Results 1 - 10 of 9,180

Table 3: Tests with which to reject a hypothesis of a degenerate structure directly

in unknown title
by unknown authors 1996
"... In PAGE 7: ... All sets contain 40 points. the underlying structure by means of the tests presented in Table3 , whichnow incorporates the uniform standard deviation of the image error distribution.... In PAGE 8: ... To the projected point position we added isotropic Gaussian noise of standard deviation = 0:5, 1:0, and 2:0 pixels. For each noise level we ran the symmetry detector with several di erent settings of the user selected rejection probability, ( Table3 ). For each setting of these parameters we determined the average number of detected and missing chords over 20 di erent image pairs.... ..."
Cited by 4

Table 3: Tests with which to reject a hypothesis of a degenerate structure directly from two or more views. P is the number of feature correspondences.

in unknown title
by unknown authors
"... In PAGE 7: ... All sets contain 40 points. the underlying structure by means of the tests presented in Table3 , which now incorporates the uniform standard deviation of the image error distribution.... In PAGE 8: ... To the projected point position we added isotropic Gaussian noise of standard deviation = 0:5, 1:0, and 2:0 pixels. For each noise level we ran the symmetry detector with several di erent settings of the user selected rejection probability, ( Table3 ). For each setting of these parameters we determined the average number of detected and missing chords over 20 di erent image pairs.... ..."

Table. 4.1: Results of the de-noising experiment. All test images have been corrupted by additive Gaussian noise with three different noise levels (28.1 dB, 24.6 dB, 22.2 dB). Four different de-noising techniques have been applied: NID: nonlinear isotropic diffusion filter; NAD: nonlinear anisotropic diffusion filter; W: local adaptive Wiener filter; EPW: edge preserving Wiener filter); fingerp.: fingerprint; moons.: moon-surface .

in The Edge Preserving Wiener Filter for Scalar and Tensor Valued Images
by Kai Krajsek, Rudolf Mester

Table 4 Deviation in mean busyness value (DMB) for diVTerent noise removal schemesa

in
by S. Mukhopadhyay, B. Ch 2001
"... In PAGE 14: ... A negative value of DMB implies the over-smoothing performed by the associated algo- rithm and it also indicates the loss of certain edge fea- tures. Table4 summarizes the observation. From the table it is evident that the overall (as well as individ- ual) score of the proposed method falls behind those of MF and CA.... ..."

Table 5 Correct processing ratio value CPR for diVTerent noise removal schemesa

in
by S. Mukhopadhyay, B. Ch 2001

Table 7 Deviation in mean busyness values (DMB) for MMS-1 and MMS-2a

in
by S. Mukhopadhyay, B. Ch 2001
"... In PAGE 16: ... The SNR values in most cases are found to improve when noise statistics is considered. Table7 shows the DMB values of the images resulting from MMS-1 and MMS-2 along with the ranks. From the table it is evi- dent that the performances of MMS-1 and MMS-2 in terms of deviation in mean business value are almost same.... ..."

Table 6.5.1-1. Results from Figure 6.5.1.1 Illustrating Noise Effects of Matching Features.

in A New Stereo Matching Paradigm for the Recovery of the Third Dimension in Two-Dimensional Images
by Frank Martin Candocia, Dean Gordon Hopkins, Malcolm Heimer, Pierre Schmidt, Malek Adjouadi, Major Professor, Dean Richard Campbell 1993

Table 6.5.1-2. Matching Results from Figure 6.5.1.2 Illustrating Noise Effects on Disparity Range.

in A New Stereo Matching Paradigm for the Recovery of the Third Dimension in Two-Dimensional Images
by Frank Martin Candocia, Dean Gordon Hopkins, Malcolm Heimer, Pierre Schmidt, Malek Adjouadi, Major Professor, Dean Richard Campbell 1993

Table 5. Gaussian noise filters.

in Easily Testable Image Operators: The Class of Circuits Where Evolution Beats Engineers
by unknown authors

Table 1. Non-Gaussian Noise Distributions

in Optimal Binary Distributed Detection
by Wei Shi, Thomas W. Sun, Richard D. Wesel 2000
"... In PAGE 3: ... There are several well known non-Gaussian noise dis- tributions [4, 6] satisfying Theorem 3. These distributions are listed in Table1 , where again ?(:) is the Gamma func- tion, a is a scale parameter related to the common variance 2 of the quadrature components, and is a shape param- eter ruling the rate of decay of the noise pdf. The general- ized Cauchy reduces to the Gaussian distribution in the case ! 1.... ..."
Cited by 3
Results 1 - 10 of 9,180