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Spectral-spatial feature extraction is an important task in hyperspectral image processing. In this paper we propose a novel method to extract distinctive invariant features from hyperspectral images for registration of hyperspectral images with different spectral conditions. Spectral condition means images are captured with different incident lights, viewing angles, or using different hyperspectral...
Boundary detection in hyperspectral image (HSI) is a challenging task due to high data dimensionality and the that is distributed over the spectral bands. For this reason, there is a dearth of research on boundary detection in HSI. In this paper, we propose a spectral-spatial feature based statistical co-occurrence method for this task. We adopt probability density function (PDF) to estimate the co-occurrence...
This paper addresses the problem of relative depth estimation using spatial defocus and spectral chromatic aberration presented in hyperspectral data. Our approach produces merged relative sparse depth map using two different methods. The first method constructs a histogram descriptor for edge pixels in each spectral band image. Due to the spectral chromatic aberration, different edge statistical...
3D reconstruction from hyper spectral images has seldom been addressed in the literature. This is a challenging problem because 3D models reconstructed from different spectral bands demonstrate different properties. If we use a single band or covert the hyper spectral image to gray scale image for the reconstruction, fine structural information may be lost. In this paper, we present a novel method...
Plant phenomics research requires different types of sensors be employed to measure the physical traits of plant surface and to estimate the plant biomass. Of particular interest is the hyperspectral imaging device which captures wavelength indexed band images that characterise material properties of objects under study. In this paper, we introduce a proof of concept research that builds 3D plant...
This paper addresses the problem of moving obstacle detection for autonomous mobile robots in unknown urban environments through the fusion of (vehicle-mounted) forward looking laser and vision sensors. In this approach we reparameterize the 2D gaussian distribution of the laser free-configuration eigenspaces by vision saliency gaussian kernel function. The approach uses bi-sensor paradigm to achieve...
This paper addresses the problem of avoiding dynamic obstacles while following the learned trajectory through non-point based maps directly through laser data. The geometric representation of free configuration area changes while a moving obstacle enters into the safety region of autonomous mobile robot. We have applied the Bézier curve properties to the free configuration eigenspaces to satisfy...
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