou, W., Qiu, X., Ke, X., Wu, X.-J., Zhang, X., Yang, X., Zhu, X., Jiang, Y., Wang, Y., Chen, Y., Ye, Y., Li, Y., Yao, Y., Lee, Y., Gu, Y., Wang, Z., Tang, Z., Feng, Z.-H., Mai, Z., Zhang, Z., Wu, Z., Ma, Z., Kristan, M., Leonardis, A., Matas, J., Felsberg, Michael, Pflugfelder, R., Kämäräinen, J.-K., Danelljan, M., Zajc, L.C., Lukežic, A., Drbohlav, O., He, Linbo, Zhang, Yushan, Yan, S., Yang, J., Fernández, G., Hauptmann, A., Memarmoghadam, A., García-Martín, Á., Robinson, Andreas, Varfolomieiev, A., Gebrehiwot, A.H., Uzun, B., Yan, B., Li, B., Qian, C., Tsai, C.-Y., Micheloni, C., Wang, D., Wang, F., Xie, F., Järemo-Lawin, Felix, Gustafsson, F., Foresti, G.L., Bhat, G., Chen, G., Ling, H., Zhang, H., Cevikalp, H., Zhao, H., Bai, H., Kuchibhotla, H.C., Saribas, H., Fan, H., Ghanei-Yakhdan, H., Li, H., Peng, H., Lu, H., Khaghani, J., Bescos, J., Li, J., Fu, J., Yu, J., Xu, J., Kittler, J., Yin, J., Lee, J., Yu, K., Liu, K., Yang, K., Dai, K., Cheng, L., Zhang, L., Wang, L., Van, Gool L., Bertinetto, L., Dunnhofer, M., Cheng, M., Dasari, M.M., Wang, N., Zhang, P., Torr, P.H.S., Wang, Q., Timofte, R., Gorthi, R.K.S., Choi, S., Marvasti-Zadeh, S.M., Zhao, S., Kasaei, S., Qiu, S., Chen, S., Schön, T.B., Xu, T., Lu, W., Hu, W., Zhou, W., Qiu, X., Ke, X., Wu, X.-J., Zhang, X., Yang, X., Zhu, X., Jiang, Y., Wang, Y., Chen, Y., Ye, Y., Li, Y., Yao, Y., Lee, Y., Gu, Y., Wang, Z., Tang, Z., Feng, Z.-H., Mai, Z., Zhang, Z., Wu, Z., and Ma, Z.

Abstract

The Visual Object Tracking challenge VOT2020 is the eighth annual tracker benchmarking activity organized by the VOT initiative. Results of 58 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The VOT2020 challenge was composed of five sub-challenges focusing on different tracking domains: (i) VOT-ST2020 challenge focused on short-term tracking in RGB, (ii) VOT-RT2020 challenge focused on “real-time” short-term tracking in RGB, (iii) VOT-LT2020 focused on long-term tracking namely coping with target disappearance and reappearance, (iv) VOT-RGBT2020 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2020 challenge focused on long-term tracking in RGB and depth imagery. Only the VOT-ST2020 datasets were refreshed. A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in the VOT-ST challenges. A new VOT Python toolkit that implements all these novelites was introduced. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net ).

Published
2020
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2. The Seventh Visual Object Tracking VOT2019 Challenge Results

Author

Kristan, M., Matas, J., Leonardis, A., Felsberg, M., Pflugfelder, R., Kämäräinen, J.-K., Čehovin Zajc, L., Drbohlav, O., Lukežič, A., Berg, A., Eldesokey, A., Käpylä, J., Fernández, G., Gonzalez-Garcia, A., Memarmoghadam, A., Lu, A., He, A., Varfolomieiev, A., Chan, A., Shekhar Tripathi, A., Smeulders, A., Suraj Pedasingu, B., Chen, B.X., Zhang, B., Wu, B., Li, B., He, B., Yan, B., Bai, B., Kim, B.H., Ma, C., Fang, C., Qian, C., Chen, C., Li, C., Zhang, C., Tsai, C.-Y., Luo, C., Micheloni, C., Tao, D., Gupta, D., Song, D., Wang, D., Gavves, E., Yi, E., Khan, F.S., Zhang, F., Wang, F., Zhao, F., De Ath, G., Bhat, G., Chen, G., Wang, G., Li, G., Cevikalp, H., Du, H., Zhao, H., Saribas, H., Jung, H.M., Bai, H., Hu, H., Peng, H., Lu, H., Li, H., Li, J., Fu, J., Chen, J., Gao, J., Zhao, J., Tang, J., Wu, J., Liu, J., Wang, J., Qi, J., Zhang, J., Tsotsos, J.K., Lee, J.H., van de Weijer, J., Kittler, J., Zhuang, J., Zhang, K., Wang, K., Dai, K., Chen, L., Liu, L., Guo, L., Zhang, L., Wang, L., Zhou, L., Zheng, L., Rout, L., Van Gool, L., Bertinetto, L., Danelljan, M., Dunnhofer, M., Ni, M., Kim, M.Y., Tang, M., Yang, M.-H., Paluru, N., Martinel, N., Xu, P., Zhang, P., Zheng, P., Torr, P.H.S., Zhang, Q., Wang, Q., Guo, Q., Timofte, R., Gorthi, R.K., Everson, R., Han, R., Zhang, R., You, S., Zhao, S.-C., Zhao, S., Li, S., Ge, S., Bai, S., Guan, S., Xing, T., Xu, T., Yang, T., Zhang, T., Vojíř, T., Feng, W., Hu, W., Wang, W., Tang, W., Zeng, W., Liu, W., Chen, X., Qiu, X., Bai, X., Wu, X.-J., Yang, X., Li, X., Sun, X., Tian, X., Tang, X., Zhu, X.-F., Huang, Y., Chen, Y., Lian, Y., Gu, Y., Liu, Y., Zhang, Y., Xu, Y., Wang, Y., Li, Y., Zhou, Y., Dong, Y., Wang, Z., Luo, Z., Zhang, Z., Feng, Z.-H., He, Z., Song, Z., Chen, Z., Wu, Z., Xiong, Z., Huang, Z., Teng, Z., Ni, Z., and Intelligent Sensory Information Systems (IVI, FNWI)

