Al Tinghua, Yaolin Liu · Geo-spatial Information Science · 2003
This paper attempts to regard spatial relation transformation as an important process in map generalization. The spatial relation generalization can be divided into the components of abstraction: topology, disttance and orientation. The concept ‘spatial relation resolution’ is introduced to describe the constraints of relative spatial relation. On the basis of nine intersection models, the cardinal direction models and the iso-distance-relation models, this paper gives three sorts of relation resolution representations for topological, distance and orientation relation, respectively. Two mapping implementations in map generalization is discussed.
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W. A. Petka, J. L. Harden, K. P. Mcgrath et al. · 1998
into which 943 ml of tris(hydroxymethyl)aminometh-ane (tris) (buffering range of 6.9 to 7.1) and 57 ml of SPS solution were added. The sample was mixed thoroughly before 1 ml volumes of KOH (8 M) were added incre-mentally to change the pH. The gel containing the SPS beads was left for approximately 3 days to equilibrate, at which time additional KOH was added if the desired pH was not found. The final volume of the gel contain-ing SPS beads was adjusted to 1 ml once the pH of the gel stabilized. The final concentration of the gel was 5% w/v. Concentration studies were made with samples prepared by diluting this volume.
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Hamilton-Hollaway, Amanda · Americanae (AECID Library) · 2016
Attempt at elicting spatial relation terms and case suffixes by setting up various arrangements with a jar, rock, and two small sticks (and, at one point, a roll of toilet paper).. Language as given: Mudburra; speakers also readily identify their own variety as Eastern Mudburra
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Huilin Peng, Yang Wang, Hao Ge · Journal of Physics Conference Series · 2023
Abstract Spatial relation recognition, which aims to predict a spatial relation predicate, has attracted increasing attention in the computer vision study. During tackling this problem, modeling spatial relation of the subjects and objects is of great importance. We find that only using spatial features leads to poor results in predicting the spatial relation. To overcome these challenges, we propose an effective spatial attention module to enhance spatial features using semantic features. After identifying the importance of spatial attention mechanism, we propose a spatial transformer module with encoder layers to recognize unseen spatial relation based on spatial attention mechanism. Extensive experiments on the benchmark dataset (SpatialSense) show that, by using refined spatial feature, our spatial transformer model and spatial attention model achieve state-of-the-art performance on overall accuracy.
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Nanxi Chen, Xu Wang, Qi Sun et al. · Pattern Recognition Letters · 2024
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行方, 常幸, 3314 · Barrel (Otaru University of Commerce) · 1994
論説
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LIU HUI TING · National University of Singapore · 1997
Master's
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Dimitris Papadias · 1993
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Elizabeth Gunderson, Nicole Hallinen · OSF Preprints (OSF Preprints) · 2018
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Isabelle Bloch, Olivier Colliot, Roberto M. César · Greater South Information System · 2006
The spatial relation "between" is a notion which is intrinsically both fuzzy and contextual, and depends, in particular, on the shape of the objects. The literature is quite poor on this and the few existing definitions do not take into account these aspects. In particular, an object B that is in a concavity of an object A/sub 1/ not visible from an object A/sub 2/ is considered between A/sub 1/ and A/sub 2/ for most definitions, which is counter intuitive. Also, none of the definitions deal with cases where one object is much more elongated than the other. Here, we propose definitions which are based on convexity, morphological operators, and separation tools, and a fuzzy notion of visibility. They correspond to the main intuitive exceptions of the relation. We distinguish between cases where objects have similar spatial extensions and cases where one object is much more extended than the other. Extensions to cases where objects, themselves, are fuzzy and to three-dimensional space are proposed as well. The original work proposed in this paper covers the main classes of situations and overcomes the limits of existing approaches, particularly concerning nonvisible concavities and extended objects. Moreover, the definitions capture the intrinsic imprecision attached to this relation. The main proposed definitions are illustrated on real data from medical images.
