ライフサイエンスコーパス: scene

1 ence of category selectivity (e.g., faces or scenes).

2 t with the expected target's size within the scene.

3 icted the same overall coloring as the study scene.

4 based representations of the entire auditory scene.

5 to detect target objects within a background scene.

6 lowly varying, global property of the visual scene.

7 ntially sample different objects in a visual scene.

8 hich are crucial in understanding the visual scene.

9 lone target in a cluttered array or natural scene.

10 Fs that define fine-scale edges in a complex scene.

11 ntribute to the encoding of an entire visual scene.

12 xy for "gist" level, global information in a scene.

13 its encoding different aspects of the visual scene.

14 bstantially alter the visual appearance of a scene.

15 gnals regarding eye movement relative to the scene.

16 n-noise mixtures, here referred to as Global scene.

17 s to encode different features of the visual scene.

18 alternative interpretations of the auditory scene.

19 peech stream rather than the entire auditory scene.

20 ition of percepts to deduce the meaning of a scene.

21 the location of the emotional versus neutral scene.

22 ping retinal images into a consistent visual scene.

23 extract and encode features from the visual scene.

24 speech stream within a multitalker auditory scene.

25 ts and shifts due to movements of the visual scene.

26 f three-dimensional (3D) data from an imaged scene.

27 context, to compute the segmentation of the scene.

28 to the most conspicuous regions in a visual scene.

29 re rarely available for bystander use at the scene.

30 demonstrated using measurements of a dynamic scene.

31 ances (odometry), irrespective of the visual scene.

32 measurements of tungsten light and a static scene.

33 ng the navigational affordances of the local scene.

34 tem based on where a user looks in a virtual scene.

35 which the field scatters from objects in the scene.

36 correct choice was a duplicate of the study scene.

37 two radically different features of a visual scene.

38 s and is trained to store and replay a movie scene.

39 gaze, while monkeys watched video of natural scenes.

40 facial cues embedded in realistic background scenes.

41 be used to predict the affordances of novel scenes.

42 in their ability to visually search complex scenes.

43 transmit information about complex acoustic scenes.

44 us unaware of minor inconsistencies between scenes.

45 gnals under simulated active view of natural scenes.

46 s to actively sample regions of interests in scenes.

47 r fine-scale edge representations of natural scenes.

48 l cortex (PHC) and the processing of spatial scenes.

49 e characteristics of most realistic auditory scenes.

50 mulus categories, such as faces, bodies, and scenes.

51 ow these factors affect detection in natural scenes.

52 lso in physical understanding of objects and scenes.

53 ic conditions or scanned pictures of natural scenes.

54 ther using a diverse set of complex, natural scenes.

55 othness and longitudinal sparsity of natural scenes.

56 egorical targets (cars or people) in natural scenes.

57 ed with increased responses relative to RAND scenes.

58 are being used to link individuals to crime scenes.

59 isual stimuli is a salient feature of visual scenes.

60 namics toward the coding of complex auditory scenes.

61 ocessing of navigationally relevant, complex scenes.

62 ed to successful mnemonic encoding of visual scenes.

63 multitude of objects contained in real-world scenes?

64 select goal-relevant objects from cluttered scenes?

65 s efficiently once the cachers have left the scene [1].

66 s the visual responses in V1 to a stationary scene, 2) that depolarized VIP cells enhance V1 response

67 or analyzing the encoding and memory of such scenes [3-5].

68 memory test), participants reported for each scene (32 studied, 64 nonstudied) whether it reminded th

69 memory test), participants reported for each scene (32 studied, 64 nonstudied) whether it was recolle

70 ing viewing of complex social and non-social scenes alike.

71 etition between objects in cluttered natural scenes, allowing for the rapid neural representation of

72 Fourier-domain sparsity in natural scenes allows FSI to recover sharp images from undersamp

73 e presence of scene context, even though the scenes alone did not evoke category-selective response p

74 or a correct interpretation of the presented scene also correlated significantly with performance in

75 An important aspect of auditory scene analysis is auditory stream segregation-the organi

76 mplex tasks performed by sensory systems is "scene analysis": the interpretation of complex signals a

77 imodal displays of information, (c) auditory scene analysis, (d) enabling and understanding shared au

78 antly by judgments associated with pictorial scene analysis, whereas its anterior section is more act

79 othalamic circuit plays an important role in scene analysis.