Subjects
Source code, Computer science, business.industry, media_common.quotation_subject, Object tracking, Performance evaluation, VOT challenge, 020206 networking & telecommunications, 02 engineering and technology, Visualization, Datorseende och robotik (autonoma system), Robustness (computer science), Video tracking, 0202 electrical engineering, electronic engineering, information engineering, RGB color model, 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, business, Computer Vision and Robotics (Autonomous Systems), media_common
Abstract

The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOT-ST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2019 focused on long-term tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard short-term, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website(1). Funding Agencies|Slovenian research agencySlovenian Research Agency - Slovenia [J2-8175, P2-0214, P2-0094]; Czech Science Foundation Project GACR [P103/12/G084]; MURI project - MoD/DstlMURI; EPSRCEngineering & Physical Sciences Research Council (EPSRC) [EP/N019415/1]; WASP; VR (ELLIIT, LAST, and NCNN); SSF (SymbiCloud); AIT Strategic Research Programme; Faculty of Computer Science, University of Ljubljana, Slovenia

Published
2019

4. Automatic Optimization of Alignment Parameters for Tomography Datasets

Author

Bleichrodt, Folkert, Batenburg, Joost, Kämäräinen, J.-K., Koskela, M., and Scientific Computing

Subjects
0303 health sciences, Tomographic reconstruction, Optimization algorithm, business.industry, Estimation theory, Stability (learning theory), ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, alignment, 02 engineering and technology, Iterative reconstruction, tomography, Set (abstract data type), 03 medical and health sciences, Electron tomography, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, Tomography, business, parameter estimation, 030304 developmental biology, Mathematics
Abstract

As tomographic imaging is being performed at increasingly smaller scales, the stability of the scanning hardware is of great importance to the quality of the reconstructed image. Instabilities lead to perturbations in the geometrical parameters used in the acquisition of the projections. In particular for electron tomography and high-resolution X-ray tomography, small instabilities in the imaging setup can lead to severe artifacts. We present a novel alignment algorithm for recovering the true geometrical parameters \emph{after} the object has been scanned, based on measured data. Our algorithm employs an optimization algorithm that combines alignment with reconstruction. We demonstrate that problem-specific design choices made in the implementation are vital to the success of the method. The algorithm is tested in a set of simulation experiments. Our experimental results indicate that the method is capable of aligning tomography datasets with considerably higher accuracy compared to standard cross-correlation methods.

Published
2013

5. Automatic Optimization of Alignment Parameters for Tomography Datasets

Author

Kämäräinen, J.-K., Koskela, M., Bleichrodt, F. (Folkert), Batenburg, K.J. (Joost), Kämäräinen, J.-K., Koskela, M., Bleichrodt, F. (Folkert), and Batenburg, K.J. (Joost)

Abstract

As tomographic imaging is being performed at increasingly smaller scales, the stability of the scanning hardware is of great importance to the quality of the reconstructed image. Instabilities lead to perturbations in the geometrical parameters used in the acquisition of the projections. In particular for electron tomography and high-resolution X-ray tomography, small instabilities in the imaging setup can lead to severe artifacts. We present a novel alignment algorithm for recovering the true geometrical parameters \emph{after} the object has been scanned, based on measured data. Our algorithm employs an optimization algorithm that combines alignment with reconstruction. We demonstrate that problem-specific design choices made in the implementation are vital to the success of the method. The algorithm is tested in a set of simulation experiments. Our experimental results indicate that the method is capable of aligning tomography datasets with considerably higher accuracy compared to standard cross-correlation methods.

Published
2013
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