Gruvrätten vid Stora Kopparberget 1641-1682 : en undersökning över rannsakade brott och utdömda straff

The purpose of this essay is to examine and explain how the Swedish mining court of Stora Kopparberget (the Great Copper Mountain) implemented its judicial legislation between 1641-1682. Questions are asked about which counts of indictments the court tried, which sentences they handed out, in what quantities and how these results looks in comparison with other contemporary courts. The index cards of the court judicial protocols are the primary source of information. The methods are those of quantity- and comparative analysis.The results show that theft of copper ore was the most common crime ransacked by the court. Other common crimes were (in order): sin of omission, transgression of work directions, fights, slander and disdain, trade of stolen ore, failing appearance in court etc.Fines were by far the most common sentence followed by shorter imprisonments, gauntlets, loss of right to mine possession, twig beating, loss of work, penal servitude, banishment, “wooden horse riding” and finally military transcription. Even though previous re-search, in the field of Swedish specialized courts, is almost non existent evidence confirms great similarities between the Stora Kopparberget mining court and Sala mining court. This essay will, hopefully, enrich our knowledge of specialized courts, of 17th century mining industry and society and let us reach a broader understanding of the working conditions of the mountain.

Benchmark Evaluation of HOG Descriptors as Features for Classification of Traffic Signs

The purpose of this paper is to analyze the performance of the Histograms of Oriented Gradients (HOG) as descriptors for traffic signs recognition. The test dataset consists of speed limit traffic signs because of their high inter-class similarities.   HOG features of speed limit signs, which were extracted from different traffic scenes, were computed and a Gentle AdaBoost classifier was invoked to evaluate the different features. The performance of HOG was tested with a dataset consisting of 1727 Swedish speed signs images. Different numbers of HOG features per descriptor, ranging from 36 features up 396 features, were computed for each traffic sign in the benchmark testing. The results show that HOG features perform high classification rate as the Gentle AdaBoost classification rate was 99.42%, and they are suitable to real time traffic sign recognition. However, it is found that changing the number of orientation bins has insignificant effect on the classification rate. In addition to this, HOG descriptors are not robust with respect to sign orientation.