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Ivo Düntsch, Hui Wang, Stephen Mccloskey · 1999
The formalization of the “part – of ” relationship goes back to the mereology of S. Le´sniewski, subsequently taken up by Leonard & Goodman (1940), and Clarke (1981). In this paper we investigate relation algebras obtained from different notions of “part–of”, respectively, “connectedness” in various domains. We obtain minimal models for the relational part of mereology in a general setting, and when the underlying set is an atomless Boolean algebra. 1
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Isabelle Bloch, Olivier Colliot, Roberto M. César · Greater South Information System · 2006
The spatial relation "between" is a notion which is intrinsically both fuzzy and contextual, and depends, in particular, on the shape of the objects. The literature is quite poor on this and the few existing definitions do not take into account these aspects. In particular, an object B that is in a concavity of an object A/sub 1/ not visible from an object A/sub 2/ is considered between A/sub 1/ and A/sub 2/ for most definitions, which is counter intuitive. Also, none of the definitions deal with cases where one object is much more elongated than the other. Here, we propose definitions which are based on convexity, morphological operators, and separation tools, and a fuzzy notion of visibility. They correspond to the main intuitive exceptions of the relation. We distinguish between cases where objects have similar spatial extensions and cases where one object is much more extended than the other. Extensions to cases where objects, themselves, are fuzzy and to three-dimensional space are proposed as well. The original work proposed in this paper covers the main classes of situations and overcomes the limits of existing approaches, particularly concerning nonvisible concavities and extended objects. Moreover, the definitions capture the intrinsic imprecision attached to this relation. The main proposed definitions are illustrated on real data from medical images.
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ZHOU XIAOMING · National University of Singapore · 1998
Master's
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Sandeep Kumar Dash, Y. V. Sureshchandra, Yatharth Mishra et al. · Computación y Sistemas · 2020
Spatial Relation identification is one of the integral parts of Spatial Information Retrieval. It deals with identifying the spatially related objects in view of their physical orientation or placement with respect to each other. The concept is widely used in many fields such as Robotics, Image Caption Generation and many more such areas. In this work the focus is to gather information from multiple modalities such as Image and its corresponding Text so as to strengthen the learning process for the identification of Spatial Relation pairs from a given text. Two different multimodal approaches are proposed in this work. In the first approach, information is explored as a sequential learning process where the individual Spatial Roles are identified as connected entities, which makes the Spatial Relation retrieval easy and efficient enough. To counter the small size of the dataset along with necessity to avoid overfitting, an efficient backward propagation based Neural Network was used to classify the candidate roles and the relations. The feature selection was different for all the classification tasks. Building on the selected feature from the first approach, the second approach uses a transfer learning method that utilizes an existing image caption generation model to retrieve the vital topic based information from image which is then used for the task. Thereby both approaches used information from two modalities which are further used to train the system in the respective approach. The model achieves state-of-the-art performance in terms of Precision for two of the Spatial Roles identification. This validates the advantage of using multimodal learning when compared with other partial-multimodal processes.
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Vincent Ng · 2015
SpaceEval (SemEval 2015 Task 8), which concerns spatial information extraction, builds on the spatial role identification tasks intro-duced in SemEval 2012 and used in SemEval 2013. Among the host of subtasks presented in SpaceEval, we participated in subtask 3a, which focuses solely on spatial relation ex-traction. To address the complexity of a MOVELINK, we decompose it into smaller re-lations so that the roles involved in each rela-tion can be extracted in a joint fashion with-out losing computational tractability. Our sys-tem was ranked first in the official evaluation, achieving an overall spatial relation extraction F-score of 84.5%. 1
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Supervisor Timos Sellis, Dimitris Papadias, Dimitris Papadias · 1994
. Various relation-based systems, concerned with the representation and processing of spatial relations, have been developed in numerous application domains. In this thesis we identify the common concepts underlying qualitative spatial knowledge representation, we compare the representational properties of the different systems and we outline the computational tasks involved in relation-based spatial information processing. The thesis also proposes symbolic spatial indexes, relation-based structures which combine several ideas in spatial knowledge representation. A symbolic spatial index is an array that preserves only a set of spatial relations among distinct objects in an image, called the modelling space; the index array discards information, such as shape and size of objects, and irrelevant spatial relations. The construction of a symbolic spatial index from an input image can be thought of as a transformation that keeps only a set of representative points needed for the definition...
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Gabriel Babatunde Iwasokun, Oluwole Charles Akinyokun, Cleopas O. Angaye · Studies in computational intelligence · 2014
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William M. Ringle (10720277), Tomás Gallareta Negrón (10720280), Rossana May Ciau (10720283) et al. · Figshare · 2021
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Sebastian Brand · Lecture notes in computer science · 2004
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Randi A. Doyle, Daniel Voyer, Isabelle D. Cherney · Journal of Applied Developmental Psychology · 2012
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