Circle Hough Transform Research Articles

Circle Hough Transform (CHT) method applied to coin identifier and recognition based on computer vision and android developer

The coin recognition system is a very important role in currency processing systems which is aims to minimize human miscalculation and time to calculate coin values. The coin identifier and recognition to calculate the value of Indonesian coins based on android developer presented in this paper. The two coin parameters are used for coin recognition which are radius and colour of the coins. The Circle Hough Transform (CHT) method with gray scaling and erosion process as pre-processing image used to calculate radius parameters, based on android developer and computer vision. After the radius value is obtained then the average RGB value of the coins is detected to determine the colour parameters. By calculating the diameter and determining the colour, the coins can be recognized and the total value of the coins in the input image can be known. The experimental results show that the system has been obtained indicate that the system successfully recognized coins based on determined algorithm. That is mean the application using computer vision can recognition coin and count the value of coins.

Iris feature extraction using Gray Level Co-occurrence Matrix and Gabor Kernel filter its impact on iris Huffman compression image

Algorithms developed to identify people with iris image data have been tested in many field and laboratory experiment. This paper analysis some a parameters of iris image used to recognize human. Iris recognition system, which is applied based on segmentation, normalization, encoding, and matching is also describe in this paper. Circle Hough Transform segmentation module used to find the inner and outer boundaries of the iris. The experiment was carried out using CASIA v1 iris database with grayscale images. Shape, intensity, and location information for localizing the pupil or iris and normalizing the iris area a used iris segmentation by unwrapping circular area into a rectangular area. Normalized area will be used to extract the features using Gray Level Co-occurrence Matrix (GLCM) and Gabor filter, the feature compared the recognition accuracy using Support Vector Machines (SVM) and Naive Bayes classifiers. GLCM feature test results achieved 95.24% SVM classification accuracy, whereas using achieved 85.71% Naive Bayes. Gabor feature test results achieved 95.24% SVM classification accuracy, whereas using achieved 95.23% Naive Bayes. The classification process based on GLCM and Gabor features show that the SVM method have to highest recognition accuracy compare to Naive Bayes classifier.

Finding a suitable threshold value for an iris-based authentication system

<span>Authentication is the first line of defense of any information technology systems. One of the popular methods used today is biometric, and iris authentication is gaining popularity. However, the threshold value is deemed to be secure and appropriate has not been thoroughly studied. Threshold is a value that defines the acceptable amount of the correct bits of the image before securely passing the authentication process. Therefore, the main aim of this research was to find a secure and suitable threshold value used in iris authentication system, where iris localization was done by using Circle Hough Transform technique. Iris image databases v.4 from the Chinese Academy of Sciences Institute of Automatic (CASIA) were used in this research. The way to find the appropriate threshold was to test for the right balance of the GAR, FRR and FAR values when trying to verify the person’s identity. The results of the test revealed that the appropriate threshold had the value of 72.9246 percent of all the available bits of the iris image. Both had a high GAR and very low FAR and FRR values. It can be concluded that the obtained threshold value was suitable and secure.</span>

Preliminary study of ring artifact detection in SPECT imaging using Jaszczak phantom

Inspection of imaging SPECT system performance was controlled by Quality Control (QC) regularly. Tomographic imaging parameters in QC, i.e. homogeneity, contrast, and spatial resolution should be tested. These results make it reliable whether the SPECT system will be used. The modular Jaszczak phantom was used for that purpose, as it offers the investigation to all three parameters with a single measurement. Compared to the contrast and spatial resolution, an objective identification of possible ring artifact in the homogeneity is challenging. Artifact simulation of SPECT images which have been generated by disturbance of 4 × 4 cm2 copper sheet located on surface LEHR collimator have been conducted to study the detectability of ring artifact. Uniformity section of Jaszczak phantom which filled with 99mTc was used for acquisition. The confirmed ring artifact was analyzed by Edge Detection and Circle Hough Transform provided by imageJ, and students t-test statistically. From the evaluation by edge detection and circle Hough transform, the present of ring artifact only detected in artifact SPECT images using 1.00, 0.75, and 0.50 mm copper sheet thickness, while in t-test method all copper sheet thickness (including 0.25 mm) detect ring artifact in images.

