Recognizing Material Properties from Images
نویسندگان
چکیده
منابع مشابه
Recognizing Material Properties from Images
Humans implicitly rely on properties of the materials that make up ordinary objects to guide our interactions. Grasping smooth materials, for example, requires more care than rough ones, and softness is an ideal property for fabric used in bedding. Even when these properties are not purely visual (softness is a physical property of the material), we may still infer the softness of a fabric by l...
متن کاملDeep Visuo-Tactile Learning: Estimation of Material Properties from Images
Estimation of materials properties, such as softness or roughness from visual perception is an essential factor in deciding our way of interaction with our environment in e.g., object manipulation tasks or walking. In this research, we propose a method for deep visuo-tactile learning in which we train a encoder-decoder network with an intermediate layer in an unsupervised manner with images as ...
متن کاملRecognizing Car Fluents from Video Supplementary Material
Our Car-Fluent dataset includes 647 video clips, containing basically 10 types of semantic parts and 16 types of car part fluents with diverse camera viewpoints and occlusion conditions. Fig. 3 shows the whole scene context of these videos. The videos are collected from various sources (youtube, movies, VIRAT [3], etc.), and captured by both static cameras and moving cameras. As can be seen fro...
متن کاملRecognizing 3d Objects from 2d Images: an Error Analysis Recognizing 3d Objects from 2d Images: an Error Analysis
Many recent object recognition systems use a small number of pairings of data and model features to compute the 3D transformation from a model coordinate frame into the sensor coordinate system. In the case of perfect image data, these systems seem to work well. With uncertain image data, however, the performance of such methods is less well understood. In this paper, we examine the eeects of t...
متن کاملCardiac material markers from tagged MR images
Tagged magnetic resonance imaging (MRI) has shown great promise in non-invasive analysis of heart motion. To replace implanted markers as a gold standard, however, tagged MRI must be able to track a sparse set of material points, so-called material markers, with high accuracy. This paper presents a new method for generating accurate motion estimates over a sparse set of material points using st...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 2020
ISSN: 0162-8828,2160-9292,1939-3539
DOI: 10.1109/tpami.2019.2907850