Circle Hough Transform Research Articles

Deep learning enables accurate analysis of images generated from droplet-based digital polymerase chain reaction (dPCR)

Accurate analysis of digital polymerase chain reaction (dPCR) images is a key to the successful miniaturization of a dPCR system, which would allow point-of-care testing, but has been hampered by several issues, mainly out-of-focus images, and variations in shape, size, and fluorescence intensities of the droplets. Herein, we performed semantic segmentation by using a deep learning model combined with the circle Hough transform (CHT). For the deep learning model, Attention DeepLabV3+ was modified and used to analyze positive and negative droplets in the low-quality dPCR images, while CHT helps to count the droplets. The modified Attention DeepLabV3+ was trained with augmented 4224 dPCR images, which were sufficient for deep learning model training. As a proof of concept, encapsulated genomic DNA (gDNA) of Escherichia coli O157:H7, a foodborne pathogen, was amplified, and the corresponding droplets were analyzed with the deep learning model to demonstrate its rapid, absolute quantification and accurate identification of the target gDNA. Our analysis method achieved similar accuracy to the commercial, bulky dPCR system, which is based on continuous-flow analysis methods. Furthermore, the deep learning-based approach exhibited a great sensitivity for detecting pathogenic bacteria from low-quality images with a low detection limit of 4.0 × 101 copies/μL. Compared with the commercially available instrument, the deep learning method demonstrated a similar molecular analysis performance. The presented results may lay the ground for efficient absolute quantification of target nucleic acids from the dPCR images as well as the establishment of a point-of-care analysis platform.

Research on grading algorithm of diabetic retinopathy based on cross-layer bilinear pooling

Considering the small differences between different types in the diabetic retinopathy (DR) grading task, a retinopathy grading algorithm based on cross-layer bilinear pooling is proposed. Firstly, the input image is cropped according to the Hough circle transform (HCT), and then the image contrast is improved by the preprocessing method; then the squeeze excitation group residual network (SEResNeXt) is used as the backbone of the model, and a cross-layer bilinear pooling module is introduced for classification. Finally, a random puzzle generator is introduced in the training process for progressive training, and the center loss (CL) and focal loss (FL) methods are used to further improve the effect of the final classification. The quadratic weighted Kappa (QWK) is 90.84% in the Indian Diabetic Retinopathy Image Dataset (IDRiD), and the area under the receiver operating characteristic curve (AUC) in the Messidor-2 dataset (Messidor-2) is 88.54%. Experiments show that the algorithm proposed in this paper has a certain application value in the field of diabetic retina grading.

Spatiotemporal Identification of Cell Divisions Using Symmetry Properties in Time-Lapse Phase Contrast Microscopy

A variety of biological and pharmaceutical studies, such as for anti-cancer drugs, require the quantification of cell responses over long periods of time. This is performed with time-lapse video microscopy that gives a long sequence of frames. For this purpose, phase contrast imaging is commonly used since it is minimally invasive. The cell responses of interest in this study are the mitotic cell divisions. Their manual measurements are tedious, subjective, and restrictive. This study introduces an automated method for these measurements. The method starts with preprocessing for restoration and reconstruction of the phase contrast time-lapse sequences. The data are first restored from intensity non-uniformities. Subsequently, the circular symmetry of the contour of the mitotic cells in phase contrast images is used by applying a Circle Hough Transform (CHT) to reconstruct the entire cells. The CHT is also enhanced with the ability to “vote” exclusively towards the center of curvature. The CHT image sequence is then registered for misplacements between successive frames. The sequence is subsequently processed to detect cell centroids in individual frames and use them as starting points to form spatiotemporal trajectories of cells along the positive as well as along the negative time directions, that is, anti-causally. The connectivities of different trajectories enhanced by the symmetry of the trajectories of the daughter cells provide as topological by-products the events of cell divisions together with the corresponding entries into mitoses as well as exits from cytokineses. The experiments use several experimental video sequences from three different cell lines with many cells undergoing mitoses and divisions. The quantitative validations of the results of the processing demonstrate the high performance and efficiency of the method.

