In this paper, we present a novel method to calculate trifocal tensor based on hybrid particle swarm optimization. This method takes pole coordinates in three views as particles and the fitness function is to minimize geometric error. The proposed method is evaluated both in synthetic and real data. Experiments show that our method is more robust and accuracy than other typical methods. Rotation matrices and translation vectors estimated by the proposed method have high precision compared with ground truth data.
{"title":"Robust Computation of Trifocal Tensor Based on Hybrid Particle Swarm Optimization","authors":"Jingtian Guan, Ji Li, J. Xi","doi":"10.1145/3469951.3469958","DOIUrl":"https://doi.org/10.1145/3469951.3469958","url":null,"abstract":"In this paper, we present a novel method to calculate trifocal tensor based on hybrid particle swarm optimization. This method takes pole coordinates in three views as particles and the fitness function is to minimize geometric error. The proposed method is evaluated both in synthetic and real data. Experiments show that our method is more robust and accuracy than other typical methods. Rotation matrices and translation vectors estimated by the proposed method have high precision compared with ground truth data.","PeriodicalId":313453,"journal":{"name":"Proceedings of the 2021 3rd International Conference on Image Processing and Machine Vision","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128296702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the images processing field, we tend to use auxiliary information to assist the network for deep analysis, and perspective value is one of the auxiliary information that we frequently use. It can effectively solve the issue of perspective distortion. But most datasets cannot provide the perspective value of the image, so we devote to building a network, named perspective estimation network (PENet), that can extract the perspective value from the input image. In this paper, we propose an innovative training method that can accurately predict the perspective value. We trained the PENet on the WorldExpo’10 dataset and the test results show that our method is highly effective.
{"title":"Encoder-Decoder based Neural Network for Perspective Estimation","authors":"Yutong Wang, Qi Zhang, Joongkyu Kim, Huifang Li","doi":"10.1145/3469951.3469967","DOIUrl":"https://doi.org/10.1145/3469951.3469967","url":null,"abstract":"In the images processing field, we tend to use auxiliary information to assist the network for deep analysis, and perspective value is one of the auxiliary information that we frequently use. It can effectively solve the issue of perspective distortion. But most datasets cannot provide the perspective value of the image, so we devote to building a network, named perspective estimation network (PENet), that can extract the perspective value from the input image. In this paper, we propose an innovative training method that can accurately predict the perspective value. We trained the PENet on the WorldExpo’10 dataset and the test results show that our method is highly effective.","PeriodicalId":313453,"journal":{"name":"Proceedings of the 2021 3rd International Conference on Image Processing and Machine Vision","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130513482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cancer is a disease which dividing of abnormal cells cannot controlled and can invade nearby tissues. Cancer cells can also spread to other body organs. Moreover, we know that the genetic is a cause of cancer. So, we used SIR model (Susceptible-Infected-Recovered) for focusing on the mathematical model of cancer. We examined the dynamics of the disease and use dynamic analysis for analyzing the stability of the model. Then we found the equilibrium states and the basic reproductive number of the mathematical model of cancer. By the numerical simulations, the comparison of the parameters effect to the model, result, and conclusion are presented. CCS CONCEPTS • Applied computing; • Life and medical sciences; • Computational biology;
{"title":"Analyze of the Model for Cancer Transmission","authors":"A. Suvarnamani, P. Pongsumpun","doi":"10.1145/3469951.3469965","DOIUrl":"https://doi.org/10.1145/3469951.3469965","url":null,"abstract":"Cancer is a disease which dividing of abnormal cells cannot controlled and can invade nearby tissues. Cancer cells can also spread to other body organs. Moreover, we know that the genetic is a cause of cancer. So, we used SIR model (Susceptible-Infected-Recovered) for focusing on the mathematical model of cancer. We examined the dynamics of the disease and use dynamic analysis for analyzing the stability of the model. Then we found the equilibrium states and the basic reproductive number of the mathematical model of cancer. By the numerical simulations, the comparison of the parameters effect to the model, result, and conclusion are presented. CCS CONCEPTS • Applied computing; • Life and medical sciences; • Computational biology;","PeriodicalId":313453,"journal":{"name":"Proceedings of the 2021 3rd International Conference on Image Processing and Machine Vision","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121761624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years the classification of images has made great progress and has been used in many fields. However, it may not be possible to classify images perfectly through the CNN because of overfitting and gradient vanishing. Most existing CNNs have too many parameters, as a result, it will take a long time to train the CNN and then to classify images. In this paper, an improved CNN, with fewer parameters, can perfectly solve the problems such as overfitting, gradient vanishing was developed. The number of designed CNN's parameters is 13M, less than that of other CNNs. In order to check the performance of the designed CNN, the database such as MNIST and CIFAR-10 were used to test the CNNs. The test result was 99.467% and 91.167% respectively. These results are similar to test accuracy of other existing CNNs. Therefore, it was confirmed that the designed CNN not only has fewer parameters than the other CNNs but also shows high test accuracy.
