Deeply Semantic Inductive Spatio-Temporal Learning

08/09/2016
by   Jakob Suchan, et al.
0

We present an inductive spatio-temporal learning framework rooted in inductive logic programming. With an emphasis on visuo-spatial language, logic, and cognition, the framework supports learning with relational spatio-temporal features identifiable in a range of domains involving the processing and interpretation of dynamic visuo-spatial imagery. We present a prototypical system, and an example application in the domain of computing for visual arts and computational cognitive science.

READ FULL TEXT
research
03/17/2021

A Survey on Spatio-temporal Data Analytics Systems

Due to the surge of spatio-temporal data volume, the popularity of locat...
research
07/02/2014

Cortical spatio-temporal dimensionality reduction for visual grouping

The visual systems of many mammals, including humans, is able to integra...
research
12/13/2021

A Data- and Task- Oriented Design Framework for Bivariate Communication of Uncertainty

The communication of uncertainty estimates, predictions and insights bas...
research
08/13/2015

Talking about the Moving Image: A Declarative Model for Image Schema Based Embodied Perception Grounding and Language Generation

We present a general theory and corresponding declarative model for the ...
research
09/09/2019

GLoG: Laplacian of Gaussian for Spatial Pattern Detection in Spatio-Temporal Data

Boundary detection has long been a fundamental tool for image processing...
research
05/19/2023

PastNet: Introducing Physical Inductive Biases for Spatio-temporal Video Prediction

In this paper, we investigate the challenge of spatio-temporal video pre...
research
02/23/2016

δ-MAPS: From spatio-temporal data to a weighted and lagged network between functional domains

We propose δ-MAPS, a method that analyzes spatio-temporal data to first ...

Please sign up or login with your details

Forgot password? Click here to reset