{"title":"Active environment perception: a novel theoretical modelling based on synchronization of interactions","authors":"F. Gianfelici","doi":"10.1109/ROSE.2005.1588330","DOIUrl":"https://doi.org/10.1109/ROSE.2005.1588330","url":null,"abstract":"A novel modelling of active environment perception is proposed in this paper. This approach is based on a specific formalization of active perception, expressed in terms of interactions between environment and sensors. In our work, a synchronization property of active perception is identified, and exploited in theoretical characterization of this problem. The regulation of nondeterministic behavior, the description in terms of automata, and the direct connection with the state-space, represent the main advantages of our approach, which has even some great implications in modelling-checking field, process-algebra theory, and soft-computation techniques. The expressiveness of this formalization, and the generality of the proposed approach, make this modelling suitable in theoretical characterizations, and applications: thus representing an improvement of the actual best practices of this domain","PeriodicalId":244890,"journal":{"name":"International Workshop on Robotic Sensors: Robotic and Sensor Environments, 2005.","volume":"60 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":"132440063","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":"Unsupervised texture segmentation for 2D probabilistic occupancy maps","authors":"Bassel Abou Merhy, P. Payeur, E. Petriu","doi":"10.1109/ROSE.2005.1588334","DOIUrl":"https://doi.org/10.1109/ROSE.2005.1588334","url":null,"abstract":"This paper presents a novel method for the segmentation of probabilistic two-dimensional occupancy maps, based on the analysis of their texture characteristics. The texture is represented by means of a double distribution of \"local binary pattern\" and \"contrast\". The logarithmic likelihood ratio, G-statistic, is used to measure the degree of similarity between different regions; this pseudo metric measure compares LBP/C distributions linked to different segments. The innovative algorithm is used to segment the probabilistic images in regions that characterize the space according to the certainty of its occupancy level. For a better interaction between an autonomous system and its environment, the segmentation scheme is also able to differentiate between objects present in the scene by analyzing the proximity between occupied segments. Along with experimental results, a comparison with other algorithms is provided in order to demonstrate the efficiency of the proposed approach","PeriodicalId":244890,"journal":{"name":"International Workshop on Robotic Sensors: Robotic and Sensor Environments, 2005.","volume":"1 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":"123296112","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}