A fast external force field for parametric active contour segmentation

Active contours or snakes are widely used for segmentation and tracking. We propose a new active contour model, which converges reliably even when the initialization is far from the object of interest. The proposed segmentation technique uses an external energy function where the energy slowly decreases in the vicinity of an edge. Based on this energy a new external force field is defined. Both energy function and force field are calculated using an efficient dual scan line algorithm. The proposed force field is tested on computational speed, its effect on the convergence speed of the active contour and the segmentation result. The proposed method gets similar segmentation results as the gradient vector flow and vector field convolution active contours, but the force field needs significantly less time to calculate

On the accuracy of 3D landscapes from UAV image data

Our surroundings change all the time. Applications that require 3D models of a changing terrain, such as urban planning, are becoming ever more demanding with respect to the cost to create them and the accuracy of the result. A novel, cheap and fast solution for this problem is given by a UAV to take aerial images of the terrain in question, in combination with structure from motion algorithms to create a 3D model from those aerial images. However the question remains whether these on-the-fly 3D maps can match the accuracy of classical surveyor based models, which require more time to create. In this paper we investigate this question, and find that under certain conditions the accuracy of the UAV based model matches the accuracy of surveyor generated measurements

2D Mapping of strongly deformed cell nuclei based on contour warping

The dynamics of genome regions are associated to the functional or dysfunctional behaviour of the human cell. In order to study these dynamics it is necessary to remove perturbations coming from movement and deformation of the nucleus, i.e. the container holding the genome. In literature models have been proposed to cope with the transformations corresponding to nuclear dynamics of healthy cells. However for pathological cells, the nucleus deforms in an apparently random way, making the use of such models a non trivial task. In this paper we propose a mapping of the cell nucleus which is based on the matching of the nuclear contours. The proposed method does not put constraints on the possible shapes nor on the possible deformations, making this method suited for the analysis of pathological nuclei

Automatic and robust external camera calibration for high accuracy mobile mapping

A mobile mapping system (MMS) is the answer of the geoinformation community to the exponentially growing demand for various geospatial data with increasingly higher accuracies and captured by multiple sensors. As the mobile mapping technology is pushed to explore its use for various applications on water, rail, or road, the need emerges to have an external sensor calibration procedure which is portable, fast and easy to perform. This way, sensors can be mounted and demounted depending on the application requirements without the need for time consuming calibration procedures. A new methodology is presented to provide a high quality external calibration of cameras which is automatic, robust and fool proof. The MMS uses an Applanix POSLV420, which is a tightly coupled GPS/INS positioning system. The cameras used are Point Grey color video cameras synchronized with the GPS/INS system. The method uses a portable, standard ranging pole which needs to be positioned on a known ground control point. For calibration a well studied absolute orientation problem needs to be solved. Here, a mutual information based image registration technique is studied for automatic alignment of the ranging pole. Finally, a few benchmarking tests are done under various lighting conditions which proves the methodology’s robustness, by showing high absolute stereo measurement accuracies of a few centimeters

Online wear detection using high-speed imaging

In this paper, the change detection of a fast turning specimen is studied at micro-level, whereas the images are acquired without stopping the rotation. In the beginning of the experiment, the imaging system is focused on the surface of the specimen. By starting the rotation of the specimen, the diameter of the specimen changes due to wear, which results in de-focusing of the imaging system. So the amount of blur in the images can be used as evidence of the wear phenomenon. Due to the properties of the microscope, the corners of the frames were dark and had to be cropped. So, each micrograph reflects only a small area of the surface. Nevertheless, techniques like stitching of multiple images can provide a significant surface area for micro-level investigation which increases the effectiveness of analyzing the material modification. Based on the results computer vision could detect a change of about 1.2 mu m in the diameter of the specimen. More important is that we could follow the same locations of the surface in the microscopic images despite blurring, uneven illumination, change on the surface, and relatively a high-speed rotation

A non-rigid registration method for multispectral imaging of plants

Registration of multispectral images remains a challenging task due to the lack of stable feature points. Methods based on intensities are generally more robust for multi-modal image registration, but are computationally demanding or are restrictive to the transformation model allowed in the registration. This paper proposes a new registration framework which overcomes these drawbacks. The proposed method optimizes the location of a set of virtual landmarks in order to get robust and accurate registration

Automatic camera to laser calibration for high accuracy mobile mapping systems using INS

A mobile mapping system (MMS) is a mobile multi-sensor platform developed by the geoinformation community to support the acquisition of huge amounts of geodata in the form of georeferenced high resolution images and dense laser clouds. Since data fusion and data integration techniques are increasingly able to combine the complementary strengths of different sensor types, the external calibration of a camera to a laser scanner is a common pre-requisite on today's mobile platforms. The methods of calibration, nevertheless, are often relatively poorly documented, are almost always time-consuming, demand expert knowledge and often require a carefully constructed calibration environment. A new methodology is studied and explored to provide a high quality external calibration for a pinhole camera to a laser scanner which is automatic, easy to perform, robust and foolproof. The method presented here, uses a portable, standard ranging pole which needs to be positioned on a known ground control point. For calibration, a well studied absolute orientation problem needs to be solved. In many cases, the camera and laser sensor are calibrated in relation to the INS system. Therefore, the transformation from camera to laser contains the cumulated error of each sensor in relation to the INS. Here, the calibration of the camera is performed in relation to the laser frame using the time synchronization between the sensors for data association. In this study, the use of the inertial relative movement will be explored to collect more useful calibration data. This results in a better intersensor calibration allowing better coloring of the clouds and a more accurate depth mask for images, especially on the edges of objects in the scene

Dynamic subsampling for image registration speedup using the mutual information variance estimate

Intensity based image registration methods are necessary in situations where feature point based methods fail, e.g multispectral images or images with repetitive textures. However the higher robustness comes at the cost of being slower than the feature point alternative. This problem can be alleviated by subsampling the pixels used to calculate the intensity matching criterion. Unfortunately there are no guidelines for the amount of allowed subsampling. In this paper we present a model for a lower bound on the number of pixel samples in image sets to keep a certain confidence about the correctness of the result, using the variance on the estimated mutual information value. With this model we can speed up the intensity based registration while remaining confident about getting correct results