DETEKSI RODA KENDARAAN DENGAN CIRCLE HOUGH TRANSFORM (CHT) DAN SUPPORT VECTOR MACHINE (SVM)

In the digital image processing, many methods have been developed, the purpose of developing these methods is how computers can detect and recognize objects in an image with a precisely and process them in a relatively short time. Wheels are components that are always present in every vehicle, whether the vehicle is a bus, car or truck, it must have wheels with the same shape. If a wheel can be detected and recognized then the vehicle recognition and classification can be determined. This research focuses on capturing circle images, detecting wheel circles by applying Circle Hough Transformation (CHT). This transformation is able to recognize the object based on its boundaries and is resistant to noise. After obtaining the image of the circle, the next step is to classify it into Wheels and Non Wheels using the Support Vector Machine (SVM) method. The development of the wheel circle detection model on the side view image of this vehicle can be used as one of the first steps in research on wheel-based automatic vehicle recognition and classification systems.

An autonomous robot for shell and tube heat exchanger inspection

AbstractShell and tube heat exchangers (STHEs) are critical to energy conversion efficiency of power plants. Eddy current examination is a way to evaluate working conditions of these tubes. However, the current testing apparatus requires human to manually insert an eddy current testing (ECT) probe into and extract it out of individual tubes, and meanwhile monitor measurement results for diagnosis. It is a time‐consuming and labor‐intensive procedure even for an experienced technician. To tackle this challenge, in this study, we developed a robot enabled ECT system for autonomous inspection of STHEs. The robotic platform employs Mecanum wheeled chassis for high mobility, machine vision to locate tube bundle and tube inlets, a rotational Cartesian mechanism to operate at planes with all possible inclinations, and a task‐specific mechanism for ECT probe delivery. Machine vision locates tube bundle and tube inlets by an April tag detection algorithm and a Circle Hough Transform algorithm, respectively. Assisted by a guiding cone, the ECT probe is continuously fed into the tubes with a fill factor of 0.819. During this process, the eddy current data are automatically collected and real‐time analyzed by convolutional neural networks, showing accuracy of nearly 100% for identifying defective and nondefective tubes and 85% for four types of defective tubes and nondefective tubes.

Algorithmic Scheme for Concurrent Detection and Classification of Printed Circuit Board Defects

An ideal printed circuit board (PCB) defect inspection system can detect defects and classify PCB defect types. Existing defect inspection technologies can identify defects but fail to classify all PCB defect types. This research thus proposes an algorithmic scheme that can detect and categorize all 14-known PCB defect types. In the proposed algorithmic scheme, fuzzy c-means clustering is used for image segmentation via image subtraction prior to defect detection. Arithmetic and logic operations, the circle hough transform (CHT), morphological reconstruction (MR), and connected component labeling (CCL) are used in defect classification. The algorithmic scheme achieves 100% defect detection and 99.05% defect classification accuracies. The novelty of this research lies in the concurrent use of CHT, MR, and CCL algorithms to accurately detect and classify all 14-known PCB defect types and determine the defect characteristics such as the location, area, and nature of defects. This information is helpful in electronic parts manufacturing for finding the root causes of PCB defects and appropriately adjusting the manufacturing process. Moreover, the algorithmic scheme can be integrated into machine vision to streamline the manufacturing process, improve the PCB quality, and lower the production cost.

Image-based Automatic Counting of Bacillus cereus Colonies using Smartphone

Substantial amounts of Bacillus cereus bacteria present in food indicates that the food is unsafe to eat, so counting B. cereus colonies in food samples is a common test for food cleanliness. Manual counting of B. cereus bacteria colonies requires approximately 2-5 minutes per Petri dish, depending on the number of colonies present. This study presents a new smartphone-based method called Bacillus Cereus Image Counting System (BCICS, “B. kiks”) for automatic counting of B. cereus colonies. BCICS uses image processing techniques including Projection Profiling, Circle Hough Transformation, Adaptive Thresholding, and Power-Law Transformation to achieve high image clarity and then uses the Connected-Component Labeling (CCL) technique to correctly count the colonies, including overlapping colonies. These techniques are built into a convenient Android smartphone application. Results of counting the colonies with BCICS were compared with results of hand counting the same dishes. The accuracy rate of each dish count was calculated, as well as the average dish accuracy across all dishes. BCICS counted total colonies with an accuracy of 90.14%, which is close to that of hand counting accuracy since hand counting itself commonly involves an error rate of 5-10%. Importantly, the application took only 3-5 seconds to count one Petri dish, which is more than 74 times faster than the time required for manual counting.

