A review of the literature showed that there is no good systematic study of a series of rigorously diagnosed “kleptomaniac” persons, other than those referred by the courts for expert opinions. This applies in particular to German-speaking and other European countries. We therefore report here on the phenomenology, demographics, psychopathology, family histories, treatment histories, behaviour therapy oriented analyses, and treatment results of 12 individuals meeting Diagnostic and Statistical Manual (DSM)-III-R criteria for kleptomania. All patients described an uncontrollable impulse to steal and a reduction in anxiety or tension during or after the act. All had at least one further psychiatric diagnosis. Although the kleptomaniac symptomatology did not seem to be part of these other disorders, the extent of stealing corresponded (positively or negatively) to other psychiatric symptoms such as depressive feelings, excessive urges to eat or migraine attacks. Microanalysis of the act of theft, as well as the preceding and following events (explored by applying behaviour therapy oriented analysis) showed intraindividually consistent, but inter-individually differing triggering factors. There was no evidence of correlation with obsessive-compulsive disorders, but some indications that kleptomania may be associated with affective disorders in terms of an “affective spectrum disorder”. According to a broader definition of “multi-impulsive-disorder” (analogous to Lacey and Evans or Fichter et al), one third of the patients might be diagnosed as “bi-impulsive disorder”. Eight of the 12 patients could be investigated at a mean follow-up time of 2 years after discharge showing favourable results. In conclusion, kleptomaniac behaviour seems to be multiconditionally caused and sustained.

In this paper the feasibility to extract the proportion of pigs located in different areas of a pig pen by advanced image analysis technique is explored and discussed for possible applications. For example, pigs generally locate themselves in the wet dunging area at high ambient temperatures in order to avoid heat stress, as wetting the body surface is the major path to dissipate the heat by evaporation. Thus, the portion of pigs in the dunging area and resting area, respectively, could be used as an indicator of failure of controlling the climate in the pig environment as pigs are not supposed to rest in the dunging area. The computer vision methodology utilizes a learning based segmentation approach using several features extracted from the image. The learning based approach applied is based on extended state-of-the-art features in combination with a structured prediction framework based on a logistic regression solver using elastic net regularization. In addition, the method is able to produce a probability per pixel rather than form a hard decision. This overcomes some of the limitations found in a setup using grey-scale information only. The pig pen is a difficult imaging environment because of challenging lighting conditions like shadows, poor lighting and poor contrast between pig and background. In order to test practical conditions, a pen containing nine young pigs was filmed from a top view perspective by an Axis M3006 camera with a resolution of 640×480 in three, 10-min sessions under different lighting conditions. The results indicate that a learning based method improves, in comparison with greyscale methods, the possibility to reliable identify proportions of pigs in different areas of the pen. Pigs with a changed behaviour (location) in the pen may indicate changed climate conditions. Changed individual behaviour may also indicate inferior health or acute illness.