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SVM DNN The system performance using the SVM and DNN classification model in terms of accuracy is shown in Figure 7 and Table 3. Figure 8 - Accuracy of different types of Feature vector used 96 95 5. (...) The HGN-DNN 89 model’s precision and accuracy signify the fact that multiple Traning Accuracy Test Accuracy feature-based models help in achieving higher efficiency. (...) It can be standardized as an extension of the test accuracy of 95.6% and 93.8% respectively. The training intelligent visual surveillance system architecture specified accuracy is obtained by running the model against the in Recommendation ITU-T H.626.5. training data set while the test accuracy is obtained by predicting based on the test data set.
Language:English
Score: 983544.8 - https://www.itu.int/en/publica.../files/basic-html/page215.html
Data Source: un
Table 2 – Chat classification module accuracy Chat Classification Actual Module Positive Negative Positive 46 4 Predicted Negative 4 46 Figure 6 – Variation of model accuracy Table 2 shows the actual and predicted classes for the complete classification module. (...) The detailed accuracy results in terms of precision, recall and F1-score of the model are given in Table 1. (...) The final accuracy of the classification module is 92%. From Figure 8, it can be seen that the accuracy achieved by the proposed technique is comparable with that of the other existing techniques. 6.
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Score: 978431.8 - https://www.itu.int/en/publica.../files/basic-html/page129.html
Data Source: un
P: bilinear warping using the current specification of Annex P, 1/16: 1/16 pel accuracy, 1/2: half pel accuracy) (a) QUANT=10 (b) QUANT=20 (c) QUANT=31 Figure 3. (...) P: bilinear warping using the current specification of Annex P, 1/16: 1/16 pel accuracy, 1/2: half pel accuracy). (a) QUANT=10 (b) QUANT=20 (c) QUANT=31 Figure 4. (...) P: bilinear warping using the current specification of Annex P, 1/16: 1/16 pel accuracy, 1/2: half pel accuracy) (a) QUANT=10 (b) QUANT=20 (c) QUANT=31 Figure 5.
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Score: 978024.7 - https://www.itu.int/wftp3/av-a...deo-site/9706_Por/q15a34r1.doc
Data Source: un
In subsection 4.1 the RD plots for 1/4-pel MV-accuracy and in subsection 4.2 the RD plots for 1/8 pel accuracy are shown, respectively. (...) Kerofsky, “ H.26L Core Experiment on Adaptive Motion Accuracy (AMA) for 1/2, 1/4, and 1/8 Sample Accuracy Motion Compensation”, ITU-T SG16/Q15, doc. (...) Wedi, “Results of core experiment on Adaptive Motion Accuracy (AMA) with 1/2, 1/4 and 1/8-pel accuracy”, ITU-T SG16/Q15, doc.
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Score: 974425.8 - https://www.itu.int/wftp3/av-a...video-site/0005_Osa/q15j14.doc
Data Source: un
Nachrichtentechnik und Informationsverarbeitung, University of Hannover Appelstr. 9A D-30167 Hannover, Germany Tel: Fax: Email: +49 511 7625304 +49 511 7625333 wedi@tnt.uni-hannover.de Title: Results of core experiment on Adaptive Motion Accuracy (AMA) with 1/2, 1/4 and 1/8-pel accuracy Purpose: Proposal _____________________________ 1 Introduction Documents Q15i59 and Q15j09 define a H.26L core experiment on Adaptive Motion Accuracy (AMA) for 1/2, 1/4 and 1/8 pel accuracy. (...) In Table 3 the filter combinations for AMA-4 (AMA with 1/2 and 1/4 MV-accuracy) and for AMA-8 (AMA with 1/2, 1/4 and 1/8-pel MV-accuracy) are shown. (...) Kerofsky, “ H.26L Core Experiment on Adaptive Motion Accuracy (AMA) for 1/2, 1/4, and 1/8 Sample Accuracy Motion Compensation”, ITU-T SG16/Q15, doc.
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Score: 972865.2 - https://www.itu.int/wftp3/av-a...video-site/0005_Osa/q15j15.doc
Data Source: un
Both A and B have 1/8th pixel MV accuracy. (b). A has 1/4th pixel MV accuracy and B has 1/8th pixel MV accuracy. (...) A has 1/8th pixel MV accuracy and B has 1/4th pixel MV accuracy. (d). Both A and B have 1/4th pixel MV accuracy. · Encoder always maintains MV and MVD information at 1/8th pixel resolution (by left-shift if necessary). If the current block has only 1/4th pixel motion accuracy, the MV prediction is converted to 1/4th pixel accuracy by right-shifting and then the MVD is formed.
