Abstract Aim: The aim was to evaluate height measurements made with the single view metrology method and to investigate the influence of standing position and different phases of gait and running on vertical height. Method: Ten healthy men were recorded simultaneously by a 2D web camera and a 3D motion analysis system. They performed six trials, three standing and three during gait and running. The vertical height was measured with the single view metrology method and in Qualisys Track Manager. The results were compared for evaluation. The vertical height in the different postures was compared to the actual height. Results: The measurements made with the single view metrology method were significantly higher than the measurements made with Qualisys Track Manager (p<0.001). The vertical height in the two standing positions was significantly lower than the actual height (p<0.05). The vertical height in midstance was significantly lower than actual height in the walking trials (p<0.05). No significant difference was found between maximum vertical height and actual height during running (p>0.05). Conclusion: The single view metrology method measured vertical heights with a mean error of +2.30 cm. Posture influence vertical body height. Midstance in walking is the position where vertical height corresponds best with actual height, in running it is the non-support phase.

<p><p><strong>Abstract</strong></p><p><strong>Aim: </strong>The aim was to evaluate height measurements made with the single view metrology method and to investigate the influence of standing position and different phases of gait and running on vertical height.</p><p><strong>Method: </strong>Ten healthy men were recorded simultaneously by a 2D web camera and a 3D motion analysis system. They performed six trials, three standing and three during gait and running. The vertical height was measured with the single view metrology method and in Qualisys Track Manager. The results were compared for evaluation. The vertical height in the different postures was compared to the actual height.</p><p><strong>Results: </strong>The measurements made with the single view metrology method were significantly higher than the measurements made with Qualisys Track Manager (p<0.001). The vertical height in the two standing positions was significantly lower than the actual height (p<0.05). The vertical height in midstance was significantly lower than actual height in the walking trials (p<0.05). No significant difference was found between maximum vertical height and actual height during running (p>0.05).</p><p><strong>Conclusion: </strong>The single view metrology method measured vertical heights with a mean error of +2.30 cm. Posture influence vertical body height. Midstance in walking is the position where vertical height corresponds best with actual height, in running it is the non-support phase.</p><p> </p></p><p> </p>

Multiple SAR interferograms with judiciously selected height sensitivities can be iteratively combined to create a high accuracy digital elevation map. An initial height estimate is refined by iteratively using larger baselines to obtain a height estimation accuracy limited by the spatial decorrelation of the antenna baseline. Spatial filtering is used to reduce the propagation of errors for accurate height estimation. Images containing regions isolated by phase discontinuities, as often found in urban environments, can be resolved by this iterative multi-baseline technique. Computationally demanding and potentially unreliable phase unwrapping is not required to determine scene elevation using SAR inMultiple SAR interferograms with judiciously selected height sensitivities can be iteratively combined to create a high accuracy digital elevation map. An initial height estimate is refined by iteratively using larger baselines to obtain a height estimation accuracy limited by the spatial decorrelation of the antenna baseline. Spatial filtering is used to reduce the propagation of errors for accurate height estimation. Images containing regions isolated by phase discontinuities, as often found in urban environments, can be resolved by this iterative multi-baseline technique. Computationally demanding and potentially unreliable phase unwrapping is not required to determine scene elevation using SAR interferometry.

