Corrigendum for "Almost vanishing polynomials and an application to the Hough transform"

In this note we correct a technical error occurred in [M. Torrente and M.C. Beltrametti, "Almost vanishing polynomials and an application to the Hough transform", J. Algebra Appl. 13(8), (2014)]. This affects the bounds given in that paper, even though the structure and the logic of all proofs remain fully unchanged.Comment: 30 page

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

Classifying Cinematographic Shot Types

3noIn film-making, the distance from the camera to the subject greatly effects the narrative power of a shot. By the alternate use of Long shots, Medium and Close-ups the director is able to provide emphasis on key passages of the filmed scene. In this work we investigate five different inherent characteristics of single shots which contain indirect information about camera distance, without the need to recover the 3D structure of the scene. Specifically, 2D scene geometric composition, frame colour intensity properties, motion distribution, spectral amplitude and shot content are considered for classifying shots into three main categories. In the experimental phase, we demonstrate the validity of the framework and effectiveness of the proposed descriptors by classifying a significant dataset of movie shots using C4.5 Decision Trees and Support Vector Machines. After comparing the performance of the statistical classifiers using the combined descriptor set, we test the ability of each single feature in distinguishing shot types.Published on-line Nov. 2011; Print publication Jan. 2013partially_openpartially_openCanini L.; Benini S.; Leonardi R.Canini, Luca; Benini, Sergio; Leonardi, Riccard

SPHEROID DETECTION IN 2D IMAGES USING CIRCULAR HOUGH TRANSFORM

Three-dimensional endothelial cell sprouting assay (3D-ECSA) exhibits differentiation of endothelial cells into sprouting structures inside a 3D matrix of collagen I. It is a screening tool to study endothelial cell behavior and identification of angiogenesis inhibitors. The shape and size of an EC spheroid (aggregation of ~ 750 cells) is important with respect to its growth performance in presence of angiogenic stimulators. Apparently, tubules formed on malformed spheroids lack homogeneity in terms of density and length. This requires segregation of well formed spheroids from malformed ones to obtain better performance metrics. We aim to develop and validate an automated imaging software analysis tool, as a part of a High-content High throughput screening (HC-HTS) assay platform, to exploit 3D-ECSA as a differential HTS assay. We present a solution using Circular Hough Transform to detect a nearly perfect spheroid as per its circular shape in a 2D image. This successfully enables us to differentiate and separate good spheroids from the malformed ones using automated test bench

Image Processing for Cartographic Applications

The goal of classifying objects of cartographic interest in aerial photographs was approached using techniques from pattern recognition and image processing. Bridge and airport images were chosen as the initial objects of interest and segments of photographs containing them were digitized for the data base. Edge-detection and Hough transform algorithms identified structures as candidate bridges; additional decision logic (using global contrast and other attributes) further reduced the set. Results indicate the feasibility and low computational cost of the approach

A study of hough transform for weld extraction

The process of joining metals is called welding. At times, selecting a poor quality material or improper usage of welding technologies may cause defects in welded joints. Some of these welded joints have to be tested nondestructively, because their failure can cause lot of damage, for instance in power plants. Radiography is a very common method for non-destructive testing of welds. It is done by certified weld inspectors who have knowledge about weld flaws, looking at the radiograph of the welded joint with naked eye. The judgment of the weld inspector can be biased; subjective, because it is dependent on his/her experience. This manual method can also become very time consuming. Many researches were exploring computer aided examination of radiographic images in early 1990’s. With much advancement in computer vision and image processing technologies, they are being used to find more effective ways of automatic weld inspection. These days, fuzzy based methods are being widely used in this area too. The first step in automatic weld inspection is to locate the welds or find a Region of Interest (ROI) in the radiographic image [7]. In this thesis, a Standard Hough Transform (SHT) based methodology is developed for weld extraction. Firstly, we have done binarization of image to remove the background and non-welds. For binarization, optimal binary threshold is found by a metaheuristic –Simulated annealing. Secondly, we use SHT to generate the Hough Transform matrix of all non-zero points in the binary image. Thirdly, we have explored two different paths to find a meaningful set of lines in the binarized image that are welds. Finally, these lines are verified as weld using a weld-peak detection procedure. Weld-peak detection is also helpful to remove any non-welds that were remaining. We have used 25 digitized radiographic images containing 100 welds to test the method in terms of true detection and false alarm rate