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.
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.
In this paper we demonstrate how the interaction between innovative methods in the field of computer vision and methods for multi-spectral image classification can help in extracting detailed land-cover / land-use information from Very High Resolution (VHR) satellite imagery. We introduce the novel concept of "geometric activity images", which we define as images encoding the strength of the relationship between a pixel and surrounding features detected through dedicated computer vision methods. These geometric activity images are used as alternatives to more traditional texture images that better describe the geometry of man-made structures and that can be included as additional information in a non-parametric supervised classification framework. We present a number of findings resulting from the integration of geometric activity images and multi-spectral bands in an artificial neural network classification. The geometric activity images we use result from the use of a ridge detector for straight line detection, calculated for different window sizes and for all multi-spectral bands and band-ratio images in a VHR scene. A selection of the most relevant bands to use for classification is carried out using band selection based on a genetic algorithm. Sensitivity analysis is used to assess the importance of each input variable. An application of the proposed methods to part of a Quickbird image taken over the suburban fringe of the city of Ghent (Belgium) shows that we are able to identify roads with much higher accuracy than when using more traditional multi-spectral image classification techniques.
In this paper, we examine sensor specific distributions of local image operators (edge and line detectors), which describe the appearance of people in video sequences. The distributions are used to describe a probabilistic articulated motion model to track the gestures of a person in terms of arms and body movement. The distributions are based on work of Sidenbladh where general distributions are examined, collected over images found on the internet. In our work, we focus on the statistics of one sensor, in our case a standard webcam, and examine the influence of image noise and scale. We show that although the general shape of the distributions published by Sidenbladh are found, important anomalies occur which are due to image noise and reduced resolution. Taking into account the effects of noise and blurring on the scale space response of edge and line detectors improves the overall performance of the model. The original distributions introduced a bias towards small sharp boundaries over large blurred boundaries. In the case of arms and legs which often appear blurred in the image, this bias is unwanted. Incorporating our modifications in the distributions removes the bias and makes the tracking more robust.
The problem of shape recognition is studied through the use of relational models based on the hypergraph representation and the context similarity measure. Formal definitions are introduced and graph properties are calculated important to the matching process. A conflict is shown to exist between the interclass distance and the semantical distance between the vertices within a model. The representation is extended with the notion of vertex neighborhood, which increases the semantical distance and makes the processing of complex scenes feasible.