waashk/dblp huggingface dataset:
unsupervised learning of a finite gamma mixture using mml: application to sar image analysis. this paper discusses the unsupervised learning problem for a mixture of gamma distributions. an important part of the unsupervised problem is determining the number of components which best describes some data. we apply the minimum message length (mml) criterion to the unsupervised learning problem in the case of a mixture of gamma distributions. we give a comparison of criteria in the literature for estimating the number of components in a data set. the comparison concerns synthetic and radarsat sar images.
improved adaptive gaussian mixture model for background subtraction. background subtraction is a common computer vision task. we analyze the usual pixel-level approach. we develop an efficient adaptive algorithm using gaussian mixture probability density. recursive equations are used to constantly update the parameters and but also to simultaneously select the appropriate number of components for each pixel.
content-based image retrieval: on theway to object features. content-based systems retrieve images based on lowlevel features (color,texture) while the user usually seeks some objects from real world. as segmentation is never accurate, such systems do not allow the use of powerful feature during retrieval (shape, structure). we propose a new system that relies on a hierarchy of segmentations, in order to handle some artifacts. besides, it allows using some object-related features for indexing (shape, structure). we also present some result on a 600 images database from corel.
virtual audio system customization using visual matching of ear parameters. applications in the creation of virtual auditory spaces (v as) and sonification require individualized head related transfer functions (hrtfs) for perceptual fidelity. hrtfs exhibit significant variation from person to person due to differences between their pinnae, and their body sizes. in this paper we propose and preliminarily implement a simple hrtf customization based on use of a recently published database of hrtfs [i] that also contains geometricalmeasurements of subject pinnae. we measure some of these features via simple image processing, and select the hrtf that has features most closely corresponding to the individual's features. this selection procedure is implemented along with the virtual auditory system described in [2], and listener tests conducted comparing the "customized" hrtf and a fixed hrtf. despite the simplicity of the method, tests reveal average improvement in localization accuracy of about 25 percent, though performance improvement varies with source location and individuals.
human activity classification based on gait energy image and coevolutionary genetic programming. in this paper, we present a novel approach based on gait energy image (gei) and co-evolutionary genetic programming (cgp) for human activity classification. specifically, hu's moment and normalized histogram bins are extracted from the original geis as input features. cgp is employed to reduce the feature dimensionality and learn the classifiers. the strategy of majority voting is applied to the cgp to improve the overall performance in consideration of the diversification of genetic programming. this learningbased approach improves the classification accuracy by approximately 7 percent in comparison to the traditional classifiers.
the generalization performance of learning machine based on phi-mixing sequence. the generalization performance is the important property of learning machines. it has been shown previously by vapnik, cucker and smale that, the empirical risks of learning machine based on i.i.d. sequence must uniformly converge to their expected risks as the number of samples approaches infinity. this paper extends the results to the case where the i.i.d. sequence is replaced by phi-mixing sequence. we establish the rate of uniform convergence of learning machine by using bernstein's inequality for phimixing sequence, and estimate the sample error of learning machine. in the end, we compare these bounds with known results.
evaluation of model-based interactive flower recognition. we introduce the concept of computer assisted visual interactive recognition (caviar). in caviar, a parameterized geometrical model serves as the human-computer communication channel. we implemented a flower recognition system and evaluated it on 30 inexperienced subjects. major conclusions include: 1) the accuracy of the caviar system is much higher than that of the machine alone; 2) its recognition time is much lower than that of the human alone; 3) it can be initialized with as few as one training sample per class and still achieve high accuracy; 4) it demonstrates a self-learning ability, which suggests that instead of initializing the caviar system with many training samples, we can trust the system's self-learning ability.
experimental comparison of combination rules using simulated data. in this paper, we report an experimental comparison between widely used combination rules, i.e. sum, product, maximum, borda count and best rank rules. we focus on the behavior of the considered combination rules for ensembles of classifiers exhibiting different performance of recognition. to this end, a simulation method using as input a specified performance matrix (i.e. recognition rates for each position of the true class and rejection rates) is proposed. this simulator generates ensembles of classifier outputs with specified profiles of performance within their list of solutions. our experimental results tend to show that these profiles of performance are one of the factors that affect the behavior of the combination rules.
simulating classifier ensembles of fixed diversity for studying plurality voting performance. this paper presents a new method for the artificial generation of classifier outputs in order to analyse the performance of plurality voting according both to the accuracies of the combined classifiers and to the agreement among them. this analysis is conducted in parallel with majority voting in order to compare the efficiency of these two methods when combining dependent classifiers. the experimental results show that the plurality voting is more efficient in achieving the trade-off between rejection rate and recognition rate.
