Particulate texture image analysis with applications

Texture analysis and textural cues have been applied for image classification, segmentation and pattern recognition. Dominant texture descriptors include directionality, coarseness, line-likeness etc. In this dissertation a class of textures known as particulate textures are defined, which are predominantly coarse or blob-like. The set of features that characterise particulate textures are different from those that characterise classical textures. These features are micro-texture, macro-texture, size, shape and compaction. Classical texture analysis techniques do not adequately capture particulate texture features. This gap is identified and new methods for analysing particulate textures are proposed. The levels of complexity in particulate textures are also presented ranging from the simplest images where blob-like particles are easily isolated from their back- ground to the more complex images where the particles and the background are not easily separable or the particles are occluded. Simple particulate images can be analysed for particle shapes and sizes. Complex particulate texture images, on the other hand, often permit only the estimation of particle dimensions. Real life applications of particulate textures are reviewed, including applications to sedimentology, granulometry and road surface texture analysis. A new framework for computation of particulate shape is proposed. A granulometric approach for particle size estimation based on edge detection is developed which can be adapted to the gray level of the images by varying its parameters. This study binds visual texture analysis and road surface macrotexture in a theoretical framework, thus making it possible to apply monocular imaging techniques to road surface texture analysis. Results from the application of the developed algorithm to road surface macro-texture, are compared with results based on Fourier spectra, the auto- correlation function and wavelet decomposition, indicating the superior performance of the proposed technique. The influence of image acquisition conditions such as illumination and camera angle on the results was systematically analysed. Experimental data was collected from over 5km of road in Brisbane and the estimated coarseness along the road was compared with laser profilometer measurements. Coefficient of determination R2 exceeding 0.9 was obtained when correlating the proposed imaging technique with the state of the art Sensor Measured Texture Depth (SMTD) obtained using laser profilometers.

Identification of Road Irregularities via Vehicle Accelerations

A periodic monitoring of the pavement condition facilitates a cost-effective distribution of the resources available for maintenance of the road infrastructure network. The task can be accurately carried out using profilometers, but such an approach is generally expensive. This paper presents a method to collect information on the road profile via accelerometers mounted in a fleet of non-specialist vehicles, such as police cars, that are in use for other purposes. It proposes an optimisation algorithm, based on Cross Entropy theory, to predict road irregularities. The Cross Entropy algorithm estimates the height of the road irregularities from vehicle accelerations at each point in time. To test the algorithm, the crossing of a half-car roll model is simulated over a range of road profiles to obtain accelerations of the vehicle sprung and unsprung masses. Then, the simulated vehicle accelerations are used as input in an iterative procedure that searches for the best solution to the inverse problem of finding road irregularities. In each iteration, a sample of road profiles is generated and an objective function defined as the sum of squares of differences between the ‘measured’ and predicted accelerations is minimized until convergence is reached. The reconstructed profile is classified according to ISO and IRI recommendations and compared to its original class. Results demonstrate that the approach is feasible and that a good estimate of the short-wavelength features of the road profile can be detected, despite the variability between the vehicles used to collect the data.

Profile equipment evaluation.

Texas Department of Transportation, Austin

Pavement roughness evaluations using a Mays ride meter. Final report.

Mode of access: Internet.

Calibration services for pavement survey equipment. Executive summary. Final report.

Federal Highway Administration, Office of Engineering and Highway Operations Research and Development, Washington, D.C.

Feasibility study for an improved acoustic probe for road roughness profiling.

Federal Highway Administration, Washington, D.C.

Rideability equipment evaluation. Final report.

Federal Highway Administration, Washington, D.C.

Upgrade of 690D surface dynamics profilometer for non-contact measurements. Final report.

Texas State Department of Highways and Public Transportation, Austin

The Walker Roughness Device for roughness measurements. Final report.

Texas State Department of Highways and Public Transportation, Austin

Performance of the analog and the digital profilometer with wheels and with non-contract transducers. Final report.

Texas State Department of Highways and Public Transportation, Transportation Planning Division, Austin

Calibration procedures for roadmeters. Final report.

Federal Highway Administration, Office of Implementation, Washington, D.C.

Road profile study. Final report.

Mode of access: Internet.

Demonstration project no. 72, automated pavement data collection equipment roughness and profile measurement. Equipment summary.

Mode of access: Internet.

Integration of inertial profilometer in ODOT Pavement Management System. Final report.

Ohio Department of Transportation, Columbus.

Evaluation of a K. J. Law Model 8300 Ultrasonic Roughness Testing Device. Final report.

Federal Highway Administration, Washington, D.C.