Fast Object Recognition Using DP in .NET

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Fast Object Recognition Using DP
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5 4 # of Junctions (%) 3 2 1 0
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5 6 7 8 4 Quality measure ( 0.1) Parts Blocks Cars
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Figure 18.5 The occupying percentage of junctions according to changes of the quality measure.
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Table 18.1 Junction number vs. quality measure. QJ = 0 3 # of lines Ln Parts Blocks Cars 100 130 137 # of junctions (Vn ) 376 543 466 Vn Ln 3 78 4 11 34 QJ = 0 5 Vn 196 196 180 Vn Ln 1 96 1 51 1 31 QJ = 0 7 Vn Vn Ln
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under uncontrolled lighting conditions has a higher dependence on the change of junction quality as a detection threshold. With some additional experiments, we identify that the number of junctions in scenes does not vary much, in spite of a low QJ . Usually, a junction quality of 0.5 is sufficient to give an adequate number of junctions for most test scenes while not skipping the salient junctions, and without increasing the time complexity of the DP-based search. Web Service qr code integratedin .net
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8. Experiments
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We applied the proposed method to find a group of optimal line junctions. Test scenes included some lines distorted by a variation of viewpoint. First, input images were processed to detect only the strongest step edges. Edges were further filtered by discarding shorter edge segments. Junctions were then inferred between the remaining line segments. When junctions were extracted from the lines, then relative relations between the junctions were searched by using the criterion of Section 4, with the collinear constraint that links two junctions. To reduce the repeated computation for relations between line segments, all possible relations such as inter-angles, collinear properties and junction qualities between lines were previously computed.
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8.1 Line Group Extraction
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As an example of 2D line matching for a viewpoint variation, the rear-view of a vehicle was used. The description of the model lines was given as a trapezoidal object. The model pattern could have a clockwise combination of the constituting lines. Figure 18.6(a-1) shows the first and the last image among a sequence of 30 images to be tested. With the cluttered background lines, a meaningful boundary extraction of the object of interest was difficult, as shown in Figure 18.6(a-2). Figure 18.6 (a-3) shows the extraction of junctions in the two frames. A threshold for the quality measure QJ was set at 0.5. Figure 18.6(a-4) shows optimal matching lines having the smallest accumulation energy of Equation (18.7). In spite of some variations from the model shape, a reasonable matching result was obtained. Unary and binary properties of Equation (18.4) were both used. Figure 18.6(b) shows a few optimal matching results. In Figure 18.6(b), the model shape is well matched as the minimum DP energy of Equation (18.7), in spite of the distorted shapes in the scenes. Matching was successful for 25 frames out of 30 a success ratio of 83 %. Failing cases result from line extraction errors in low-level processing, in which lines could not be defined on the rear windows of vehicles.
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Figure 18.6 Object matching under weak perspective projection: a rear-window of a vehicle on the highway was used. (a-1) The first and last images to be tested; (a-2) line extraction; (a-3) junction detection for QJ = 0 5; (a-4) optimal model matching; (b) a few optimal matching results between the first and last images.
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Fast Object Recognition Using DP
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Figure 18.7 Object matching in a synthetic image with broken and noisy lines.
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Figure 18.7 shows experimental results for extracting a 2D polyhedral object. Figure 18.7(a) shows a model as a pentagon shape with noisy and broken lines in the background region. All lines except for the pentagon were randomly generated. Figure 18.7(b) shows a few matching results with small DP energy. A total of six candidates were extracted as the matched results for generating hypotheses for the object recognition. Each one is similar to the pentagon model shape. It is interesting to see the last result because we did not expect this extraction. Two topological combinations of line junctions are shown as model shapes in Figure 18.8. J1 and J2 junctions are combined with a collinear constraint that also denotes the same rotating condition as the clockwise direction in the case of Figure 18.8(a). The three binary relations in Section 4 all appear in the topology of Figure 18.8. In the combination of J2 and J3 , the rotating direction between the two junctions is reversed. In Figure 18.8(b), similar topology to Figure 18.8(a) is given, except for the difference of rotating direction of the constituting junctions. Figure 18.9 presents an example of extracting topological line groups to guide 3D object recognition. The topological shapes are invariant to wide changes of view. That is, if there is no self-occlusion on the object, the interesting line groups are possible to extract. Figure 18.9(a) shows the original image to be tested. After discarding the shorter lines, Figure 18.9(b) presents the extracted lines with the numbering indicating the line index, and Figure 18.9(c) and 18.9(d) give the matched line groups corresponding to the model shape of
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