Hough Technique Research Articles

A web application for sex and stature estimation from radiographic proximal femur for a Thai population

In both forensic and archaeological domains, the discovery of incomplete human remains is a frequent occurrence. Nevertheless, the estimation of biological profiles from such remains presents a challenge due to the absence of crucial skeletal elements, such as the skull and pelvis. This study aimed to assess the utility of the proximal femur in the forensic identification process by creating a web application for osteometric analysis of the proximal femur. The aim was to determine the sex and stature of an individual from radiographs of the left anteroposterior femur. To accomplish this, an automated method was developed for acquiring linear measurements from radiographic images of the proximal femur using Python tools. The application of Hough techniques and Canny edge detection was utilized to generate linear femoral dimensions from radiographs. A total of 354 left femora were radiographed and measured by the algorithm. The sex classification model employed in this study was the Naïve Bayes algorithm (accuracy = 91.2 %). Results indicated that Gaussian process regression (GPR) was the most effective method for estimating stature (mean error = 4.68 cm, SD = 3.93 cm). The proposed web application holds the potential to serve as a valuable asset in the realm of forensic investigations in Thailand, particularly in the estimation of biological profiles from fragmentary skeletal remains.

Quasi-real time acquisition and processing for biomedical IR and conventional imaging in surgery applications

Surgery decision making is almost basically adopted by imaging, and for internal organs, imaging is the only tool for proceeding on. Interested imaging is obtained by as X-rays, CT (Computer Tomography), MRI (Magnetic Resonance Imaging), NMR (Nuclear Magnetic Resonance), ultrasound, scintigraphy, and PET (Positron Emission Tomography). All the above techniques are correlated to specific instrumentation, and their manufacturers include dedicated software to output high quality images. However, despite advances in biomedical instrumentation technology, new issues, regarding insurgence of pathologies so far unknown, require as much techniques to be developed. In this spirit, infrared thermography, for instance, can ride to help us to solve this thorny issue. Infrared images have been used to discriminate interested organs with respect to tissues and adherences that must be taken in consideration for continuing and optimizing the surgery process. Modified linear and circular Hough transform have been used to process captured images. This paper illustrates findings related to quasi-real time acquisition and processing during surgery. Infrared processing has been compared to another technique based on atomic force microscopy-based algorithm. Hough technique is also treated.

Detection and Segmentation of 3D Objects in Urban Environments Using Indexation

A procedure for automobile detection on 3D point clouds of urban areas is presented in this work. Point clouds are obtained using an HDL-64E Velodyne LIDAR. The work is divided into two sections: Segmentation, in which the base plane (floor) and its perpendicular planes are extracted using Hough's technique. Next every other object is segmented using MeanShift method; and Indexation, in which all segmented objects are modeled according to a normal direction so that its histograms can be obtained and compared to a pre-loaded histogram database. The reconstructed environment is considered to be semi-structured, meaning that it can be modeled using planes. In the process ROC analysis is used for thresholds optimization.

Numerical study of Hough technique in surgery of otosclerosis, using the finite element method.

Otosclerosis is a metabolic bone disease of the otic capsule that can cause the stapes fixation, resulting in conductive hearing loss or, in a profound sensorineural deafness threshold. Surgery is one of the possible treatments for the otosclerosis. To repair small focus of otosclerosis in the anterior crus of the stapes, in 1960, Hough suggested the implementation of a technique in which part of the anterior crus is fractured and the stapes turned. As a result, the posterior crus of the stapes is the only connection with the inner ear. In this work, the outcome of Hough's surgical technique was simulated. Based on computerized images, a finite element model of middle ear ossicles and tympanic membrane was created, as well as a model where the stapes has changed. The discretization of the tridimensional solid model was made using the ABAQUS software. The mechanical properties used were taken from the literature and adequate boundary conditions were applied. The results obtained with the Hough technique simulation were compared with a representative model of the normal ear, taking into account the displacements obtained on the central part of the stapes footplate and the maximum principal stress in the stapes crus. The results obtained are closer to the normal ear model, therefore Hough technique stands out as a good option to correct small focus of otosclerosis.

Pellet Size Distribution Using Circular Hough Transform in Simulink

This paper presents fast and effective technique for determining the size of pellets from real-time video stream. The circular Hough transforms had been used by several researches in iris detection for face recognition, automatic ball recognition and detecting fingertips position. Here we used circular Hough transform to determine size of pellets in steel plant. This system consists of five steps i.e., take input image, convert into grey scale image, normalize grey scale image, detect edges & perform circular hough transform and do the size analysis. Initially the real-time RGB image is being read from the webcam, as the image is obtained it is converted to grey scale image following that the light normalization of the image is done, after all the circular hough technique is used to detect the pellets and to determine the size of pellets. Since detection and recognition in noisy and cluttered images is challenging problem in computer vision. Some of the pellets are overlapping each other and some itself noise that make the recognition process challenging. So to get rid from all such difficulties the technique used is circular hough transform. This technique helps in determining the centres of the pellets and to measure the radius of pellets and to mark the pellets which are selected. The system is so fast that any change in the input image causes change in the output.

