Silhouette-based 3-D model reconstruction from multiple images

Mülayim, Adem Yasar; Yılmaz, Ulaş; Atalay, Volkan

doi:10.1109/tsmcb.2003.814303

Cited by 96 publications

(42 citation statements)

References 33 publications

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“…1, A and C), custom software was written in the Java programming language (Sun Microsystems) that reconstructs 3D root system models from 2D image sequences and quantifies 3D root system traits. This software, RootReader3D, utilizes a silhouette-based back-projection algorithm (Mulayim et al, 2003;Zhu et al, 2006) combined with crosssectional volume segmentation to generate 3D root models (Supplemental Fig. S1).…”

Section: Rootreader3d Reconstruction and Analysis Softwarementioning

confidence: 99%

Three-Dimensional Root Phenotyping with a Novel Imaging and Software Platform

et al. 2011

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A novel imaging and software platform was developed for the high-throughput phenotyping of three-dimensional root traits during seedling development. To demonstrate the platform's capacity, plants of two rice (Oryza sativa) genotypes, Azucena and IR64, were grown in a transparent gellan gum system and imaged daily for 10 d. Rotational image sequences consisting of 40 two-dimensional images were captured using an optically corrected digital imaging system. Three-dimensional root reconstructions were generated and analyzed using a custom-designed software, RootReader3D. Using the automated and interactive capabilities of RootReader3D, five rice root types were classified and 27 phenotypic root traits were measured to characterize these two genotypes. Where possible, measurements from the three-dimensional platform were validated and were highly correlated with conventional two-dimensional measurements. When comparing gellan gum-grown plants with those grown under hydroponic and sand culture, significant differences were detected in morphological root traits (P , 0.05). This highly flexible platform provides the capacity to measure root traits with a high degree of spatial and temporal resolution and will facilitate novel investigations into the development of entire root systems or selected components of root systems. In combination with the extensive genetic resources that are now available, this platform will be a powerful resource to further explore the molecular and genetic determinants of root system architecture.

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Section: Rootreader3d Reconstruction and Analysis Softwarementioning

confidence: 99%

Three-Dimensional Root Phenotyping with a Novel Imaging and Software Platform

et al. 2011

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“…The user provides segmented images as input, and our software provides a visual hull model of the object. Other methods [10,8] typically require specialist equipment such as turntables, calibration objects, or multi-camera setups. Fig.…”

Section: Introductionmentioning

confidence: 99%

Shape-from-Silhouette with Two Mirrors and an Uncalibrated Camera

Forbes

Nicolls

Jager

et al. 2006

Computer Vision – ECCV 2006

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Abstract. Two planar mirrors are positioned to show five views of an object, and snapshots are captured from different viewpoints. We present closed form solutions for calculating the focal length, principal point, mirror and camera poses directly from the silhouette outlines of the object and its reflections. In the noisy case, these equations are used to form initial parameter estimates that are refined using iterative minimisation. The self-calibration allows the visual cones from each silhouette to be specified in a common reference frame so that the visual hull can be constructed. The proposed setup provides a simple method for creating 3D multimedia content that does not rely on specialised equipment. Experimental results demonstrate the reconstruction of a toy horse and a locust from real images. Synthetic images are used to quantify the sensitivity of the self-calibration to quantisation noise. In terms of the silhouette calibration ratio, degradation in silhouette quality has a greater effect on silhouette set consistency than computed calibration parameters.

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“…This approach handles tracking, but not recognition, and it assumes an initial unoccluded view of the target. Mulayim et al [21] fuse target color and target texture in a semiparametric statistical model, which they use to define an active contour model in which they can detect a partial occlusion when it occurs, and recover the missing component.…”

Section: Prior Workmentioning

confidence: 99%