6,344 research outputs found
Spatial Aggregation: Theory and Applications
Visual thinking plays an important role in scientific reasoning. Based on the
research in automating diverse reasoning tasks about dynamical systems,
nonlinear controllers, kinematic mechanisms, and fluid motion, we have
identified a style of visual thinking, imagistic reasoning. Imagistic reasoning
organizes computations around image-like, analogue representations so that
perceptual and symbolic operations can be brought to bear to infer structure
and behavior. Programs incorporating imagistic reasoning have been shown to
perform at an expert level in domains that defy current analytic or numerical
methods. We have developed a computational paradigm, spatial aggregation, to
unify the description of a class of imagistic problem solvers. A program
written in this paradigm has the following properties. It takes a continuous
field and optional objective functions as input, and produces high-level
descriptions of structure, behavior, or control actions. It computes a
multi-layer of intermediate representations, called spatial aggregates, by
forming equivalence classes and adjacency relations. It employs a small set of
generic operators such as aggregation, classification, and localization to
perform bidirectional mapping between the information-rich field and
successively more abstract spatial aggregates. It uses a data structure, the
neighborhood graph, as a common interface to modularize computations. To
illustrate our theory, we describe the computational structure of three
implemented problem solvers -- KAM, MAPS, and HIPAIR --- in terms of the
spatial aggregation generic operators by mixing and matching a library of
commonly used routines.Comment: See http://www.jair.org/ for any accompanying file
From Unstructured 3D Point Clouds to Structured Knowledge - A Semantics Approach
International audienc
From Multiview Image Curves to 3D Drawings
Reconstructing 3D scenes from multiple views has made impressive strides in
recent years, chiefly by correlating isolated feature points, intensity
patterns, or curvilinear structures. In the general setting - without
controlled acquisition, abundant texture, curves and surfaces following
specific models or limiting scene complexity - most methods produce unorganized
point clouds, meshes, or voxel representations, with some exceptions producing
unorganized clouds of 3D curve fragments. Ideally, many applications require
structured representations of curves, surfaces and their spatial relationships.
This paper presents a step in this direction by formulating an approach that
combines 2D image curves into a collection of 3D curves, with topological
connectivity between them represented as a 3D graph. This results in a 3D
drawing, which is complementary to surface representations in the same sense as
a 3D scaffold complements a tent taut over it. We evaluate our results against
truth on synthetic and real datasets.Comment: Expanded ECCV 2016 version with tweaked figures and including an
overview of the supplementary material available at
multiview-3d-drawing.sourceforge.ne
Representations for Cognitive Vision : a Review of Appearance-Based, Spatio-Temporal, and Graph-Based Approaches
The emerging discipline of cognitive vision requires a proper representation of visual information including spatial and temporal relationships, scenes, events, semantics and context. This review article summarizes existing representational schemes in computer vision which might be useful for cognitive vision, a and discusses promising future research directions. The various approaches are categorized according to appearance-based, spatio-temporal, and graph-based representations for cognitive vision. While the representation of objects has been covered extensively in computer vision research, both from a reconstruction as well as from a recognition point of view, cognitive vision will also require new ideas how to represent scenes. We introduce new concepts for scene representations and discuss how these might be efficiently implemented in future cognitive vision systems
Spatial and Visual Perspective-Taking via View Rotation and Relation Reasoning for Embodied Reference Understanding
Embodied Reference Understanding studies the reference understanding in an
embodied fashion, where a receiver is required to locate a target object
referred to by both language and gesture of the sender in a shared physical
environment. Its main challenge lies in how to make the receiver with the
egocentric view access spatial and visual information relative to the sender to
judge how objects are oriented around and seen from the sender, i.e., spatial
and visual perspective-taking. In this paper, we propose a REasoning from your
Perspective (REP) method to tackle the challenge by modeling relations between
the receiver and the sender and the sender and the objects via the proposed
novel view rotation and relation reasoning. Specifically, view rotation first
rotates the receiver to the position of the sender by constructing an embodied
3D coordinate system with the position of the sender as the origin. Then, it
changes the orientation of the receiver to the orientation of the sender by
encoding the body orientation and gesture of the sender. Relation reasoning
models the nonverbal and verbal relations between the sender and the objects by
multi-modal cooperative reasoning in gesture, language, visual content, and
spatial position. Experiment results demonstrate the effectiveness of REP,
which consistently surpasses all existing state-of-the-art algorithms by a
large margin, i.e., +5.22% absolute accuracy in terms of Prec0.5 on YouRefIt.Comment: ECCV 2022. Code: http://github.com/ChengShiest/REP-ER
- …