Automatic Segmentation of Wood Logs by Combining Detection and Segmentation

Gutzeit, Enrico; Voskamp, Jörg

doi:10.1007/978-3-642-33179-4_25

Cited by 14 publications

(17 citation statements)

References 6 publications

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“…Our more generalize approach extends the specific approach for wood log segmentation in (Gutzeit and Voskamp, 2012). Similar as in the wood log segmentation case, the objects of a specific organo-group in one image are mostly similar in color and texture.…”

Section: Our Approachmentioning

confidence: 85%

“…This approach needs to have wood logs in the center of the image. It was extended in (Gutzeit and Voskamp, 2012) by relaxing these restrictions using an object detection to estimate the center region of the stack of wood. Each individual wood log is segmented in contrast to the complete wood area.…”

Section: Related Workmentioning

confidence: 99%

“…With the pixel sets, the image can be binary segmented with a kd-tree accelerated density estimation (KD-NN) followed by an optimization based segmentation. This method by using graph-cut applied on wood logs is called KD-NN-A in (Gutzeit and Voskamp, 2012). In the KD-NN-A approach, a trimap I tri is used to mark a pixel as unknown, foreground or background.…”

Section: Fore-and Background Estimationmentioning

confidence: 99%

See 2 more Smart Citations

Optimization-based Automatic Segmentation of Organic Objects of Similar Types

Gutzeit

Radolko

Kuijper

et al. 2015

Proceedings of the 10th International Conference on Computer Vision Theory and Applications

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For the segmentation of multiple objects on unknown background in images, some approaches for specific objects exist. However, no approach is general enough to segment an arbitrary group of organic objects of similar type, like wood logs, apples, or tomatoes. Each approach contains restrictions in the object shape, texture, color or in the image background. Many methods are based on probabilistic inference on Markov Random Fields-summarized in this work as optimization based segmentation. In this paper, we address the automatic segmentation of organic objects of similar types by using optimization based methods. Based on the result of object detection, a fore-and background model is created enabling an automatic segmentation of images. Our novel and more general approach for organic objects is a first and important step in a measuring or inspection system. We evaluate and compare our approaches on images with different organic objects on very different backgrounds, which vary in color and texture. We show that the results are very accurate.

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Section: Our Approachmentioning

confidence: 85%

Section: Related Workmentioning

confidence: 99%

Section: Fore-and Background Estimationmentioning

confidence: 99%

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Optimization-based Automatic Segmentation of Organic Objects of Similar Types

Gutzeit

Radolko

Kuijper

et al. 2015

Proceedings of the 10th International Conference on Computer Vision Theory and Applications

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“…Gutzeit and Voskamp [2] train and apply Haar-like features [11] to roughly determine the region of interest, where wood logs are located. Based on this approach we show in [4] how different feature detectors in combination with Gaussian mixture models and iterative classification can achieve a higher true positive and lower false negative object detection rate.…”

Section: Related Researchmentioning

confidence: 99%

“…The main motivation for the development of this method is the segmentation of wood log clusters as shown in figure 1(a) and (c). These clusters have recently become of interest in the field of object detection and segmentation, since there exists a strongly increasing demand for such methods, as shown in [1], [2], [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Detection and Segmentation of Quasi-Planar Surfaces Through Expectation Maximization Under a Planar Homography Constraint

Herbon

Schumann

Tönnies

et al. 2015

2015 12th Conference on Computer and Robot Vision

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We propose a novel method capable of detecting and segmenting quasi-planar surfaces based on homography decomposition and Semi-Global-Matching without the need for extrinsic calibration or stereo rectification. Existing methods require coplanarity of all points on the dominant plane and are thus unsuited for unconstrained quasi-planar surfaces. In contrast to state of the art methods we can account for local depth variations. This is achieved by introducing a novel planar inter-image rectification technique, which enables us to perform block matching without epipolar rectification.Experiments are performed on two different databases. Firstly we quantitatively evaluate the general feasibility of our method on a database containing indoor and outdoor scenes with available ground truth data. Secondly we apply our method to the new publicly available HAWKwood database. Our experiments have shown that the true positive rate of our segmentation procedure exceeds 94.0% while the false positive rate is below 4.9%.

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The Algorithm and Software for Timber Batch Measurement by Using Image Analysis

Mehrentsev

Kruglov

2019

Communications in Computer and Information Science

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Automatic Segmentation of Wood Logs by Combining Detection and Segmentation

Cited by 14 publications

References 6 publications

Optimization-based Automatic Segmentation of Organic Objects of Similar Types

Optimization-based Automatic Segmentation of Organic Objects of Similar Types

Detection and Segmentation of Quasi-Planar Surfaces Through Expectation Maximization Under a Planar Homography Constraint

The Algorithm and Software for Timber Batch Measurement by Using Image Analysis

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