By Institute of Electrical and Electronics Engineers
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Otherwise it is reasonable to rotate the image regions to their dominant orientation before feature extraction to gain rotation invariance similar to . 34 R. Sorschag Discussion. From the object detection view, two different challenges arise from these observations. The first challenge is it to figure out which segment (or combination of segments) captures a trained object and which ones only capture background. The second challenge is which segment captures an object best if many overlapping hypotheses are given.
Segmentation Accuracy. As shown in Fig. 2, we use two different segmentation accuracies. On the one hand, perfect segmentations are given from the Pascal VOC dataset . The object pixels are thereby used as foreground and all others are used as background. On the other hand, we simulate an inaccurate segmentation using the convex hull of all pixels that belong to a perfectly segmented object. No holes are retained in this approach but the actual object shape is heavily changed. In the case study of this work, we further use an automatic object segmentation approach.
This indicates that the effect of changing the object’s aspect ratio is worse than using a larger amount of background. However, for Gabor wavelets no significant changes have been measured between square bounding boxes and rectangle ones. Segmentation Accuracy. In order to simulate inaccurate segmentations from the given test set, we used the convex hull around perfectly segmented objects. Table 3 shows the classification results of perfectly and inaccurately segmented objects. These results indicate that accurate segmentation can improve the classification significantly 32 R.
2006 IEEE International Solid-state Circuits Conference Digest of Technical Papers by Institute of Electrical and Electronics Engineers