ライフサイエンスコーパス: scene

1 ies (e.g. faces, bodies, hands, objects, and scenes).

2 segregate many diverse features of a sensory scene.

3 mage, encodes valuable information about the scene.

4 nts, thereby serving to stabilize the visual scene.

5 eparation from the background of an auditory scene.

6 arent level of stardom on the drug discovery scene.

7 the background and from other objects in the scene.

8 ain regions to convey features of the visual scene.

9 peech stream rather than the global auditory scene.

10 elevant sound sources in a changing acoustic scene.

11 different objects or streams present in the scene.

12 med on hypotensive injured patients from the scene.

13 inescent gunshot residue (LGSR) at the crime scene.

14 umerosity as a primary feature of the visual scene.

15 ion of directional information from a visual scene.

16 ine an object's overall distinctiveness in a scene.

17 ach preceded by a line-drawing sketch of the scene.

18 ng saccadic interrogation of a simple visual scene.

19 esponding to specific features of the visual scene.

20 that process distinct features of the visual scene.

21 nd neutral items) and photographs of neutral scenes.

22 s-and a 180( ) field of view for large-scale scenes.

23 work in concert for recognition of faces and scenes.

24 shifts, known as saccades, to explore visual scenes.

25 increase in neuronal preference for natural scenes.

26 ies naturally encode aspects of novel visual scenes.

27 and consistent across individual objects and scenes.

28 antially improve motion estimates in natural scenes.

29 ibution of salience to fixation selection in scenes.

30 ants explored novel, real-world, 360 degrees scenes.

31 s to detect relevant cues in complex sensory scenes.

32 ndings that may not generalize to real-world scenes.

33 tention manifests itself in dynamic auditory scenes.

34 tion of target vergence in three-dimensional scenes.

35 a principled analysis of natural images and scenes.

36 mediate effects of TBS on encoding of visual scenes.

37 the probable spatial arrangements of natural scenes.

38 riability in motion estimates across natural scenes.

39 l exploration is normally studied over large scenes.

40 generated by the local structure of natural scenes.

41 presence of light and dark stimuli in visual scenes.

42 nuous color spectrum and 360-degree panorama scenes.

43 le ability to perceive and understand visual scenes.

44 ich signal light and dark features in visual scenes.

45 a while presenting partially occluded visual scenes.

46 al-life, complex sounds and complex auditory scenes.

47 especially the case in complex, naturalistic scenes.

48 is a key step in interpreting complex visual scenes.

49 ceiving and understanding complex real-world scenes.

50 ic discrimination of similar objects but not scenes.

51 l visual features that are characteristic of scenes.

52 nced later memory for concurrently presented scenes.

53 s that underlie the organization of auditory scenes.

54 r knowledge about the composition of natural scenes.

55 urrent location encoding in complex auditory scenes.

56 ayers trained to identify visual objects and scenes.

57 OPA selectively impaired the recognition of scenes.

58 relations among object velocities in dynamic scenes.

59 rious statistical properties of novel visual scenes.

60 e the relevance of each direction to natural scenes.

61 that match the spatial statistics of natural scenes.

62 a that extrapolates the views of a presented scene [8], and it has been used to provide evidence for

63 culated from 21 high-resolution COSMO-SkyMed scenes acquired over Mexico City and obtain components o

64 ding and decoding global motion in a natural scene across large populations of ooDSGCs.

65 Local motion in a visual scene allows the detection of prey or predator and predi

66 r, multidimensional photography resolves the scene along with other information dimensions, such as w

67 lmost always experienced at the fovea, while scenes always extend across the entire periphery, these

68 Uncertainty in such grouping arises from scene ambiguity and sensory noise.

69 behavior matching expectations from natural scene analyses.

70 rformed significantly worse on both auditory scene analysis tasks relative to healthy controls and pa

71 rsing of the auditory environment ('auditory scene analysis')-has been poorly characterized.

72 ns for non-musical functions (e.g., auditory scene analysis).

73 c cognitive operations underpinning auditory scene analysis-sound source segregation and sound event

74 Both natural scene and grating discriminability were higher in standa

75 Further, in all classic scene and object regions, reachable-scale views dissocia

76 istinct representational signature from both scene and object views in visual cortex.