constrained structure and motion estimation from optical flow. unbiased and consistent estimates of structure and motion can be obtained by least squares minimization of the differential epipolar constraint. previous work on this subject does not make use of geometrical constraints that often are present in natural and man built scenes. this paper shows how linear constraints among feature points (collinearity and coplanarity) and bilinear relations among such entities (parallelism and incidence) can be incorporated in the minimization process to improve the structure and motion estimates. there are 2 main contributions: (i) the formulation of a constrained minimization problem for structure and motion estimation from optical flow and (ii) the solution of the optimization problem by levenberg-marquardt and direct projection. we show that the proposed approach is stable, fast and efficient.
browsing graphics without prior knowledge. in this paper, we present a system to browse a set of graphical documents without prior knowledge of their content. this system relies on a new structural method locating regions potentially embedding a symbol, using an unsupervised reconstruction of the documents from their representation as chain of points. the method is complemented by a querying mechanism that retrieves from these regions those that are the most similar to a given input symbol. this mechanism uses a specific user feedback scheme to improve the accuracy of the retrieval.
texture based segmentation: automatic selection of co-occurrence matrices. texture is one of the least understood areas in computer vision. one of the major short-comings of texture segmentation approaches has been the ad-hoc selection of the set of feature vectors. we present an approach to qualitatively select a sub-set of a large (in principle infinite) set of co-occurrence matrices. a transportation measure is used to determine the difference between co-occurrence matrices resulting from various textures. this results in an ordered set of matrices, of which the resulting segmentation performance is directly related to the transportation measure. by combining segmentation results from various matrices the overall performance improves only when the matrices enhance different image areas. the most probable candidates for this can be obtained by using the same transportation measure applied to dimensional co-occurrence data. again, this results in an ordered set. texture segmentation results indicate a monotone increase in performance when adding subsequent matrices results from the ordered set.
multi-order standard deviation based distance metrics and its application in handwritten chinese character recognition. distance metric is the most popular metrics in the area of pattern recognition, and it is always used as a measure of similarity between the test pattern and the reference patterns. in this paper, a new distance metric based on manhattan distance is proposed. in the distance metric, not only the standard deviation but also the multi-order standard deviation of the reference patterns' feature vectors is involved. this paper develops this metric and the experiments based on the distance metric are discussed. according to our experiments on hcl2004 handwritten chinese characters database, the proposed distance metric shows its efficiency by improving the recognition accuracy of the system 4.01% compared with the system performance based on the standard deviation weighted distance metric
camera calibration using circle and right angles. in this paper, a new method is proposed for camera calibration using planar pattern which includes a circle and right angles. it is shown that a dual conic constraint on the vanishing line of a plane can be derived from a right angle and a circle on the same plane. given an image of a model plane containing a circle and three right angles, the vanishing line and the images of the circular points can be uniquely determined. as a result, camera calibration can be achieved by taking several images of the model plane from different view angles. there are three advantages of the proposed method. first, no correspondences between images need to be established. secondly, conics and lines forming the right angles in an image can be extracted and fitted very accurately. thirdly, it is practical as circular patterns and right angles are widely available. experiments with real images demonstrate the performance of the presented method.
reconstruction from plane mirror reflection. the 3d reconstruction problem from a single view of an object and its image reflected by a mirror plane is addressed in this paper. it is shown that the object and its mirror counterpart constitute a bilateral symmetric structure, in which correspondences are related by a harmonic homology associated with the mirror plane and the plane normal in 3d space. a novel method utilizing the profile as well as features of the object is proposed to determine the images of the plane normal and point correspondences. once they are recovered, 3d reconstruction can be computed using general stereo vision technique. experiment with real image is given to demonstrate the performance of the proposed method.