Genetic Algorithm for the Extraction of Nonanalytic Objects from Multiple Dimensional Parameter Space

A new approach of the Hough transform is proposed, which makes use of the genetic searching algorithm. By using this proposed algorithm, we can resolve the main obstacle of the Hough transform, which demands an enormous amount of storage for the Hough space. The idea of this genetic Hough technique is applicable to the recognition of both analytic and nonanalytic patterns. Based on the analysis of peak formation in the 4D generalized Hough transform's parameter space, a fitness function is derived, which represents the statistical weight of the existence of desired objects. By using the genetic approach to extract peaks in the parameter space, the physical storage for the 4D Hough parameter domain is not required during the detection while the accuracy of the detected parameters can be significantly improved.

An Optimizing Line Finder Using a Hough Transform Algorithm

In this paper we present an optimization algorithm for locating peaks in the accumulator of the Hough algorithm with robust voting kernel. We present a detailed discussion of the accuracy that can be achieved by locating these peaks in the accumulator, and show that the error bounds on the estimates of line parameters are always within those based upon least squares. This arises from the robust nature of the voting kernel. We describe the optimization algorithm in some detail since the shape of the peaks in the standard parameter space for straight lines are sinusoidal ridges. Standard approaches therefore fail, but the method described is shown to be robust from the experimental results presented. Some discussion of post-processing is also made, in which the shortcomings of standard Hough techniques, splitting long lines across parameter bins, can be remedied. We also discuss the use of a confidence measure in the line parameters based upon the value of the accumulator, and show that this is related to the mean squared distance from the line of the edge pixels associated with it. Finally, we present results produced by this optimizing Hough technique on a disparate set of images, with various application areas in mind, to demonstrate the versatility of the method and the accuracy that can be achieved at little computational overhead.

A New Generalized Hough Transform for the Detection of Irregular Objects

In this paper, we introduce a new generalized Hough transform for the recognition of nonanalytic objects in a 2-D image. The main idea of our approach is to use pairs of boundary points with the same gradient angle to derive some rotation-invariant parameters to effect the fast Hough transform. Each voting to the Hough domain is contributed by a pair of edge pixels with the same gradient angle. The primary obstacle to using the Hough techniques, a large memory requirement, is overcome by our new voting approach. The conventional 4-D Hough domain is significantly reduced to a 2-D domain. This approach provides an easy method for determining the parameters of the object in question and an extremely effective solution for eliminating false votes in the transform.

Hough techniques for fast optimization of linear constant velocity motion in moving influence fields

Consider a projectile that must be launched at a given time from a given point into linear constant velocity motion, facing possible encounters with other objects moving at known constant speeds. The cost (or utility) of interaction between the projectile and each object is quantified by an essentially arbitrary, possibly object-specific, function of proximity. It is shown that this constrained problem framework allows fast approximation of the globally optimal projectile velocity using Hough-Transform techniques.

Acquisition of the curves of the human crystalline lens from slit lamp images: an application of the Hough transform

To accurately model lens-based functions such as accommodation and image formation on the retina, it is essential to know anterior chamber depth, anterior segment length, lens thickness, and, most importantly, lens curvature both on the surfaces and internally. With the exception of lens curvatures, all these data can be obtained with a high degree of precision by one or more techniques (i.e., A-scan ultrasonography and pachymetry). Lens curvatures can be collected by Scheimpflug slit lamp photography, but the curvature data must be extracted from these images, a problem complicated by both linear and nonlinear image distortion. Previous approaches have involved significant magnification of the image combined with major subjective input and judgment. We present here a computer-based application of the Hough technique for measurement of curvature of lens surfaces observed in Scheimpflug slit lamp photography and related evaluation of (and solutions for) the associated image distortion. Minimal user input is required for successful application of this method, but the time required to obtain a fit for each surface is >1 min. Results obtained by this technique on test images compare favorably with those obtained by independent methods.

New parallel Hough transform for circles

The Hough transform is a well known medium-level image recognition technique for the detection of curves. The conventional Hough technique [1, 2] requires a three-dimensional accumulator array (AA) for the detection of circles. Because shape parameterisation and data structure of the accumulator array significantly affect the memory space and computation loading requirements of any hardware or software realisation, they should be carefully selected for any fast and efficient algorithm. A new scheme which uses only a pair of two-dimensional accumulator arrays to reduce the storage and computation time by an order of magnitude or more is proposed. This new scheme is capable of discriminating multiple (including concentric) circles in a complex real life image with a recognition rate of 95–100%. Various parallel realisations of this Hough scheme for circles on a general purpose MIMD (a reconfigurable transputer network) machine are discussed and a comparison of their performances with the conventional approach on the basis of execution time and recognition rate is presented.

