Three-dimensional (3D) vision measurement technology based on encoding structured light plays an important role and has become the main development trend in the field of 3D non-contact measurement. However, how to synthetically improve measurement speed, accuracy and sampling density is still a difficult problem. Thus in the present work, a novel 3D measurement method based on temporal encoding structured light by combining trapezoidal phase-shifting pattern and cyclic code pattern is proposed. Due to trapezoidal phase-shifting has the advantages of high sampling density and high-speed, the proposed method can maintain these advantages by using cyclic code to expand the range of trapezoidal phase-shifting. In addition, the correction scheme is designed to solve the problem of cycle dislocation. Finally, simulation experimental platform is built with 3ds max and MATLAB. Experimental analyses and results show that, the maximal error is less than 3 mm in the range from 400 mm to 1100 mm, cycle dislocation correction has a good effect.
Pavement crack detection plays an important role in the pavement maintaining and management. Recently, the laser scanning technique for pavement crack detection becomes more and more popular due to its ability of discriminating dark areas, which are not caused by pavement distress such as tire marks, oil spills, and shadows. However, this technique still bears some errors for pavement crack recognition errors, thus in the present work, the factors contributed to these errors in laser scanning system are first analyzed, and then a decision model for the laser scanning pavement crack detection system based on the hypothesis test is proposed. Experimental analyses and results show that this model not only allows us to build the relationship between the contribution factors and crack detection accuracy and to provide the criteria to compare the detection accuracy for the different roads, but also can be used to judge whether the crack exists with a reasonable number of deformed light stripes. Therefore, the proposed decision model can provide guidance on the pavement crack detection and has a practical value.
Pavement roughness is an important index to reflect the quality of pavement. To improve the efficiency of pavement
roughness measurement, a novel pavement roughness measurement system based on structured light vision inspection is
proposed in this paper. By describing the principle of structured light inspection and measurement system design, it is
stressed that the proposed system can be used to acquire a continuous longitudinal profile of pavement and International
Roughness Index by using the structured light illumination and CCD. Some experiments are performed to test orientation
precision and performance of the system. The experimental results show that the system has some pros such as high
orientation precision, good stability and low cost.
Many methods have been proposed to avoid radiologists fail to diagnose small pulmonary nodules. Recently, support
vector machines (SVMs) had received an increasing attention for pattern recognition. In this paper, we present a
computerized system aimed at pulmonary nodules detection; it identifies the lung field, extracts a set of candidate
regions with a high sensitivity ratio and then classifies candidates by the use of SVMs. The Computer Aided Diagnosis
(CAD) system presented in this paper supports the diagnosis of pulmonary nodules from Computed Tomography (CT)
images as inflammation, tuberculoma, granuloma..sclerosing hemangioma, and malignant tumor. Five texture feature
sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the
most robust features. The selected feature set was fed into an ensemble of SVMs classifiers. The achieved classification
performance was 100%, 92.75% and 90.23% in the training, validation and testing set, respectively. It is concluded that
computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and
may contribute to more efficient diagnosis.