77 requires some kind of representation of the scene and of the observer's location but the form this m

78 ess multiple objects simultaneously within a scene and update their spatial positions in real time.

79 n be utilized on-site for detection at crime scenes and can be used for analyzing multiple sample typ

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

81 ave limited improvement in capturing natural scenes and displaying the images in real time.

82 rounded in the statistics of tilt in natural scenes and images.

83 ate estimates of groundtruth tilt in natural scenes and provides a better account of human performanc

84 tions that generalize across observed action scenes and written descriptions.

85 Participants studied 30 scenes and, after a distractor task, drew as many images

86 ation followed by an AC-association, so B (a scene) and C (an object) were indirectly linked through

87 ce tilts are spatially related in real-world scenes, and show that humans pool information across spa

88 Natural scenes are characterized by individual objects as well a

89 owever, is somewhat limiting, since everyday scenes are composed of complex images, consisting of inf

90 ITD) statistics inherent in natural acoustic scenes are parameters determining spatial discriminabili

91 improves when object-context associations in scenes are semantically consistent, thus predictable fro

92 However, visual scenes are typically composed of multiple categories.

93 ction task on low, medium or high complexity scenes as determined by two biologically plausible natur

94 and associative memory (cued recognition of scenes associated with objects).

95 nce (whiten) the spectral density of natural scenes at low spatial frequencies and follow the externa

96 In dense, extended biosonar scenes, bats have to emit sounds rapidly to avoid collis

97 d lunch, my friend, and a street sign in the scene before me?).

98 ally extrapolate the visual information in a scene beyond its boundaries (scene construction), and on

99 nfluences the appearance not only of overall scene brightness, but also of low-frequency patterns.

100 ted associations (i.e., choosing the correct scene but the incorrect photograph) significantly predic

101 The world we see consists of complex visual scenes, but rarely is the entire picture visible to us.

102 n exploit spatial correlations in the visual scene by using retinotopy, the organizing principle by w

103 Seed set in scenes postfire exceeded other scenes by 55%, and annual fecundity nearly doubled (88%

104 g for more than one object in complex visual scenes can be detrimental for search performance.

105 l-color scene images drawn from 30 different scene categories while having their brain activity measu

106 Many types of features are associated with scene categories, ranging from low-level properties, suc

107 Human scene categorization is characterized by its remarkable

108 Together, these results suggest that scene categorization is primarily a high-level process,

109 onal layers of a DCNN trained for object and scene categorization with neural representations in huma

110 time course of their unique contributions to scene categorization.

111 to determine their relative contributions to scene categorization.

112 tand each property's unique contributions to scene categorization.

113 ean enough information to describe a general scene category.

114 systematic definition quantifies how natural scene complexity interacts with decision-making.

115 rmation processing was affected by low-level scene complexity.

116 sampled such that CE and SC both influenced scene complexity.

117 is activated regardless of whether these two scene components are integrated in the same percept.

118 suggest that the mental representation of a scene consists of an intermingling of sensory informatio

119 ismiss theoretical explanations that include scene construction [2,3], and suggest removal of BE from

120 ivileged link between boundary extension and scene construction in memory to begin with.

121 Further, peak responses from the scene construction task were anterior to perceptual peak

122 She claims that scene construction will lead to transformations exclusiv

123 posterior of those elicited by memory-based scene construction within the broader RSC.

124 nformation in a scene beyond its boundaries (scene construction), and one in which we normalize our m

125 nto additional cognitive processes than just scene construction.

126 e simplified, artificial stimuli, real-world scenes contain low-level regularities that are informati

127 adults freely viewing a large set of complex scenes containing thousands of semantically annotated ob

128 each trial, participants had to categorize a scene context and an object briefly presented within the

129 about where to expect certain objects given scene context, might be learned implicitly and unconscio

130 indicated whether briefly presented natural scenes depicted one of three attended object categories.

131 ly reconstruct the 3D profile of an obscured scene, despite 34-fold spectral-temporally overlapping n

132 not impose any assumptions about the imaged scene, despite relying on the mathematically simple proc

133 hat early visual experience enhances natural scene discriminability by directly increasing responsive

134 sing decoding methods and found that natural scene discriminability increased by 75% between the ages

135 ippocampus [10, 11] in the schematization of scenes during memory.

