Relevant bibliographies by topics / Visual pruning

Academic literature on the topic 'Visual pruning'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Visual pruning"

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Iorio, Carmela, Massimo Aria, Antonio D’Ambrosio, and Roberta Siciliano. "Informative trees by visual pruning." Expert Systems with Applications 127 (August 2019): 228–40. http://dx.doi.org/10.1016/j.eswa.2019.03.018.

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Kim, G., J. A. Lewis-Peacock, K. A. Norman, and N. B. Turk-Browne. "Pruning of visual memories based on contextual prediction error." Journal of Vision 13, no. 9 (July 25, 2013): 930. http://dx.doi.org/10.1167/13.9.930.

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Zhang, Fan, Yang Song, Weidong Cai, Alexander G. Hauptmann, Sidong Liu, Sonia Pujol, Ron Kikinis, Michael J. Fulham, David Dagan Feng, and Mei Chen. "Dictionary pruning with visual word significance for medical image retrieval." Neurocomputing 177 (February 2016): 75–88. http://dx.doi.org/10.1016/j.neucom.2015.11.008.

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Che Manqiang, 车满强, 李树斌 Li Shubin, and 葛金鹏 Ge Jinpeng. "Convolutional Channel Pruning and Weighting for Accurate Location Visual Tracking." Laser & Optoelectronics Progress 57, no. 16 (2020): 161505. http://dx.doi.org/10.3788/lop57.161505.

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Zabadal, Thomas J., and Thomas W. Dittmer. "QUANTITATIVE MEASUREMENT OF CLUSTER COMPACTNESS IN SEVERAL TABLE GRAPE CULTIVARS." HortScience 27, no. 6 (June 1992): 599g—600. http://dx.doi.org/10.21273/hortsci.27.6.599g.

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Cluster compactness can affect fruit quality by influencing pesticide spray penetration into clusters and by predisposing berries to cracking and subsequent decay. Compactness of clusters can be altered through gibberellic acid sprays, flower cluster thinning and pruning severity. Assessment of cluster compactness has often been performed using a visual rating system which may not provide adequate quantitative measurement. Evaluation of cluster compactness by insertion of wedges between randomly chosen pairs of berries on a cluster revealed a high correlation with a visual rating system and a more sensitive measure of cluster compactness than the visual rating system. Several pruning severity treatments were applied to table grape cultivars to determine their influence on cluster compactness. It was possible to measure statistically significant differences in cluster compactness among these treatment using this wedge measurement technique.
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Alqahtani, Ali, Xianghua Xie, Mark W. Jones, and Ehab Essa. "Pruning CNN filters via quantifying the importance of deep visual representations." Computer Vision and Image Understanding 208-209 (July 2021): 103220. http://dx.doi.org/10.1016/j.cviu.2021.103220.

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Suchocka, Marzena, Tatiana Swoczyna, Joanna Kosno-Jończy, and Hazem M. Kalaji. "Impact of heavy pruning on development and photosynthesis of Tilia cordata Mill. trees." PLOS ONE 16, no. 8 (August 23, 2021): e0256465. http://dx.doi.org/10.1371/journal.pone.0256465.

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Tree pruning is carried out to reduce conflict with infrastructure, buildings, and any other human activity. However, heavy pruning may result in a diminished tree crown capacity for sugar production and exposure to fungal infection. This risk leads to a decrease in tree stability or vigour. In this work, we analysed the effect of heavy pruning of roadside trees on the photosynthetic performance process compared to neighbouring unpruned trees. Four years of tree crown growth was studied by terrestrial imaging. Tree vitality (Roloff’s classification) and risk (Visual Tree Assessment) were evaluated. Over-pruned trees showed intensified photosynthetic efficiency during the growing season following pruning. Particularly ET0/TR0 and PIABS tended to increase in pruned trees while higher Fv/Fm was noted only in late October, suggesting delayed leaf senescence. After four years, pruned trees rebuilt their crowns, however not in their entirety. Results obtained from biometric, vitality, and risk assessment showed high differentiation in pruned tree crown recovery. Our results revealed that despite the intensified efforts of trees to recover from wounding effects, severe pruning evokes dieback occurrence and a higher risk of failure in mature trees.
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Yamahachi, Homare, Sally A. Marik, Justin N. J. McManus, Winfried Denk, and Charles D. Gilbert. "Rapid Axonal Sprouting and Pruning Accompany Functional Reorganization in Primary Visual Cortex." Neuron 64, no. 5 (December 2009): 719–29. http://dx.doi.org/10.1016/j.neuron.2009.11.026.