lennard-jones force field for geometric active contour. this paper presents a new geometric active contour (gac) model based on lennard-jones (l-j) force field, which is inspired by the theory of intermolecular interaction. different from conventional gradient based gac models, the proposed model does not rely on any pre-computed edge map. we take each pixel of image as a particle, and design an l-j force field for gac model according to interaction between pixels. we introduce a parameter of distance regularization to make the force tunable, and define an energy function for the l-j function to integrate various image features efficiently. a switch parameter c generates two different characteristics for the l-j force field: in the case of c=0, the force vector flows bi-directionally converge to boundaries, while in the case of c
choosing good distance metrics and local planners for probabilistic roadmap methods. this paper presents a comparative evaluation of different distance metrics and local planners within the context of probabilistic roadmap methods for motion planning. both c-space and workspace distance metrics and local planners are considered. the study concentrates on cluttered three-dimensional workspaces typical, e.g., of mechanical designs. our results include recommendations for selecting appropriate combinations of distance metrics and local planners for use in motion planning methods, particularly probabilistic roadmap methods. our study of distance metrics showed that the importance of the translational distance increased relative to the rotational distance as the environment become more crowded. we find that each local planner makes some connections than none of the others do --- indicating that better connected roadmaps will be constructed using multiple local planners. we propose a new local planning method we call {\em rotate-at-s} that outperforms the common straight-line in c-space method in crowded environments.
probabilistic roadmap methods are embarrassingly parallel. in this paper we report on our experience parallelizing probabilistic roadmap motion planning methods (prms). we show that significant, scalable speedups can be obtained with relatively little effort on the part of the developer. our experience is not limited to prms, however. in particular, we outline general techniques for parallelizing types of computations commonly performed in motion planning algorithms, and identify potential difficulties that might be faced in other efforts to parallelize sequential motion planning methods.
descrete events models + temporal logic = supervisory controller: automatic synthesis of locomotion controllers. in this paper, we address the problem of the synthesis of controller programs for a variety of robotics and manufacturing tasks. the problem we choose for test and illustrative purposes is the standard ``walking machine problem,'''' a representative instance of a real "hybrid" problem with both logical/discrete and continuous properties and strong mutual influence without any reasonable separation. we aim to produce a ``compiler technology'''' for this class of problems in a manner analogous to the development of the so-called ``silicon compilers'''' for the vlsi technology. to cope with the difficulties inherent to the problem, we resort to a novel approach that combines many key ideas from a variety of disciplines: namely, ``discrete event supervisory systems'''', petri nets approaches and ``temporal logic''''. notes: will appear in the 1995 ieee international conference on robotics and automation, nagoya, japan
enhancing randomized motion planners: exploring with haptic hints. in this paper, we investigate methods for enabling a human operator and an automatic motion planner to cooperatively solve a motion planning query. our work is motivated by our experience that automatic motion planners sometimes fail due to the difficulty of discovering &lsquo;critical&rsquo; configurations of the robot that are often naturally apparent to a human observer.our goal is to develop techniques by which the automatic planner can utilize (easily generated) user-input, and determine &lsquo;natural&rsquo; ways to inform the user of the progress made by the motion planner. we show that simple randomized techniques inspired by probabilistic roadmap methods are quite useful for transforming approximate, user-generated paths into collision-free paths, and describe an iterative transformation method which enables one to transform a solution for an easier version of the problem into a solution for the original problem. we also illustrate that simple visualization techniques can provide meaningful representations of the planner's progress in a 6-dimensional c-space. we illustrate the utility of our methods on difficult problems involving complex 3d cad models.
sensorless manipulation using massively parallel microfabricated actuator arrays. this paper investigates manipulation tasks with arrays of microelectromechanical structures (mems). we develop a geometric model for the mechanics of microactuators and a theory of sensorless, parallel manipulation, and we describe efficient algorithms for their evaluation. the theory of limit surfaces offers a purely geometric characterization of microscale contacts between actuator and moving object, which can be used to efficiently predict the motion of the object on an actuator array. it is shown how simple actuator control strategies can be used to uniquely align a part up to symmetry without sensor feedback. this theory is applicable to a wide range of microactuator arrays. our actuators are oscillating structures of sigle-crystal silicon fabricated in a ic-compatible process. calculations show that these actuators are strong enough to levitate and move e.g. a piece of paper.
automatic sensor configuration for task-directed planning. we consider the problem of planning the configuration of a sensor within the context of a robotic task. in this paper, we focus on geometrically specified tasks in the plane, and give algorithms for computing the regions from which an idealized point-and-shoot sensor can detect a polygonal robot. our main algorithm allows sensor configurations from which the robot may be partially obstructed, and computes the regions from which the robot can be detected as it translates through the goal at a known orientation. this algorithm runs in time $o(kmn^{3}(n+m))$ for an environment of complexity $n$, a robot of complexity $m$, and a goal of complexity $k$. the regions constructed can be used by an active sensing system to configure sensors that are guaranteed to observe the robot as it enters the goal.
