Field Imaging and Volumetric Reconstruction of Riprap Rock and Large-Sized Aggregates: Algorithms and Application

Riprap rock and large-sized aggregates have been used extensively in geotechnical and hydraulic engineering. They essentially provide erosion control, sediment control, and scour protection. The sustainable and reliable use of riprap materials demands efficient and accurate evaluation of their large particle sizes, shapes, and gradation information at both quarry production lines and construction sites. Traditional methods for assessing riprap geometric properties involve subjective visual inspection and time-consuming hand measurements. As such, achieving the comprehensive in-situ characterization of riprap materials still remains challenging for practitioners and engineers. This paper presents an innovative approach for characterizing the volumetric properties of riprap by establishing a field imaging system associated with newly developed color image segmentation and three-dimensional (3-D) reconstruction algorithms. The field imaging system described in this paper with its algorithms and field application examples is designed to be portable, deployable, and affordable for efficient image acquisition. The robustness and accuracy of the image segmentation and 3-D reconstruction algorithms are validated against ground truth measurements collected in stone quarry sites and compared with state-of-the-practice inspection methods. The imaging-based results show good agreement with the ground truth and provide improved volumetric estimation when compared with currently adopted inspection methods. Based on the findings of this study, the innovative imaging-based system is envisioned for full development to provide convenient, reliable, and sustainable solutions for the onsite Quality Assurance/Quality Control tasks relating to riprap rock and large-sized aggregates.

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Microscopic Biopsy Image Segmentation Using Hybrid Color K-Means Approach

International Journal of Computer Vision and Image Processing ◽

10.4018/ijcvip.2017010105 ◽

2017 ◽

Vol 7 (1) ◽

pp. 79-90

Author(s):

Rajesh Kumar ◽

Rajeev Srivastava ◽

Subodh Srivastava

Keyword(s):

Image Segmentation ◽

Color Image ◽

False Positive Rate ◽

Ground Truth ◽

Color Image Segmentation ◽

Data Set ◽

Global Consistency ◽

Segmentation Approach ◽

Sensitivity Specificity ◽

Biopsy Image

The color image segmentation is a fundamental requirement for microscopic biopsy image analysis and disease detection. In this paper, a hybrid combination of color k-means and marker control watershed based segmentation approach is proposed to be applied for the segmentation of cell and nuclei of microscopic biopsy images. The proposed approach is tested on breast cancer microscopic data set with ROI segmented ground truth images. Finally, the results obtained from proposed framework are compared with the results of popular segmentation algorithms such as Fuzzy c-means, color k-means, texture based segmentation as well as adaptive thresholding approaches. The experimental analysis shows that the proposed approach is providing better results in terms of accuracy, sensitivity, specificity, FPR (false positive rate), global consistency error (GCE), probability random index (PRI), and variance of information (VOI) as compared to other segmentation approaches.

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Color Image Segmentation Using Fuzzy C-Regression Model

Advances in Fuzzy Systems ◽

10.1155/2017/4582948 ◽

2017 ◽

Vol 2017 ◽

pp. 1-15 ◽

Cited By ~ 5

Author(s):

Min Chen ◽

Simone A. Ludwig

Keyword(s):

Image Segmentation ◽

Regression Model ◽

Fuzzy Clustering ◽

Spatial Information ◽

Color Image ◽

Clustering Algorithms ◽

Ground Truth ◽

Color Image Segmentation ◽

Learning Techniques ◽

Hard Clustering

Image segmentation is one important process in image analysis and computer vision and is a valuable tool that can be applied in fields of image processing, health care, remote sensing, and traffic image detection. Given the lack of prior knowledge of the ground truth, unsupervised learning techniques like clustering have been largely adopted. Fuzzy clustering has been widely studied and successfully applied in image segmentation. In situations such as limited spatial resolution, poor contrast, overlapping intensities, and noise and intensity inhomogeneities, fuzzy clustering can retain much more information than the hard clustering technique. Most fuzzy clustering algorithms have originated from fuzzy c-means (FCM) and have been successfully applied in image segmentation. However, the cluster prototype of the FCM method is hyperspherical or hyperellipsoidal. FCM may not provide the accurate partition in situations where data consists of arbitrary shapes. Therefore, a Fuzzy C-Regression Model (FCRM) using spatial information has been proposed whose prototype is hyperplaned and can be either linear or nonlinear allowing for better cluster partitioning. Thus, this paper implements FCRM and applies the algorithm to color segmentation using Berkeley’s segmentation database. The results show that FCRM obtains more accurate results compared to other fuzzy clustering algorithms.

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