A Quantitative Index Representing Crack Shapes and Corresponding Techniques

In order to quantificationally describe the soil cracks due to dry-wet cycles, the concept of gray level entropy is applied according to the physical significance of the information entropy to represent various shapes of cracks. Then a piece of simple and easy-to-use equipment for taking photos is used to monitor and record the crack propagation. A grayscale image and the corresponding gray level entropy are obtained automatically by a program. Test results showed that gray level entropy can quantificationally describe the shape of cracks reasonably well and evaluate the degree of crack development effectively.

A Spatial-Spectral Feature Descriptor for Hyperspectral Image Matching

Hyperspectral Images (HSIs) have been utilized in many fields which contain spatial and spectral features of objects simultaneously. Hyperspectral image matching is a fundamental and critical problem in a wide range of HSI applications. Feature descriptors for grayscale image matching are well studied, but few descriptors are elaborately designed for HSI matching. HSI descriptors, which should have made good use of the spectral feature, are essential in HSI matching tasks. Therefore, this paper presents a descriptor for HSI matching, called HOSG-SIFT, which ensembles spectral features with spatial features of objects. First, we obtain the grayscale image by dimensional reduction from HSI and apply it to extract keypoints and descriptors of spatial features. Second, the descriptors of spectral features are designed based on the histogram of the spectral gradient (HOSG), which effectively preserves the physical significance of the spectral profile. Third, we concatenate the spatial descriptors and spectral descriptors with the same weights into a new descriptor and apply it for HSI matching. Experimental results demonstrate that the proposed HOSG-SIFT performs superior against traditional feature descriptors.

Performance Evaluation of Digital Image Processing by Using Scilab

Scilab is an open-source, cross-platform computational environment software available for academic and research purposes as a free of charge alternative to the matured computational copyrighted software such as MATLAB. One of important library available for Scilab is image processing toolbox dedicated solely for image and video processing. There are three major toolboxes for this purpose: Scilab image processing toolbox (SIP), Scilab image and video processing toolbox (SIVP) and recently image processing design toolbox (IPD). The target discussion in this paper is SIVP due to its vast use out there and its capability to handle streaming video file as well (note that IPD also supports video processing). Highlight on the difference between SIVP and IPD will also be discussed. From testing, it is found that in term of looping test, Octave and FreeMat are faster than Scilab. However, when converting RGB image to grayscale image, Scilab outperform Octave and FreeMat.

A Blind Watermarking Model of the 3D Object and the Polygonal Mesh Objects for Securing Copyright

In this paper, we propose a novel model for 3D object watermarking. The proposed method is based on the properties of the discrete cosine transform (DCT) of the 3D object vertices to embed a secret grayscale image three times. The watermarking process takes place by using the vertices coefficients and the encrypted image pixels. Moreover, the extraction process is totally blind based on the reverse steps of the embedding process to recover the secret grayscale image. Various performance aspects of the method are measured and compared between the original 3D object and the watermarked one using Euclidean distance, Manhattan distance, cosine distance, and correlation distance. The obtained results show that the proposed model provides better performances in terms of execution time and invisibility.

A Real-Time Car Towing Management System Using ML-Powered Automatic Number Plate Recognition

Automatic Number Plate Recognition (ANPR) has been widely used in different domains, such as car park management, traffic management, tolling, and intelligent transport systems. Despite this technology’s importance, the existing ANPR approaches suffer from the accurate identification of number plats due to its different size, orientation, and shapes across different regions worldwide. In this paper, we are studying these challenges by implementing a case study for smart car towing management using Machine Learning (ML) models. The developed mobile-based system uses different approaches and techniques to enhance the accuracy of recognizing number plates in real-time. First, we developed an algorithm to accurately detect the number plate’s location on the car body. Then, the bounding box of the plat is extracted and converted into a grayscale image. Second, we applied a series of filters to detect the alphanumeric characters’ contours within the grayscale image. Third, the detected the alphanumeric characters’ contours are fed into a K-Nearest Neighbors (KNN) model to detect the actual number plat. Our model achieves an overall classification accuracy of 95% in recognizing number plates across different regions worldwide. The user interface is developed as an Android mobile app, allowing law-enforcement personnel to capture a photo of the towed car, which is then recorded in the car towing management system automatically in real-time. The app also allows owners to search for their cars, check the case status, and pay fines. Finally, we evaluated our system using various performance metrics such as classification accuracy, processing time, etc. We found that our model outperforms some state-of-the-art ANPR approaches in terms of the overall processing time.