136 ally encoded negative, neutral, and positive scenes, each preceded by a line-drawing sketch of the sc

137 tual organization is the process of grouping scene elements into whole entities.

138 ty of the targeted (left) hippocampus during scene encoding and increased subsequent recollection.

139 second nearly identical movie in which some scenes ended differently.

140 The improved postfire mating scene enhanced reproduction by increasing pollination ef

141 heir empirical work is an admirable study of scene errors, but the bridge between their data and thei

142 ts are not based on a stable 3D model of the scene, even a distorted one.

143 -19 pandemic outbreak is the most astounding scene ever experienced in the XXI century.

144 to assess how long-term memory for auditory scenes facilitates detection of an auditory target in as

145 ) and compared neuronal responses to natural scene features in relation to simpler grating stimuli th

146 irectly increasing responsiveness to natural scene features.

147 , epoxides, and heterocumulenes and sets the scene for a host of new applications for CO(2)-derived p

148 nomic variation for future work and sets the scene for a new understanding of the evolution and genet

149 When searching a visual scene for a target, we tend not to look at items or loca

150 sual system is devoted to sifting the visual scene for the few bits of behaviorally relevant informat

151 easured fMRI and EEG responses to incomplete scene fragments and used representational similarity ana

152 mate the three-dimensional (3D) structure of scenes from information in two-dimensional (2D) retinal

153 performed a task that required constructing scenes from memory and completed a scene selectivity loc

154 ere enables the reconstruction of room-sized scenes from non-confocal, parallel multi-pixel measureme

155 When used to discriminate visual scenes from other stimuli, the same approach reveals a l

156 eason flowering synchrony in postfire mating scenes further increased mating potential.

157 ve for categories such as faces, bodies, and scenes have been found(1-5), but large parts of IT corte

158 entional dominance of faces in active social scenes, highlighting the importance of using a variety o

159 ing automated external defibrillators at the scene hold the promise of improving survival after OHCA.

160 tributing attention more globally across the scene (i.e., ensemble grouping).

161 were presented in color and the rest of the scene (i.e., the visual periphery) was entirely desatura

162 ructures known to be engaged during face and scene identification.

163 velocities (deg/s), infrared eye images and scene imagery (RGB + D).

164 hould exist during the construction of novel scene imagery.

165 icipants (both sexes) viewed 2250 full-color scene images drawn from 30 different scene categories wh

166 Subjects viewed prefamiliarized scene images intermixed with new scenes and classified t

167 e detection task with a set of eight natural scene images.

168 he rapid recognition and memory of faces and scenes implies the engagement of category-specific compu

169 of artificial life - such as the laboratory scene in Goethe's Faust - can help us to understand the

170 ting increasingly complex features of visual scenes in an easily decodable format.

171 ic discrimination of similar objects but not scenes in male and female cognitively unimpaired older a

172 ight the importance of statistics of natural scenes in shaping human visual perception.

173 capable of reconstructing 3-dimensional (3D) scenes in the presence of strong background noise are hi

174 yperspectral datasets collected from natural scenes in the UK and India.

175 In most vertebrates large, moving visual scenes induce an optokinetic response (OKR) control of e

176 Line drawings of the missing scene information correlate with our recorded activity p

177 is known about how humans process real-world scene information during active viewing conditions.

178 We conclude that, when viewing natural scenes, information about the seen category is encoded v

179 Motivations, behind-the-scenes insights, importance of new technologies, and the

180 attention to individuate a complex, dynamic scene into a few focal objects (i.e., object individuati

181 hese implants encode luminance of the visual scene into electrical stimulation, however, leaving out

182 the resulting transformation of the spatial scene into temporal modulations on the retina constitute

183 ween LOC and OPA in representing objects and scenes is currently limited, however.

184 rmation present in visual stimuli in natural scenes is evaluating their fractal dimension.

185 Whether fixation selection in real-world scenes is guided by image salience or by objects has bee

186 al place area (OPA), were shown to represent scene layout (but not object content).

