Automatic object recognition is an active area of interest for military and commercial applications. In this paper, a
system addressing autonomous recognition of ship types in infrared images is proposed. Firstly, an approach of
segmentation based on detection of salient features of the target with subsequent shadow removing is proposed, as is the
base of the subsequent object recognition. Considering the differences between the shapes of various ships mainly lie in
their superstructures, we then use superstructure moment functions invariant to translation, rotation and scale differences
in input patterns and develop a robust algorithm of obtaining ship superstructure. Subsequently a back-propagation
neural network is used as a classifier in the recognition stage and projection images of simulated three-dimensional ship
models are used as the training sets. Our recognition model was implemented and experimentally validated using both
simulated three-dimensional ship model images and real images derived from video of an AN/AAS-44V Forward
Looking Infrared(FLIR) sensor.
In this paper, we develop a method for the reconstruction of 3D coronary artery based on two perspective projections acquired on a standard single plane angiographic system in the same systole. Our reconstruction is based on the model of generalized cylinders, which are generated by sweeping a two-dimensional cross section along an axis in three-dimensional space. We restrict the cross section to be circular and always perpendicular to the tangent of the axis. Firstly, the vascular centerlines of the X-ray angiography images on both projections are semiautomatically extracted by multiscale vessel tracking using Gabor filters, and the radius of the coronary are also acquired simultaneously. Secondly, the relative geometry of the two projections is determined by the gantry information and 2D matching is realized through the epipolar geometry and the consistency of the vessels. Thirdly, we determine the three-dimensional (3D) coordinates of the identified object points from the image coordinates of the matched points and the calculated imaging system geometry. Finally, we link the consequent cross sections which are processed according to the radius and the direction information to obtain the 3D structure of the artery. The proposed 3D reconstruction method is validated on real data and is shown to perform robustly and accurately in the presence of noise.
In clinical practice, digital subtraction angiography (DSA) is a powerful technique for the visualization of blood vessels in the human body. Blood vessel segmentation is a main problem for 3D vascular reconstruction. In this paper, we propose a new adaptive thresholding method for the segmentation of DSA images. Each pixel of the DSA images is declared to be a vessel/background point with regard to a threshold and a few local characteristic limits depending on some information contained in the pixel neighborhood window. The size of the neighborhood window is set according to a priori knowledge of the diameter of vessels to make sure that each window contains the background definitely. Some experiments on cerebral DSA images are given, which show that our proposed method yields better results than global thresholding methods and some other local thresholding methods do.