When a monocular vision-based unmanned aerial vehicle (UAV) is flown to the final approach to intercept the glide slope, the position and orientation of the airport runway in the image must be detected accurately for a host of suitable procedures to be followed. The approaching marking on the runway is showed as some white spots of high intensity as well as the complicated backgrounds. In our paper, we use pin-hole perspective principle, the constraint condition of the rectangle in inertial space, the front shot constraint condition of the target, as well as the clustering algorithm to identify the runway and output its position and orientation in image space. The results of the experiments show that by this algorithm, even from a place far away from the runway with marks being unclear, effective detection is possible. After all, single-frame detection errors exist, so we extend the basic runway-detection algorithm to the runway tracking. A full filtering strategy using particle filter can guard against potentially catastrophic results and improve the detection rate. Apparently, the whole algorithm of our paper can be treated as a special vision sensor for landing equipment of UAV.

Detecting and tracking dim moving small targets in infrared image sequences containing cloud clutter is an important area of research. The paper proposes a novel algorithm for the dim moving small target detection in cloudy background. The algorithm consists of three courses. The first course consists of the image spatial filtering and the sequence temporal filtering, it can be realized by two parallel calculative parts. The second course is the fusion and the segmentation processing. The last course is the targets acquiring and tracking, it can be achieved by the Kalman tracker. The results of our experiment prove that the algorithm is very effective.

This paper presents a novel curve based algorithm of stereo vision to reconstruct 3D line-like objects. B-spline approximations of 2D edge curves are selected as primitives for the reconstruction of their corresponding space curves so that, under the assumption of affine camera model, a 3D curve can be derived from reconstructing its control points according to the affine invariant property of B-Spline curves. The superiority of B-spline model in representing free-form curves gives good geometric properties of reconstruction results. Both theoretical analysis and experimental results demonstrate the validity of our approach.

This correspondence is concerned with a method for image segmentation on the visual principle. The inconsistency between the conventional discriminating criterion and the human vision mechanism in perceiving an object and its background is analyzed and an improved discriminating criterion with visual nonlinearity is defined. A new model and an algorithm for image segmentation calculation are proposed based on the spatially adaptive principle of human vision and the relevant hypotheses about object recognition. This is a two-stage process of image segmentation. First, initial segmentation is realized with the bottom-up segmenting algorithm, followed by the goal-driven segmenting algorithm to improve the segmentation results concerning certain regions of interest. Experimental results show that, compared with some conventional and gradient-based segmenting methods, the new method has the excellent performance of extracting small objects from the images of natural scenes with a complicated background.

This paper deals with the adaptive image matching and displacement estimation problems of dissimilar sequential images. Adaptive matching may be referred to a kind of functional optimization. Based on the additive measuring model for sequential images with gray-level deviation and the finite element technique, a class of gray-level correction model and robust matching criterion function are proposed, and an adaptive hierarchical searching algorithm is studied, which can automatically evaluate the temporal and spatial gray-level deviation existed in sequential images and filter out the interference thereof to increase the probability of correct estimation of displacement. Experimental results for the estimation of displacement fields with translation and/or small angle rotation in medical digital subtraction angiography (DSA) imaging and aerial remote sensing pictures are presented.>

Dip logging is a method of well logging. From the raw data obtained by dip logging, a dip arrow plot can be obtained using the relation comparison method of pattern recognition. The data tend to be very much scattered and random. The design principle and method of an expert dip arrow plot pattern recognition system are described. It incorporates a mathematical model method and an artificial intelligence technique. An angle processing algorithm and an ordered sample grouping algorithm are proposed. This system has been implemented on an IBM-PC/XT and on compatible machines. Its classification rate is 98%, obtained by processing 21 data patterns from the Zhongyuan oil field in China.>

It is shown that the statistical properties of the mean absolute difference mismatch measure surface is dependent on the autocorrelation function of image and signal-to-noise ratio. A method for finding the match point is presented, based on the idea of testing a set of hypotheses. Using the threshold estimation formulas and the optimized search scheme developed by the authors, a fast algorithm which may be adaptable to various expected operating conditions can be implemented.>