Automatic Detection of Calibration Grids in Time-of-Flight Images

It is convenient to calibrate time-of-flight cameras by established methods, using images of a chequerboard pattern. The low resolution of the amplitude image, however, makes it difficult to detect the board reliably. Heuristic detection methods, based on connected image-components, perform very poorly on this data. An alternative, geometrically-principled method is introduced here, based on the Hough transform. The projection of a chequerboard is represented by two pencils of lines, which are identified as oriented clusters in the gradient-data of the image. A projective Hough transform is applied to each of the two clusters, in axis-aligned coordinates. The range of each transform is properly bounded, because the corresponding gradient vectors are approximately parallel. Each of the two transforms contains a series of collinear peaks; one for every line in the given pencil. This pattern is easily detected, by sweeping a dual line through the transform. The proposed Hough-based method is compared to the standard OpenCV detection routine, by application to several hundred time-of-flight images. It is shown that the new method detects significantly more calibration boards, over a greater variety of poses, without any overall loss of accuracy. This conclusion is based on an analysis of both geometric and photometric error.Comment: 11 pages, 11 figures, 1 tabl

Double Hough Transform for Estimating the Position of the Mandibular Canal in Dental Radiographs

In this work, a multiple generalised anisotropic Hough transform (AGHT) is used to detect the mandibular canal in dental panoramic radiographs. The proposed method relies on a sequential application of the Hough transform that we call double Hough transform. The recognition of the mandibular canal is based on a double template matching compared with the clinical detection using the fact that the shape of the mandibular canal is usually the same and it is situated inside the mandibular bone. The experiments performed on real orthopantomographic images shown that the risk of false detection is significantly decreased, while the recognition is not affected by occlusion and by the presence of additional structures e.g. teeth, projection errors

Aplikasi Segmentasi Huruf Jawa

Javanese letter are a representative and a proof of Indonesia's rich and diverse. Nowadays the use of Javanese language in oral and written conversation Indonesia is getting rare. Our people prefer to learn our national language or International languages such as English and Chinese. Another work to conserve Javanese letter is make Javanese letter optical recognition. Therefore, in this thesis an application that could be used in the preprocessing phase of Javanese letter recognition was developed.Hough transform and projection profile are the methods that used in this application. Hough transform is a method that perform skew detection on the document. Projection profile is a method that process segmentation.In Chapter 5 light intensity, consistency range between rows, font size and type, the difference of font weight would affect the result. The result of projection profile method can segment 77% of document with consistent range between rows. The hough transform method can detect skew on the document up to 94%

Implementation of the Hough Transform for 3D Track Reconstruction in Drift Chambers

The paper is devoted to the method for 3D reconstruction of the straight tracks in the tracking system consisting of the drift-chamber stereo layers. The method is based on the Hough-transform approach - the discrete case of more general Radon transform - and takes into account both coordinates of the hit wires and drift distances not only for the measurements in one projection, but also in the rotated stereo layers. The proposed method allows one to resolve the right-left ambiguity and provides the accordance between vertical and horizontal projections of the track.Comment: 6 pages, 4 figures, submitted to the VIth International School-Seminar "Actual Problems of High Energy Physics", August 7-16, 2001, Gomel, Belarus, see http://gomelschool.hep.b

Vanishing Point Detection with Direct and Transposed Fast Hough Transform inside the neural network

In this paper, we suggest a new neural network architecture for vanishing point detection in images. The key element is the use of the direct and transposed Fast Hough Transforms separated by convolutional layer blocks with standard activation functions. It allows us to get the answer in the coordinates of the input image at the output of the network and thus to calculate the coordinates of the vanishing point by simply selecting the maximum. Besides, it was proved that calculation of the transposed Fast Hough Transform can be performed using the direct one. The use of integral operators enables the neural network to rely on global rectilinear features in the image, and so it is ideal for detecting vanishing points. To demonstrate the effectiveness of the proposed architecture, we use a set of images from a DVR and show its superiority over existing methods. Note, in addition, that the proposed neural network architecture essentially repeats the process of direct and back projection used, for example, in computed tomography.Comment: 9 pages, 9 figures, submitted to "Computer Optics"; extra experiment added, new theorem proof added, references added; typos correcte

Image Segmentation and Multiple skew estimation, correction in printed and handwritten documents

Analysis of handwritten document has always been a challenging task in the field of image processing. Various algorithms have been developed in finding solution to this problem. The algorithms implemented here for segmentation and skew detection works not only on printed or scanned document images but for also handwritten document images which creates an edge over other methodologies. Here Line segmentation for both printed and handwritten document image is done using two methods namely Histogram projections and Hough Transform assuming that input document image consists of no major skews. For Histogram Projection to work correct, the document must not contain even slight skews. Hough transform gives better results than the former case. Word Segmentation can be done using the connected components analysis. Here, we first identify connected components in the printed or handwritten document image. A methodology is being used here which detects multiple skews in multi handwritten documents or printed ones. Using clustering algorithms, we detect multiple skew blocks in a handwritten document image or printed document image or a combination of both. The algorithm used here also works for skewed multi handwritten text blocks

2D and 3D Polar Plume Analysis from the Three Vantage Positions of STEREO/EUVI A, B, and SOHO/EIT

Polar plumes are seen as elongated objects starting at the solar polar regions. Here, we analyze these objects from a sequence of images taken simultaneously by the three spacecraft telescopes STEREO/EUVI A and B, and SOHO/EIT. We establish a method capable of automatically identifying plumes in solar EUV images close to the limb at 1.01 - 1.39 R in order to study their temporal evolution. This plume-identification method is based on a multiscale Hough-wavelet analysis. Then two methods to determined their 3D localization and structure are discussed: First, tomography using the filtered back-projection and including the differential rotation of the Sun and, secondly, conventional stereoscopic triangulation. We show that tomography and stereoscopy are complementary to study polar plumes. We also show that this systematic 2D identification and the proposed methods of 3D reconstruction are well suited, on one hand, to identify plumes individually and on the other hand, to analyze the distribution of plumes and inter-plume regions. Finally, the results are discussed focusing on the plume position with their cross-section area.Comment: 22 pages, 10 figures, Solar Physics articl

Image Skew Detection and Correction in Regular Images and Document Images

During any Document scanning and processing of regular images in our daily life activities image skew is a very important part that should be kept in mind before processing the images. Skew is generally referred to the degree of rotation of an image in comparison with its actual position. So before proceeding to any further activity with the images we need to assure the skew of an image is correct or not. So detection of skew of an image would be the first thing to be applied to regular images some times and specially scanned documents when transforming them to appropriate format. There are different algorithms for detection of skew of an image that have been implemented in different kind of works. The basic and very commonly used one is Scan line based skew detection. In this technique several lines are passed through the image from left to right, right to left, top to bottom and bottom to top and then the number of black pixels encountered in different projection of line are counted. The projection with maximum black pixels encountered is to be taken to consider the skew of the image. There is another approaches like Hough transform, Base-point method etc. In Hough transform method the pixel value is calculated for each value of θ. The angle producing maximum variance is considered to be the skew angle of the image. These two algorithms have been implemented and the results have been represented to compare the accurac