Geometric data understanding : deriving case specific features

There exists a tradition using precise geometric modeling, where uncertainties in data can be considered noise. Another tradition relies on statistical nature of vast quantity of data, where geometric regularity is intrinsic to data and statistical models usually grasp this level only indirectly. This work focuses on point cloud data of natural resources and the silhouette recognition from video input as two real world examples of problems having geometric content which is intangible at the raw data presentation. This content could be discovered and modeled to some degree by such machine learning (ML) approaches like deep learning, but either a direct coverage of geometry in samples or addition of special geometry invariant layer is necessary. Geometric content is central when there is a need for direct observations of spatial variables, or one needs to gain a mapping to a geometrically consistent data representation, where e.g. outliers or noise can be easily discerned. In this thesis we consider transformation of original input data to a geometric feature space in two example problems. The first example is curvature of surfaces, which has met renewed interest since the introduction of ubiquitous point cloud data and the maturation of the discrete differential geometry. Curvature spectra can characterize a spatial sample rather well, and provide useful features for ML purposes. The second example involves projective methods used to video stereo-signal analysis in swimming analytics. The aim is to find meaningful local geometric representations for feature generation, which also facilitate additional analysis based on geometric understanding of the model. The features are associated directly to some geometric quantity, and this makes it easier to express the geometric constraints in a natural way, as shown in the thesis. Also, the visualization and further feature generation is much easier. Third, the approach provides sound baseline methods to more traditional ML approaches, e.g. neural network methods. Fourth, most of the ML methods can utilize the geometric features presented in this work as additional features.Geometriassa käytetään perinteisesti tarkkoja malleja, jolloin datassa esiintyvät epätarkkuudet edustavat melua. Toisessa perinteessä nojataan suuren datamäärän tilastolliseen luonteeseen, jolloin geometrinen säännönmukaisuus on datan sisäsyntyinen ominaisuus, joka hahmotetaan tilastollisilla malleilla ainoastaan epäsuorasti. Tämä työ keskittyy kahteen esimerkkiin: luonnonvaroja kuvaaviin pistepilviin ja videohahmontunnistukseen. Nämä ovat todellisia ongelmia, joissa geometrinen sisältö on tavoittamattomissa raakadatan tasolla. Tämä sisältö voitaisiin jossain määrin löytää ja mallintaa koneoppimisen keinoin, esim. syväoppimisen avulla, mutta joko geometria pitää kattaa suoraan näytteistämällä tai tarvitaan neuronien lisäkerros geometrisia invariansseja varten. Geometrinen sisältö on keskeinen, kun tarvitaan suoraa avaruudellisten suureiden havainnointia, tai kun tarvitaan kuvaus geometrisesti yhtenäiseen dataesitykseen, jossa poikkeavat näytteet tai melu voidaan helposti erottaa. Tässä työssä tarkastellaan datan muuntamista geometriseen piirreavaruuteen kahden esimerkkiohjelman suhteen. Ensimmäinen esimerkki on pintakaarevuus, joka on uudelleen virinneen kiinnostuksen kohde kaikkialle saatavissa olevan datan ja diskreetin geometrian kypsymisen takia. Kaarevuusspektrit voivat luonnehtia avaruudellista kohdetta melko hyvin ja tarjota koneoppimisessa hyödyllisiä piirteitä. Toinen esimerkki koskee projektiivisia menetelmiä käytettäessä stereovideosignaalia uinnin analytiikkaan. Tavoite on löytää merkityksellisiä paikallisen geometrian esityksiä, jotka samalla mahdollistavat muun geometrian ymmärrykseen perustuvan analyysin. Piirteet liittyvät suoraan johonkin geometriseen suureeseen, ja tämä helpottaa luonnollisella tavalla geometristen rajoitteiden käsittelyä, kuten väitöstyössä osoitetaan. Myös visualisointi ja lisäpiirteiden luonti muuttuu helpommaksi. Kolmanneksi, lähestymistapa suo selkeän vertailumenetelmän perinteisemmille koneoppimisen lähestymistavoille, esim. hermoverkkomenetelmille. Neljänneksi, useimmat koneoppimismenetelmät voivat hyödyntää tässä työssä esitettyjä geometrisia piirteitä lisäämällä ne muiden piirteiden joukkoon

