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This paper presents an evaluation of the SIFT (scale invariant feature transform), Colour SIFT, and SURF (speeded up robust feature) descriptors on very low resolution images. The performance of the three descriptors are compared against each other on the precision and recall measures using ground truth correct matching data. Our experimental results show that both SIFT and colour SIFT are more robust...
Localization is of vital importance to a mobile vehicle system. Most of the existing algorithms are based on laser range finders, sonar sensors, artificial landmarks or GPS information. In this paper, we present a sequential probability location method for mobile vehicle, which uses scale-invariant image features as natural landmarks in unmodified environments. First, we construct a ground truth map...
This paper proposed a parallel particle filter algorithm with the help of GPU (Graphic Processing Unit) in face tracking. Due to illumination and occlusion problems, face tracking usually does not work stably based on a single cue. Three different visual cues, color histogram, edge orientation histogram and wavelet feature, are integrated under the framework of particle filter to improve the tracking...
In this paper, a fast depth estimation method using arbitrary configured stereo vision is proposed. The key idea of the method is to use wavelet transform modulus maxima as feature points in the process of epipolar line rectification and image pair matching. Wavelet transform modulus maxima are first extracted for the image pair at coarse scale. Then stereo rectification process is implemented only...
In this paper, a hybrid stereo matching algorithm which is based on feature and area process (HAFA) is presented. At first edge features are extracted and matched using wavelet transform modulus maxima representation to get a sparse disparity map. In this step, edge points are detected by getting the maxima modulus of the wavelet transform of the stereo image. At coarse scales, the local maxima of...
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