Circular Hough Transform Research Articles

Remote Sensing of Mars: Detection of Impact Craters on the Mars Global Surveyor DTM by Integrating Edge- and Region-Based Algorithms

This study integrates two different computer vision approaches, namely the circular Hough transform (CHT) and the determinant of Hessian (DoH), to detect automatically the largest number possible of craters of any size on the digital terrain model (DTM) generated by the Mars Global Surveyor mission. Specifically, application of the standard version of CHT to the DTM captured a great number of craters with diameter smaller than ~ 50 km only, failing to capture larger craters. On the other hand, DoH was successful in detecting craters that were undetected by CHT, but its performance was deterred by the irregularity of the topographic surface encompassed: strongly undulated and inclined (trended) topographies hindered crater detection. When run on a de-trended DTM (and keeping the topology unaltered) DoH scored higher. Current results, although not optimal, encourage combined use of CHT and DoH for routine crater detection undertakings.

Contour Extraction based on Circular Hough Transform for Forest Canopy Digital Hemispherical Photography

Contour Extraction based on Circular Hough Transform for Forest Canopy Digital Hemispherical Photography

Circular Traffic Signs Detection in Natural Environments

This paper describes an efficient method for the detection of circular traffic signs on color images in different natural environments. First, a RGB image was converted into HSV color space which was segmented by the hue and saturation thresholds, so that we can extract the region of interests (ROIs). Due to which, A Circular Hough Transform (CHT) based algorithm is proposed to find circles in the image, after that the traffic signs are detected from the images under natural scenes. Experiments were conducted for the detection of traffic signs which involved two traffic sign image databases. The experimental results indicate that the proposed algorithm possess of high detection accuracy, and good robust performance.

Micro-Droplet Detection Method for Measuring the Concentration of Alkaline Phosphatase-Labeled Nanoparticles in Fluorescence Microscopy.

This paper developed and evaluated a quantitative image analysis method to measure the concentration of the nanoparticles on which alkaline phosphatase (AP) was immobilized. These AP-labeled nanoparticles are widely used as signal markers for tagging biomolecules at nanometer and sub-nanometer scales. The AP-labeled nanoparticle concentration measurement can then be directly used to quantitatively analyze the biomolecular concentration. Micro-droplets are mono-dispersed micro-reactors that can be used to encapsulate and detect AP-labeled nanoparticles. Micro-droplets include both empty micro-droplets and fluorescent micro-droplets, while fluorescent micro-droplets are generated from the fluorescence reaction between the APs adhering to a single nanoparticle and corresponding fluorogenic substrates within droplets. By detecting micro-droplets and calculating the proportion of fluorescent micro-droplets to the overall micro-droplets, we can calculate the AP-labeled nanoparticle concentration. The proposed micro-droplet detection method includes the following steps: (1) Gaussian filtering to remove the noise of overall fluorescent targets, (2) a contrast-limited, adaptive histogram equalization processing to enhance the contrast of weakly luminescent micro-droplets, (3) an red maximizing inter-class variance thresholding method (OTSU) to segment the enhanced image for getting the binary map of the overall micro-droplets, (4) a circular Hough transform (CHT) method to detect overall micro-droplets and (5) an intensity-mean-based thresholding segmentation method to extract the fluorescent micro-droplets. The experimental results of fluorescent micro-droplet images show that the average accuracy of our micro-droplet detection method is 0.9586; the average true positive rate is 0.9502; and the average false positive rate is 0.0073. The detection method can be successfully applied to measure AP-labeled nanoparticle concentration in fluorescence microscopy.

Repeatability and reproducibility of TEM soot primary particle size measurements and comparison of automated methods

Transmission electron microscopy (TEM) is used widely to measure the size and structure of aerosols, however the repeatability and reproducibility of these measurements has never been investigated. In this study, the primary particle size distributions of three soot samples from a propylene/air co-flow diffusion flame are measured from TEM images by four independent operators. The overall uncertainty, accounting for repeatability and variation across samples and operators, is quantified through gage repeatability and reproducibility (Gage R & R) analysis. A randomized measurement procedure coupled with a semi-automated algorithm called center-selected edge scoring, is found to limit operator bias while also taking a fifth as long as manual measurement. Accounting for all sources of uncertainty, the 95% confidence intervals determined from a single measurement of primary particle mean and standard deviation are ± 14% and ± 33%, which is greater than is commonly reported. Since Gage R & R analysis requires more replicate measurements than is often practical, a simplified approach for obtaining confidence intervals is presented which can capture Gage R & R uncertainties while requiring only a few replicate measurements of a given sample. The Gage R & R results are used to evaluate three automated methods, two employing a Circular Hough Transform (CHT) algorithm and one utilizing Euclidian distance mapping (EDM). The CHT methods underestimate the mean primary particle diameter, while the EDM method overestimates it. Both the CHT method with Canny edge detection and the EDM method assess reasonably well the standard deviation of the size distribution.

