Circle Hough Transform Research Articles

Analysis of advanced techniques of image processing based on automatic detection system and road signs recognition

This paper describes implementations of the Hough Circle Transform and the Radon Line Transform. The presented transforms were used to build a system for identify- ing road signs from selected images and recordings. This can be very important and espe- cially useful for monitoring and prevention in driver assistance systems. The design of our system assumed that the objects should be found automatically from images or video sequence. Furthermore, the detection was based on the shapes of common traffic signs which correspond to used transforms. Additionally, the Huffman method was applied to encode the data before being compared. Consequently, the traffic signs were matched directly with the Euclidean distance.

An Automatic Assembly Technology for Perforated Discs Based on Contour Optimized Hough Circle Transform

Aiming at the low efficiency of handwork and high labor intensity, an automatic solution for perforated discs assembly is developed based on computer vision. This technology consists of the processes of material feeding, parts handling, circle detecting and assembling. The mechatronic structure includes the pneumatic elements and electric actuators that controlled by PLC and stepper motor drivers. This method has solved the problem of the large cost of human force since this product has a big industrial production. Meanwhile, a contour optimized Hough circle transform (CHCT) is proposed. It can overcome the standard Hough circle transform (HCT) s disadvantages, such as redundant calculation and probability of failures. It enhances the reliability in order to satisfy the demand of industrial automatic production. The image processing takes only about 60ms and reaches 100% success rate with a small detection error. This method also has the generality for the similar assembly system based on machine vision.

스마트폰 영상에서의 개선된 실시간 눈동자 검출 방법

In this paper, we propose a novel method for enhancing the detection speed and rate by reducing the computation in Hough Circle Transform on real-time iris detection of smartphone camera image. First of all, we find a face and eyes from input image to detect iris and normalize the iris region into fixed size to prevent variation of size for iris region according to distance from camera lens. Moreover, we carry out histogram equalization to get regular image in bright and dark illumination from smartphone and calculate minimal iris range that contains iris with the distance between corner of the left eye and corner of the right eye on the image. Subsequently, we can minimize the computation of iris detection by applying Hough Circle Transform on the range including the iris only. The experiment is carried out in two case with bright and dark illumination. Our proposed method represents that detection speed is 40% faster and detection rate is 14% better than existing methods.

Peak Trekking of Hierarchy Mountain for the Detection of Cerebral Aneurysm using Modified Hough Circle Transform

The Circle of Willis is the junction of two carotid arteries and two vertebral arteries that supply the brain with nutrition. Junctions where these arteries come together may develop weak spots that can balloon out and fill with blood, creating aneurysms. These sac-like areas may leak or rupture, spilling blood into surrounding tissues which may cause artery spasm leading to potential stroke or even death. Clipping and coiling are two treatment options preferred by neurosurgeon which require proper detection of aneurysm. Medical practitioners are therefore emphasizing on the prior detection of cerebral aneurysm (CA) before rupture occurs leading to subarachnoid haemorrhage (SAH). This paper presents an efficient detection technique rooted in Modified Hough Circle Transform (MHCT) on the image extracted from Digital Subtraction Angiogram (DSA). Experimental results have firmly substantiated that the proposed method is highly efficient in properly detecting the location, size and type of aneurysm.

Combining Multiple Cues for Pedestrian Detection in Crowded Situations

This paper proposes a vision-based pedestrian detection in crowded situations based on a single camera. The main idea behind our work is to fuse multiple cues so that the major challenges, such as occlusion and complex background facing in the topic of crowd detection can be successfully overcome. Based on the assumption that human heads are visible, circle Hough transform (CHT) is applied to detect all circular regions and each of which is considered as the head candidate of a pedestrian. After that, the false candidates resulting from complex background are firstly removed by using template matching algorithm. Two proposed cues called head foreground contrast (HFC) and block color relation (BCR) are incorporated for further verification. The rectangular region of every detected human is determined by the geometric relationships as well as foreground mask extracted through background subtraction process. Three videos are used to validate the proposed approach and the experimental results show that the proposed method effectively lowers the false positives at the expense of little detection rate.

Vision Based Starting Position Recognition and Positioning Controlfor Thin Steel Sheet Welding Robot

For the thin steel plate butt welding of containers, a visual control method of welding robot is proposed to recognize starting position and drive the weld torch to accurately track the starting position. Firstly, an iterative circle Hough transformation is presented, which can automatically obtain the region of interest including the torch characteristic point. In the second iteration, the torch characteristic point is recognized and extracted, robustly and quickly. Then the seam is recognized and its characteristic point is extracted by using line Hough transformation. Furthermore, an image space based visual control scheme of starting position is designed, and the accurate starting position control is achieved. The experimental results show that the proposed scheme can extract the torch's and seam's characteristic point coordinates, accurately and robustly. Moreover, the torch can be driven to the starting position quickly and accurately by using the visual control. Finally, test results in the container factory also verify the effectiveness of these methods, and the satisfactory results are obtained.