187 e shown to represent object content (but not scene layout), while scene-selective regions, including

188 ility to recognize one aspect of a cluttered scene, like color, offers no guarantees for the correct

189 niquely associated with a color, a panoramic scene location, and an emotional sound while fMRI data w

190 Instead, our memory for a scene may be largely driven by its visual composition, w

191 on and put into question the assumption that scene memory automatically combines visual information w

192 s the existence of two separate processes in scene memory: one in which we automatically extrapolate

193 by low-pressure sodium light, which renders scenes monochromatic.

194 connectivity with foveal V1, while the proto scene network shows biased functional connectivity with

195 c to first-person perspective motion through scenes, not motion on faces or objects, and was not foun

196 be solved in two ways: One can individuate a scene object by object, or alternatively group objects i

197 Stationary and dynamic scenes of air bubbles in water in both indoor and outdoo

198 In the real world, complex dynamic scenes often arise from the composition of simpler parts

199 Natural scenes often contain multiple objects and surfaces.

200 g a connection between a suspect and a crime scene or demonstrate the absence of such connections.

201 thood did not induce improvements in natural scene or in grating stimulus discriminability.

202 -by-trial basis to different objects (faces, scenes, or tools).

203 iented images cause more boundary extension, scene-oriented images cause more boundary contraction.

204 0.001), but enhanced associative memory for scenes paired with alcohol (p = 0.015).

205 cortical regions that respond selectively to scenes: parahippocampal place area, retrosplenial comple

206 Secondary analyses of scene patients from the PAMPer trial demonstrated that p

207 Complex scene perception depends upon the interaction between si

208 n propelling our understanding of real-world scene perception forward.

209 This Comment article provides a behind-the-scenes perspective and update of our 2016 Review, which

210 female, and another adult female ran to the scene, physically attacked the snake (with bites and hit

211 Seed set in scenes postfire exceeded other scenes by 55%, and annual

212 ve processes including memory recall, visual scene processing and navigation, and is a core component

213 y support the distinction between object and scene processing as an organizing principle of human hig

214 The distinction between object and scene processing features prominently in visual cognitiv

215 dence for the distinction between object and scene processing in human visual cortex.

216 Scene processing is fundamentally influenced and constra

217 own that this distinction between object and scene processing is one of the main organizing principle

218 e.g., regions related to social cognition or scene processing).

219 ecessary to advance research in the field of scene processing.

220 atial layout and spatial relations influence scene processing.

221 d (ii) AD-PRS on a vulnerable cortico-limbic scene-processing network heavily implicated in AD pathop

222 er specialized applications such as infrared scene projectors.

223 d by individual objects as well as by global scene properties such as spatial layout.

224 zed both by individual objects and by global scene properties.

225 recognition, with clusters in MPC performing scene recognition bilaterally and face recognition in ri

226 tween LOC and OPA stimulation and object and scene recognition performance (Dilks et al., 2013).

227 ignificantly reduced memory performance in a scene recognition task, impaired hippocampal connectivit

228 t the MPC is topologically tuned to face and scene recognition, with clusters in MPC performing scene

229 xhibited theta-phase locking during face and scene recognition.

230 (occipital place area) selectively impaired scene recognition.

231 amework for real-time three-dimensional (3D) scene reconstruction from single-photon data.

232 creased attention to semantically meaningful scene regions, suggesting more exploratory, information-

233 roposed, such that posterior aspects process scene-related visual information (constituting a medial

234 of studies have found selective responses to scenes (relative to objects) in OPA in childhood [10-13]

235 on, integration of the left and right visual scene relies on information in the center visual field,

236 e appears in or disappears from the acoustic scene remain unclear.

237 operties that contribute to enhanced natural scene representation, we performed calcium imaging of ex

238 , multitalker background, or global auditory scene, respectively.

239 We introduce an acquisition strategy, scene response model, and reconstruction algorithm that

240 ility to assess whether motion of the visual scene results from the animal's head movements.

241 tra-arrest transport vs 7.1% who received on-scene resuscitation (risk difference, 4.2% [95% CI, 3.5%

242 tra-arrest transport vs 8.5% who received on-scene resuscitation (risk difference, 4.6% [95% CI, 4.0%

243 ing resuscitation compared with continued on-scene resuscitation is unclear.

244 sport to hospital compared with continued on-scene resuscitation was associated with lower probabilit

245 ransport and 12.6% for those who received on-scene resuscitation.

246 ous circulation), compared with continued on-scene resuscitation.