Development of an Automatic Grading System Based on Energy Circular Hough Transform and Causal Median Filter

Optical mark recognition machines are used for performing automatic grading of the exam papers that have multiple choice answers. They use some mathematical operations to achieve recognizing the answers marked by the ones who take the exam. In this study, an automatic grading system developed by the use of Hough transform and a filtering system is proposed. The system introduced brings a new perspective for grading the multiple choice exam papers. It focuses on adapting the energy based circular Hough transform for identifying the marked answer bubbles. The procedure is also combined with a data filtering method known as casual median filter. The filtering system, which targets for detecting the outliers and removing them, is commonly used by the robotics and mechatronics researchers for cleaning the unwanted data. The whole system is verified by testing more than 2500 exam answer sheets of the Technical English course offered to the second year Mechanical Engineering students of the Bulent Ecevit University located in Zonguldak, Turkey. The system performance is also tested by observing the results obtained in three different case studies designed and conducted for different goals.

Hardware Accelerators for Iris Localization

This paper presents field programmable logic array (FPGA) based hardware accelerators for iris localization, which can be used to accelerate the iris localization task in reliable and affordable embedded iris recognition systems. This work uses edge-map generation and circular Hough transform (CHT) based algorithm to localize irises in the images captured under near infrared (NIR) illumination. The proposed hardware accelerators for iris localization are: 1) Edge-map generation hardware for pupillary boundary detection; 2) Edge-map generation hardware for limbic boundary detection; and 3) CHT hardware for pupillary and limbic boundary detection. These hardware accelerators have processing time of 390.46 μsec, 393.67 μsec and 3.46 msec (average) respectively for an image of 320 × 240 pixels and achieve the iris localization accuracy of 96.52%. The proposed CHT and median filter hardware implementations show better results than the previous work.

Developing Iris Recognition System for Smartphone Security

Smartphones have become an important way to store sensitive information; therefore, users’ privacy needs to be highly protected. This can be done by using the most reliable and accurate biometric identification system available today: iris recognition. This paper develops and tests an iris recognition system for smartphones. The system uses eye images that rely on visible wavelength; these images are acquired by the smartphone built-in camera. The development of the system passes through four main phases: the first phase is the iris segmentation phase, which is done in three steps to detect the iris region from the captured image, which contains the eye and part of the face using Haar Cascade Classifier training, pupil localization, and iris localization using a Circular Hough Transform. In the second phase, the system applies normalization using a Rubber Sheet model, which converts the iris image to a fixed size pattern. In the third phase, unique features are extracted from that pattern using a Deep Sparse Filtering algorithm. Finally, in the matching phase, seven different matching techniques are investigated to decide the most appropriate one the system will use to verify the user. Two types of testing are conducted: Offline and Online tests. The BIPLab database and a collected dataset are used to measure the accuracy of the system phases and to calculate the Equal Error Rate (EER) for the whole system. The average EER is 0.18 for the BIPLab database and 0.26 for the collected dataset.

Natural Inspired Intelligent Visual Computing and Its Application to Viticulture.

This paper presents an investigation of natural inspired intelligent computing and its corresponding application towards visual information processing systems for viticulture. The paper has three contributions: (1) a review of visual information processing applications for viticulture; (2) the development of natural inspired computing algorithms based on artificial immune system (AIS) techniques for grape berry detection; and (3) the application of the developed algorithms towards real-world grape berry images captured in natural conditions from vineyards in Australia. The AIS algorithms in (2) were developed based on a nature-inspired clonal selection algorithm (CSA) which is able to detect the arcs in the berry images with precision, based on a fitness model. The arcs detected are then extended to perform the multiple arcs and ring detectors information processing for the berry detection application. The performance of the developed algorithms were compared with traditional image processing algorithms like the circular Hough transform (CHT) and other well-known circle detection methods. The proposed AIS approach gave a Fscore of 0.71 compared with Fscores of 0.28 and 0.30 for the CHT and a parameter-free circle detection technique (RPCD) respectively.

