### Table 3 Algorithm for dynamic programming-based stereo matching.

### Table 2: Stereo correspondence results

"... In PAGE 4: ... Any points for which the least squares matching solution di- verges, or does not converge fast enough (a threshold of 20 iterations is currently used), are also invalidated, and removed from the set of correspondences. Results from the stereo correspondence, using the above matching strategy, are shown in Table2 . A graphical depic- tion of the nal point correspondences for the image pair is depicted in Figure 4.... ..."

### TABLE II THE COMPARISON OF COLOR CHAIN STEREO DYNAMIC PROGRAMMING WITH AND WITHOUT MEDIAN FILTER.

### Table 2 The stereo matching results by the proposed method for test image pairs Image pair Windows Correct (%) All errors (%) Error at depth discontinuities (%) Invalid (%)

2005

"... In PAGE 8: ... 11. Table2 shows the stereo matching result for test image pairs. For Tsukuba image pair, we compare the pro- posed algorithm with fast stereo matching algo- rithm, such as SAD method, multi-windowing method (Fusiello et al.... ..."

### Table 1. possible moves for the dynamic programming algorithm

"... In PAGE 5: ... D is used to record the cost of a match or a substitution, V and H are used to record the cost of an insertion respectively in Av and Ah. Table1 represents the possible moves in the dynamic programming matrix. For example, DH means a match or substituion between Av and Ah followed by an insertion in Ah.... ..."

### Table 2 : Distribution of computation rimes for stereo match

### TABLE VI NUMBER OF MATCHES FOR TSUKUBA STEREO PAIR

2002

### Table 3. Results for stereo

"... In PAGE 5: ... The cost of labelling a pixel as oc- cluded was fixed at a16 a35a4 in all cases. Table3 shows the per- centage of bad matching pixels obtained as a result of ap- plying our algorithm to the four data sets available. For comparison purpose, the results obtained from other dense stereo algorithms have also been included.... ..."

### TABLE II: The average suboptimality bounds (columns 2-4-6-8-10), obtained when applying our stereo matching algorithms to one scanline at a time (instead of the whole image). In this case, we are also able to compute the true average suboptimality (columns 3-5-7-9-11) of the generated solutions, using dynamic programming. As can be seen, by inspecting the table, the suboptimality bounds approximate the true suboptimality relatively well, meaning that they can be safely used as a measure for judging the goodness of the generated solution in this case.

2007

Cited by 3

### Table 1 Comparison of the on-line string matching algorithms Algorithm Dynamic programming Backward filtering

2007

"... In PAGE 2: ... Current existing on-line string matching algo- rithms for packet inspection can be classified into four categories: dynamic programming, bit parallel, filtering, and automaton algorithms. As summa- rized in Table1 , dynamic programming [3] and bit parallel [4] algorithms are inappropriate for vari- able-length and multiple patterns, and the filtering algorithms [5] have poor worst-case time complexity O(nm), where n and m are the length of the text and patterns, respectively. Only the automaton based algorithms such as Aho-Corasick (AC) [6] support variable-length and multiple patterns, and also have the deterministic worst-case time complexity O(n).... ..."