Tristage Hough transform for multiple ellipse extraction

The importance of rapid and accurate extraction of ellipses in images for model-based computer vision systems has long been recognised. The Hough transform technique has established itself as a robust method for shape extraction, however, its application to ellipses has been restricted by excessive computational and storage requirements. Direct extension of the Hough technique to ellipses is particularly demanding since a five-dimensional accumulator array is required. This paper presents a novel tristage technique for ellipse extraction based on decomposing the problem into sequentially executed stages. Candidate ellipse centres are determined using an improved centre-finding procedure and novel Hough-based procedures extract the remaining parameters in two further stages. Although multistage parameter extraction is not new, many of the problems that exist in previous schemes are resolved. In the new technique, the dimensionality of the accumulator arrays used does not exceed two, while their ranges are predefined. Memory savings are therefore significant. Concentric ellipses of the same orientation are shown to be extracted with little extra computational demand. Two extensions of the tristage technique to multiple ellipse extraction are presented and compared. When applied to real images, the new technique extracted multiple, concentric ellipses reliably and with reduced complexity.

Invariant pattern recognition, symmetry, and Radon transforms

A framework for a large class of invariant-pattern-recognition problems, is suggested. The proposed approach combines the properties of uniqueness, registration, and optimality with the ability to use natural symmetries of input patterns. The approach is based on cross correlation. The cross-correlation function with respect to a group of transformations is defined. The properties of this function and methods of its calculation are discussed. As a special case, it is shown formally that the cross correlation with respect to the motion group is the basis of such techniques as Radon transforms and the Hough technique. The roles of symmetry and parameters are examined. A technique for decomposition of pictures into sums of separable functions associated with groups of transformations is applied to the calculation of the cross-correlation function. Several examples of applications of the proposed approach are given.

Hough technique for 3D object recognition

Advances in imaging techniques make it efficient to extract information such as the length and orientation of a straight edge in 3D space. An algorithm using the Hough technique is proposed for the recognition of objects which can be represented or characterised by a set of vertices and straight lines. Assuming the scale is known a priori, the orientation and translation of an object are determined sequentially in two stages. The method is robust and can handle objects with curved or irregularly shaped surfaces, and surface reconstruction is not required.

Structural shape recognition in a multiresolution environment

A new structural approach for shape recognition in a multiresolution environment is illustrated. It can be divided into several steps: in the first a local classification of the instances of the detected contour is performed, considering a fixed number of paradigm patterns. Following a bottom-up procedure based on the generalized Hough technique, the local features are analyzed to look for greater aggregates of the recognized sub-shapes. The implementations of the process on an array processor SIMD-machine and on a pyramid machine are considered. In the latter case an improvement of the performances is obtained in several practical cases by adopting planning strategies on hierarchies and multiresolution facilities. Some experimental results on artificial images are finally discussed.

Image processing of seismograms: (a) Hough transformation for the detection of seismic patterns; (b) thinning processing in the seismogram

Hough transformation is used to detect the seismic patterns on the seismogram. The system of Hough technique includes envelope generation, thresholding, Hough transformation, and parameter determination. Visual inspection method, local peak detection method, and clustering algorithm are used to determine the parameters. Thinning algorithms are used for the skeletonization of the seismic reflectors in the seismogram. The thinning algorithms used are fast parallel thinning algorithm and 2D thinning algorithm preserving 8- and 4-point neighbor connectivities.

Viewer Independent Shape Recognition

An important problem in vision is to detect the presence of a known rigid 3-D object. The general 3-D object recognition task can be thought of as building a description of the object that must have at least two parts: 1) the internal description of the object itself (with respect to an object-centered frame); and 2) the transformation of the object-centered frame to the viewer-centered (image) frame. The reason for this decomposition is parsimony: different views of the object should have minimal impact on its description. This is achieved by factoring the object's description into two sets of parameters, one which is view-independent (the object-centered component) and one which is view-varying (the viewing transformation). Often a description of the object is known beforehand and the task reduces to finding the objectframe to viewer-frame transformation. This paper describes a method for handling this case: a known object is detected by finding changes in orientation, translation, and scale of the object from its canonical description. The method is a Hough technique and has the characteristic insensitivity to occlusion and noise.

On the Hough Technique for Curve Detection

The usual Hough procedure for detecting curves embedded in digitized images involves a transformation from image space I to parameter space P. In this note we suggest that insight into this transformation may be enhanced by mapping P back into I.