کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6862415 | 677243 | 2015 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Exploiting semantic knowledge for robot object recognition
ترجمه فارسی عنوان
بهره برداری از دانش معنایی برای شناسایی شیء ربات
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کلمات کلیدی
دانش معنایی، بشریت، تشخیص شی، مدل های گرافیکی احتمالی ربات های مستقل،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
This paper presents a novel approach that exploits semantic knowledge to enhance the object recognition capability of autonomous robots. Semantic knowledge is a rich source of information, naturally gathered from humans (elicitation), which can encode both objects' geometrical/appearance properties and contextual relations. This kind of information can be exploited in a variety of robotics skills, especially for robots performing in human environments. In this paper we propose the use of semantic knowledge to eliminate the need of collecting large datasets for the training stages required in typical recognition approaches. Concretely, semantic knowledge encoded in an ontology is used to synthetically and effortless generate an arbitrary number of training samples for tuning Probabilistic Graphical Models (PGMs). We then employ these PGMs to classify patches extracted from 3D point clouds gathered from office environments within the UMA-offices dataset, achieving a â¼90% of recognition success, and from office and home scenes within the NYU2 dataset, yielding a success of â¼81% and â¼69.5% respectively. Additionally, a comparison with state-of-the-art recognition methods also based on graphical models has been carried out, revealing that our semantic-based training approach can compete with, and even outperform, those trained with a considerable number of real samples.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 86, September 2015, Pages 131-142
Journal: Knowledge-Based Systems - Volume 86, September 2015, Pages 131-142
نویسندگان
José-Raúl Ruiz-Sarmiento, Cipriano Galindo, Javier Gonzalez-Jimenez,