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Rentmeister, M.H. Kiani, K. Pister, and J.T. Stauth, \u201cA 120-330v, sub-\u00b5a, 4-channel driver for microrobotic actuators with wireless-optical power delivery and over 99% current efficiency,\u201d IEEE Symp. VLSI Circuits, pp.1-2, IEEE, 2020. 10.1109\/vlsicircuits18222.2020.9162908","DOI":"10.1109\/VLSICircuits18222.2020.9162908"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] S. Du, Y. Li, and T. Ikenaga, \u201cTemporally forward nonlinear scale space for high frame rate and ultra-low delay a-kaze matching system,\u201d IEICE Trans. Inf. & Syst., vol.E103.D, no.6, pp.1226-1235, 2020. 10.1587\/transinf.2019mvp0019","DOI":"10.1587\/transinf.2019MVP0019"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] T. Hu, R. Fuchikami, and T. Ikenaga, \u201cHigh temporal resolution-based temporal iterative tracking for high framerate and ultra-low delay dynamic tracking system,\u201d IEICE Trans. Inf. & Syst., vol.E105.D, no.5, pp.1064-1074, 2022. 10.1587\/transinf.2021edp7200","DOI":"10.1587\/transinf.2021EDP7200"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] S. Du, Z. Dong, Y. Li, and T. Ikenaga, \u201cStraight-line detection within 1 millisecond per frame for ultra-high-speed industrial automation,\u201d IEEE Trans. Ind. Informat., vol.19, no.4, pp.5965-5975, 2023. 10.1109\/tii.2022.3170585","DOI":"10.1109\/TII.2022.3170585"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] C. Zhao, W. Zhu, and S. Feng, \u201cSuperpixel guided deformable convolution network for hyperspectral image classification,\u201d IEEE Trans. Image Process., vol.31, pp.3838-3851, 2022. 10.1109\/tip.2022.3176537","DOI":"10.1109\/TIP.2022.3176537"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] Z. Wu, H. Zhu, L. He, Q. Zhao, J. Shi, and W. Wu, \u201cReal-time stereo matching with high accuracy via spatial attention-guided upsampling,\u201d Appl. Intell., vol.53, no.20, pp.24253-24274, 2023. 10.1007\/s10489-023-04646-w","DOI":"10.1007\/s10489-023-04646-w"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. S\u00fcsstrunk, \u201cSlic superpixels compared to state-of-the-art superpixel methods,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.34, no.11, pp.2274-2282, 2012. 10.1109\/tpami.2012.120","DOI":"10.1109\/TPAMI.2012.120"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] Z. Li and J. Chen, \u201cSuperpixel segmentation using linear spectral clustering,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp.1356-1363, 2015.","DOI":"10.1109\/CVPR.2015.7298741"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] Y. Li, T. Hu, R. Fuchikami, and T. Ikenaga, \u201cGrid sample based temporal iteration and compactness-coefficient distance for high frame and ultra-low delay slic segmentation system,\u201d Int. Conf. Mach. Vis. Appl. (MVA), 2023. 10.23919\/mva57639.2023.10215797","DOI":"10.23919\/MVA57639.2023.10215797"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] Y. Li, T. Hu, R. Fuchikami, and T. Ikenaga, \u201cGrid sample based temporal iteration for fully pipelined 1-ms slic superpixel segmentation system,\u201d IEICE Trans. Inf. & Syst., vol.E107.D, no.4, pp.515-524, 2024. 10.1587\/transinf.2023edp7128","DOI":"10.1587\/transinf.2023EDP7128"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] V. Jampani, D. Sun, M.-Y. Liu, M.-H. Yang, and J. Kautz, \u201cSuperpixel sampling networks,\u201d Proc. Eur. Conf. Comput. Vis. (ECCV), pp.352-368, 2018. 