{"title":"Deep CNN for Classification of Image Contents","authors":"Hu Shuo, Hoon Kang","doi":"10.1145/3469951.3469962","DOIUrl":"https://doi.org/10.1145/3469951.3469962","url":null,"abstract":"In recent years the classification of images has made great progress and has been used in many fields. However, it may not be possible to classify images perfectly through the CNN because of overfitting and gradient vanishing. Most existing CNNs have too many parameters, as a result, it will take a long time to train the CNN and then to classify images. In this paper, an improved CNN, with fewer parameters, can perfectly solve the problems such as overfitting, gradient vanishing was developed. The number of designed CNN's parameters is 13M, less than that of other CNNs. In order to check the performance of the designed CNN, the database such as MNIST and CIFAR-10 were used to test the CNNs. The test result was 99.467% and 91.167% respectively. These results are similar to test accuracy of other existing CNNs. Therefore, it was confirmed that the designed CNN not only has fewer parameters than the other CNNs but also shows high test accuracy.","PeriodicalId":313453,"journal":{"name":"Proceedings of the 2021 3rd International Conference on Image Processing and Machine Vision","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128122762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To improve the accuracy of kerf angle, an automatic calibration method for kerf angle in wafer automated optical inspection is presented. First, the error model of inspection system is established and system angle deviations are calibrated. Next, normalized positioning-based the kerf edges of interest in multiple images are extracted. Then, the coordinate transformation considering the system angle deviation compensation is performed. Finally, the kerf edge line is fitted based on the least squares method to obtain the kerf angle and the kerf angle can be automatically calibrated by rotating the stage. The experimental results show that the kerf angle obtained is relatively stable by coordinate transformation of multiple images to enhance the information of kerf edge and the accuracy of kerf angle can reach within 0.02 degree. Besides, the kerf angle is more sensitive to the system angle deviation and the result is basically a linear increase.
{"title":"An Automatic Calibration Method for Kerf Angle in Wafer Automated Optical Inspection","authors":"Chao Meng, J. Shi, Fei Hao, Yuan Chao","doi":"10.1145/3469951.3469953","DOIUrl":"https://doi.org/10.1145/3469951.3469953","url":null,"abstract":"To improve the accuracy of kerf angle, an automatic calibration method for kerf angle in wafer automated optical inspection is presented. First, the error model of inspection system is established and system angle deviations are calibrated. Next, normalized positioning-based the kerf edges of interest in multiple images are extracted. Then, the coordinate transformation considering the system angle deviation compensation is performed. Finally, the kerf edge line is fitted based on the least squares method to obtain the kerf angle and the kerf angle can be automatically calibrated by rotating the stage. The experimental results show that the kerf angle obtained is relatively stable by coordinate transformation of multiple images to enhance the information of kerf edge and the accuracy of kerf angle can reach within 0.02 degree. Besides, the kerf angle is more sensitive to the system angle deviation and the result is basically a linear increase.","PeriodicalId":313453,"journal":{"name":"Proceedings of the 2021 3rd International Conference on Image Processing and Machine Vision","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121869697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Proceedings of the 2021 3rd International Conference on Image Processing and Machine Vision","authors":"","doi":"10.1145/3469951","DOIUrl":"https://doi.org/10.1145/3469951","url":null,"abstract":"","PeriodicalId":313453,"journal":{"name":"Proceedings of the 2021 3rd International Conference on Image Processing and Machine Vision","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127962578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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