Corner points play an important role in moving object detection, especially in the case of free-moving camera. Corner points provide more accurate information than other pixels and reduce the computation which is unnecessary. Previous works only use intensity information to locate the corner points, however, the information that former and the last frames provided also can be used. We utilize the information to focus on more valuable area and ignore the invaluable area. The proposed algorithm is based on reinforcement learning, which regards the detection of corner points as a Markov process. In the Markov model, the video to be detected is regarded as environment, the selections of blocks for one corner point are regarded as actions and the performance of detection is regarded as state. Corner points are assigned to be the blocks which are seperated from original whole image. Experimentally, we select a conventional method which uses marching and Random Sample Consensus algorithm to obtain objects as the main framework and utilize our algorithm to improve the result. The comparison between the conventional method and the same one with our algorithm show that our algorithm reduce 70% of the false detection.

Overlapping cells segmentation is one of the challenging topics in medical image processing. In this paper, we propose to approximately represent the cell contour as a set of sparse contour points, which can be further partitioned into two parts: the strong contour points and the weak contour points. We consider the cell contour extraction as a contour points locating problem and propose an effective and robust framework for segmentation of partially overlapping cells in cervical smear images. First, the cell nucleus and the background are extracted by a morphological filtering-based K-means clustering algorithm. Second, a gradient decomposition-based edge enhancement method is developed for enhancing the true edges belonging to the center cell. Then, a dynamic sparse contour searching algorithm is proposed to gradually locate the weak contour points in the cell overlapping regions based on the strong contour points. This algorithm involves the least squares estimation and a dynamic searching principle, and is thus effective to cope with the cell overlapping problem. Using the located contour points, the Gradient Vector Flow Snake model is finally employed to extract the accurate cell contour. Experiments have been performed on two cervical smear image datasets containing both single cells and partially overlapping cells. The high accuracy of the cell contour extraction result validates the effectiveness of the proposed method.

This paper presents a fully distributed algorithm for target tracking using a mobile sensor network. It tries to maintain the target being visible to the mobile network all the time while consuming as little motion energy as possible. Meanwhile the network connectivity is maintained. At every time, only the nodes around the target are activated while other nodes keep idle. Certain functions are defined to quantify the main aspects in the tracking such as the target escaping probability and the network connectivity status. They transform the tracking into a multi-objective optimization problem. To solve this global problem, a local motion strategy is proposed. Simulation results show that our algorithm yields good performance.

This paper presents a novel approach for image-based visual servoing of a robot manipulator with an eye-in-hand camera when the camera parameters are not calibrated and the 3-D coordinates of the features are not known. Both point and line features are considered. This paper extends the concept of depth-independent interaction (or image Jacobian) matrix, developed in earlier work for visual servoing using point features and fixed cameras, to the problem using eye-in-hand cameras and point and line features. By using the depth-independent interaction matrix, it is possible to linearly parameterize, by the unknown camera parameters and the unknown coordinates of the features, the closed-loop dynamics of the system. A new algorithm is developed to estimate unknown parameters online by combining the Slotine-Li method with the idea of structure from motion in computer vision. By minimizing the errors between the real and estimated projections of the feature on multiple images captured during motion of the robot, this new adaptive algorithm can guarantee the convergence of the estimated parameters to the real values up to a scale. On the basis of the nonlinear robot dynamics, we proved asymptotic convergence of the image errors by the Lyapunov theory. Experiments have been conducted to demonstrate the performance of the proposed controller.

This paper presents an effective algorithm for calibrating the extrinsic parameters between a camera and a laser range finder whose trace is invisible. On the basis of an analysis of three possible features, we propose to design a right-angled triangular checkerboard and to employ the invisible intersection points of the laser range finder’s slice plane with the edges of the checkerboard to set up the constraints equations. The extrinsic parameters are then calibrated by minimizing the algebraic errors between the measured intersections points and their corresponding projections on the image plane of the camera. We compared our algorithm with the existing methods by both simulations and the real data of a stereo measurement system. The simulation and experimental results confirmed that the proposed algorithm can yield more accurate results.