Target Recognition and Localization of Mobile Robot with Monocular PTZ Camera

The target recognition and location based on the vision sensor is a kind of more intuitive and effective method. The paper designs a human mobile robot and the monocular PTZ camera fixed on the differential driver of the mobile robot for the target recognition and localization. The camera parameters are calibrated using the Zhang Zhengyou calibration method; target recognition is based on the combination of the color and the edge detection. First, the objects with the same color are extracted by the color recognition method (by setting an appropriate threshold value for the HSV color space of the acquired image). Then the target is further extracted by the edge detection (by Hough circle transformation); target location is based on the Similar Triangle Principle because the PTZ camera has pitch, tilt, and zoom characteristics and the monocular vision sensor of the camera with different pitching angles is used to measure the distance between the robot and the target. According to the characteristics of the monocular PTZ camera the mobile robot realizes the target localization and tracking. The simulation and experimental results demonstrate that the mobile robot shows better target recognition and localization in mobile tracking target process and proves the effectiveness of the method.

Semi-automated counting model for arbuscular mycorrhizal fungi spores using the Circle Hough Transform and an artificial neural network.

Arbuscular Mycorrhizae (AM) are mutualistic associations between Arbuscular Mycorrhizal Fungi (AMF) and the roots of many plant species. AMF spores give rise to filaments that develop in the root system of plants and contribute to the absorption of water and some nutrients. This article introduces a semi-automated counting model of AMF spores in slide images based on Artificial Neural Network (ANN). The semi-automated counting of AMF spores facilitates and accelerates the tasks of researchers, who still do the AMF spore counting manually. We built a representative database of spore images, processing images through the Circle Hough Transform (CHT) method and training an ANN to classify patterns automatically. The classification analysis and the performances of the proposed method against the manual method are presented in this paper. The accuracy for the identification of spores by CHT in conjunction to ANN classification in the images was 90%. The results indicate that this method can accurately detect the presence of AMF spores in images as well as count them with a high level of confidence.

Memory‐efficient architecture of circle Hough transform and its FPGA implementation for iris localisation

This study presents a circle Hough transform (CHT) architecture that provides memory reduction between 74 and 93% without and with little degradation in the accuracy, respectively. For an image of P × Q pixels, the standard (direct) CHT requires a two-dimensional (2D) accumulator array of P × Q cells, but the proposed CHT uses a 2D accumulator array of (P/m) × (Q/n) cells for coarse circle detection and two 1D accumulator arrays of P × 1 and Q × 1 cells for fine detection, therein reducing the memory by a factor of m × n (approximately). The proposed CHT architecture was applied to iris localisation application and carried out its comprehensive evaluation. The average accuracy of the proposed CHT for iris localisation (inner plus outer iris-circle detection) is 98% with memory reduction of 87% compared with the direct CHT. The proposed CHT architecture was implemented on field programmable logic array targeting Xilinx Zynq device. The proposed CHT hardware takes processing time of 6.25 ms (average) for iris localisation in an image of 320 × 240 px2. The proposed work is compared with the previous work, which shows improved results. Finally, the effect of additive Gaussian noise on the CHT performance is investigated.

Automated Measurement of <em>Cryptococcal</em> Species Polysaccharide Capsule and Cell Body

The purpose of this technique is to provide a consistent, accurate, and manageable process for large numbers of polysaccharide capsule measurements. First, a threshold image is generated based on intensity values uniquely calculated for each image. Then, circles are detected based on contrast between the object and background using the well-established Circle Hough Transformation (CHT) algorithm. Finally, the detected cell capsules and bodies are matched according to center coordinates and radius size, and data is exported to the user in a manageable spreadsheet. The advantages of this technique are simple but significant. First, because these calculations are performed by an algorithm rather than a human both accuracy and reliability are increased. There is no decline in accuracy or reliability regardless of how many samples are analyzed. Second, this approach establishes a potential standard operating procedure for the Cryptococcus field instead of the current situation where capsule measurement varies by lab. Third, given that manual capsule measurements are slow and monotonous, automation allows rapid measurements on large numbers of yeast cells that in turn facilitates high throughput data analysis and increasingly powerful statistics. The major limitations of this technique come from how the algorithm functions. First, the algorithm will only generate circles. While Cryptococcus cells and their capsules take on a circular morphology, it would be difficult to apply this technique to non-circular object detection. Second, due to how circles are detected the CHT algorithm can detect enormous pseudo-circles based on the outer edges of several clustered circles. However, any misrepresented cell bodies caught within the pseudo-circle can be easily detected and removed from the resulting data sets. This technique is meant for measuring the circular polysaccharide capsules of Cryptococcus species based on India Ink bright field microscopy; though it could be applied to other contrast based circular object measurements.

