A stereo matching algorithm based on top-view transformation and dynamic programming for road-vehicle detection

Lee, Ki-Yong; Lee, Joon-Woong; Houshangi, N.

doi:10.1007/s12555-009-0208-6

Cited by 7 publications

(1 citation statement)

References 29 publications

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“…In this figure, the left figure denotes the similarity matrix in which the light stripes correspond to high similarity values. Moreover, the right figure denotes the dynamic programming [17,18] matrix and it can be seen that the lowest-cost path between the opposite corners visibly follows the dark stripe. The diagonal line denotes the distance (similarity) between the two persons.…”

Section: Gabor Feature Extraction For Ammmentioning

confidence: 99%

Dynamic time warping based identification using gabor feature of Adaptive Motion Model for walking humans

Park

Lee

2009

Int. J. Control Autom. Syst.

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In this paper, we propose a novel feature extraction method for the identification of humans. The main objective of our method is to identify each human being by extracting the Gabor feature based on the Adaptive Motion Model (AMM) for the motion of humans. In our method, the adaptive motion model, which can represent the temporal motion for each walking human is first made from the sequence images and, then, the Gabor features of the eight directions which can represent the spatial motion information for humans are extracted. The proposed feature extraction method can make a more accurate motion model by adjusting the weight between the previous and current model for each person. Moreover, our method has the advantage of allowing more information such as the Gabor features for the eight directions extracted from the AMM. Since the conventional method uses the face feature for each human being, it has disadvantages in the case of images of small size, while our method has better identification performance this case, because it only uses the spatio-temporal motion information. Finally, we identify each person by finding the minimum value of the extended dynamic time warping (DTW) for the eight Gabor features. The accuracy of the identification conducted using the proposed feature is better than that of the conventional method using the Gait Energy Image (GEI) and Face Image feature.

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Section: Gabor Feature Extraction For Ammmentioning

confidence: 99%