In this thesis, we develop algorithms to estimate crop heights as well as to detect plots infarms. Plant height estimation is needed in precision agriculture to monitor plant health andgrowth cycles. We use a 3D LiDAR mounted on an Unmanned Aerial Vehicle (UAV) anduse the LiDAR data for height and plot estimation. We present a general methodology forextracting plant heights from 3D LiDAR with two specific variants for the two environments:row-crops and pasture. The main algorithm is based on ground plane estimation from 3DLiDAR scans, which is then used to determine the height of plants in the scans. For rowcrops, the plot detection uses a K-means clustering algorithm to find the bounding boxes ofthese clusters, and a voting scheme to determine the best-fit width, height, and orientationof the clusters/plots. This best-fit box is then used to create a grid over the LiDAR dataand the plots are extracted. For pasture, relative heights are estimated using data collectedweekly. Both algorithms we evaluated using data collected from actual farms and pasture.The accuracy in plot height estimation was +/- 5.36 % and that for growth estimates was+/- 7.91 %. / Master of Science / Plant height estimation and measurement is a vital task when it comes to farming. Knowing these characteristics help determine whether the plants are growing healthy and when to harvest them. On similar lines, accurate estimates of the plant heights can be used to prevent overgrazing and undergrazing of pastures. However, as farm and plot size increases, getting consistent and accurate measurements becomes a more time-consuming and manually intensive task. Using robots can help solve this problem because they can be used to estimate the height. With sensors that are already available, such as the 3D LiDAR that we use, we can use aerial robots to fly over the farm and collect plant data. This data can then be processed to estimate the plant height, eliminating the need to go out and manually measure every single plant. This thesis discusses a methodology of doing exactly this, as well as detecting plots within a farm. The algorithms are evaluated using data collected from actual farms and pasture.

Remote sensing offers many advantages to previous forest measurements, such as limiting costs and time in the field. Light detection and ranging (lidar) has been shown to enable accurate estimates of forest height. Lidar does produce precise measurements for ground elevation and forest height, where and when it is available. However, it is expensive to collect and does not have wall-to-wall coverage in the United States. In this study, we estimated height using the National Agricultural Imagery Program (NAIP) photogrammetric point clouds to create a predicted height map for managed loblolly pine stands in the southeastern United States. Recent studies have investigated the ability of digital aerial photogrammetry (DAP), and more specifically NAIP, as an alternative to lidar as a means of estimating forest height due to its lower costs, frequency of acquisition, and wall-to-wall coverage across the United States. Field-collected canopy height for 534 plots in Virginia and North Carolina were regressed against the 90th percentile derived from NAIP point clouds. The model for predicted pine height using the 90th percentile of height (P90) is predicted pine height = 1.09(P90) – 0.43. The adjusted R^2 is 0.93, and the RMSE is 1.44 m. This model is being used to produce a 5 m x 5 m canopy height model for all pine stands across Virginia, North Carolina, and Tennessee. NAIP-derived point clouds are thus a viable means of predicting canopy height in southern pines. / M.S. / Collecting accurate measurements of pine plantations is essential to managing them to maximize various ecosystem goods and services. However, it can be difficult and time-consuming to collect these measurements by hand. This study demonstrates that point clouds derived from digital stereo aerial photograms enable calculating forest height to an accuracy sufficient for pine plantation management. We developed a simple linear regression model to predict forest canopy height using the 90th percentile of the photo-derived heights above the ground in a given area. With this model, we created a map of pine plantation canopy heights (consisting of 5 m x 5 m grid cells, each containing a canopy height estimate) for pine forests in Virginia, North Carolina, and Tennessee. Digital aerial photography from the National Agricultural Imagery Program (NAIP) is repeated every three years for a given state, allowing growth to be mapped over time. Photography collected by NAIP and similar programs also has uniform acquisition parameters in a given year applicable over large regions. State- and national photography programs like NAIP are also less expensive than other data sets, like airborne laser scanning data, that enable estimation of tree height.

<p>Detta examensarbete undersöker forensisk längdmätning i bild, t ex längduppskattning av människor i bilder rörande brottsmål. Problemen identifieras och några av dagens befintliga längdmätningsmetoder diskuteras.</p><p>Den metod som bäst uppfyller de i arbetet ställda kraven, d v s snabb handläggning, minimal systeminformation, minimalt arbete på plats och exakthet, har valts ut, anpassats och utvärderats. Metoden bygger på att hitta s k gränspunkter och grundplanets gränslinje i bilden och utifrån en i världen känd referenslängd beräkna den sökta längden. Den bakomliggande teorin presenteras och metoden beskrivs i detalj. Funktioner, algoritmer och ett användargränssnitt har implementerats i beräkningsprogrammet MatLab. Tester har utförts för att validera metodens noggrannhet och parameterberoende. Metoden visar sig ge mycket bra resultat då rätt förutsättningar ges, men har konstaterats vara känslig för variation på gränslinjen. En rad förbättringsförslag presenteras för att utveckla metoden och stabilisera resultatet.</p><p>Examensarbetet omfattar 20 högskolepoäng och utgör ett obligatoriskt moment i utbildningsprogrammet civilingenjör i datateknik som ges av Linköpings universitet. Arbetet är utfört vid och på uppdrag av Statens kriminaltekniska laboratorium (SKL) i Linköping.</p>