Automated Measurement of Cryptococcal Species Polysaccharide Capsule and Cell Body.

The purpose of this technique is to provide a consistent, accurate, and manageable process for large numbers of polysaccharide capsule measurements. First, a threshold image is generated based on intensity values uniquely calculated for each image. Then, circles are detected based on contrast between the object and background using the well-established Circle Hough Transformation (CHT) algorithm. Finally, the detected cell capsules and bodies are matched according to center coordinates and radius size, and data is exported to the user in a manageable spreadsheet. The advantages of this technique are simple but significant. First, because these calculations are performed by an algorithm rather than a human both accuracy and reliability are increased. There is no decline in accuracy or reliability regardless of how many samples are analyzed. Second, this approach establishes a potential standard operating procedure for the Cryptococcus field instead of the current situation where capsule measurement varies by lab. Third, given that manual capsule measurements are slow and monotonous, automation allows rapid measurements on large numbers of yeast cells that in turn facilitates high throughput data analysis and increasingly powerful statistics. The major limitations of this technique come from how the algorithm functions. First, the algorithm will only generate circles. While Cryptococcus cells and their capsules take on a circular morphology, it would be difficult to apply this technique to non-circular object detection. Second, due to how circles are detected the CHT algorithm can detect enormous pseudo-circles based on the outer edges of several clustered circles. However, any misrepresented cell bodies caught within the pseudo-circle can be easily detected and removed from the resulting data sets. This technique is meant for measuring the circular polysaccharide capsules of Cryptococcus species based on India Ink bright field microscopy; though it could be applied to other contrast based circular object measurements.

Mobile based Automated Complete Blood Count (Auto-CBC) Analysis System from Blood Smeared Image

<span>Blood cells diagnosis is becoming essential to ensure a proper treatment can be proposed to a blood related disease patient. In current research trending, automated complete blood count analysis system is required for pathologists or researchers to count the blood cells from the blood smeared images. Hence, a portable mobile-based complete blood count (CBC) analysis framework with the aid of microscope is proposed, and the smartphone camera is mounted to the viewing port of the light microscope by adding a smartphone support. Initially, the blood smeared image is acquired from a light microscope with objective zoom of 100X magnifications view the eyepiece zoom of 10X magnification, then captured by the smartphone camera. Next, the areas constitute to the WBC and RBC are extracted using combination of color space analysis, threshold and Otsu procedure. Then, the number of corresponding cells are counted using topological structural analysis, and the cells in clumped region is estimated using Hough Circle Transform (HCT) procedure. After that, the analysis results are saved in the database, and shown in the user interface of the smartphone application. Experimental results show the developed system can gain 92.93% accuracy for counting the RBC whereas 100% for counting the WBC.</span>

White blood cell (WBC) counting analysis in blood smear images using various color segmentation methods

White blood cells (WBCs) is closely related to human immunity system which is useful to fight viruses and bacteria. Cancer therapy effectiveness and some of the blood-related diseases can be determined from the count of cell region. Traditionally, WBC count is done manually which yields inaccurate results as the blood sample increases. Moreover, even though hematology counter device is fast and accurate, it is very costly. These problems has led to the invention of cost effective Computer Aided System (CAS) which analyzes the blood smeared images obtained from microscope. In CAS, the most important step is segmentation and any failure at this stage will cause inaccuracy in the subsequent stages. Realizing the importance of segmentation, this paper investigated various segmentation methods for the purpose of WBC counting based on color band thresholding procedure. Initially, color space correction based on L∗a∗b∗ color space was applied to standardize the image color intensity. Next, segmentation process was conducted to prune out the WBC region from the background by combining color analysis of RGB, CMYK and HSV with Otsu thresholding. Morphological filter was employed as the segmented image contained noises would affect the system performance. Henceforth, Connected Component Labelling (CCL) was done to distinguish the small particles that still existed in the image. Eventually, Circle Hough Transform (CHT) was applied to identify and count the WBC including the one in the clump region. Overall system performance was accessed and it was found that by using S color component of HSV, color space provided the highest WBC segmentation accuracy which was 96.92%. Meanwhile, for the combination of two color bands; S-C produced 96.56% of WBC counting accuracy. Another interesting finding was that the usage of nucleus based detection was superior compared to cytoplasm-based detection for WBC counting purpose.

