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Table 1 Mean Error Rates for Experiment 1 as a Function of Trial Type, Location Uncertainty, and Color Discriminability Color Discrimination

in Negative Priming Depends on Ease of Selection

by Gordon Logan, Cathleen Moore, Toby Mordkoff, Janice Murray, Hal Pashler, Doug Rohrer, Eric Ruthruff, Jeff Miller

"... In PAGE 4: ... Figure 2 shows the amount of negative priming as a function of location uncertainty and the discriminability of the target and distractor col- ors. Table1 shows the mean error rates. Repeated measures analyses of variance (ANOVAs) were conducted on mean RTs and PCs to the probe dis- play in the ignored-repetition and control conditions.... ..."

Table 2. Discrimination capacity of di#0Berent color indexing techniques

in EigenHistograms: using Low Dimensional Models of Color Distribution for Real Time Object Recognition

by Jordi Vitria, Petia Radeva, Xavier Binefa 1999

"... In PAGE 6: ... Table2 shows the results obtained for each method of color object inspection considering 21 blisters of di#0Berent rois where each blister contains correct and wrong objects. The purpose of this test is to observe the global probabilistic distributions of wrong and correct color objects.... ..."

Cited by 2

Table 2. Q Estimated from Discrimination Task at Two Distractor Color Locationsa Color Location (3.10, 2.65, 1.84)

in Task-dependent color discrimination

by Allen B. Poirson, Brian A. Wandell 1990

Cited by 10

Table l. Q Estimated from Discrimination Task at Distractor Color Location (1.71, 1.48, 1.96) Distractor Grid Size 3 X 3

in Task-dependent color discrimination

by Allen B. Poirson, Brian A. Wandell 1990

Cited by 10

Table 1. Comparison of discriminative power of two groups of descriptors: (i impulsive, p pseudo-periodic, c colored noise and s sinusoidal)

in Audio Features for Noisy Sound Segmentation

by Pierre Hanna, Nicolas Louis, Myriam Desainte-catherine, Jenny Benois-pineau, Scrime Labri, Université De Bordeaux

Cited by 1

Table 3 : Examples in a regression problem. Precision threshold is set to 10?1. Domain of quest length, according to observations, is [-1, 3999] ; so Ganelon, Bryan and Triboulet are counter- examples to Arthur, Iago is not. Domain of Nb of Questions is known to be [0, 10] ; with a precision threshold = 10?1, this attribute discriminates Arthur from Ganelon, Bryan and Triboulet. The corresponding constraints become accordingly6: Name Color Nb Questions

in Inductive Learning of Membership Functions and Fuzzy Rules

by Michèle Sebag, W. J. Maas, Marc Schoenauer

Table 3. Percent signal change relative to fixation within the visual response selection ROIs in the color-matching task (Experiment 2) Left Hemisphere ROI Right Hemisphere ROI

in Common neural mechanisms for response selection and perceptual processing

by Yuhong Jiang, Nancy Kanwisher

"... In PAGE 8: ...l.,1999 for a role of this region in color perception), pre-SMA (Rushworth et al., 2001), and the anterior and inferior prefrontal cortex. (2) ROI Analysis: Are the response selection ROIs activated by nonspatial perceptual discrimination? ---------------------- Insert Table3 here --------------------- To determine whether the response selection ROIs are activated by nonspatial perceptual discrimination, we measured percent signal change relative to fixation in each ROI in the easy and difficult color-matching task (see Table 3). Among the 13 ROIs that showed response selection activity, 10 showed a significant effect for perceptual discrimination in the color task, including the anterior and posterior IPS, ventral and dorsal lateral prefrontal cortex, the frontal operculum/insula and right cerebellum.... In PAGE 8: ...l.,1999 for a role of this region in color perception), pre-SMA (Rushworth et al., 2001), and the anterior and inferior prefrontal cortex. (2) ROI Analysis: Are the response selection ROIs activated by nonspatial perceptual discrimination? ---------------------- Insert Table 3 here --------------------- To determine whether the response selection ROIs are activated by nonspatial perceptual discrimination, we measured percent signal change relative to fixation in each ROI in the easy and difficult color-matching task (see Table3 ). Among the 13 ROIs that showed response selection activity, 10 showed a significant effect for perceptual discrimination in the color task, including the anterior and posterior IPS, ventral and dorsal lateral prefrontal cortex, the frontal operculum/insula and right cerebellum.... ..."