Screening analysis of seston from a domestic wastewater treatment plant using digital images

This paper concerns the use of digital images of seston to monitor different ponds of a domestic wastewater treatment plant. For this purpose, a commercial camera was employed to photograph the seston samples and an automatic procedure based on the Hough Circle Transform was used to segment the resulting picture. Multivariate techniques were then employed to analyze the seston images in terms of their RGB colour components. The results indicate that such colour components undergo a significant change after each stage of the wastewater treatment process, according to a Hotelling T2 test (p-value smaller than 10−4). This finding was corroborated by the use of quadratic discriminant analysis (QDA), which resulted in the correct classification of all samples according to the treatment stage. In addition, it was found that differences in the illumination of the samples can be standardized by converting the RGB data to the YCbCr format and correcting the luminance (Y) component. This correction may unveil changes in the seston features that were previously masked by the illumination variability.

Feature Detection in Radio Astronomy using the Circle Hough Transform

AbstractWhile automatic detection of point sources in astronomical images has experienced a great degree of success, less effort has been directed towards the detection of extended and low-surface-brightness features. At present, existing telescopes still rely on human expertise to reduce the raw data to usable images and then to analyse the images for non-pointlike objects. However, the next generation of radio telescopes will generate unprecedented volumes of data making manual data reduction and object extraction infeasible. Without developing new methods of automatic detection for extended and diffuse objects such as supernova remnants, bent-tailed galaxies, radio relics and halos, a wealth of scientifically important results will not be uncovered. In this paper we explore the response of the Circle Hough Transform to a representative sample of different extended circular or arc-like astronomical objects. We also examine the response of the Circle Hough Transform to input images containing noise alone and inputs including point sources.

On the computation of the Circle Hough Transform by a GPU rasterizer

This paper presents an alternative for a fast computation of the Hough transform by taking advantage of commodity graphics processors that provide a unique combination of low cost and high performance platforms for this sort of algorithms. Optimizations focus on the features of a GPU rasterizer to evaluate, in hardware, the entire spectrum of votes for circle candidates from a small number of key points or seeds computed by the GPU vertex processor in a typical CPU manner. Number of votes and fidelity of their values are analyzed within the GPU using mathematical models as a function of the radius size for the circles to be detected and the resolution for the texture storing the results. Empirical results validate the output obtained for a much faster execution of the Circle Hough Transform (CHT): On a 1024×1024 sample image containing 20 circles of r=50 pixels, the GPU accelerates the code an order of magnitude and its rasterizer contributes with an additional 4× factor for a total speed-up greater than 40× versus a baseline CPU implementation.

Size invariant circle detection

The Circle Hough Transform (CHT) has become a common method for circle detection in numerous image processing applications. Various modifications to the basic CHT operation have been suggested which include: the inclusion of edge orientation, simultaneous consideration of a range of circle radii, use of a complex accumulator array with the phase proportional to the log of radius, and the implementation of the CHT as filter operations. However, there has also been much work recently on the definition and use of invariance filters for object detection including circles. The contribution of the work presented here is to show that a specific combination of modifications to the CHT is formally equivalent to applying a scale invariant kernel operator. This work brings together these two themes in image processing which have herewith been quite separate. Performance results for applying various forms of CHT filters incorporating some or all of the available modifications, along with results from the invariance kernel, are included. These are in terms of an analysis of the peak width in the output detection array (with and without the presence of noise), and also an analysis of the peak position in terms of increasing noise levels. The results support the equivalence between the specific form of the CHT developed in this work and the invariance kernel.

Lower order circle and ellipse Hough transform

In this work we present two new algorithms for the detection of circles and ellipses which use the FHT algorithm as a basis: Fast Circle Hough Transform (FCHT) and Fast Ellipse Hough Transform (FEHT). The first stage of these two algorithms, devoted to obtaining the centers of the figures, is computationally the most costly. With the objective of improving the execution times of this stage it has been implemented using a new focusing algorithm instead of the typical polling process in a parameter space. This new algorithm uses a new strategy that manages to reduce the execution times, especially in the case where multiple figures appear in the image, or when they are of very different sizes. We also perform a labeling of the image points that permits discriminating which of these belong to each figure, saving computations in subsequent stages.