10.1007\/978-3-030-01234-2_22","DOI":"10.1007\/978-3-030-01234-2_22"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] F. Yang, Q. Sun, H. Jin, and Z. Zhou, \u201cSuperpixel segmentation with fully convolutional networks,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp.13964-13973, 2020.","DOI":"10.1109\/CVPR42600.2020.01398"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] Y. Wang, Y. Wei, X. Qian, L. Zhu, and Y. Yang, \u201cAinet: Association implantation for superpixel segmentation,\u201d Proc. IEEE Int. Conf. Comput. Vis. (ICCV), pp.7078-7087, 2021.","DOI":"10.1109\/ICCV48922.2021.00699"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] P. Zhang, T. Hu, D. Luo, S. Du, and T. Ikenaga, \u201cHighly-parallel hardwired deep convolutional neural network for 1-ms dual-hand tracking,\u201d IEEE Trans. Circuits Syst. Video Technol., vol.32, no.12, pp.8192-8203, 2022. 10.1109\/tcsvt.2021.3103784","DOI":"10.1109\/TCSVT.2021.3103784"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] W.-C. Tu, M.-Y. Liu, V. Jampani, D. Sun, S.-Y. Chien, M.-H. Yang, and J. Kautz, \u201cLearning superpixels with segmentation-aware affinity loss,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp.568-576, 2018.","DOI":"10.1109\/CVPR.2018.00066"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] U. Gaur and B.S. Manjunath, \u201cSuperpixel embedding network,\u201d IEEE Trans. Image Process., vol.29, pp.3199-3212, 2020. 10.1109\/tip.2019.2957937","DOI":"10.1109\/TIP.2019.2957937"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] X. Pan, Y. Zhou, Y. Zhang, and C. Zhang, \u201cFast generation of superpixels with lattice topology,\u201d IEEE Trans. Image Process., vol.31, pp.4828-4841, 2022. 10.1109\/tip.2022.3188155","DOI":"10.1109\/TIP.2022.3188155"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] L. Zhu, Q. She, B. Zhang, Y. Lu, Z. Lu, D. Li, and J. Hu, \u201cLearning the superpixel in a non-iterative and lifelong manner,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp.1225-1234, 2021.","DOI":"10.1109\/CVPR46437.2021.00128"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] T. Suzuki, \u201cSuperpixel segmentation via convolutional neural networks with regularized information maximization,\u201d IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), pp.2573-2577, IEEE, 2020. 10.1109\/icassp40776.2020.9054140","DOI":"10.1109\/ICASSP40776.2020.9054140"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] P. Li and W. Ma, \u201cOversegnet: A convolutional encoder-decoder network for image over-segmentation,\u201d Comput. Electr. Eng., vol.107, p.108610, 2023. 10.1016\/j.compeleceng.2023.108610","DOI":"10.1016\/j.compeleceng.2023.108610"},{"key":"21","unstructured":"[21] M. Miyama, \u201cFpga accelerator for super-pixel segmentation featuring clear detail and short boundary,\u201d IIEEJ Trans. Image Electron. Visual Comput., vol.5, no.2, pp.83-91, 2017."},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] A. Akagic, E. Buza, R. Turcinhodzic, H. Haseljic, N. Hiroyuki, and H. Amano, \u201cSuperpixel accelerator for computer vision applications on arria 10 soc,\u201d IEEE Int. Symp. Design Diagnostics Electron. Circuits & Syst. (DDECS), pp.55-60, IEEE, 2018. 10.1109\/ddecs.2018.00-12","DOI":"10.1109\/DDECS.2018.00-12"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] P.A. Khamaneh, A. Khakpour, M. Shoaran, and G. Karimian, \u201cReal-time memory efficient slic accelerator for low-power applications,\u201d Multimed. Tools. Appl., vol.81, no.22, pp.32449-32467, 2022. 10.1007\/s11042-022-12594-9","DOI":"10.1007\/s11042-022-12594-9"},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] M. Mighani and A. Khakpour, \u201cFmslic: Fast memory-efficient structure for implementation of slic on fpga,\u201d Circuits, Syst. Signal Process., vol.42, no.8, pp.5065-5078, 2023. 