Localization in Unstructured Environments: Towards Autonomous Robots in Forests with Delaunay Triangulation

Autonomous harvesting and transportation is a long-term goal of the forest industry. One of the main challenges is the accurate localization of both vehicles and trees in a forest. Forests are unstructured environments where it is difficult to find a group of significant landmarks for current fast feature-based place recognition algorithms. This paper proposes a novel approach where local observations are matched to a general tree map using the Delaunay triangularization as the representation format. Instead of point cloud based matching methods, we utilize a topology-based method. First, tree trunk positions are registered at a prior run done by a forest harvester. Second, the resulting map is Delaunay triangularized. Third, a local submap of the autonomous robot is registered, triangularized and matched using triangular similarity maximization to estimate the position of the robot. We test our method on a dataset accumulated from a forestry site at Lieksa, Finland. A total length of 2100\,m of harvester path was recorded by an industrial harvester with a 3D laser scanner and a geolocation unit fixed to the frame. Our experiments show a 12\,cm s.t.d. in the location accuracy and with real-time data processing for speeds not exceeding 0.5\,m/s. The accuracy and speed limit is realistic during forest operations

Refinements for Intragastric Gavage in Rats

Intragastric (IG)-gavage is widely used in laboratory rats in pharmacological, toxicological and pharmacokinetic  studies. This technique has been claimed to result in severe stress and a variety of complications.  This study was designed to compare the stress response caused by IG-gavage with steel and teflon probes,  to determine whether any habituation occurred to repeated gavaging and to find out whether the use of different  administration volumes within the recommended range influenced the stress response.  Telemetrically registered cardiovascular responses were used to assess the stress-producing effects. During  laparoscopy, transmitters with a catheter extending into the abdominal aorta were implanted into the peritoneal  cavity of male Wistar rats. IG-gavage induced a significant increase in diastolic and systolic blood  pressure and heart rate, lasting for about 40 minutes. IG-gavage with a stainless steel probe induced greater  changes in cardiovascular parameters. It can be concluded that teflon probes are preferable because they  elicit less discomfort to the animals. Repeating the IG-gavage with a teflon probe daily evoked a decrease  of all parameters on the fourth day as compared with the previous days, but this did not occur in the stainless  steel group. The volume administered through IG-gavage had significant effects on diastolic blood  pressure, systolic blood pressure and heart rate. Surprisingly, volumes of 2 and 4 ml / kg body weight  resulted in a greater response in cardiovascular parameters than volumes of 6 and 8 ml/kg. It appears that  there is a window of preferred administration volumes. A routine cage change induced an increase in diastolic  blood pressure, systolic blood pressure and heart rate comparable to the changes observed after IGgavage.  In conclusion, our data indicate that use of IG-gavage with a soft teflon probe and volumes 6 and  8 ml/kg are obvious refinements for the procedure.

Effects of Litter Origin and Weight on Behaviour of Outbred NIH/S Mice in Plus-maze and Staircase Tests

The objective of this study was to investigate the effects of litter and weight on the behavior of mice.  Male outbred NIH/S mice from 8 litters were randomly distributed among 6 cages and subjected to the  plus-maze and staircase tests. The litter from which the animals had originated had a significant effect on  the behavior of mice in the plus-maze test; furthermore addition of the covariates final weight and weight  gain had no effect on significance or explanatory value. It is proposed that litter origin might influence the  adaptation processes, the development of social status and consequently, the behavior of mice. Differences  attributable to litter were not observed in the staircase test, but when both weight parameters were added as  covariates this proved to be significant. Though the source of these litter-related differences remains to be  clarified, these differences do have a significant effect on the behavior of mice. Therefore they need to be  considered since knowledge of the litter where the outbred mice originated can partly explain differences in  the behavior of the animals. The comparison of models showed that incorporation of the natural features of  the animals (as derived from their biological origin) into a calculation can help rationalise the results; and  provide ample opportunities for discussion and understanding of this complex issue.