80 ure of sounds is very important for auditory scene analysis.

81 therefore both important factors in auditory scene analysis.

82 portant auditory functions, such as auditory scene analysis.

83 ompletes the requirements for true olfactory scene analysis.

84 The fibers are randomly distributed in the scene and are packed on the camera end, thus making a br

85 The acoustic scene and diving behaviour of tagged individuals were re

86 able of multiplexing information of a visual scene and encoding relative depth perception from motion

87 Microbes are present at every crime scene and have been used as physical evidence for over a

88 tended speech stream from the whole auditory scene and how increasing background noise corrupts this

89 rates information already acquired about the scene and knowledge of the statistical structure of patt

90 ggests that, although functionally distinct, scene and object processing pathways do interact at a pe

91 ted to interpret the physical structure of a scene and predict how physical events will unfold.

92 nvolved in parsing the physical content of a scene and preparing an appropriate action.

93 r to correctly interpret the PIT 360 degrees scene and tended to significantly focus on details of th

94 amiliarized scene images intermixed with new scenes and classified them as indoor versus outdoor (enc

95 ing pathways.SIGNIFICANCE STATEMENT Although scenes and objects are known to contextually interact in

96 ations of humans freely viewing naturalistic scenes and performing exemplar and categorical search ta

97 Human participants studied a set of scenes and then took two types of memory test while unde

98 the visual system (high-level vision: faces, scenes) and relatively late in visual development (start

99 in activity, a video of the real-life visual scene, and free oculomotor behavior were simultaneously

100 ce and time, functional interactions between scene- and object-processing pathways.SIGNIFICANCE STATE

101 has primarily provided evidence for separate scene- and object-selective cortical pathways.

102 itally blind participants with face-, body-, scene-, and object-related natural sounds and presented

103 Still, it is unclear why such complex scenes appear to be the same from moment to moment despi

104 dividuals with regard to which features of a scene are fixated [6-8], large individual differences ar

105 NCE STATEMENT Individual objects in a visual scene are seen as distinct entities or as parts of a who

106 ation and recognition of objects in a visual scene are two problems that are hard to solve separately

107 Temporal signatures of fiber tips in the scene are used to localize each fiber.

108 Natural scenes are often characterized by a dominant orientation

109 d the emphasis on theories of how images and scenes are synthesized is misleading.

110 ited (or inhibited) by light from across the scene as expected for irradiance detectors.

111 hus, the neural representation of the visual scene as one progresses up the cortical hierarchy become

112 novel therapies for all patients and set the scene as the field prepares to enter an era of novel the

113 could deviate significantly with the object scene, as well as OSH systems with different opical sett

114 g a DMD and capture 256-by-256-pixel dynamic scenes at a speed of 10 frames per second.

115 involved in fine-grained neural encoding of scenes at a subordinate-level, in our case, architectura

116 ect recognition and neural representation by scene background, even in the absence of contextually as

117 MEG decoding results revealed that scene-based facilitation of object processing peaked at

118 nal neuroanatomy and neural dynamics of such scene-based object facilitation.

119 By actively moving the scene between each trial, we show here that neurons in t

120 ic) of five different object classes (faces, scenes, bodies, objects, scrambled objects).

121 subiculum subfield of the hippocampus during scene, but not face or object, discriminations.

122 ty quickly tracks the presence of objects in scenes, but crucially only for those objects that were i

123 lso predicted by perceptual responses to the scenes, but not by their semantic properties.

124 es a structured representation of the visual scene by co-selecting image elements that are part of be

125 s utilize rapidly acquired information about scenes by guiding search toward likely target sizes.

126 bout target and hand positions from a visual scene, calculates a difference vector between them, and

127 ABSTRACT: Complex natural scenes can be decomposed into their oriented spatial fre

128 is that barn owls, by active scanning of the scene, can induce adaptation of the tectal circuitry to

129 ing of architectural styles than entry-level scene categories.

130 n each cluster did not correspond to typical scene categories.

131 surface attitude statistics, conditioned on scene category and viewing elevation.

132 gnals that inform visual cortex about visual scene changes not predicted by the animal's own actions.

133 erns as early as 160 ms, despite substantial scene clutter and variation in the visual appearance of

134 In such scenes, colour contributes to the segmentation of object

135 s, have an attentional bias toward emotional scenes compared with conspecifics showing a neutral expr

136 ink, the caudate showed a higher response to scenes compared with faces, similar to the PPA.

