Asphalt Pavement Texture Level and Distribution Uniformity Evaluation Using Three-Dimensional Method

Collagen is the most prominent protein in the human body, making up 30% of the total protein content. Quantitative studies have shown structural differences between collagen fibers of the normal and diseased tissues, due to the remodeling of the extracellular matrix during the pathological process. The dominant orientation, which is an important characteristic of collagen fibers, has not been taken into consideration for quantitative collagen analysis. Based on the conventional gray level co-occurrence matrix (GLCM) method, the authors proposed the orientation-dependent GLCM (OD-GLCM) method by estimating the dominant orientation of collagen fibers. The authors validated the utility of the OD-GLCM method on second harmonic generation (SHG) microscopic images of tendons from rats with different ages. Compared with conventional GLCM method, the authors' method has not only improved the discrimination between different tissues but also provided additional texture information of the orderliness of collagen fibers and the fiber size. The OD-GLCM method was further applied to the differentiation of the preliminary SHG images of normal and cancerous human pancreatic tissues. The combination of SHG microscopy and the OD-GLCM method might be helpful for the evaluation of diseases marked with abnormal collagen morphology.

This paper presents a method to characterize asphalt pavement macrotexture using the gray level co-occurrence matrix (GLCM). Data collected at 37 field sites are included in the analysis, representing 6 types of asphalt surface layers such as dense asphalt concrete (DAC), stone matrix asphalt (SMA), rubber asphalt concrete (RAC), ultra-thin wearing course (UTWC), micro-surfacing (MS), and open graded friction course (OGFC). This paper documents the investigation into the differences of GLCM indicators under various pixel pair spatial relationships. Then, the average of each GLCM indicator in some pixel pair spatial relationships is selected for mean texture depth (MTD) correlation. The correlation analysis shows there are 2 GLCM indicators, f_8 and f_9, have strong relationship with MTD, which are entropy of the gray level sum distribution and the gray level combination distribution of pixel pairs of pavement macrotexture respectively. The larger the values of f_8 and f_9, the more complex of the pavement macrotexture. The correlation coefficients between MTD and f_8, f_9 are 0.9601 and 0.9493 respectively. The exponential models are better choice for connecting f_8 and f_9 with MTD, which are highly significant. The mean square errors (MSE) of the exponential models with f_8 and f_9 are 0.00343 and 0.00351 respectively.

Pavement texture is an important surface characteristic for safety evaluation. Through a series of laboratory and field testing, this study presents an automatic non-contact method using high resolution 3D images for accurate and efficient pavement texture evaluation. Firstly, a 3D laser device was applied to capture pavement texture. Subsequently, the 3D texture images were denoised using image processing methods. Then, the 3D seed filling algorithm was developed to calculate mean texture depth (MTD) and mean profile depth (MPD). The computed MTD and MPD values from 3D images showed a good correlation with results of sand patch testing and high repeatability. Further, through the follow-up field testing, the proposed method using 3D images showed better performance in texture evaluation than the 2D texture profiler or sand patch testing. The results indicate it is accurate and efficient to evaluate pavement texture using high resolution 3D images in a non-contact and automatic manner.

Machine learning methods are used to detect elderly accidents while image processing is used to support machine learning performance so that detection performance can be better. This study used two methods used simultaneously, namely GLCM and ANN. The study consisted of preparation of human gesture datasets, preprocessing stage, application of GLCM, analysis of feature extraction results, classification using ANN and analysis of motion class detection results. Overall, the GLCM method with homogeneity parameters and ANN as a classifier obtained an accuracy of 24.32%. The GLCM method with contrast parameters and ANN as a classifier gets an accuracy of 99.84%. The GLCM method with mean parameters and ANN as the classifier gets an accuracy of 99.99%. The GLCM method with dissimilarity parameters and ANN as a classifier to classify hand movement images gets the best accuracy of 100%.

Image analysis-based classification of pigmentation patterns in fish: A case study of pseudo-albinism in Senegalese sole

Preliminary study on the correlation between two-dimension surface texture and friction

One of the most important problems in pattern recognition is texture-based image classification. In this paper, the combination of Discrete Cosine Transform (DCT) and Gray Level Co-Occurrence Matrix (GLCM) methods for feature extraction was proposed. The attributes extracted from DCT method were mean and variance, while the attributes extracted from GLCM method were energy and entropy. Adaptive Neuro-Fuzzy Inference System (ANFIS) was used as a classifier. The classifier model was trained using 50% of the texture images and remaining images were used for testing. Four classes of texture images were downloaded from KTH-TIPS (Textures under varying Illumination, Pose and Scale) image database, three of which were used in each experiments thus there were four data combination. The best data testing accuracy result towards textures of crumpled aluminium foil, corduroy, and orange peel is 98.3%, which is 1.6% better than one hidden-layer feed forward neural network classifier. In average, testing accuracy result of ANFIS excelled one hidden-layer feed forward neural network with 93.7% over 90.4%.