Area-based cell colony surviving fraction evaluation: A novel fully automatic approach using general-purpose acquisition hardware

BackgroundThe current methodology for the Surviving Fraction (SF) measurement in clonogenic assay, which is a technique to study the anti-proliferative effect of treatments on cell cultures, involves manual counting of cell colony forming units. This procedure is operator-dependent and error-prone. Moreover, the identification of the exact colony number is often not feasible due to the high growth rate leading to the adjacent colony merging. As a matter of fact, conventional assessment does not deal with the colony size, which is generally correlated with the delivered radiation dose or the administered cytotoxic agent. MethodConsidering that the Area Covered by Colony (ACC) is proportional to the colony number and size as well as to the growth rate, we propose a novel fully automatic approach exploiting Circle Hough Transform, to automatically detect the wells in the plate, and local adaptive thresholding, which calculates the percentage of ACC for the SF quantification. This measurement relies just on this covering percentage and does not consider the colony number, preventing inconsistencies due to intra- and inter-operator variability. ResultsTo evaluate the accuracy of the proposed approach, we compared the SFs obtained by our automatic ACC-based method against the conventional counting procedure. The achieved results (r = 0.9791 and r = 0.9682 on MCF7 and MCF10A cells, respectively) showed values highly correlated with the measurements using the traditional approach based on colony number alone. ConclusionsThe proposed computer-assisted methodology could be integrated in laboratory practice as an expert system for the SF evaluation in clonogenic assays.

A hybrid defect detection method for compact camera lens

Production technology has increased rapidly with the development of industrial technology. Conventional human visual inspection is insufficient for conducting quality control under this increased capacity. Therefore, high-speed and high-accuracy automated optical inspection is becoming increasingly crucial. In this article, we propose an automated inspection method for a compact camera lens using a circle Hough transformation, weighted Sobel filter, and polar transformation. Our analysis of defects in the compact camera lens identified problems including of the circular texture and the non-fixed position of the inspection region. To overcome these problems, we design an inspection algorithm for locating and inspecting a circular region. A machine learning support vector machine method is then applied for obtaining a precise detection result. The experimental results show that the proposed inspection method is suitable for detecting defects in a complicated circular inspection region, and that the proposed system exhibited high performance.

Smart Choice – Content Based Clothing Recommendation System

Shopping has always been a basic necessity but also a time consuming nuisance. With the trending e-platforms, people are shopping on the go! In this ever growing technology, finding something with pattern of your choice is difficult. The plethora of options often gets the customers confused and eventually drops the idea. Our project will help the consumers to search for the clothes referring the pattern in the picture they upload on our platform. It aims at using image processing techniques to provide with much better results for our customers and aid them in shopping. Exploring choices become easy as the clothes having similar patterns will be shown as the result. Rather than searching for hours, our project will bring the searched results to them. The texture and print on the image of cloth will be analyzed for patterns like lines at different angles and circular designs. These identified patterns will help to find similar clothes which the customer may desire. Keywordss: Image Retrieval, Shopping, Content based image retrieval, Gabor function, Circle Hough Transform.

Incremental circle hough transform: An improved method for circle detection

The Circle Hough Transform (CHT) is one of the popular circle detection algorithm in image processing and machine vision application, favored for its tolerance to noise. Nevertheless, it involves huge computation and excessive memory requirements. Because of its drawbacks, various modifications have been suggested to increase its performances. In this paper, we present a new modification of the CHT method developed for an automatic biometric iris recognition system. The novelty of this method resides on the use of the incremental property in order to reduce the resources requirement and the parallel property for decreasing the computation time. The incremental property is obtained using the approximation of the used trigonometric functions, while the parallel property is achieved by calculating, at the same time, several point coordinates of the circle. The software implementation and the validation have been done in C++ and MATLAB on real images. The errors analysis and the performances of the proposed method against the basic CHT method are presented in this paper.

A Simple and Quantitative Method to Evaluate Ice Recrystallization Kinetics Using the Circle Hough Transform Algorithm

The formation of large ice crystals via recrystallization processes in foods and water-based materials often decreases the quality and structural integrity of the materials. Hence, there is a widespread academic and commercial interest in natural and synthetic ice crystal growth modifiers that inhibit the recrystallization of ice. Well-known natural ice crystal growth modifiers are antifreeze proteins (AFPs), which inhibit ice recrystallization by adsorbing on the surface of ice crystals. Reliable quantification of the ice recrystallization inhibition (IRI) efficiency is a long-sought goal. In this work, we describe a simple method to quantitatively evaluate IRI efficiency, based on automated image analysis using the circle Hough transform (CHT) algorithm. It enables robust and high throughput analysis of natural and synthetic ice recrystallization inhibitors. Here we use the method to evaluate the impact of a single-point mutation in the ice-binding site of QAE on its IRI activity. We find that the T18N mutant of QAE has virtually the same effective ice recrystallization inhibitory concentration as the wild-type QAE. This is in contrast to thermal hysteresis activity, evaluated by cryoscopy or sonocrystallization, where the mutation greatly decreases the activity.