Evaluation of effectiveness of eye massage therapy via classification of periocular images.

This paper proposes a method to evaluate the effectiveness of the eye message therapy. The existing methods are via the diagnoses conducted by the medical professions based on the measurements acquired by the optical instruments. However, this approach is very expensive. To address this issue, this paper performs the classification between the periocular images taken before performing the eye massage therapy and those after performing the eye massage therapy to address the above difficulty. First, the median filtering is used to suppress the solitary point noise with preserving the edges of the image without causing the significant blurring. Then, the Canny operator is employed to accurately locate the edges. Next, the circle Hough transform (CHT) is used for performing the iris segmentation. Finally, various classifiers are used to perform the classification. The computer numerical simulation results show that our proposed method can achieve the high classification accuracies. This implies that there is a significant difference on the iris before performing the eye massage therapy and after performing the eye massage therapy. In addition, the comparisons with the state of art Daugman method have been performed. It is found that the classification performance achieved by the CHT based method is better than those achieved by the Daugman method.

Automatic Assembly of Bolts and Nuts Based on Machine Vision Recognition

In this study, based on the computer vision technology, we developed a recognition system for nuts positioning to complete the automatic bolt assembly part of the automatic production line. The actual image of the nut was captured by an industrial camera, which would be processed by the following edge detection and Hough circle transformation. After that, the coordinates of the nuts were obtained in the pixel scale. Finally, the real position of nuts would be fed back to the robot arm, according to which the automatic assembly of the bolt would be completed. This computer vision based recognition system is an indispensable part for the efficiency and accuracy improvement of automatic production line.

Assistive Framework for Automatic Detection of All the Zones in Retinopathy of Prematurity Using Deep Learning.

Retinopathy of prematurity (ROP) is a potentially blinding disorder seen in low birth weight preterm infants. In India, the burden of ROP is high, with nearly 200,000 premature infants at risk. Early detection through screening and treatment can prevent this blindness. The automatic screening systems developed so far can detect "severe ROP" or "plus disease," but this information does not help schedule follow-up. Identifying vascularized retinal zones and detecting the ROP stage is essential for follow-up or discharge from screening. There is no automatic system to assist these crucial decisions to the best of the authors' knowledge. The low contrast of images, incompletely developed vessels, macular structure, and lack of public data sets are a few challenges in creating such a system. In this paper, a novel method using an ensemble of "U-Network" and "Circle Hough Transform" is developed to detect zones I, II, and III from retinal images in which macula is not developed. The model developed is generic and trained on mixed images of different sizes. It detects zones in images of variable sizes captured by two different imaging systems with an accuracy of 98%. All images of the test set (including the low-quality images) are considered. The time taken for training was only 14min, and a single image was tested in 30ms. The present study can help medical experts interpret retinal vascular status correctly and reduce subjective variation in diagnosis.

Real-time Signal Light Detection based on Yolov5 for Railway

To improve the safety and efficiency of train operation, autonomous driving train have developed rapidly in recent years. Among them, the signal detection is one of the most basic functions. However, due to the small size of signal light and the complicated of the railway environment, the signal detection is still a huge problem. The existing methods, such as the approach based on Hough circle transformation, are hard to meet the practical application requirements. In this paper, a real time railway signal lights detection based on Yolov5 is introduced. And a lot of experiments were conducted to prove the effectiveness of the proposed method. The experimental results show that the proposed method achieved 0.972 for both average recall rate and average accuracy rate. Besides, the detection speed of the proposed method reached astonishing 100FPS. Overall, the detection speed and accuracy both meet the practical application requirements.