The quality of fruit production is very important because it is related to the value of sales. Data from the Directorate General of Horticulture at the Ministry of Agriculture in 2017 showed that 94,3% of the total yield of citrus fruits is a type of tangerine. In the classification of the quality, the visual observation process is strongly influenced by subjectivity so that in certain conditions such as tired eyes and the number of oranges that want to classify too many the process can be inconsistent and also take a long time. Therefore, a technology is needed to accelerate the classification process and make it more objective. This study combines the Gray level Co-occurrence Matrix (GLCM) method for texture, Hue, Saturation, Value (HSV) features for color features and the k-Nearest Neighbor (k-NN) classification method. The data used were 60 images of rotten tangerines and 60 images of not rotten tangerines divided using a 4-fold cross-validation method to find the best combination of data training and data testing. 3 main processes will be carried out, namely preprocessing, feature extraction and classification. This study produced the highest accuracy of 80% from the combined of GLCM and HSV features extraction with value k = 5 for k-NN .

The major hurdle for segmenting lung lobes in computed tomographic (CT) images is to identify fissure regions, which encase lobar fissures. Accurate identification of these regions is difficult due to the variable shape and appearance of the fissures, along with the low contrast and high noise associated with CT images. This paper studies the effectiveness of two texture analysis methods - the gray level co-occurrence matrix (GLCM) and the gray level run length matrix (GLRLM) - in identifying fissure regions from isotropic CT image stacks. To classify GLCM and GLRLM texture features, we applied a feed-forward back-propagation neural network and achieved the best classification accuracy utilizing 16 quantized levels for computing the GLCM and GLRLM texture features and 64 neurons in the input/hidden layers of the neural network. Tested on isotropic CT image stacks of 24 patients with the pathologic lungs, we obtained accuracies of 86% and 87% for identifying fissure regions using the GLCM and GLRLM methods, respectively. These accuracies compare favorably with surgeons/radiologists' accuracy of 80% for identifying fissure regions in clinical settings. This shows promising potential for segmenting lung lobes using the GLCM and GLRLM methods.

Evaluation of Asphalt Pavement Texture Using Multiview Stereo Reconstruction Based on Deep Learning

In order to obtain the asphalt pavement texture information in real time and accurately monitor the anti-skid performance of the road pavement, an automatic close range photogrammetry system (ACPR system) was proposed and built based on the circle arranged three cameras close range photogrammetry (CPR) technology to obtain the asphalt pavement surface texture. Automatic image acquisition and 3D reconstruction were achieved by the ACPR system. Sand patch method and laser scanning method (ZGScan) were used to collect the on-site comparison test of the asphalt pavement texture. Mean texture depth (MTD) and root mean square roughness (RSMR) were chosen as the statistical indicators of road surface texture. The results show that the texture data obtained by ACPR system has relatively high accuracy and efficiency, and the recognition accuracy is close to 0.02mm. The ACPR system improves the efficiency and accuracy of traditional close range photogrammetry and provides real-time and effective road surface anti-skid information for subsequent safety braking of autonomous vehicle.

PURPOSES : Skid resistance and noise of roads highly depend on the characteristics of pavement texture. Therefore, estimation of texture characteristics may give useful information for the skid resistance and noise of road. Generally, Sand Patch Test is performed in order to estimate MTD(Mean Texture Depth). However, it is time-consuming and needs traffic control. This study aimed to investigate the effectiveness of measurement texture depth using the Portable Laser Profiler that give the MPD(Mean Profile Depth). METHODS : MTD and MPD were collected on the number of expressway sections including Central Inland Test Road sections in Korea. Statistical analysis are performed to establish the relationship between MTD data based on Sand Patch Test and MPD data obtained by the Portable Laser Profiler. RESULTS : Linear relationship MPD and MTD is observed for both of asphalt pavement and concrete pavement such as R-square of 0.51 to 0.58. CONCLUSIONS : Even though, the test method and definition of MPD and MTD are different. EMTD(Estimated Mean Texture Depth) can be obtained by using the correlationship between MPD with MTD.

Concave distribution characterization of asphalt pavement surface segregation using smartphone and image processing based techniques

Parijoto fruit is a typical fruit that grows around Mount Muria, Kudus Regency and Mount Merapi in Yogyakarta, Indonesia. This fruit has many health benefits, especially for pregnant women. This fruit production is not much because of its limited growth around Mount Muria. So parijoto fruit is made into powder drink products and syrup, so that it can be consumed in a longer period of time and not only during the harvest. To get a good processed product requires good quality ingredients. Parijoto fruit needs to be sorted and classified. Current technology allows classification to be done by digital image processing. The Gray Level Cooccurrence Matrix (GLCM) method is proposed to extract the texture features from the parijoto fruit and then classify them using the K-Nearest Neighbor (KNN) method. GLCM can describe a spatial linear relationship of the frequency at which gray values are determined by other gray values in one area of investigation. It can simply use the statistical approach of appearance or histogram of the image matrix. In this way, information will be easily relative position of neighboring pixels that are suitable for the classification process using KNN. KKN was chosen because this method was proven to be used for relatively few datasets, but a normalization process was needed to increase accuracy. Based on the results of the implementation of the GLCM and KNN methods for parijoto fruit classification the classification accuracy was 80%.

Three-dimensional technique for accurate pavement macrotexture measurement using Surface Volume Parameters