This paper presents a new controller for controlling a number of feature points on a robot manipulator to trace desired trajectories specified on the image plane of a fixed camera. It is assumed that the intrinsic and extrinsic parameters of the camera are not calibrated. A new adaptive algorithm is developed to estimate the unknown parameters online, based on three original ideas. First, we use the pseudoinverse of the depth-independent interaction matrix to map the image errors onto the joint space of the manipulator. By eliminating the depths in the interaction matrix, we can linearly parameterize the closed-loop dynamics of the manipulator. Second, to guarantee the existence of the pseudoinverse, the adaptive algorithm introduces a potential force to drive the estimated parameters away from the values that result in a singular Jacobian matrix. Third, to ensure that the estimated parameters are convergent to their true values up to a scale, we combine the Slotine-Li method with an online algorithm for minimizing the error between the estimated projections and real image coordinates of the feature points. We have proved asymptotic convergence of the image errors to zero by the Lyapunov theory based on the nonlinear robot dynamics. Experiments have been carried out to verify the performance of the proposed controller.

This paper presents a new controller for controlling a number of feature points on a robot manipulator to trace desired trajectories specified on the image plane of a fixed camera, provided that the intrinsic and extrinsic parameters of the camera are not calibrated. A new adaptive algorithm is developed to estimate the unknown parameters on-line based on three original ideas. First, we use the pseudo-inverse of the depth-independent interaction matrix, proposed in our earlier work, to map the image errors onto the joint space so that we can linearly parameterize the closed-loop dynamics of the system. Second, to guarantee existence of the pseudo-inverse, we introduce a potential force to drive the estimated parameters away from the values resulting in singular image interaction matrix. Third, to ensure that the estimated parameters are convergent to their true values up to a scale, we combine the Slotine-Li method with an on-line algorithm for minimizing the errors between the estimated projections and real image coordinates of the feature points. We have proved asymptotic convergence of the image errors by Lyapunov method and demonstrated the performance by experiments

Traditionally, the problem of stereo matching has been addressed either by a sparse feature-based approach or a dense area-based approach. This technique still suffers from a lack in accuracy or long computation time needed to match stereo images. In this paper, we employ color segments on scan lines of stereo images as primitives for matching, and introduce the widely used fuzzy set theory to the matching processes in order to handle ambiguities in matching. During the stage of accurate pixel correspondences of matched color segments, we use the technique of dynamic time warping (DTW), which is used in speech recognition. We found that DTW works robustly and rapidly regardless of the number of points to be matched. Covering more image space than point elements and providing more information than gray level features, color segments are less sensitive to noise and photometric variations and are thus more easily matched, and can reduce the false matches. Furthermore, a post-processing step can find more matches and alleviate the influence of under-segmentation or over-segmentation. A fast median filter can be applied to the results to further remove outliers. Experiments have been performed to demonstrate the accuracy and efficiency of the algorithm.

Segmentation of moving objects in image sequences is a fundamental step in many computer vision applications such as mineral processing industry and automated visual surveillance. In this paper, we introduce a novel approach to detect moving objects in a noisy background. Our approach combines a modified adaptive Gaussian mixture model (GMM) for background subtraction and optical flow methods supported by temporal differencing in order to achieve robust and accurate extraction of the shapes of moving objects. The algorithm works well for image sequences having many moving objects with different sizes as demonstrated by experimental results on real image sequences.

Matching corners between images is an important and difficult problem in stereo vision and many other vision applications, and no effective method has been developed to cope with general cases. In this paper, we present an improved SUSAN (smallest univalue segment assimilating nucleus) corner detection algorithm and an effective algorithm to establish corner correspondence between two images based on Delaunay triangulation. First we construct Delaunay triangulations among corners of each image and compute interior angles of the triangles. The corner correspondence is established based on an observation that these angles completely and uniquely characterize the corners and their values are not affected by scale change, less affected by rotation and translation to some extent. At the matching stage, we first obtain the most similar triangle pairs, and then extend their edges circularly until all matching corners are triangulated and mismatching corners are discarded. Experimental results are provided which illustrate the good performance of the algorithm.