Table 1: Comparaisons of average, minimal and maximum NS scores for the 35 most frequent words in training set for various experiments. Experiment 40DIM takes into account all the visual features. Experiments 5SAMEBEST and 10SAMEBEST are carried out on the spaces reduced to the 5 same (respectively the 10 same) most discriminating dimensions for all words. LABSTD is carried out on space made up of 6 dimensions of space LAB with its standard deviations. COLOR on visual spaces RGB, RGS, LAB and their standard deviations. Method 40DIM gives the best results. 40DIM 5SAMEBEST 10SAMEBEST LABSTD COLOR

in heterogeneity for image content-based

by Sabrina Tollari, Hervé Glotin

"... In PAGE 6: ... Thus we wish to reduce the number of dimensions of visual space while improving the scores obtained for each word, but we do not know a priori which dimensions to keep. We rst associate visual features which gave scores NS weaker than experiment 40DIM (see Table1 ). For example, experiments LABSTD and COLOR consisting in taking only the visual subspaces based on color.... In PAGE 6: ... Then we carried out experiments 5SAMEBEST and 10SAMEBEST consisting in taking into account for all the words the 5 (respectively 10) most dis- criminating dimensions not for each word but all words. Table1 compares the scores NS of experiments 40DIM, 5SAMEBEST, 10SAMEBEST, LABSTD and COLOR. We notice that these methods give average scores quite lower than method 40DIM.... ..."

Table 1: Feature saliency: Qualitative attributes of various outdoor vacation photograph classification problems and associated low-level features used for discrimination.

in A Bayesian Framework for Semantic Classification of Outdoor Vacation Images

by Aditya Vailaya, Mário Figueiredo, Anil Jain, Hongjiang Zhang 1999

"... In PAGE 7: ... Since, landscape images have characteristic distribution of colors (sky is generally blue, grass is green, etc), we also use color features (histograms and coherence vectors, described in [16]) as another set of features. Table1 briefly describes the qualitative attributes of the various classes... ..."

Cited by 18

Table 1: Feature saliency: Qualitative attributes of various outdoor vacation photograph classification problems and associated low-level features used for discrimination.

in A Bayesian framework for semantic classification of outdoor vacation images

by Aditya Vailaya, Mário Figueiredo, Anil Jain, Hongjiang Zhang 1999

Cited by 18

Results 1 - 10 of 5,804

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Table 1 Mean Error Rates for Experiment 1 as a Function of Trial Type, Location Uncertainty, and Color Discriminability Color Discrimination

Table 2. Discrimination capacity of di#0Berent color indexing techniques

Table 2. Q Estimated from Discrimination Task at Two Distractor Color Locationsa Color Location (3.10, 2.65, 1.84)

Table l. Q Estimated from Discrimination Task at Distractor Color Location (1.71, 1.48, 1.96) Distractor Grid Size 3 X 3

Table 1. Comparison of discriminative power of two groups of descriptors: (i impulsive, p pseudo-periodic, c colored noise and s sinusoidal)

Table 3. Percent signal change relative to fixation within the visual response selection ROIs in the color-matching task (Experiment 2) Left Hemisphere ROI Right Hemisphere ROI

Table 1: Feature saliency: Qualitative attributes of various outdoor vacation photograph classification problems and associated low-level features used for discrimination.

Table 1: Feature saliency: Qualitative attributes of various outdoor vacation photograph classification problems and associated low-level features used for discrimination.