We present an application of machine learning and statistics to the problem of distinguishing between defective and non-defective industrial workpieces, where the defect takes the form of a long and thin crack on the surface of the piece. From the images of pieces a number of features are extracted by using the Hough transform and the Correlated Hough transform. Two datasets are considered, one...

Rapid computation of the Hough Transform is necessary in very many computer vision applications. One of the major approaches for fast Hough Transform computation is based on the use of a small random sample of the data set rather than the full set. Two diierent algorithms within this family are the Randomized Hough Transform (RHT) and the Probabilistic Hough Transform (PHT). There have been con...

This paper presents a technique to improve the performance of motion detection using Hough Transform. The proposed technique uses edge pixels around object corners instead of all of the object edge pixels as used in the Motion Detection using Hough Transform: Direct Approach. As a result, with this corner technique, a number of computations can be reduced and hence, motion detection can be sign...

A new method is proposed to detect circular arcs. The detection of a circular arc includes the determination of its center, radius, length, and midpoint. The Hough transform is applied to detect circular arcs by using center and radius as parameters. A peak value in the parameter space of the Hough transform indicates the existence of a circular arc. The center and radius of the circular arc ar...

The decomposition of binary images using rectangular blocks of foreground pixels as primitives is considered. Based on this type of decomposition, a fast method for evaluating the Hough transform is introduced. A complexity analysis of the proposed block Hough transform algorithm sets constraints on the complexity of algorithms used for block decomposition, so that the total decomposition and H...

In this chapter, several advanced detection algorithms for Track-Before-Detect (TBD) procedures using the Hough Transform (HT) are proposed and studied. The detection algorithms are based on the scheme described in (Carlson et al., 1994) to use the Hough transform for simultaneous target detection and trajectory estimation. The concept described in (Carlson et al., 1994) accepts that a target m...

Hough transform (HT) has been the most common method for circle detection exhibiting robustness but adversely demanding a considerable computational load and large storage. Alternative approaches include heuristic methods that employ iterative optimization procedures for detecting multiple circles under the inconvenience that only one circle can be marked at each optimization cycle demanding a ...

A highly efficient circle positioning algorithm, called the two-step optimization Hough transform (TSHT), based on multi-resolution segmentation is proposed to solve problems of offset transform, namely, its large memory overhead, long time consumption, and low recognition accuracy. First, using image feature printed circuit board (PCB) circular identifier, target obtained adaptive preprocessin...

The Hough Transform [6] -HTis a standard tool in image analysis that allows recognizing global patterns in an image space. To detect shapes in noisy data, preserving the idea of the conventional HT, but allowing detection of approximate shapes, the Fuzzy Hough Transform -FHTwas introduced [8]. Based on the FHT way of work, we propose an Edge Orientation-based Fuzzy Hough Transform -EOFHTwherein...