10.1007\/s00034-023-02353-y","DOI":"10.1007\/s00034-023-02353-y"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] A. Ghaderi, C. Ahlberg, M. \u00d6stgren, F. Ekstrand, and M. Ekstr\u00f6m, \u201cFp-slic: A fully-pipelined fpga implementation of superpixel image segmentation,\u201d Euromicro Conf. Digit. Syst. Des. (DSD), pp.109-117, IEEE, 2022. 10.1109\/dsd57027.2022.00024","DOI":"10.1109\/DSD57027.2022.00024"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] J. Zhang, L. Cheng, C. Li, Y. Li, G. He, N. Xu, and Y. Lian, \u201cA low-latency fpga implementation for real-time object detection,\u201d Proc. IEEE Int. Symp. Circuits Syst. (ISCAS), pp.1-5, IEEE, 2021.","DOI":"10.1109\/ISCAS51556.2021.9401577"},{"key":"27","doi-asserted-by":"publisher","unstructured":"[27] X. Wang, C. Wang, J. Cao, L. Gong, and X. Zhou, \u201cWinonn: Optimizing fpga-based convolutional neural network accelerators using sparse winograd algorithm,\u201d IEEE Trans. Comput.-Aided Design Integr. Circuits Syst., vol.39, no.11, pp.4290-4302, 2020. 10.1109\/tcad.2020.3012323","DOI":"10.1109\/TCAD.2020.3012323"},{"key":"28","doi-asserted-by":"publisher","unstructured":"[28] J. Li, K.-F. Un, W.-H. Yu, P.-I. Mak, and R.P. Martins, \u201cAn fpga-based energy-efficient reconfigurable convolutional neural network accelerator for object recognition applications,\u201d IEEE Trans. Circuits Syst., II, Exp. Briefs, vol.68, no.9, pp.3143-3147, 2021. 10.1109\/tcsii.2021.3095283","DOI":"10.1109\/TCSII.2021.3095283"},{"key":"29","doi-asserted-by":"publisher","unstructured":"[29] L. Xuan, K.-F. Un, C.-S. Lam, and R.P. Martins, \u201cAn fpga-based energy-efficient reconfigurable depthwise separable convolution accelerator for image recognition,\u201d IEEE Trans. Circuits Syst., II, Exp. Briefs, vol.69, no.10, pp.4003-4007, 2022. 10.1109\/tcsii.2022.3180553","DOI":"10.1109\/TCSII.2022.3180553"},{"key":"30","doi-asserted-by":"crossref","unstructured":"[30] S. Yan, Z. Liu, Y. Wang, C. Zeng, Q. Liu, B. Cheng, and R.C.C. Cheung, \u201cAn fpga-based mobilenet accelerator considering network structure characteristics,\u201d Proc. Int. Conf. Field Program. Log. Appl. (FPL), pp.17-23, IEEE, 2021.","DOI":"10.1109\/FPL53798.2021.00011"},{"key":"31","doi-asserted-by":"publisher","unstructured":"[31] X. Wu, Y. Ma, M. Wang, and Z. Wang, \u201cA flexible and efficient fpga accelerator for various large-scale and lightweight cnns,\u201d IEEE Trans. Circuits Syst. I, Reg. Papers, vol.69, no.3, pp.1185-1198, 2021. 10.1109\/tcsi.2021.3131581","DOI":"10.1109\/TCSI.2021.3131581"},{"key":"32","doi-asserted-by":"publisher","unstructured":"[32] D.T. Nguyen, H. Kim, and H.-J. Lee, \u201cLayer-specific optimization for mixed data flow with mixed precision in fpga design for cnn-based object detectors,\u201d IEEE Trans. Circuits Syst. Video Technol., vol.31, no.6, pp.2450-2464, 2020. 10.1109\/tcsvt.2020.3020569","DOI":"10.1109\/TCSVT.2020.3020569"},{"key":"33","doi-asserted-by":"publisher","unstructured":"[33] E. Wang, J.J. Davis, P.Y.K. Cheung, and G.A. Constantinides, \u201cLutnet: Learning fpga configurations for highly efficient neural network inference,\u201d IEEE Trans. Comput., vol.69, no.12, pp.1795-1808, 2020. 