MULTI-PATCHES IRIS BASED PERSON AUTHENTICATION SYSTEM USING PARTICLE SWARM OPTIMIZATION AND FUZZY C-MEANS CLUSTERING

Abstract. Locating the boundary parameters of pupil and iris and segmenting the noise free iris portion are the most challenging phases of an automated iris recognition system. In this paper, we have presented person authentication frame work which uses particle swarm optimization (PSO) to locate iris region and circular hough transform (CHT) to device the boundary parameters. To undermine the effect of the noise presented in the segmented iris region we have divided the candidate region into N patches and used Fuzzy c-means clustering (FCM) to classify the patches into best iris region and not so best iris region (noisy region) based on the probability density function of each patch. Weighted mean Hammimng distance is adopted to find the dissimilarity score between the two candidate irises. We have used Log-Gabor, Riesz and Taylor’s series expansion (TSE) filters and combinations of these three for iris feature extraction. To justify the feasibility of the proposed method, we experimented on the three publicly available data sets IITD, MMU v-2 and CASIA v-4 distance.

Iris localization using Hybrid Algorithm containing Circular Hough Transform, Fuzzy Clustering Method and Canny Edge Detector

Iris segmentation comprises of a sequence of operations including initial Iris and pupil detection as a most prominent region of image with pronounced round shape, outlining outer iris border and final refinement of visible iris part by rejecting regions occluded by reflection spots, eyelids and eyelashes. The results are mask of iris region i.e. set of pixels, which are visible points of iris together with its inner and outer borders. This paper presents an efficient hybrid algorithm to segment iris in unconstrained environment where human recognition is developing for images which can be captured without asking humans i.e. CCTV surveillance etc. by removing noise such as eyelashes and eyelids. It is a challenging task to get proper iris region from input image to get it recognized from trained dataset of the individuals. The proposed algorithm gives high accuracy rates in classification and matching. Keywords: Circular Hough Transform, Fuzzy Clustering, edge detector, sclera region

Certain investigation on iris image recognition using hybrid approach of Fourier transform and Bernstein polynomials

In the era of biometric research the recent developments in real time biometric world, iris recognition is considered as one of the most important approach used in person identification based biometric authentication. This approach is considered than the other biometric authentication methods such as behavioral biometrics which specially includes Keystroke, Speaker Recognition whereas the another side of biometric authentication is Physical biometrics which specially includes Fingerprint Recognition, Voice Recognition, Finger Geometry Recognition and Facial Recognition. From these considerations, the hybrid approach of Fourier transform and Bernstein polynomial for iris recognition has been proposed. The novelty of this proposed method results in improving the Accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR) and Equal Error Rate (EER) for the given iris image. In addition to this, Singular Value Decomposition (SVD) is used for iris image pre-processing. Circular Hough transform (CHT) and Canny edge detection (CED) are applied for iris image segmentation which segments the individual region of the input images. After the image is segmented, Fourier transform and Bernstein polynomial have been applied to extract the features from the segmented iris image, which is the most important step for obtaining the texture details, which are independent and uncorrelated even for identical pairs. Support Vector Machine (SVM) is used for image classification. Perhaps, Feature extraction and Classification of iris image are mainly based on the rich texture details present in iris image. Finally, our proposed system is applied on UBIRIS database and our research experiment provides better accuracy and recognition rates compared to the combined iris recognition techniques such as Fourier transform with SVM, Bernstein polynomial with SVM, Fourier transform with KSVM, Bernstein polynomial with KSVM and hybridization of Fourier and Bernstein polynomial with SVM.