Tracking and recognition algorithm for a robot harvesting oscillating apples

Apple fruits on trees tend to swing because of wind or other natural causes, therefore reducing the accuracy of apple picking by robots. To increase the accuracy and to speed up the apple tracking and identifying process, tracking and recognition method combined with an affine transformation was proposed. The method can be divided into three steps. First, the initial image was segmented by Otsu’s thresholding method based on the two times Red minus Green minus Blue (2R-G-B) color feature; after improving the binary image, the apples were recognized with a local parameter adaptive Hough circle transformation method, thus improving the accuracy of recognition and avoiding the long, time-consuming process and excessive fitted circles in traditional Hough circle transformation. The process and results were verified experimentally. Second, the Shi-Tomasi corners detected and extracted from the first frame image were tracked, and the corners with large positive and negative optical flow errors were removed. The affine transformation matrix between the two frames was calculated based on the Random Sampling Consistency algorithm (RANSAC) to correct the scale of the template image and predict the apple positions. Third, the best positions of the target apples within 1.2 times of the prediction area were searched with a de-mean normalized cross-correlation template matching algorithm. The test results showed that the running time of each frame was 25 ms and 130 ms and the tracking error was more than 8% and 20% in the absence of template correction and apple position prediction, respectively. In comparison, the running time of our algorithm was 25 ms, and the tracking error was less than 4%. Therefore, test results indicate that speed and efficiency can be greatly improved by using our method, and this strategy can also provide a reference for tracking and recognizing other oscillatory fruits. Keywords: apple picking robot, tracking and recognition algorithm, oscillating apple, Hough transform, pyramid LK optical flow algorithm, affine transform, template matching DOI: 10.25165/j.ijabe.20201305.5520 Citation: Yang Q H, Chen C, Dai J Y, Xun Y, Bao G J. Tracking and recognition algorithm for a robot harvesting oscillating apples. Int J Agric & Biol Eng, 2020; 13(5): 163–170.

An effective deep learning features based integrated framework for iris detection and recognition

In recent years, Iris recognition has emerged as an important and trustworthy biometric model to recognize humans. The application of automatic iris recognition models find useful in different fields namely border control, citizen confirmation, and criminal to commercial products. This paper introduces an effective deep learning (DL) based integrated model for precise iris detection, segmentation and recognition. The projected model involves different stages namely preprocessing, detection, segmentation and recognition. Initially, preprocessing of images takes place to improve the quality of the input image using Black Hat filtering, Median filtering and Gamma Correction. Then, Hough Circle Transform model is applied to localize the region of interest, i.e. iris in an effective way. Afterwards, Mask region proposal network with convolution neural network (R-CNN) with Inception v2 model is applied for trustworthy iris recognition and segmentation i.e., recognizing iris/non-iris pixels. For validating the results of the presented model, a detailed simulation takes place using a benchmark CASIA-Iris Thousand dataset and the results are validated interms of detection accuracy. The attained simulation outcome depicted that the projected technique shows maximum recognition accuracy of 99.14% which is superior to other methods such as UniNet.V2, AlexNet, VGGNet, Inception, ResNet and DenseNet models in a significant way.

A vision based method for automated measurement of circular fiber cross-sections

Abstract There are many synthetic fibers with circular cross-sections, and characteristics of the fiber cross-sections have a major influence on the fiber properties. Direct measurements made with microscopic techniques on the fiber cross-sections can output maximum information on transverse dimensions. However, measuring parameters of the fiber sections in the microscopic images is a laborious task. In this paper, a hybrid method based on machine vision is proposed to measure the diameter and ellipticity of the circular fiber cross-sections. The hybrid method consists of modified Circle Hough Transform (CHT) and edge tracing algorithm, and the two sub-algorithms are performed successively. The modified CHT is used for detection and initial measurement of the fiber sections. Then the results are refined by the edge tracing algorithm, outputting elliptical results along with the ellipticity information of each fiber section. Experimental results show that the proposed method outperforms existing methods in circular fiber cross-section measurement.

