Hough Transform and Clustering for a 3-D Building Reconstruction with Tomographic SAR Point Clouds

Seismic Discrimination between Earthquakes and Explosions Using Support Vector Machine

The discrimination between earthquakes and explosions is a serious issue in seismic signal analysis. This paper proposes a seismic discrimination method using support vector machine (SVM), wherein the amplitudes of the P-wave and the S-wave of the seismic signals are selected as feature vectors. Furthermore, to improve the seismic discrimination performance using a heterodyne laser interferometer for seismic wave detection, the Hough transform is applied as a compensation method for the periodic nonlinearity error caused by the frequency-mixing in the laser interferometric seismometer. In the testing procedure, different kernel functions of SVM are used to discriminate between earthquakes and explosions. The outstanding performance of a laser interferometer and Hough transform method for precision seismic measurement and nonlinearity error compensation is confirmed through some experiments using a linear vibration stage. In addition, the effectiveness of the proposed discrimination method using a heterodyne laser interferometer is verified through a receiver operating characteristic curve and other performance indices obtained from practical experiments.

Voting-Based Document Image Skew Detection

Optical Character Recognition (OCR) is an indispensable tool for technology users nowadays, as our natural language is presented through text. We live under the need of having information at hand in every circumstance and, at the same time, having machines understand visual content and thus enable the user to be able to search through large quantities of text. To detect textual information and page layout in an image page, the latter must be properly oriented. This is the problem of the so-called document deskew, i.e., finding the skew angle and rotating by its opposite. This paper presents an original approach which combines various algorithms that solve the skew detection problem, with the purpose of always having at least one to compensate for the others’ shortcomings, so that any type of input document can be processed with good precision and solid confidence in the output result. The tests performed proved that the proposed solution is very robust and accurate, thus being suitable for large scale digitization projects.