User-assisted Object Detection by Segment Based Similarity Measures in Mobile Laser Scanner Data

This paper describes a method that aims to find all instances of a certain object in Mobile Laser Scanner (MLS) data. In a userassisted approach, a sample segment of an object is selected, and all similar objects are to be found. By selecting samples from multiple classes, a classification can be performed. Key assumption in this approach is that a one-to-one relationship exists between segments and objects. In this paper the focus is twofold: (1) to explain how to get proper segments, and (2) to describe how to find similar objects. Point attributes that help separating neighbouring objects are presented. These point attributes are used in an attributed connected component algorithm where segments are grown, based on proximity and attribute values. Per component, a feature vector is proposed that consists of two parts. The first is a height histogram, containing information on the height distribution of points within a component. The second contains size and shape information, based on the components’ bounding box. A simple correlation function is used to find similarities between samples, as selected by a user, and other components. Our approach is tested on a MLS dataset, containing over 300 objects in 13 classes. Detection accuracies heavily depend on the success of the segmentation, and the number of selected samples in combination with the variety of object types in the scene.

LOSS Evaluation for Continuous Downgrades Segments Based on Accident Frequency Prediction

In order to evaluate traffic safety performance of continuous downgrades segment more accurately and quantitatively, a new safety evaluation method namely level of safety service (LOSS) is introduced. Based on the current traffic safety situation in China, the LOSS definition and classification method are studied systematically. In this paper, LOSS is built as a 5 levels framework and the graded threshold values are derived from accident frequency prediction models. The empirical research dataset includes 322 accidents data on a 15.27km continuous downgrades segment. The dataset is grouped into 16 fixed longitudinal grade sections to highlight the geometrical characteristics. Negative binomial model is best fitted among four candidate models, while the parameter estimation result indicates that geometrical factors, oversize vehicles mixed rate and average speed have significant positive effect on accident frequency. The overall LOSS of sample segment locates into the Level 3 interval, which means less safety performance than expected. As an extended application, LOSS specific to each fixed longitudinal grade section is analyzed.

Automated Fluorometric Method for the Determination of Morphine in Urine

A sensitive and specific automated method for the fluorometric determination of morphine in urine was developed. The analysis is performed at a rate of 40 samples/hr. The method is based on the oxidation of morphine to the fluorescent pseudo-morphine dimer. Morphine is extracted from urine at pH 9.4 with a mixture of organic solvents. The organic phase is washed with a dilute buffer at pH 9.4 and is further extracted with a dilute alkaline solution. Following the third extraction step, 2 equal portions of the alkali phase are buffered to pH 9.4. Potassium ferricyanide solution is added to the sample segment and water is added to the “blank” segment. Each of these solutions enters a separate fluorometer and the fluorescence is recorded. An increment in fluorescence of the sample as compared to the blank indicates the presence of morphine. Concentrations as low as 0.2 μg free morphine/ml urine are easily detected. Five hundred subjects from a Methadone Maintenance Program have been tested. Results show excellent agreement with determinations on the same samples by thin layer chromatography.