scholarly journals Selective Adaptation for Temporal Information: Evidence from the Classification of Visual Ternus Apparent Motion

i-Perception ◽  
10.1068/ic761 ◽  
2011 ◽  
Vol 2 (8) ◽  
pp. 761-761
Author(s):  
Huihui Zhang ◽  
Lihan Chen ◽  
Xiaolin Zhou
Keyword(s):  
Apparent Motion ◽  
Selective Adaptation ◽  
Temporal Information

scholarly journals Classification of apparent motion percepts based on temporal factors

2008 ◽  
Vol 8 (4) ◽  
pp. 31 ◽  
Author(s):  
Vebjørn Ekroll ◽  
Franz Faul ◽  
Jürgen Golz
Keyword(s):  
Apparent Motion ◽  
Temporal Factors

A cross-linguistic investigation of the combinational possibilities of the ‘for X time’ adverbial with different aspectual verb classes

2012 ◽  
Vol 12 (2) ◽  
pp. 211-231
Author(s):  
Athina Sioupi
Keyword(s):  
Temporal Information ◽  
Perfective Aspect ◽  
Imperfective Aspect ◽  
Verb Classes ◽  
Change Of State ◽  
Viewpoint Aspect

The paper observes that the Vendler classification is not sufficient as a classification of verbs, since it cannot explain why some telic verbs, such as change of state (COS) verbs and degree achievements (DAs) appear with the durational adverbial (d-adverbial) ‘for X time’ in Greek, in English and in German, while some atelics like semelfactives appear with the frame adverbial (f-adverbial) se X ora (‘in X time’) in Greek. In the spirit of Iatridou et al. (2003) it is proposed that the d-adverbial ‘for X time’ tests not only for (a)telicity but also for (im)perfectivity. It also argues that the two d-adverbials in Greek ja X ora and epi X ora (‘for X time’) are to be found with different grammatical (viewpoint) aspect: the former with perfective aspect and the latter with imperfective aspect. This is due to the fact that the ja X ora gives not only durative temporal information but also a lexical aspectual one, while the epi X ora gives only a durative temporal.

scholarly journals On The Possibility of Identification of the Moving Celestial Objects Observed with the Space Astrometric Telescope

1998 ◽  
Vol 11 (1) ◽  
pp. 585-585
Author(s):  
O.P. Bykov
Keyword(s):  
Angular Velocity ◽  
Apparent Motion ◽  
Statistical Treatment ◽  
Moving Object ◽  
Heavenly Body ◽  
Angular Velocities ◽  
Celestial Bodies ◽  
Motion Parameters ◽  
Special Algorithm

In connection with the creation of the scientific grounds of the Russian Project named “Struve” Space Astrometric System, the main principles of classification and identification of any celestial moving object observed with this System were formulated and basic algorithms were elaborated. These algorithms are invariable for a short or long observational arc, for a known or unknown heavenly body and for an artificial or natural celestial object. For classification of observed celestial bodies the angular velocities of motion are used. These values are calculated from the statistical treatment of the 5 nearest spherical celestial body positions with a time intervalbetween them near 7 seconds. For an identification of well known sky objects ordinary procedure may be used by means of traditional ephemeris calculations. For an identification of unknown natural celestial bodies a special algorithm was developed. Ituses a calculated angular velocity of moving object from one scan to another close scan of observations. Then, having several accurate positions of fixed object during 5-10 hours per day, we can determine an initial elliptical object’s orbit by the Apparent Motion Parameters Method created at Pulkovo Observatory. It deals with a position of object, its angular velocity and acceleration, position’s angle and a curvature of trajectory on a short observational arc. These last four quantities are named the Apparent Motion Parameters. In spite of a preliminary character of the AMP-method orbits we can identify an observed object through a large interval of time, for example over 2-4 months after orbit determination. In this problem the observed and calculated angular velocities of the object’s motion are very useful. They are new and important ephemeris parameters at the epoch of Space Telescope astrometric positional observations. Examples of simulations of orbit determinations will be presented.

scholarly journals SI-STSAR-7: A Large SAR Images Dataset with Spatial and Temporal Information for Classification of Winter Sea Ice in Hudson Bay

2021 ◽  
Vol 14 (1) ◽  
pp. 168
Author(s):  
Wei Song ◽  
Wen Gao ◽  
Qi He ◽  
Antonio Liotta ◽  
Weiqi Guo
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Sea Ice ◽  
Incidence Angle ◽  
Temporal Information ◽  
Hudson Bay ◽  
Sar Images ◽  
Learning Methods ◽  
Image Patches

Remote sensing satellites have been broadly applied to sea ice monitoring. The substantial increase in satellite imagery provides a large amount of data support for deep learning methods in the sea ice classification field. However, there is a lack of public remote sensing datasets to facilitate sea ice classification with spatial and temporal information and to benchmark the deep learning methods. In this paper, we provide a labeled large sea ice dataset derived from time-series sentinel-1 SAR images, dubbed SI-STSAR-7, and a validated dataset construction method for sea ice classification research. The SI-STSAR-7 dataset includes seven different sea ice types corresponding to different sea ice development stages in Hudson Bay during winter, and its samples are time sequences of SAR image patches in order to embody the differences of backscattering intensity and textures between different sea ice types, as well as the change of sea ice with time. We construct the dataset by first performing noise reduction and mitigation of incidence angle dependence on SAR images, and then producing data samples and labeling them based on our proposed sample-producing principles and the weekly regional ice charts provided by Canadian Ice Service. Three baseline classification methods are developed on SI-STSAR-7 to establish benchmarks, which are evaluated with accuracy and kappa coefficient. The sample-producing principles are verified through experiments. Based on the experimental results, sea ice classification can be implemented well on SI-STSAR-7.

Capture of Intermodal Visual/Tactile Apparent Motion by Moving and Static Sound

2011 ◽  
Vol 24 (4) ◽  
pp. 369-389 ◽  
Author(s):  
Lihan Chen ◽  
Xiaolin Zhou
Keyword(s):  
Apparent Motion ◽  
Visual Stimulus ◽  
Tactile Stimulus ◽  
Temporal Information ◽  
Auditory Information ◽  
Motion Direction ◽  
Temporal Ventriloquism ◽  
Temporal Interval ◽  
Visual Apparent Motion ◽  
Task Irrelevant

AbstractApparent motion can occur within a particular modality or between modalities, in which a visual or tactile stimulus at one location is perceived as moving towards the location of the subsequent tactile or visual stimulus. Intramodal apparent motion has been shown to be affected or 'captured' by information from another, task-irrelevant modality, as in spatial or temporal ventriloquism. Here we investigate whether and how intermodal apparent motion is affected by motion direction cues or temporal interval information from a third modality. We demonstrated that both moving and asynchronous static sounds can capture intermodal (visual–tactile and tactile–visual) apparent motion; moreover, while the auditory direction cues have less impact upon the perception of intramodal visual apparent motion than upon the perception of intramodal tactile or intermodal visual/tactile apparent motion, the auditory temporal information has equivalent impacts upon both intramodal and intermodal apparent motion. These findings suggest intermodal apparent motion is susceptible to the influence of dynamic or static auditory information in similar ways as intramodal visual or tactile apparent motion.

Sign in / Sign up

Export Citation Format

Share Document