137 e and quicker to detect source appearance in scenes comprised of temporally-regular (REG), rather tha

138 Human subjects listened to 'scenes' comprised of concurrent tone-pip streams (source

139 nment, we investigate how a complex acoustic scene consisting of multiple speech sources is represent

140 women, we investigate how a complex acoustic scene consisting of multiple speech sources is represent

141 gs suggest that visuospatial integration and scene construction processes might partly mediate indivi

142 psin enhances the thalamic representation of scenes containing local correlations in radiance, compen

143 n isolation and testing these classifiers on scenes containing one of the two categories.

144 ly, impacting how both social and non-social scene content are fixated, as well as general visual exp

145 s general tendency for visual exploration of scene content, as well as the precise moment-to-moment s

146 tex was strongly enhanced by the presence of scene context, even though the scenes alone did not evok

147 but easy to recognize within their original scene context, in which no other associated objects were

148 The presence of a specific category in a scene could be reliably decoded from MEG response patter

149 Participants studied a scene depicting four hills of different shapes and sizes

150 -binned photon detections to highly accurate scene depth and reflectivity by exploiting both the tran

151 nd has revealed selective impairments during scene discrimination following hippocampal lesions.

152 A scene distant from their nest area may not look as unfam

153 Scene domains were biased toward the peripheral visual f

154 to a single shape class, but to a variety of scene elements that are typically aligned with gravity:

155 and parietal cortex in REG relative to RAND scenes, emerging ~400 ms of scene-onset.

156 y roles in our ability to parse the auditory scene, enabling us to attend to one auditory object or s

157 ir size is inconsistent with the rest of the scene, even when the targets were made larger and more s

158 during a perceptual discrimination task for scenes, faces, and objects.

159 ires subjects to discriminate highly similar scenes, faces, or objects from multiple viewpoints, and

160 t ants use egocentric visual memories of the scene for guidance [1, 2, 6].

161 three times every second to scan the visual scene for interesting things to look at.

162 ssion cascade as a framework, and to set the scene for the articles in this series, which address spe

163 ired out-of-equilibrium systems will set the scene for the next generation of molecular materials wit

164 trieval (yes responses) for recently studied scenes for the two test types revealed pronounced differ

165 ate different kinds of boundaries in natural scenes, for example, those arising from surface reflecta

166 acting pattern motion direction from complex scenes--for which decisive evidence for the involvement

167 task, the correct choice depicted the study scene from a shifted perspective.

168 Recovering 3D scenes from 2D images is an under-constrained task; opti

169 identifying 20 different objects classes in scenes from a standard computer vision data set (the PAS

170 human subiculum in discrimination of complex scenes from different viewpoints.SIGNIFICANCE STATEMENT

171 curately infers the speed of complex natural scenes from this set of spatiotemporal channels [6-14].

172 cities by using 50 million images of street scenes gathered with Google Street View cars.

173 lobal serial dependencies: the appearance of scene gist is sequentially dependent on the gist perceiv

174 ven approach to describe a large database of scenes (>100,000 images) in terms of their visual proper

175 me filter that the brain imposes on a visual scene has been elusive.

176 ent response to a sinusoidally moving visual scene has been shown to get smaller with faster stimuli,

177 unts of blood traces can be found at a crime scene, having a method that is nondestructive, and provi

178 spective cohort study of patients undergoing scene HT or ground transport in the National Trauma Data

179 When searching real-world scenes, human attention is guided by knowledge of the pl

180 mics, human subjects viewed pre-familiarized scene images intermixed with new scenes and classified t

181 are very adept at extracting the "gist" of a scene in a fraction of a second.

182 Once the scene in terms of feedstocks is introduced, we carefully

183 ce area, represents pathways for movement in scenes in a manner that is tolerant to variability in ot

184 we show that variation in viewing of social scenes, including levels of preferential attention and t

185 In visual scenes, individual objects that are distinct and discrim

186 sults suggest that expectations derived from scene information, processed in scene-selective cortex,

187 ficantly focus on details of the 360 degrees scene instead of the most informative elements.

188 when they are inconsistent in size with the scene; instead, it is a byproduct of a useful strategy t

189 ts act together to help decompose the visual scene into parallel information channels.