Language:English
Score: 971943.6 - https://www.itu.int/wftp3/av-a..._07_B_Geneva/JCTVC-B006_r1.doc
Data Source: un
Problems in the current specification (1) Accuracy of motion vectors The 1/16-pel accuracy motion vectors adopted in Annex P requires at least three multiplications for each pixel. (...) Simulation results 4.1 Comparison of execution time UltraSparc, 200MHz with gcc ver. 2.7.2 (-O3) Annex P warping (modified software) 1/16 pel accuracy: 12.1 frames/sec half pel accuracy: 28.5 frames/sec virtual frame warping 1/16 pel accuracy: 13.8 frames/sec half pel accuracy: 56.0 frames/sec Pentium, 120MHz with gcc ver. 2.7.2p (-O6) Annex P warping (modified software) 1/16 pel accuracy: 8.3 frames/sec half pel accuracy: 13.4 frames/sec virtual frame warping 1/16 pel accuracy: 9.6 frames/sec half pel accuracy: 19.3 frames/sec 4.2 Computational complexity of the H.263+ decoder In order to provide a measure for the complexity of the H.263+ decoder, the execution time of the tmndecode-ver1.6 decoder was investigated in the same environment. (...) The results indicate that the execution time of RPR warping can be reduced by 78% for the UltraSparc processor and by 57% for the Pentium processor, by adopting the virtual frame warping algorithm and half pel accuracy motion vectors. Considering these results, we propose the following: (1) Supported modes for negotiation Due to the low computational complexity of arbitrary warping with half pel accuracy, it is suggested that the following three modes be negotiated for Annex P: mode 1: factor of four size changes only at 1/16 pel accuracy mode 2: arbitrary scaling without motion at 1/16 pel accuracy OR arbitrary warping at half pel accuracy mode 3: arbitrary warping at 1/16 pel accuracy The half pel / 1/16pel accuracy indication is achieved by a DA bit in the Reference Picture Resampling Parameters (RPRP) field (see section 6 of this document). (2) Usage of the virtual frame warping method in the RPR mode The simulation results indicate that the execution time of the warping process is substantially reduced by the using the virtual frame warping method, especially when half pel accuracy motion vectors are adopted.
Language:English
Score: 970207.4 - https://www.itu.int/wftp3/av-a...deo-site/9706_Por/q15a34r0.doc
Data Source: un
This is why the present core experiment is defined as an addition to q15i-59 to focus on the performance of AMA also for other motion accuracies than used in TML2. 2 Definition of the experiment The guidelines in q15i-59 are still valid. 2.1 When using ¼ motion resolution Make a decision on the MB level whether to use ½ or ¼ accuracy. (...) Compare the total performance to using ¼ pixel accuracy constantly. 2.2 When using 1/8 motion resolution Make a decision on the MB level whether to use ½, ¼ or 1/8 accuracy. (...) Compare the total performance to using 1/8 pixel accuracy constantly.
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Score: 968523.4 - https://www.itu.int/wftp3/av-a...video-site/0005_Osa/q15j09.doc
Data Source: un
FAO - SFM Tool Detail: Good practices for estimating area and assessing accuracy of land change FAO.org english français Español Sustainable Forest Management (SFM) Toolbox Background Modules Tools Cases News E-learning Gateway Register Tool Details Good practices for estimating area and assessing accuracy of land change Author Olofsson, P., Foody, G.M., Herold, M., Stehman, S.V., Woodcock, C.E. & Wulder, M.E. (...) Statistically robust and transparent approaches for assessing accuracy and estimating area of change are critical to ensure the integrity of land change information. (...) The primary good practice recommendations for assessing accuracy and estimating area are: (i) implement a probability sampling design that is chosen to achieve the priority objectives of accuracy and area estimation while also satisfying practical constraints such as cost and available sources of reference data; (ii) implement a response design protocol that is based on reference data sources that provide sufficient spatial and temporal representation to accurately label each unit in the sample (i.e., the “reference classification” will be considerably more accurate than the map classification being evaluated); (iii) implement an analysis that is consistent with the sampling design and response design protocols; (iv) summarize the accuracy assessment by reporting the estimated error matrix in terms of proportion of area and estimates of overall accuracy, user's accuracy (or commission error), and producer's accuracy (or omission error); (v) estimate area of classes (e.g., types of change such as wetland loss or types of persistence such as stable forest) based on the reference classification of the sample units; (vi) quantify uncertainty by reporting confidence intervals for accuracy and area parameters; (vii) evaluate variability and potential error in the reference classification; and (viii) document deviations from good practice that may substantially affect the results.
Language:English
Score: 968523.4 - https://www.fao.org/sustainabl...tools/tool-detail/en/c/411863/
Data Source: un
FAO - SFM Detalles de herramientas: Good practices for estimating area and assessing accuracy of land change FAO.org english français Español Conjunto de Herramientas para la Gestión Forestal Sostenible (GFS) Antecedentes Módulos Herramientas Casos News Aprendizaje electrónico Gateway Registrarse Tool Details Good practices for estimating area and assessing accuracy of land change Author Olofsson, P., Foody, G.M., Herold, M., Stehman, S.V., Woodcock, C.E. & Wulder, M.E. (...) Statistically robust and transparent approaches for assessing accuracy and estimating area of change are critical to ensure the integrity of land change information. (...) The primary good practice recommendations for assessing accuracy and estimating area are: (i) implement a probability sampling design that is chosen to achieve the priority objectives of accuracy and area estimation while also satisfying practical constraints such as cost and available sources of reference data; (ii) implement a response design protocol that is based on reference data sources that provide sufficient spatial and temporal representation to accurately label each unit in the sample (i.e., the “reference classification” will be considerably more accurate than the map classification being evaluated); (iii) implement an analysis that is consistent with the sampling design and response design protocols; (iv) summarize the accuracy assessment by reporting the estimated error matrix in terms of proportion of area and estimates of overall accuracy, user's accuracy (or commission error), and producer's accuracy (or omission error); (v) estimate area of classes (e.g., types of change such as wetland loss or types of persistence such as stable forest) based on the reference classification of the sample units; (vi) quantify uncertainty by reporting confidence intervals for accuracy and area parameters; (vii) evaluate variability and potential error in the reference classification; and (viii) document deviations from good practice that may substantially affect the results.
Language:English
Score: 966146.6 - https://www.fao.org/sustainabl...ols/tools-details/es/c/411863/
Data Source: un