Hough Transforms

This paper proposes a new idea for grading multiple-choice test which is based on a camera with reliability and efficiency. The bounds of the answer sheet image captured by the camera is first allocated using Hough transform and then skew-corrected into the proper orientation, followed by the normalization to a given size. Next, the tick mark corresponding to the answer for each question can be recognized by allocation of the mask which wraps the answer area. The experimental results showed that the proposed system has achieved significant improvement in performance in terms of accuracy, reliability, and elapsed time compared with those of the conventional optical mark recognition (OMR) systems. The proposed system also demonstrated that it can also achieve high accuracy of 99.7% while using non-transoptic answer sheet paper with lower cost.

In this paper we present a multi-camera system, which serves as the primary vision apparatus for a ceiling based swinging service robotic platform. To facilitate a smooth and unimpeded movement of the swinging platform the knowledge of the working environment is essential. To this end we examine the 3D scene reconstruction by concurrent multiple views from the distinct cameras located at the upper corners of a room for volume calculation of objects in everyday indoor environments. The 3D scene reconstruction is used to determine the working volume where the swinging robot will be able to operate. At first, we detect lines across the views using the hough transformation while the computation of multiple vanishing points serves as the platform to distinguish regions across the multi camera system. We thereafter obtain correspondences across the multiple views of the test room and finally we determine the 3D volumes of objects across the scene. The preliminary experimental results show that the volume computation is very accurate and reasonably time consuming.

The authentication of individuals using iris-based recognition is a widely developing technology. Precise and unobtrusive image acquisition is not always possible. This introduces a number of ill-affecting factors to the subsequent localisation and characterisation stages. Common problems include variations in lighting, poor image quality, noise and interference caused by eyelashes. The feature extraction and classification procedures rely heavily on the rich textural details of the iris to provide a unique digital signature for an individual. As a result, the stability and integrity of a system depends on effective segmentation of the iris to generate the iris-code. The previously mentioned problems hinder this task. A new segmentation method is presented to tackle these problems. Principal component analysis (PCA) is discussed as a preprocessing technique that removes redundant and useless data. Variations in lighting; and noise are handled using an application of median filtering and adaptive thresholding. Finally, edge detection and the Hough transform are discussed for locating the circular boundaries of the pupil and sclera

Pipeline surface defects such as cracks cause major problems for asset managers, particularly when the pipe is buried under the ground. The manual inspection of surface defects in the underground pipes has a number of drawbacks, including subjectivity, varying standards, and high costs. Automatic inspection systems using image processing and artificial intelligence techniques can overcome many of these disadvantages and offer asset managers an opportunity to significantly improve quality and reduce costs.