10.1109\/tc.2020.2978817","DOI":"10.1109\/TC.2020.2978817"},{"key":"34","doi-asserted-by":"crossref","unstructured":"[34] M. Andronic and G.A. Constantinides, \u201cPolylut: learning piecewise polynomials for ultra-low latency fpga lut-based inference,\u201d Proc. Int. Conf. Field Programmable Technol. (ICFPT), pp.60-68, IEEE, 2023.","DOI":"10.1109\/ICFPT59805.2023.00012"},{"key":"35","doi-asserted-by":"crossref","unstructured":"[35] B. Jacob, S. Kligys, B. Chen, M. Zhu, M. Tang, A. Howard, H. Adam, and D. Kalenichenko, \u201cQuantization and training of neural networks for efficient integer-arithmetic-only inference,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp.2704-2713, 2018.","DOI":"10.1109\/CVPR.2018.00286"},{"key":"36","doi-asserted-by":"crossref","unstructured":"[36] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, \u201cMobilenetv2: Inverted residuals and linear bottlenecks,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp.4510-4520, 2018.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"37","unstructured":"[37] Halcon, Available online: https:\/\/linx.jp\/product\/mvtec\/halcon\/."},{"key":"38","doi-asserted-by":"crossref","unstructured":"[38] S. Song, S.P. Lichtenberg, and J. Xiao, \u201cSun rgb-d: A rgb-d scene understanding benchmark suite,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp.567-576, 2015.","DOI":"10.1109\/CVPR.2015.7298655"},{"key":"39","doi-asserted-by":"crossref","unstructured":"[39] B. Cheng, R. Girshick, P. Doll\u00e1r, A.C. Berg, and A. Kirillov, \u201cBoundary iou: Improving object-centric image segmentation evaluation,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp.15334-15342, 2021.","DOI":"10.1109\/CVPR46437.2021.01508"},{"key":"40","doi-asserted-by":"publisher","unstructured":"[40] D. Marmanis, K. Schindler, J.D. Wegner, S. Galliani, M. Datcu, and U. Stilla, \u201cClassification with an edge: Improving semantic image segmentation with boundary detection,\u201d ISPRS J. Photogramm. Remote Sens., vol.135, pp.158-172, 2018. 10.1016\/j.isprsjprs.2017.11.009","DOI":"10.1016\/j.isprsjprs.2017.11.009"},{"key":"41","doi-asserted-by":"crossref","unstructured":"[41] J. Papon, A. Abramov, M. Schoeler, and F. Worgotter, \u201cVoxel cloud connectivity segmentation-supervoxels for point clouds,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp.2027-2034, 2013.","DOI":"10.1109\/CVPR.2013.264"},{"key":"42","doi-asserted-by":"crossref","unstructured":"[42] J. Chang, D. Wei, and J.W. Fisher III, \u201cA video representation using temporal superpixels,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp.2051-2058, 2013.","DOI":"10.1109\/CVPR.2013.267"},{"key":"43","doi-asserted-by":"crossref","unstructured":"[43] O. Miksik, D. Munoz, J.A. Bagnell, and M. Hebert, \u201cEfficient temporal consistency for streaming video scene analysis,\u201d Int. Conf. Robot. Autom. (ICRA), pp.133-139, IEEE, 2013. 10.1109\/icra.2013.6630567","DOI":"10.1109\/ICRA.2013.6630567"},{"key":"44","doi-asserted-by":"crossref","unstructured":"[44] R. Achanta and S. Susstrunk, \u201cSuperpixels and polygons using simple non-iterative clustering,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp.4651-4660, 2017.","DOI":"10.1109\/CVPR.2017.520"},{"key":"45","doi-asserted-by":"publisher","unstructured":"[45] M. Van den Bergh, X. Boix, G. Roig, and L. Van Gool, \u201cSeeds: Superpixels extracted via energy-driven sampling,\u201d Int. J. Comput. Vision, vol.111, no.3, pp.298-314, 2015. 10.1007\/s11263-014-0744-2","DOI":"10.1007\/s11263-014-0744-2"},{"key":"46","doi-asserted-by":"crossref","unstructured":"[46] M.-Y. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa, \u201cEntropy rate superpixel segmentation,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp.2097-2104, IEEE, 2011.","DOI":"10.1109\/CVPR.2011.5995323"},{"key":"47","doi-asserted-by":"crossref","unstructured":"[47] J. Yao, M. Boben, S. Fidler, and R. Urtasun, \u201cReal-time coarse-to-fine topologically preserving segmentation,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 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