190 The ability to parse a complex auditory scene into perceptual objects is facilitated by a hierar

191 redistribution is perceived as an ancestral scene involving three notional players: the needy other,

192 E STATEMENT Perception of a complex auditory scene is based on the ability of the brain to group thos

193 The scene is illuminated by two off-axis optical pulses.

194 ntation that contrasts the regularity of the scene is perceived salient for many animals as a means t

195 w these cells code together a complex visual scene is unclear.

196 tical regularities present in noisy acoustic scenes is an important biological strategy for solving c

197 on of object categories in cluttered natural scenes is remarkably rapid.

198 When segmenting an ambiguous scene, it is helpful to already know the present objects

199 e monkey ventral pathway neurons that prefer scene-like stimuli to objects [2].

200 topographical memory as assessed in tasks of scene memory where the viewpoint shifts from study to te

201 tic factors influence gaze to complex visual scenes more broadly, impacting how both social and non-s

202 nst the likely depth structure of the viewed scene, naturally reproducing key characteristics of both

203 umans and shows that the organization of the scene network is preserved across primate species.

204 Regions of the brain's "scene network" are well poised to integrate retinal inpu

205 we demonstrate that specific regions of the scene network-the retrosplenial complex (RSC) and occipi

206 MRI and MEG studies to reveal supra-additive scene-object interactions.

207 to track the neural representation of within-scene objects as a function of top-down attentional set.

208 Compared to a SF map of the same scene obtained using linear-filtered stimuli, a much lar

209 vidence, including body fluid stains, at the scene of a crime.

210 ng technology that can deliver an AED to the scene of an out-of-hospital cardiac arrest for bystander

211 hth century onward, the Indian Ocean was the scene of extensive trade of sub-Saharan African slaves v

212 patients (>/=15 years) transported from the scene of injury to our level I trauma center by air or g

213 tages in the hierarchy transform an auditory scene of multiple overlapping sources, from peripheral t

214 Many medieval images containing scenes of infectious disease come from non-medical sourc

215 A natural auditory scene often contains sound moving at varying velocities.

216 world objects presented in images of natural scenes only when these objects have been associated with

217 category level within the first 200 ms after scene onset.

218 relative to RAND scenes, emerging ~400 ms of scene-onset.

219 Instead of analyzing all the elements in a scene, our visual system has the ability to compress an

220 y by preserving stable aspects of the visual scene over time, yet, for dynamic stimuli, temporal smoo

221 ence can promote the apparent consistency of scenes over time.

222 Here, we propose an alternative, that stable scene perception is actively achieved by the visual syst

223 tional neuroanatomy of hippocampal-dependent scene perception is unknown.

224 e blindness are usually thought to stabilize scene perception, making us unaware of minor inconsisten

225 st a key role for the hippocampus in complex scene perception.

226 is crucial for detection of faces in natural scenes, performing a critical first step on which other

227 ct subfields, examining the role of these in scene processing has been previously limited by scanner

228 ric retinal alignment is required for visual scene recognition, but ants can translate this acquired

229 s delayed when listeners were focused on the scenes relative to when listening passively, consistent

230 This demonstrated that the discrimination of scenes, relative to faces and objects, recruits the ante

231 Finally, the correlation of this effect with scene-selective activity suggests that, although functio

232 esses within PPA, HD patients showed reduced scene-selective activity within PPA compared with health

233 f the STS and extend these prior findings to scene-selective cortex in the ventral-most regions of IT

234 derived from scene information, processed in scene-selective cortex, feed back to shape object repres

235 ventral IT cortex of macaques that included scene-selective cortex.

236 Multivoxel pattern analyses showed that a scene-selective region of dorsal occipitoparietal cortex

237 The neural representation in scene-selective regions of human visual cortex, such as

238 emergence of higher-level representations in scene-selective regions of the human brain.

239 iform face area (FFA) in addition to several scene-selective regions.

240 x was correlated with concurrent activity in scene-selective regions.

241 found a spatially clustered subpopulation of scene-selective units with an associated event-related f

242 observers often inspect different parts of a scene sequentially to form overall perception, suggestin

243 er to measure the range of each pixel in the scene sequentially.

244 r deposition for up to 48 h to mimic a crime scene setting.

245 ion depends upon knowledge of the underlying scene statistics.