frontal plane algorithms for dynamic bipedal walking. this paper presents two frontal plane algorithms for 3d dynamic bipedal walking. one of which is based on the notion of symmetry and the other uses reinforcement learning algorithm to learn the lateral foot placement. the algorithms are combined with a sagittal plane algorithm and successfully applied to a simulated 3d bipedal robot to achieve level ground walking. the simulation results showed that the choice of the local control law for the stance-ankle roll joint could significantly affect the performance of the frontal plane algorithms.
a unified force control approach to autonomous underwater manipulation. a unified force control scheme for an autonomous underwater robotic system is proposed in this paper. this robotic system is composed of a six degree-of-freedom autonomous underwater vehicle (auv) and a robotic arm that is mounted on the auv. a unified force control approach, which combines impedance control with hybrid position&#x002f;force control by means of fuzzy switching to perform autonomous underwater manipulation, is presented in this paper. this controller requires a dynamic model of the underwater vehicle-manipulator system. however, it does not require any model of the environment and therefore will have the potential to be useful in underwater tasks where the environment is generally unknown. the proposed approach combines the advantages of impedance control with hybrid control so that both smooth contact transition and force trajectory tracking can be achieved. in the absence of any functional autonomous underwater vehicle-manipulator system that can be used to verify the proposed controller, extensive computer simulations are performed and the results are presented in the paper.
approximating a single viewpoint in panoramic imaging devices. recent panoramic cameras present a very wide field of view from a single viewpoint. a single viewpoint is useful in mobile robotics for a number of reasons, including perspective reprojection and stereo analysis. however, the requirement of single viewpoint for panoramic cameras restricts the optical and geometrical design of these devices. in this paper, we present a method for approximating a single viewpoint in panoramic devices that allows much greater freedom in design. we illustrate the method with a compact catadioptric device using a spherical mirror and standard optics, and apply it to perspective reprojection. the resultant panoramic camera has been integrated as a surveillance device on a small mobile robot.
the synthesis of 3-d form-closure grasps. this paper presents a new formulation of computing three-dimensional (3-d) frictional form-closure grasps of n robotic fingers. as 3-d form-closure grasps involve 6-d wrench space, we first propose a recursive reduction technique to transform the complicated problem in the 6-d space into a simpler 3-d one. next, we rewrite the sufficient and necessary condition for form-closure grasps into its equivalent form of two sets of linear inequalities. then, according to the linear inequality theory, the problem is transformed to searching for a set of points which ensure the inconsistency of each of the two linear inequality systems. to search for such points, we proposed two methods: the first one is based on testing whether the convex region formed by each linear inequality system is empty, while the second one relies on the potential field method. we have implemented the algorithm and confirmed their efficiency for the synthesis of 3-d form-closure grasps.
using haptic vector fields for animation motion control. we are exploring techniques for animation authoring and editing using a haptic force-feedback device. in our system, a family of animations is encoded by a bundle of trajectories. this bundle in turn defines a time-varying, higher-order vector field on a configuration space for the animation. a haptic input device provides a low-dimensional parameterization of the resulting dynamical system, and the haptic force feedback permits browsing and editing of the space of animations, by allowing the user to experience the vector field as physical forces.
analyzing teams of cooperating mobile robots. in [don4], we described a manipulation task for cooperating mobile robots that can push large, heavy objects. there, we asked whether explicit local and global communication between the agents can be removed from a family of pushing protocols. in this paper, we answer in the affirmative. we do so by using the general methods of [don4] analyzing information invariants. we discuss several measures for the information complexity of the task of pushing with cooperating mobile robots, and we present a methodology for creating new manipulation strategies out of existing ones. we develop and analyze synchronous and asynchronous manipulation protocols for a small team of cooperating mobile robots that can push large boxes. the protocols we describe have been implemented in several forms on the cornell mobile robots in our laboratory.
designing stable finite state machine behaviors using phase plane analysis and variable structure control. this paper discusses how phase plane analysis can be used to describe the overall behavior of single and multiple autonomous robotic vehicles with finite state machine rules. the importance of this result is that we can begin to design provably stable group behaviors from a set of simple control laws and appropriate switching points with decentralized variable structure control. the ability to prove stable group behavior is especially important for applications such as locating military targets or land mines. in this paper, we demonstrate how phase plane analysis has been used to explain the behavior of a 16 cm3 autonomous line-tracking robot with four finite states. after which, the analysis is extended to include the design of a decentralized variable structure controller that guides multiple vehicles to a goal while avoiding each other.