This paper introduces a coaxial visible and infrared dual-band imager, which utilizes the visible and infrared detection technology, uptakes the scenery radiation of different wavelengths or optical energy reflected, adopts transmission and secondary reflection theory to design the coaxial optical path, and realizes the dual-band imaging of same scene. The imager can acquire the registered visible and infrared images, and effectively solves the problem of registration for visible and infrared images in different-source image fusion.
Range image can be obtained by 3D-Scanning and needs registration. Based on the classic ICP (Iterative Closest Point)
algorithm, this paper presents an improved ICP method. The classic ICP uses the 3D point-to-point distance as the error
measurement function. In our paper, the point-to-point distance will be replaced by a point-to-facet distance. By formula
derivation, this measurement function can be transformed into facet-weighted point-to-point distance. We apply this
method for range image registration and the result shows the validity of this algorithm, which has faster convergence rate
and better anti-noise attribute than previously described weighted ICP methods.
Detection of small objects in image data is fundamental to many image processing applications. Spatial domain detection of small objects is the key process of the detect before track (DBT) method for moving object detection. Most of the existing spatial detection methods are filter-based ones. We present a novel and efficient approach to spatial detection of small objects in image data, which combines the local signal-to-noise ratio (SNR) characteristic and appearance characteristic of small objects. In such a detection scheme, the nonlinear principal component analysis (NLPCA) neural network (NN) is used for modeling the appearance of small objects and constructing a saliency measure function. Based on this function and the feature vector extracted at each pixel position using the principal component analysis (PCA) technique, a small object saliency map is formed by lexicographically scanning the input image, then the saliency map is thresholded to obtain the intermediate object location map. We also treat such a saliency map as a spatially filtered result of the input image. Compared to several filter-based detection methods, experiments show that the proposed algorithm outperforms these methods.