Prolonged Exposure of Mice to a Nest Box Reduces Locomotor Activity in the Plus-Maze Test

Environmental enrichment (EE) has been associated with many effects on the behavior of laboratory animals.  The term EE is rather vague, often referring to a variety of item combinations as if what is added to  the cage has no significance. EE is indeed housing refinement, and therefore more exact terms should be  used to clarify the situation. This study was designed to assess whether access to a nest box (NB) could  modify behavior of BALB/c mice in the plus-maze test. Two series of experiments were done with an aspen  NB (11 x 11 x 7 cm, wall thickness 1.5 cm, two round holes (d = 3 cm) at opposite sides. Control mice had  no added item in the cage. The plus-maze consisted of two open (8 x 17 cm) and two closed arms (8 x 17  x 30 cm) connected by a central platform (8 x 8 cm). Mice were placed on the central platform facing an  open arm. During five minutes, the numbers of entries made onto the open and into the closed arms were  recorded. From this data, the percentages of entries made onto the open arms, and the percentage of time  spent on the open arms, were calculated. Furthermore, the number of fecal boli left by the mice in the plusmaze,  as a stress indicator, were counted. In the first series of experiments NB was present for one, two  and three weeks but no drugs were administered. NB provided for one or two weeks had no effect on the  behavior of mice. However, exposure to NB for three weeks did decrease the locomotor activity of mice in  the plus-maze test, as reflected in the decline in the total number of entries made in the test. The presence  of NB for one or two weeks resulted in more (p = 0.001) fecal boli voided when compared to the no NB  or NB for three weeks groups. In the second series of experiments we used NB for 10 days and the selective neuronal nitric oxide synthase  (nNOS) inhibitor 1-(2-trifluoromethylphenyl)-imidazole (TRIM) as a pharmacological tool (at doses  of 25.0, 50.0 and 100.0 mg/kg, i.p.). Depending on the dose, the administration of TRIM induced an anxiolytic  (50 mg/kg) or sedative effect (100 mg/kg) as seen in the increase in the percentage of entries made  onto the open arms or a decrease in the total number of entries, respectively. NB for 10 days had no effect  on the behavior of mice or on the effect of TRIM. In conclusion, NB does not appear to interfere with the anxiolytic effect of TRIM in the plus-maze test but  prolonged exposure to NB does reduce the locomotor  activity of mice.

Localization in Unstructured Environments: Towards Autonomous Robots in Forests with Delaunay Triangulation

Autonomous harvesting and transportation is a long-term goal of the forest industry. One of the main challenges is the accurate localization of both vehicles and trees in a forest. Forests are unstructured environments where it is difficult to find a group of significant landmarks for current fast feature-based place recognition algorithms. This paper proposes a novel approach where local point clouds are matched to a global tree map using the Delaunay triangularization as the representation format. Instead of point cloud based matching methods, we utilize a topology-based method. First, tree trunk positions are registered at a prior run done by a forest harvester. Second, the resulting map is Delaunay triangularized. Third, a local submap of the autonomous robot is registered, triangularized and matched using triangular similarity maximization to estimate the position of the robot. We test our method on a dataset accumulated from a forestry site at Lieksa, Finland. A total length of 200 m of harvester path was recorded by an industrial harvester with a 3D laser scanner and a geolocation unit fixed to the frame. Our experiments show a 12 cm s.t.d. in the location accuracy and with real-time data processing for speeds not exceeding 0.5 m/s. The accuracy and speed limit are realistic during forest operations.</p

Image Analysis and Development of Graphical User Interface for Pole Vault Action

In recent years, motion estimation analysis has become one of the vital research areas in sport and has attracted the interest of many researchers toward events such as swimming, pole vaulting, and hurdling. In this paper, we present a novel method for determining the step length, speed, and the feet-contact-time on the running track of a pole vault athlete using a mono-camera arrangement. The step length and step frequency are essential descriptors of the approach run in pole vaulting. The approach along a linear trajectory is familiar to many throwing and jumping events. The measurement setting and image processing, as well as the step registration stages such as the block matching and optimal flow algorithm are presented and compared to alternative methods. The validation of the step size and step frequency accuracy is provided, using manually digitized step sizes as the baseline. The proposed methodology is efficient and straightforward, providing immediate feedback to the athlete and coaches. We were also successful in building a basic Graphical User Interface (GUI) to illustrate pole-vaulting actions during a performance. This research could be used as an initial step for developing a fully interactive platform that is capable of yielding supportive instructions to the athletes and the coaches on a real-time basis for self-assessment and further improvement.</p