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Benjumeda, Isabel, Manuel Molano-Mazón, and Luis M. Martinez. "Flowers and weeds: cell-type specific pruning in the developing visual thalamus." BMC Biology 12, no. 1 (2014): 3. http://dx.doi.org/10.1186/1741-7007-12-3.

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van Kerkoerle, Timo, Sally A. Marik, Stephan Meyer zum Alten Borgloh, and Charles D. Gilbert. "Axonal plasticity associated with perceptual learning in adult macaque primary visual cortex." Proceedings of the National Academy of Sciences 115, no. 41 (September 27, 2018): 10464–69. http://dx.doi.org/10.1073/pnas.1812932115.

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Perceptual learning is associated with changes in the functional properties of neurons even in primary sensory areas. In macaque monkeys trained to perform a contour detection task, we have observed changes in contour-related facilitation of neuronal responses in primary visual cortex that track their improvement in performance on a contour detection task. We have previously explored the anatomical substrate of experience-dependent changes in the visual cortex based on a retinal lesion model, where we find sprouting and pruning of the axon collaterals in the cortical lesion projection zone. Here, we attempted to determine whether similar changes occur under normal visual experience, such as that associated with perceptual learning. We labeled the long-range horizontal connections in visual cortex by virally mediated transfer of genes expressing fluorescent probes, which enabled us to do longitudinal two-photon imaging of axonal arbors over the period during which animals improve in contour detection performance. We found that there are substantial changes in the axonal arbors of neurons in cortical regions representing the trained part of the visual field, with sprouting of new axon collaterals and pruning of preexisting axon collaterals. Our findings indicate that changes in the structure of axonal arbors are part of the circuit-level mechanism of perceptual learning, and further support the idea that the learned information is encoded at least in part in primary visual cortex.
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Dissertations / Theses on the topic "Visual pruning"

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Wu, David, and dwu8@optusnet com au. "Perceptually Lossless Coding of Medical Images - From Abstraction to Reality." RMIT University. Electrical & Computer Engineering, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080617.160025.

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This work explores a novel vision model based coding approach to encode medical images at a perceptually lossless quality, within the framework of the JPEG 2000 coding engine. Perceptually lossless encoding offers the best of both worlds, delivering images free of visual distortions and at the same time providing significantly greater compression ratio gains over its information lossless counterparts. This is achieved through a visual pruning function, embedded with an advanced model of the human visual system to accurately identify and to efficiently remove visually irrelevant/insignificant information. In addition, it maintains bit-stream compliance with the JPEG 2000 coding framework and subsequently is compliant with the Digital Communications in Medicine standard (DICOM). Equally, the pruning function is applicable to other Discrete Wavelet Transform based image coders, e.g., The Set Partitioning in Hierarchical Trees. Further significant coding gains are ex ploited through an artificial edge segmentation algorithm and a novel arithmetic pruning algorithm. The coding effectiveness and qualitative consistency of the algorithm is evaluated through a double-blind subjective assessment with 31 medical experts, performed using a novel 2-staged forced choice assessment that was devised for medical experts, offering the benefits of greater robustness and accuracy in measuring subjective responses. The assessment showed that no differences of statistical significance were perceivable between the original images and the images encoded by the proposed coder.
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Books on the topic "Visual pruning"

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Pruning made easy: A gardener's visual guide to when and how to prune everything, from flowers to trees. Pownal, Vt: Storey Pub., 1997.

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Hill, Lewis. Pruning Made Easy: A gardener's visual guide to when and how to prune everything, from flowers to trees (Storey's Gardening Skills Illustrated). Storey Publishing, LLC, 1998.

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Book chapters on the topic "Visual pruning"

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Ning, Paul, and Lambertus Hesselink. "Octree Pruning for Variable-Resolution Isosurfaces." In Visual Computing, 349–63. Tokyo: Springer Japan, 1992. http://dx.doi.org/10.1007/978-4-431-68204-2_22.

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Che, Manqiang, Runling Wang, Yan Lu, Yan Li, Hui Zhi, and Changzhen Xiong. "Channel Pruning for Visual Tracking." In Lecture Notes in Computer Science, 70–82. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11009-3_3.