246 Using naturalistic scene stimuli and multivariate pattern analysis of fMRI

247 Scenes strongly facilitate object recognition, such as w

248 we show the ventral pathway also represents scene structure aligned with the gravitational reference

249 A fundamental problem in extracting scene structure is distinguishing different physical sou

250 ct and relay specific features from a visual scene, such as the capacity to discern local or global m

251 rforms deep neural networks on a challenging scene text recognition benchmark while being 300-fold mo

252 population code that is more distributed for scenes than for other stimulus categories, and less spar

253 ly allocate attention to parts of the visual scene that may contain goal-relevant information.

254 ifically sustains representations of similar scenes that are less overlapping than in other hippocamp

255 t the DG supports representations of similar scenes that are less overlapping than those in neighbori

256 system processes objects embedded in complex scenes that vary in both luminance and colour.

257 o distinguish different objects in a natural scene, the brain must segment the image into regions cor

258 Immediately after a change in the visual scene, the motor system generates independent responses

259 As we move our gaze through a complex scene, the retinal image is constantly shifted and overw

260 To extract object boundaries in natural scenes, the primate visual system does not only rely on

261 hen infants observe an agent act in a simple scene, they infer the agent's mental states and then use

262 tive technique that could be used at a crime scene to determine the sex of a saliva donor.

263 based on the specific aspects of the visual scene to which they respond.

264 128 x 128-pixel resolution three-dimensional scenes to an accuracy of approximately 3 mm at a range o

265 l autocorrelation normally present in visual scenes to promote perceptual stability.

266 istical regularity present in noisy acoustic scenes to reduce errors in signal recognition and discri

267 t exploits short-term correlations in visual scenes to reduce noise and stabilize perception.

268 Ambulance scene-to-hospital transport times for pickups before noo

269 s (when road closures are likely) had longer scene-to-hospital transport times than on nonmarathon da

270 ysical inferences per se or, indeed, even in scene understanding; they overlap with the domain-genera

271 Images of Jupiter's poles show a chaotic scene, unlike Saturn's poles.

272 tations and processing of a complex acoustic scene up through the hierarchy of the human auditory cor

273 f different sounds in a multitalker auditory scene using magnetoencephalography and corticovocal cohe

274 sities (photons s(-1) mum(-2) ) in a natural scene vary over several orders of magnitude from shady w

275 al periphery and were selectively active for scene versus face, body, or object images.

276 s, we constructed a novel continuous natural scene video whereby phase information was maintained in

277 orks (DCNNs) and panoramic videos of natural scenes, viewed immersively through a head-mounted displa

278 humans performing a range of everyday tasks (scene viewing and exemplar and categorical search), maki

279 tially to deviant information during natural scene viewing and visual search, and suggest that humans

280 [5-8] and adapt across overlapping shifts in scene viewpoint [9, 10].

281 Further, individual scene views prime associated representations of the pano

282 l judgments regardless of stimulus category (scenes vs objects) or modality (word vs picture).

283 "This grand natural scene was a physical correlate of Francis's intellectual

284 The presence of these categories within a scene was decoded from MEG sensor patterns by training l

285 ted together but failed the moment the study scene was removed (even at a 0-s delay).

286 Over and above this, appearance in REG scenes was associated with increased responses relative

287 l subiculum and was not modulated by whether scenes were subsequently remembered or forgotten.

288 on of the hippocampus, regardless of whether scenes were subsequently remembered or forgotten.

289 rsity of environments that humans encounter, scenes were surveyed at random locations within 25 indoo

290 However in older scenes where all other entomological evidence is no long

291 cortex contains a detailed map of the visual scene, which is represented according to multiple stimul

292 s were not evoked by photographs of arousing scenes, which is indicative of selective early reactivit

293 r direction of travel based on the perceived scene while going backward.

294 a global representation of the full auditory scene with all its streams is a better candidate neural

295 ed object detector that processes the entire scene with varying resolution, uses retino-specific obje

296 pinnings of attentional selection in natural scenes with high temporal precision.

297 flage breaking that links active scanning of scenes with neural adaptation.

298 t in the selection of top salient regions of scenes with those selected by a non-foveated high resolu

299 erentially to abstract categories (faces and scenes), with a spatial organization similar to adults.

300 -based representation of the entire auditory scene, with both attended and unattended speech streams