### Table 3: Quaternary Codes, their Binary Images under the Gray Map and Associated Lattices.

"... In PAGE 26: ...4. A Table of Codes and Lattices The rst two columns of Table3 give the block length of the code de ned over Z4, and the block length of the binary image under the Gray map. The minimum distance (Lee distance in the Z4 world, Hamming distance in the Z2 world) appears in the third column.... ..."

### Table 1. Compression Results in Bits-Per-Pixel: Methods Operating on the Entire Image.

"... In PAGE 2: ... The second set (PICS) consists of nine gray scale pictorial images that are commonly used in image processing (airplane, baboon, boats, goldhill, bridge, cameraman, couple, lena, and peppers). The results for the methods that operate on the entire gray scale image are shown in Table1 . The best performers are CALIC with arithmetic encoding and JPEG-LS, followed by CALIC with Huffman encoding, the S+P transform with arithmetic coding, and FELICS.... ..."

### Table 5: Parameters of generalized Gray image of extended Hensel lift of QR(n) (left) and minimum distance of the best known linear codes of same length and size (right).

2003

"... In PAGE 6: ... Following this work, we lifted generating polynomial of QR(n) for n = 17, 23, 31, 47 to Z8 and Z16, extended the resulting codes by a parity-check sym- bol, then computed their minimum distance with computer assistance. Results are shown in Table5 , bold (resp. italic) is for codes better than (resp.... ..."

Cited by 1

### Table 1: Gray to Gray principle evaluation. Ratio of pixels with a saturation over 7.5.

2007

"... In PAGE 16: ... We compare the application of the non-local demosaicing algorithm alternated with one or two iterations of the color mismatching reduction algorithm presented in section 4. We begin the evaluation by the gray to gray principle ( Table1 and figures 6-8). We note that, if the initial condition used by POCS and the non local demosaicing is a gray image, then the demosaiced image by both methods is still gray while it is not the case for the rest of the methods which deal directly with the CFA mask.... In PAGE 16: ... However, in order to be consistent, we apply both methods with the same initial condition we shall use in general. Table1 shows the ratio of pixels of the demosaiced image with a saturation over a certain threshold. We compute the saturation as the distance of a certain color to the gray axis given by r = g = b.... ..."

### Table IX: Algorithms which benefited by using Gray Code over standard binary code.

1995

Cited by 50

### Table 2: Entropy and conditional entropy for bit planes of Gray coded and original lena image

"... In PAGE 3: ... Comparison Let us continue the discussion of weighted binary versus Gray coding by looking at the entropy of the bit planes generated using the two methods. For the lena image, Table2 shows the entropyofweighted binary coded and Gray coded bit planes. Per the previous discussion, the increased in compression performance is attributable to the fact that the binary re ected gray code preserves more of the sample to sample correlation than does the weighted binary code.... ..."

### Table 1. Some recently published systems

"... In PAGE 1: ... Nevertheless, these issues have rarely been thoroughly studied and most published systems generally rely on ad hoc strategies or assume that the items are positioned at known locations over neat backgrounds. Table1 lists the item extraction methods used by some recent sys- tems and their recognition results. However, since in some cases the quality of the data and the experimental conditions were not described, and there is no standard database to test the check processing systems, it is not possible to compare the results.... ..."

### Table 1: Z4-Codes, their Binary Images under the Gray Map and Associated Lattices.

### Table 1: Normalized vertical coe cients of scale 32 32 of images with (a) random natural scenes (without people), (b) pedestrians. We use a gray level coding scheme to visualize the patterns in the di erent classes of coe cients the val- ues of the coe cients and display them in the proper spatial layout. Coe cients close to 1 are gray, stronger coe cients are darker, and weaker coe cients are lighter. Figures 3(a)-(d) show the color coding for the

1997

"... In PAGE 3: ... Tables 1(a) and 1(b) show the average coe cient values for the set of vertical Haar coe cients of scale 32 32 for both the non-pedestrian and pedestrian classes. Table1 (a) shows that the pro- cess of averaging the coe cients within the pattern and then in the ensemble does not create spurious patterns; the average values of these non-pedestrian coe cients are near 1 since these are random images that do not share any common pattern. The pedestrian averages, on the other hand, show a clear pattern, with strong re- sponse (values over 1.... ..."

Cited by 143

### Table 1: Normalized vertical coe cients of scale 32 32 of images with (a) random natural scenes (without people), (b) pedestrians. We use a gray level coding scheme to visualize the patterns in the di erent classes of coe cients the val- ues of the coe cients and display them in the proper spatial layout. Coe cients close to 1 are gray, stronger coe cients are darker, and weaker coe cients are lighter. Figures 3(a)-(d) show the color coding for the

1997

"... In PAGE 3: ... Tables 1(a) and 1(b) show the average coe cient values for the set of vertical Haar coe cients of scale 32 32 for both the non-pedestrian and pedestrian classes. Table1 (a) shows that the pro- cess of averaging the coe cients within the pattern and then in the ensemble does not create spurious patterns; the average values of these non-pedestrian coe cients are near 1 since these are random images that do not share any common pattern. The pedestrian averages, on the other hand, show a clear pattern, with strong re- sponse (values over 1.... ..."

Cited by 143