Height estimation of objects can be implemented both for soft-biometrics and as an object tracking feature. In first case we can eliminate some possible subjects having considerably different height than the observed one, and focus on determining more distinctive remote identification features, like colour, face or ear, and search for similar subjects in a smaller set of possible candidates. For object tracking it can be used for temporal and spatial correspondence analysis as well or simultaneously for both in case of having different cameras. In this thesis we propose a novel method for automatic estimation of height using an uncalibrated camera. Nowadays such cameras can be found in any corner for different purposes like as for security reasons. A crucial moment in height estimation is finding vanishing points. In the method we use RANSAC to estimate best vanishing point from several estimated candidate points. The method has the new advantages that from different frames and their respective height estimations, automatically determines certain reasonable heights depending on height measurements distribution. With spreading of camera implementation in common applications, we believe this new software can be widely applied in as many fields as it can be imagined.

This thesis is devoted to the shadow detection and compensation in aerial images with application of the detection results to building height detection. Shadows could be defined as the parts of the scene that is not directly illuminated by a light source due to obstructing object or objects. Usually the shadows in images or video are undesirable, since they could cause degradation of the expected results during processing of the image or video for object detection, segmentation, scene surveillance or similar purposes. However shadow information could also be used for beneficial purposes like revealing information about the object&rsquo / s shape, orientation and even about the light source. In this thesis firstly shadow detection methods are overviewed. Beside the selected methods from literature, some novel approaches are also proposed and experimented. Then shadow compensation methods are overviewed and experimented. Finally an example of beneficial utilizations of shadow information is studied, where buildings&rsquo / heights are estimated from their shadow length and sun angles.

Detta examensarbete undersöker forensisk längdmätning i bild, t ex längduppskattning av människor i bilder rörande brottsmål. Problemen identifieras och några av dagens befintliga längdmätningsmetoder diskuteras. Den metod som bäst uppfyller de i arbetet ställda kraven, d v s snabb handläggning, minimal systeminformation, minimalt arbete på plats och exakthet, har valts ut, anpassats och utvärderats. Metoden bygger på att hitta s k gränspunkter och grundplanets gränslinje i bilden och utifrån en i världen känd referenslängd beräkna den sökta längden. Den bakomliggande teorin presenteras och metoden beskrivs i detalj. Funktioner, algoritmer och ett användargränssnitt har implementerats i beräkningsprogrammet MatLab. Tester har utförts för att validera metodens noggrannhet och parameterberoende. Metoden visar sig ge mycket bra resultat då rätt förutsättningar ges, men har konstaterats vara känslig för variation på gränslinjen. En rad förbättringsförslag presenteras för att utveckla metoden och stabilisera resultatet. Examensarbetet omfattar 20 högskolepoäng och utgör ett obligatoriskt moment i utbildningsprogrammet civilingenjör i datateknik som ges av Linköpings universitet. Arbetet är utfört vid och på uppdrag av Statens kriminaltekniska laboratorium (SKL) i Linköping.

During the last decade the human body detection and tracking has been a very extensive research eld within the computer vision. There are many potential applications of people tracking such as security-monitoring, anthropomorphic analysis or biometrics. In this thesis we present an algorithm and related software to detect human bodies in an indoor environment. It is part of a wider project which aims to estimate the human height. The purposed algorithm performs in real-time to detect people. The algorithm is developed using the free OpenCV library in C++ programming language. As far as this algorithm is rst part of a wider system, our software gives two outputs. The principal one is the coordinates of the detected object. With the coordinates, the aforementioned measuring system will be able to calculate the height by itself. The other output is the video sequence with the detected person bounded by a rectangle, wich provides visual feedback to the user. This software is able to communicate with Matlab Engine. It is important since the subsequent height estimation system works in Matlab®.