{"title":"Highly Accurate Tomato Maturity Recognition: Combining Deep Instance Segmentation, Data Synthesis and Color Analysis","authors":"Umme Fawzia Rahim, H. Mineno","doi":"10.1145/3508259.3508262","DOIUrl":"https://doi.org/10.1145/3508259.3508262","url":null,"abstract":"Automatic maturity recognition and counting of tomatoes during different growth stages from images is of great significance for optimal management in tomato farming, long-term yield prediction and robotic harvesting. In this study, we present a novel method that combines deep instance segmentation, data synthesis and color analysis to accurately recognize and count tomatoes during different growth stages. In our approach, we trained the Mask R-CNN instance segmentation neural network with synthetically generated dataset to accurately segment all tomato instances in an image, then color-based thresholding was applied to identify their growth stage and count the tomato number accordingly. The synthetic data generation algorithm preserved the physical structure of the data objects, thus produced photorealistic synthesized cultivation scenes. The trained model demonstrated substantial performance with maximum 92.1% average precision and 91.4% recall against the real-world test datasets for tomato segmentation. The tomato maturity recognition accuracy of the color-analysis method was evaluated by comparing estimated count with ground-truth manual counts. Our experimental results demonstrated high accuracy of tomato counting during three different growth stages: green, half ripened and fully ripened.","PeriodicalId":259099,"journal":{"name":"Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131966794","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":"Personalized Point-of-Interest Recommendation Based on Social and Geographical Influence","authors":"Chang Su, Bin Gong, Xianzhong Xie","doi":"10.1145/3508259.3508278","DOIUrl":"https://doi.org/10.1145/3508259.3508278","url":null,"abstract":"With the rapid development of location-based social networks (LBSNs), personalized Point-of-Interest (POI) recommendation has become an important personalized service to help users explore the surrounding environment. To better solve the data-sparse problem of POI recommendation, the main idea of existing research is to use neural networks to fuse context information such as social relationships and geographical influence. However, the existing models are still inadequate in integrating context information, and few studies consider privacy protection against users' activity trajectories. To solve these problems, this paper proposes a POI recommendation algorithm, SGGCN, which integrates social relationships and geographical influence. Based on desensitization of user activity trajectory, this method uses a graph convolutional neural network to explicitly learn the collaborative signal between users and users, POIs and POIs, and users and POIs to alleviate the data-sparse problem. Experiments on two real data sets show a 10% improvement over state-of-the-art POI recommendation methods.","PeriodicalId":259099,"journal":{"name":"Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115547122","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}
Maksim Siniukov, Anastasia Antsiferova, D. Kulikov, D. Vatolin
{"title":"Hacking VMAF and VMAF NEG: Vulnerability to Different Preprocessing Methods","authors":"Maksim Siniukov, Anastasia Antsiferova, D. Kulikov, D. Vatolin","doi":"10.1145/3508259.3508272","DOIUrl":"https://doi.org/10.1145/3508259.3508272","url":null,"abstract":"Video quality measurement plays a critical role in the development of video processing applications. In this paper, we show how popular quality metrics VMAF and its tuning-resistant version VMAF NEG can be artificially increased by video preprocessing. We propose a pipeline for tuning parameters of processing algorithms which allows to increase VMAF by up to 218.8%. A subjective comparison of preprocessed videos showed that with the majority of methods visual quality drops down or stays unchanged. We show that VMAF NEG scores can also be increased by some preprocessing methods by up to 21.9%.","PeriodicalId":259099,"journal":{"name":"Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114513021","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}