Extracting the three-dimensional shape of live pigs using stereo photogrammetry

Wu, Jiahua; Tillett, R.D.; McFarlane, N.J.B.; Ju, Xiangyang; Siebert, J. Paul; Schofield, P.

doi:10.1016/j.compag.2004.05.003

Cited by 98 publications

(58 citation statements)

References 22 publications

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“…Comparing the differences between the image of the calibration target and the back projected target image, the calibration errors were found between 0.2 to 0.5 pixel. Verified by comparing a surface profile with the measurement of a titled flat surface [25], the depth measurement error was found less than 0.5mm. The purpose of the range image decomposition was to remove dented range data from the range images before the elastic modeling.…”

Section: Resultsmentioning

confidence: 83%

“…Sensor information can be obtained from manufacturer. In the stereo photogrammetry imaging system developed, the exterior and inner parameters were reliably estimated by the camera calibration [24,25]. A standard bundles adjustment approach [21] was used to calculate these parameters of each camera.…”

Section: Camera Calibrationmentioning

confidence: 99%

“…There was enough skin texture information of the breast to achieve reliable point-wise correspondences [24,25]. The photogrammetry system was configured with four stereo-pairs of high resolution cameras to capture the entire breast surface.…”

Section: System Configurationmentioning

confidence: 99%

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Multi-view stereophotogrammetry for post-mastectomy breast reconstruction

Henseler

Peng

et al. 2015

Med Biol Eng Comput

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A multi-view three dimensional stereophotogrammetry system was developed to capture 3D shape of breasts for breast cancer patients. The patients had received immediate unilateral breast reconstruction after mastectomy by the extended latissimus dorsi flap and without contra-lateral surgery. In order to capture the whole breast shape including the infra mammary fold, the patients were introduced to the imaging room and leaned over the imaging rig to open up the infra mammary fold and to expose the entire area of each breast. The imaging system consisted of eight high-resolution (4504 × 3000 pixels) digital cameras and four flash units. The cameras were arranged in four stereo pairs from four different view angles to cover the whole surface of the breasts. The system calibration was carried out ahead of every capture session and the stereo images were matched to generate four range images to be integrated using an elastic model proposed. A watertight breast mesh model was reconstructed to measure the volume of the breast captured. The accuracy of using the developed multi-view stereophotogrammetry system for breast volume measurement was 11.12cc with SEM 7.74cc, comparing to the measurements of the water displacement method. It was concluded that the 3D stereophotogrammetry image system developed was more reliable than the method of water displacement.

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Section: Resultsmentioning

confidence: 83%

Section: Camera Calibrationmentioning

confidence: 99%

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Multi-view stereophotogrammetry for post-mastectomy breast reconstruction

Henseler

Peng

et al. 2015

Med Biol Eng Comput

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“…In other studies (Brandl and Jorgensen, 1996;Marchant et al, 1999), these techniques have also been used to estimate the shape and weight of pigs and fish (Lines et al, 2001). These techniques are useful to reproduce 3D models of the exterior shape of animals (Wu et al, 2004), and to guide a robotic system towards specific points on the surface of the animal (Frost et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

An automatic colour-based computer vision algorithm for tracking the position of piglets

Jover

Raya

Martínez-Gómez

et al. 2009

Span. j. agric. res.

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Artificial vision is a powerful observation tool for research in the field of livestock production. So, based on the search and recognition of colour spots in images, a digital image processing system which permits the detection of the position of piglets in a farrowing pen, was developed. To this end, 24,000 images were captured over five takes (days), with a five-second interval between every other image. The nine piglets in a litter were marked on their backs and sides with different coloured spray paints each one, placed at a considerable distance on the RGB space. The programme requires the user to introduce the colour patterns to be found, and the output is an ASCII file with the positions (column X, line Y) for each of these marks within the image analysed. This information may be extremely useful for further applications in the study of animal behaviour and welfare parameters (huddling, activity, suckling, etc.). The software programme initially segments the image in the RGB colour space to separate the colour marks from the rest of the image, and then recognises the colour patterns, using another colour space [B/(R+G+B), (G-R), (B-G)] more suitable for this purpose. This additional colour space was obtained testing different colour combinations derived from R, G and B. The statistical evaluation of the programme's performance revealed an overall 72.5% in piglet detection, 89.1% of this total being correctly detected.Additional key words: animal behaviour, image analysis, pattern recognition, piglets activity, segmentation. ResumenSistema de visión artificial basado en el color, para el seguimiento de la posición de lechones La visión artificial es una potente herramienta de observación al servicio de la investigación en el campo de la producción ganadera. En este sentido, se ha desarrollado un software de tratamiento digital de imagen, basado en la búsqueda y reconocimiento de manchas de color en la imagen, que permite la detección de la posición de los lechones en una corralina de maternidad. Para ello, se capturaron 24.000 imágenes en cinco tomas (días), con un intervalo de 5 segundos entre cada dos imágenes. Los nueve lechones presentes en estas camadas fueron pintados en el lomo y laterales con colores distintos y alejados entre sí en el espacio RGB. El programa precisa la introducción por el usuario de los patrones de color a buscar, devolviendo como salida un fichero ASCII con las posiciones (columna X, fila Y) de cada una de esas manchas en cada imagen analizada. Esta información podría ser de gran utilidad en posteriores aplicaciones para el estudio de paráme-tros de comportamiento y bienestar de los animales (hacinamiento, actividad, amamantamiento, etc.).El software realiza una segmentación previa de la imagen en el espacio RGB, para aislar las manchas de color del resto de la imagen, y posteriormente el reconocimiento de los patrones de color utilizando otro espacio de color [B/(R+G+B), (G-R), (B-G)] más adecuado para tal fin, obtenido a partir de pruebas con diferentes combinaciones ...

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“…Shape plays a role in measuring the general health of the animal. In case of pigs, this is typically estimated from fatness, which is expressed as a condition score on a scale from 1 to 5 (Wu et al, 2004). The main method for determining the score is by touch, combined with a visual inspection of the shape as seen from the rear (Whittemore and Schofield, 2000).…”

Section: Introductionmentioning

confidence: 99%

3d Scanning of Live Pigs System and Its Application in Body Measurements

Guo

Wang

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The shape of a live pig is an important indicator of its health and value, whether for breeding or for carcass quality. This paper implements a prototype system for live single pig body surface 3d scanning based on two consumer depth cameras, utilizing the 3d point clouds data. These cameras are calibrated in advance to have a common coordinate system. The live 3D point clouds stream of moving single pig is obtained by two Xtion Pro Live sensors from different viewpoints simultaneously. A novel detection method is proposed and applied to automatically detect the frames containing pigs with the correct posture from the point clouds stream, according to the geometric characteristics of pig's shape. The proposed method is incorporated in a hybrid scheme, that serves as the preprocessing step in a body measurements framework for pigs. Experimental results show the portability of our scanning system and effectiveness of our detection method. Furthermore, an updated this point cloud preprocessing software for livestock body measurements can be downloaded freely from https://github.com/LiveStockShapeAnalysis to livestock industry, research community and can be used for monitoring livestock growth status.

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Extracting the three-dimensional shape of live pigs using stereo photogrammetry

Cited by 98 publications

References 22 publications

Multi-view stereophotogrammetry for post-mastectomy breast reconstruction

Multi-view stereophotogrammetry for post-mastectomy breast reconstruction

An automatic colour-based computer vision algorithm for tracking the position of piglets

3d Scanning of Live Pigs System and Its Application in Body Measurements

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