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Davoodikakhki, Mahdi, and KangKang Yin. "Hierarchical Action Classification with Network Pruning." In Advances in Visual Computing, 291–305. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64556-4_23.

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Kamma, Koji, and Toshikazu Wada. "Reconstruction Error Aware Pruning for Accelerating Neural Networks." In Advances in Visual Computing, 59–72. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33720-9_5.

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Wahlberg, Fredrik, and Anders Brun. "Feature Weight Optimization and Pruning in Historical Text Recognition." In Advances in Visual Computing, 98–107. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41939-3_10.

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Emrich, Tobias, Hans-Peter Kriegel, Peer Kröger, Matthias Renz, Johannes Senner, and Andreas Züfle. "A Visual Evaluation Framework for Spatial Pruning Methods." In Advances in Spatial and Temporal Databases, 507–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22922-0_40.

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Artiemjew, Piotr, and Przemysław Górecki. "Visual Dictionary Pruning Using Mutual Information and Information Gain." In Artificial Intelligence and Soft Computing, 3–14. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07176-3_1.

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Ma, Huimin, and Xiaozhi Chen. "Simplification Computing for Visual Events Based on Distinct Silhouette and View-Independent Pruning Algorithm." In Communications in Computer and Information Science, 243–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37149-3_29.

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Conference papers on the topic "Visual pruning"

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Mallapragada, Pavan K., Rong Jin, and Anil K. Jain. "Online visual vocabulary pruning using pairwise constraints." In 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2010. http://dx.doi.org/10.1109/cvpr.2010.5540062.

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Huang, Han, Yao Zhao, Chunyu Lin, and Huihui Bai. "Fast bottom-up pruning for HEVC intraframe coding." In 2013 Visual Communications and Image Processing (VCIP). IEEE, 2013. http://dx.doi.org/10.1109/vcip.2013.6706389.

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Yang, Tao, and Chun-Guang Li. "Local Convex Representation with Pruning for Manifold Clustering." In 2019 IEEE Visual Communications and Image Processing (VCIP). IEEE, 2019. http://dx.doi.org/10.1109/vcip47243.2019.8965757.

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Gan, Wenyao, Li Song, Li Chen, Rong Xie, and Xiao Gu. "Identifying and Pruning Redundant Structures for Deep Neural Networks." In 2019 IEEE Visual Communications and Image Processing (VCIP). IEEE, 2019. http://dx.doi.org/10.1109/vcip47243.2019.8966025.

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Taguchi, S., S. Omachi, and H. Aso. "Fast visual search using simplified pruning rules - streamlined active search." In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, 2004. http://dx.doi.org/10.1109/icpr.2004.1333927.

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Husseini, Sahar, Pouria Babahajiani, and Moncef Gabbouj. "Color Constancy Model Optimization with Small Dataset via Pruning of CNN Filters." In 2021 9th European Workshop on Visual Information Processing (EUVIP). IEEE, 2021. http://dx.doi.org/10.1109/euvip50544.2021.9484046.

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Li, Yuchen, Yitong Liu, Hongwen Yang, and Dacheng Yang. "Fast CU splitting and pruning method based on online learning for intra coding in HEVC." In 2014 Visual Communications and Image Processing (VCIP). IEEE, 2014. http://dx.doi.org/10.1109/vcip.2014.7051603.

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Nguyen Huu, Thuc, Vinh Van Duong, and Byeungwoo Jeon. "Random-access-aware Light Field Video Coding using Tree Pruning Method." In 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP). IEEE, 2020. http://dx.doi.org/10.1109/vcip49819.2020.9301800.

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Cuevas-Velasquez, Hanz, Antonio-Javier Gallego, Radim Tylecek, Jochen Hemming, Bart van Tuijl, Angelo Mencarelli, and Robert B. Fisher. "Real-time Stereo Visual Servoing for Rose Pruning with Robotic Arm." In 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020. http://dx.doi.org/10.1109/icra40945.2020.9197272.

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Wu, David, Damian Tan, and Hong Wu. "An Improved Visual Pruning Algorithm for Perceptually Lossless Medical Image Coding." In TENCON 2005 - 2005 IEEE Region 10 Conference. IEEE, 2005. http://dx.doi.org/10.1109/tencon.2005.301333.

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