Identification and Red Blood Cell Automated Counting from Blood Smear Images using Computer Aided System

Objectives: Red blood cell count plays a vital role in identifying the overall health of the patient. Hospitals use the Haemocytometer to count the blood cells. Conventional method of placing the smear under microscope and counting the cells manually leads to erroneous results and medical laboratory technicians are put under stress. A computer aided system will help to attain precise results in less amount of time. It proposes an image processing technique for counting the number of Red blood cells. This aims to examine and process the blood smear image, in order to support the counting of red blood cells and identify the number of normal and abnormal cells in the image automatically. Methods/Statistical Analysis: K-Medoids algorithm which is robust to external noise is used to extract the WBCs from the image. Granulometric analysis is used to separate the Red blood cells from White blood cells. The Red blood cells obtained are counted using the Labeling algorithm and Circular Hough Transform. The radius range for the circle drawing algorithm is estimated by computing the distance of the pixels from the boundary which automates the entire algorithm. Findings: A comparison is done between the counts obtained using the Labelling algorithm and Circular Hough Transform. Results of the work showed that Circular Hough Transform was more accurate in counting the Red blood cells than the Labelling algorithm as it was successful in identifying even the overlapping cells. The work also intends to compare the results of cell count done using the proposed methodology and manual approach. The results showed that the proposed cell counting approach was much faster and accurate than the manual approach. The work is designed to address all the drawbacks of the previous research work. Novelty/Improvement: It can be extended to extract various texture and shape features of abnormal cells identified so that diseases like Anemia of inflammation and chronic disease can be detected at the earliest.

Foreign Circular Element Detection in Chest X-Rays for Effective Automated Pulmonary Abnormality Screening

In automated chest X-ray screening (to detect pulmonary abnormality: Tuberculosis (TB), for instance), the presence of foreign element such as buttons and medical devices hinders its performance. In this paper, using digital chest radiographs, the authors present a new technique to detect circular foreign element, within the lung regions. They first compute edge map by using several different edge detection algorithms, which is followed by morphological operations for potential candidate selection. These candidates are then confirmed by using circular Hough transform (CHT). In their test, the authors have achieved precision, recall, and F1 score of 96%, 90%, and 92%, respectively with lung segmentation. Compared to state-of-the-art work, their technique excels performance in terms of both detection accuracy and computational time.

Iris localization by means of adaptive thresholding and Circular Hough Transform

In this paper, a new iris localization method for mobile devices is presented. Our system uses both intensity and saturation threshold on the captured eye images to determine iris boundary and sclera area, respectively. Estimated iris boundary pixels which have been placed outside the sclera will be removed. The remaining pixels are mainly the boundary of iris inside the sclera. Then, circular Hough transform is applied to such iris boundary pixels in order to localize the iris. Experiments were done on 60 iris images taken by a HTC mobile device from 10 different persons with both left and right eyes images available per person. Also, we evaluate the proposed algorithm on MICHE datasets include iphone5, Samsung Galaxy S4 and Samsung Galaxy Tab2. Experimental evaluation shows that the proposed system can successfully localize iris on tested images.

Development of an Android Application for Object Detection Based on Color, Shape or Local Features

Object detection and recognition is an important task in many computer vision applications. In this paper an Android application was developed using Eclipse IDE and OpenCV3 Library. This application is able to detect objects in an image that is loaded from the mobile gallery, based on its color, shape, or local features. The image is processed in the HSV color domain for better color detection. Circular shapes are detected using Circular Hough Transform and other shapes are detected using Douglas-Peucker algorithm. BRISK (binary robust invariant scalable keypoints) local features were applied in the developed Android application for matching an object image in another scene image. The steps of the proposed detection algorithms are described, and the interfaces of the application are illustrated. The application is ported and tested on Galaxy S3, S6, and Note1 Smartphones. Based on the experimental results, the application is capable of detecting eleven different colors, detecting two dimensional geometrical shapes including circles, rectangles, triangles, and squares, and correctly match local features of object and scene images for different conditions. The application could be used as a standalone application, or as a part of another application such as Robot systems, traffic systems, e-learning applications, information retrieval and many others.

Detection of helmets on motorcyclists

The use of motorcycle accidents has rapidly increased. Although the helmet is the main safety equipment of motorcyclists, many drivers do not use it. This paper proposed a method for motorcycle detection and classification and a system for the detection of motorcyclists without helmets. For vehicle classification, we have employed the wavelet transform (WT) as the descriptor and the random forest as the classifier. For helmet detection, the circular Hough transform (CHT) and the histogram of oriented gradients (HOG) descriptor were applied to extract the image attributes, and the multilayer perceptron (MLP) classifier was used to classify the objects. The results for vehicle classification achieved an accuracy rate of 97.78 %. The algorithm step in the helmet detection accomplished an accuracy rate of 91.37 %. The results were obtained with the author’s database.

Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards

Ground vehicles equipped with monocular vision systems are a valuable source of high‐resolution image data for precision agriculture applications in orchards. This paper presents an image processing framework for fruit detection and counting using orchard image data. A general‐purpose image segmentation approach is used, including two feature learning algorithms; multiscale multilayered perceptrons (MLP) and convolutional neural networks (CNN). These networks were extended by including contextual information about how the image data was captured (metadata), which correlates with some of the appearance variations and/or class distributions observed in the data. The pixel‐wise fruit segmentation output is processed using the watershed segmentation (WS) and circular Hough transform (CHT) algorithms to detect and count individual fruits. Experiments were conducted in a commercial apple orchard near Melbourne, Australia. The results show an improvement in fruit segmentation performance with the inclusion of metadata on the previously benchmarked MLP network. We extend this work with CNNs, bringing agrovision closer to the state‐of‐the‐art in computer vision, where although metadata had negligible influence, the best pixel‐wise F1‐score of 0.791 was achieved. The WS algorithm produced the best apple detection and counting results, with a detection F1‐score of 0.861. As a final step, image fruit counts were accumulated over multiple rows at the orchard and compared against the post‐harvest fruit counts that were obtained from a grading and counting machine. The count estimates using CNN and WS resulted in the best performance for this data set, with a squared correlation coefficient of .

3D Visual Tracking of an Articulated Robot in Precision Automated Tasks.

The most compelling requirements for visual tracking systems are a high detection accuracy and an adequate processing speed. However, the combination between the two requirements in real world applications is very challenging due to the fact that more accurate tracking tasks often require longer processing times, while quicker responses for the tracking system are more prone to errors, therefore a trade-off between accuracy and speed, and vice versa is required. This paper aims to achieve the two requirements together by implementing an accurate and time efficient tracking system. In this paper, an eye-to-hand visual system that has the ability to automatically track a moving target is introduced. An enhanced Circular Hough Transform (CHT) is employed for estimating the trajectory of a spherical target in three dimensions, the colour feature of the target was carefully selected by using a new colour selection process, the process relies on the use of a colour segmentation method (Delta E) with the CHT algorithm for finding the proper colour of the tracked target, the target was attached to the six degree of freedom (DOF) robot end-effector that performs a pick-and-place task. A cooperation of two Eye-to Hand cameras with their image Averaging filters are used for obtaining clear and steady images. This paper also examines a new technique for generating and controlling the observation search window in order to increase the computational speed of the tracking system, the techniques is named Controllable Region of interest based on Circular Hough Transform (CRCHT). Moreover, a new mathematical formula is introduced for updating the depth information of the vision system during the object tracking process. For more reliable and accurate tracking, a simplex optimization technique was employed for the calculation of the parameters for camera to robotic transformation matrix. The results obtained show the applicability of the proposed approach to track the moving robot with an overall tracking error of 0.25 mm. Also, the effectiveness of CRCHT technique in saving up to 60% of the overall time required for image processing.

Human gait pattern changes detection system: A multimodal vision-based and novelty detection learning approach

This paper proposes a novel gait rehabilitation analysis system, based on an innovative multimodal vision-based sensor setup, focused on detecting gait pattern changes over time. The proposed setup is based on inexpensive technologies, without compromising performance, and was designed to be deployed on walkers, which are a typical assistive aid used in gait rehabilitation. In the medical field the evaluation of a patient's rehabilitation progress is typically performed by a medical professional through subjective techniques based on the professional's visual perception and experience. In this context, we are proposing an automatic system to detect the progress of patients undergoing rehabilitation therapy. Our approach is able to perform novelty detection for gait pattern classification based on One-Class Support Vector Machines (OC-SVM). Using point-cloud and RGB-D data, we detect the lower limbs (waist, legs and feet) by using Weighted Kernel-Density Estimation and Weighted Least-Squares to segment the legs into several parts (thighs and shins), and by fitting 3D ellipsoids to model them. Feet are detected using k-means clustering and a Circular Hough Transform. A temporal analysis of the feet's depth is also performed to detect heel strike events. Spatiotemporal and kinematic features are extracted from the lower limbs’ model and fed to a classifier based on the fusion of several OC-SVMs. Experiments with volunteers using the robotic walker platform ISR-AIWALKER, where the proposed system was deployed, revealed a lower limbs tracking accuracy of 3° and that our novelty detection approach successfully identified novel gait patterns, evidencing an overall 97.89% sensitivity.