The median filter is one of the basic building blocks in many image processing situations. However, its use has long been hampered by its algorithmic complexity of O (r) in the kernel radius. With the trend toward larger images and proportionally larger filter kernels, the need for a more efficient median filtering algorithm becomes pressing. In this correspondence, a new, simple, yet much faster, algorithm exhibiting O (1) runtime complexity is described and analyzed. It is compared and benchmarked against previous algorithms. Extensions to higher dimensional or higher precision data and an approximation to a circular kernel are presented, as well.

This paper addresses the fundamental problem of automatically discovering an unknown moving deformable object in a monocular video sequence. No prior model of the object is used; it is only assumed that the object is composed of a set of apparently rigid parts that are not necessarily visible simultaneously, making it possible to circumvent the typical constraint of model initialization. A set of rigid parts describing the object is incrementally extracted in a modeling-tracking loop with reinforced memory. In this framework, low-level segmentation is considered as a necessary but non reliable process that helps initiating hypotheses. Motion-based layer segmentation from feature points and edges is applied only when and where no modeled parts can be tracked. Using the quantity of motion measure, it is further shown how to deal with temporal scale. The interest for this approach in applications such as human tracking is demonstrated for a set of various sequences including a rapidly evolving shape

In image-based light field rendering, many efforts have been made to improve the efficiency of the modeling. This efficiency pertains to the photorealism and compression of a given model. We propose a new way to improve these two aspects. We show that it is better to sample the plenoptic function on a surface that minimizes the frequency content of all its lumispheres. We also demonstrate an additional constraint based on the visual hull that further guides the sampling of the plenoptic function around an object. We propose a corresponding algorithm that relies on images alone and only suppose that the modeled object is opaque. This algorithm is then validated on both real and synthetic data sets.

Traditional approaches for surface reconstruction from range data require that the input data be either range images or unorganized sets of points. Since a large number of range sensors provide data along curvilinear patterns such as profiles, this paper presents an approach for reconstructing a surface from a set of unorganized curves. A strategy for updating the reconstructed surface during data acquisition is described as well. Curves are accumulated in a volumetric structure in which a vector field is built and updated. The information that is needed for efficient curve registration is also directly available in this vector field. This leads to a unified modeling approach combining surface reconstruction and curve registration. The algorithm implementing the approach is of linear complexity with respect to the number of input curves and makes it suitable for interactive modeling. Simulated data based on a set of six curvilinear patterns as well as data acquired with a range sensor are used to illustrate the various steps of the algorithm.

Probabilistic occupancy grids have proved to be very useful for workspace modeling in 2D environments. Due to the expansion of computational load, this approach was not tractable for mapping a 3D environment in real applications. In this paper, the original occupancy grid scheme is revisited and a generic closed-form function is introduced to avoid numerical computation of probabilities for a range sensor with Gaussian error distribution. Occupancy probabilities are computed and stored in a multiresolution octree for improved performance and compactness. Occupancy models are built in local reference frames and linked to a global reference frame through uncertain spatial relationships that can be updated dynamically. This scheme is used for building a 3D map in a telerobotic maintenance application of electric power lines where perturbations may cause motion of object assembly.

Building on previous work on incremental natural scene modelling for mobile robot navigation, we focus in this paper on the problem of representing and managing uncertainties. The environment is composed of ground regions and objects. Objects (e.g., rocks) are represented by an uncertain state vector (location) and a variance-covariance matrix. Their shapes are approximated by ellipsoids. Landmarks are defined as objects with specific properties (discrimination, accuracy) that permit to use them for robot localization and for anchoring the environment model. Model updating is based on an extended Kalman filter. Experimental results are given that show the construction of a consistent model over tens of meters.

A mobile robot navigates in an environment and incrementally builds a map while locating itself. The quality of the map depends on the sensor models. Due to the difficulty of defining a stochastic odometric model, the classical approach based on Kalman filtering is revisited in order to decouple the information provided by the odometric sensor and exteroceptive sensors. The proposed approach is based on the observation and fusion of geometric relationships between objects. These relationships are represented in local maps and related in a global map where odometric information is exploited. The resulting local maps are invariant with respect to biases introduced in the robot location (position and orientation) due to sliding for instance. Experimental results on real data compare both the classical and the proposed approach.

This paper is concerned with the reliability of a shape description recovered from a set of scattered measurements. The recovering process of the description should not introduce a bias that is caused by specific acquisition conditions such as the presence of spurious measurements and the relative position of the sensor with respect to the object. Moreover, the recovered description should be stable for a sampling variation. While the fitting stage is based on a measurement error model which takes into account the sensor's viewpoint, the stability with sampling is tested by perturbing an hypothesized section. The validity of the approach is demonstrated by extracting reliable estimates of polynomial sections (lines, conics) from surface profile range data obtained from one or several viewpoints.

Many tasks, such as pose determination, object recognition and model building rely on a geometrical description of the visible surfaces derived from scene measurements. Even though much effort has been invested in surface description, robust extraction of surface parameters from scattered and noisy 3-D measurements is still a delicate task. For a given visible surface section, the extracted parameters should not depend on the sensor position in the scene nor on a particular measurement set on this surface. In this work, we show that a viewpoint invariant and stable local surface description can be extracted on sections where measurement constraints are redundant with respect to a polynomial model. A segmentation approach is developed to identify such stable sections. The approach is based on a measurement error model which takes into account the sensor’s viewpoint. An application of the approach to the extraction of straight line sections from single scan 3-D surface profiles is presented. The extracted stable linear sections are stored in a list that includes the estimated descriptive parameters for each section and indices of reliability for each description. The descriptive parameters obtained from images of the objects is compared with pre-measured parameters.

Robust extraction of surface parameters from multiple view scattered and noisy 3-D measurements is a delicate task. It is shown that a stable local surface description can be extracted on sections where measurement constraints are redundant with respect to a polynomial model. A segmentation approach is developed to identify these sections. The approach is based on a measurement error model which takes into account the sensor's viewpoint. An application of the approach to the extraction of straight line sections from single scan 3-D surface profiles is presented.>