Apple detection from apple tree image based on BP neural network and Hough transform

Using machine vision to accurately identify apple number on the tree is becoming the key supporting technology for orchard precision production management. For adapting to the complexity of the field environment in various detection situations, such as illumination changes, color variation, fruit overlap, and branches and leaves shading, a robust algorithm for detecting and counting apples based on their color and shape modes was proposed. Firstly, BP (back propagation) neural network was used to train apple color identification model. Accordingly the irrelevant background was removed by using the trained neural network model and the image only containing the apple color pixels was acquired. Then apple edge detection was carried out after morphological operations on the obtained image. Finally, the image was processed by using circle Hough transform algorithm, and apples were located with the help of calculating the center coordinates of each apple edge circle. The validation experimental results showed that the correlation coefficient of R2 between the proposed approaches based counting and manually counting reached 0.985. It illustrated that the proposed algorithm could be used to detect and count apples from apple trees’ images taken in field environment with a high precision and strong anti-jamming feature. Keywords: apple detecting and counting, BP neural network, Hough transform,color segmentation, edge detection DOI: 10.3965/j.ijabe.20150806.1239 Citation: Xiao C Y, Zheng L H, Li M Z, Chen Y, Mai C Y. Apple detection from apple tree image based on BP neural network and Hough transform. Int J Agric & Biol Eng, 2015; 8(6): 46－53.

An Automatic Fault Recognition Method for Side Frame Key in TFDS

Trouble of moving Freight car Detection System (TFDS) constitutes an important part of the railway safety warning systems. With TFDS, dynamic images of freight cars are captured by high-speed cameras, timely transmitted to the train inspection center by special optical fiber network and finally observed for fault recognition. In this paper, an automatic fault recognition method for side frame key in TFDS is proposed based on open source computer vision library (OpenCV) to overcome the disadvantages of manual fault recognition. At first, image preprocessing and segmentation are applied to eliminate the impact of the surrounding environment and further simplify images. Then the axle and through-hole are calibrated through Hough circle transformation and the side frame key is located indirectly according to the geometric relationship among the axle, through-hole and side frame key. Finally, the difference of mean gray values in the region of interest (ROI) is analyzed to judge whether the side frame key is missing or not. Featured by the high efficiency, reliability and practicability, the proposed method lays the foundation for engineering applications in automatic fault recognition of freight cars.

Automatic static contact angle algorithm for blurry drop images and its application in hydrophobicity measurement for insulating materials

To improve the accuracy of the static contact angle evaluation for the blurry drop images, a large number of water drop images are numerically generated or acquired by the contact angle instrument/digital camera. Based on these images, the applicability of the circle Hough transformation and circle fitting algorithms is systematically investigated and compared. The results reveal that the least-squares circle fitting algorithm is particularly suitable for the clear drop images, such as the images acquired by the contact angle instrument. The circle Hough transformation is particularly suitable for the blurry drop images, such as the images acquired by the digital camera. The influence of the contact angle on the contact angle error by the circle Hough transformation is investigated. The critical contact angle values corresponding to various water drop volumes are given, which can be used to improve the accuracy of the static contact angle measurement for the blurry drop images. A modified Hough transformation based on the critical contact angle is proposed. The modified Hough transformation is verified by the static contact angle measurements of different insulating materials. The study proposes a comparatively effective algorithm for automatic and accurate static contact angle measurement of the blurry drop images.

A high accurate static contact angle algorithm based on Hough transformation

The static contact angle for blurry drop images is more intricate to obtain. To improve the accuracy of the Hough transformation for the static contact angle calculation, the water drop images with different volumes are generated by the Laplace equation, and the influence of the volume on the accuracy of the Hough transformation is analyzed. The results reveal that the circle Hough transformation is particularly well suited to the cases with small drop volume. At the same time, the critical water drop volume value corresponding to a contact angle error of 3° is given, a modified Hough transformation algorithm in conjunction with the critical water drop volume is proposed, and at the same time, the accuracy of the static contact angle calculation for blurry water drop images is significantly improved. The proposed algorithm is a powerful approach to estimate the static contact angle for blurry water drop images.