247 as well as verbal working memory and visual scene scanning.

248 The results indicate that the accuracy of scene segmentation is sharpened by a suppressive process

249 ural differentiation index was estimated for scene-selective (PPA) and object-selective (LOC) cortica

250 impaired object recognition, while TMS over scene-selective cortex (occipital place area) selectivel

251 lves the occipital place area (OPA) [1, 2]-a scene-selective region in the dorsal stream that selecti

252 In contrast, another scene-selective region of cortex, the retrosplenial comp

253 faces or objects, and was not found in other scene-selective regions (the parahippocampal place area

254 Scene-selective regions exhibit retinotopic properties a

255 for the selective involvement of object- and scene-selective regions in processing their preferred ca

256 object content (but not scene layout), while scene-selective regions, including the occipital place a

257 ipital face area and fusiform face area) and scene selectivity (including the "proto" parahippocampal

258 structing scenes from memory and completed a scene selectivity localizer task.

259 nd posterior parahippocampal regions showing scene selectivity.

260 associations (AB pairs, either face-shape or scene-shape), and then underwent fMRI scanning while the

261 e, we find that salient events in background scenes significantly suppress phase-locking and gamma re

262 her contributed 78% of the variance of human scene similarity assessments and were within the noise c

263 The second sample mimicked a real crime scene situation and had an unknown number of GSR particl

264 ipants with objects defined solely by across-scene statistics provided either visually or through phy

265 rmined by two biologically plausible natural scene statistics, contrast energy (CE) or spatial cohere

266 tilt across space in accordance with natural scene statistics.

267 hing video relative to listening to auditory scenes, stronger physiological responses were recorded f

268 l system also uses visual context-the visual scene surrounding a stimulus-to predict the content of t

269 timulus are modulated by context, the visual scene surrounding the stimulus.

270 that, although OPA already responded more to scenes than objects by age 5, responses to first-person

271 dimensional depth scan of an emulated street scene that consisted of a model car and a human figure u

272 the alternating perception of entire visual scenes that can be instigated by interocular conflict.

273 enables a flexible representation of complex scenes that can be modulated by high-level cognitive sys

274 nd flexible representation of complex visual scenes that can be modulated by higher-level cognitive s

275 ial, in patches taken from images of natural scenes that either contained or did not contain color in

276 neocortical elements into spatially coherent scenes that form the basis of unfolding memory events.

277 ch for multiple classes of target in complex scenes that occur only once (e.g., As I emerge from the

278 st or for recognizing objects in a cluttered scene, the position of the target in the visual field go

279 readily adapt the acquisition scheme to the scene, thereby maximising the measurement flexibility.

280 environments such that only the parts of the scene they were looking at were presented in color and t

281 and surface properties that allow individual scenes to be recognized and their spatial structure asce

282 statistics of only elemental features of the scenes to relying on co-occurrence frequencies of elemen

283 stroke, and because of the difficulty of on-scene triage decision making with respect to the target

284 recognize and extract features from complex scenes using limited sensory information.

285 ese biases are present from the beginning of scene-viewing.

286 of reachable-scale views from both navigable scene views and close-up object views.

287 Infants looked longer when the social scene was incongruent with the type of colaughter.

288 ral benefit of repeated exposures to certain scenes was inversely related to explicit awareness of su

289 Complex auditory scenes were modeled with stochastic figure-ground stimul

290 ucture by hierarchically decomposing dynamic scenes: When we see a person walking on a train or an an

291 lity to encode global features of the visual scene, whereas V1, LM, and AL may be more specialized fo

292 schedules, we investigated Echinacea mating scenes, which quantify isolation from potential mates an

293 uences activity within the HC in response to scenes, while other perceptual nodes remained intact.

294 ion in which observers consistently recall a scene with visual information beyond its boundaries, is

295 cale views dissociated from both objects and scenes with an intermediate response magnitude.

296 nformation from celestial cues and panoramic scenes with distance information from an intrinsic odome

297 Overall, performance was best for scenes with intermediate complexity.

298 on to calculate an avatar's perspective on a scene without any prompt to do so.

299 n adults who repeatedly studied and recalled scene-word associations using a mnemonic imagery strateg

300 ior hippocampus and posteromedial cortex for scene-word pairs.