A novel image measurement algorithm for common mushroom caps based on convolutional neural network

In a mushroom greenhouse, for example, there are no sensors that can measure the diameter of clustered mushrooms, and farmers can only empirically estimate the relationship between the mushroom growth and greenhouse microclimate data. Our team developed an image measurement system that can record the circle diameter of Agaricus bisporus (hereinafter referred to as “common mushroom”) caps during the fruiting period. To continuously record the size of the mushroom caps for purpose-specific analysis (such as data analysis for optimized greenhouse microclimate control, calculation of growth rate, or harvest reminder), a new algorithm is proposed for calculating the diameter of the circle of round mushroom caps. This algorithm is used to estimate the mushroom circles using the images continuously captured by a camera and generate a growth record of the mushroom caps. The automatic adjustment of the camera aperture and focus during the growth of the mushroom results in a color deviation between images, which makes it difficult to detect the circles of the mushroom caps. The proposed algorithm is sufficiently robust to overcome the challenges presented by the color deviation in achieving accurate estimation. It is based on the identification results of the convolutional neural network and combines the innovative Score-Punishment (SP) algorithm in this study to calculate the circle diameter of the mushroom caps. The proposed algorithm outperformed the current Circle Hough Transform (OpenCV’s implementation). Besides, the proposed algorithm is more robust without the need to set parameters for the images of each time period, making it more feasible for practical application. In addition to describing the algorithm in detail, in this work, the effectiveness and practicality of the proposed algorithm through experiments were also verified.

Automatic detection and classification of honey bee comb cells using deep learning

In a scenario of worldwide honey bee decline, assessing colony strength is becoming increasingly important for sustainable beekeeping. Temporal counts of number of comb cells with brood and food reserves offers researchers data for multiple applications, such as modelling colony dynamics, and beekeepers information on colony strength, an indicator of colony health and honey yield. Counting cells manually in comb images is labour intensive, tedious, and prone to error. Herein, we developed a free software, named DeepBee©, capable of automatically detecting cells in comb images and classifying their contents into seven classes. By distinguishing cells occupied by eggs, larvae, capped brood, pollen, nectar, honey, and other, DeepBee© allows an unprecedented level of accuracy in cell classification. Using Circle Hough Transform and the semantic segmentation technique, we obtained a cell detection rate of 98.7%, which is 16.2% higher than the best result found in the literature. For classification of comb cells, we trained and evaluated thirteen different convolutional neural network (CNN) architectures, including: DenseNet (121, 169 and 201); InceptionResNetV2; InceptionV3; MobileNet; MobileNetV2; NasNet; NasNetMobile; ResNet50; VGG (16 and 19) and Xception. MobileNet revealed to be the best compromise between training cost, with ~9 s for processing all cells in a comb image, and accuracy, with an F1-Score of 94.3%. We show the technical details to build a complete pipeline for classifying and counting comb cells and we made the CNN models, source code, and datasets publicly available. With this effort, we hope to have expanded the frontier of apicultural precision analysis by providing a tool with high performance and source codes to foster improvement by third parties (https://github.com/AvsThiago/DeepBee-source).

High-efficiency sub-microscale uncertainty measurement method using pattern recognition

This study presents a fast precision measurement method that uses pattern recognition. First, a specific micro-structured surface was designed and manufactured, providing a unique pattern for recognition and matching. Second, a measurement system was proposed based on the algorithms of circle Hough transform (CHT), neural classifier (NC), template matching (TM) and sub-pixel interpolation (SI). Then, a series of experiments were carried out from three aspects: circle detection, length uncertainty, and measurement speed and range. The results showed the correct circle classification percentage was more than 96% and the CHT search accuracy was within a two-pixel level. The length uncertainty test demonstrated the method was able to achieve 90-nm length uncertainty, and a comparison of measurement speeds showed it helped to speed up measurements by a factor of 1000 compared to the original one.

Circle Object Detection Of A Moveable Vision Robot Using Hough Circle Method

The object detection system is very important in an automatic mobile robot system. The functions of this system for a navigation robot are to find objects and also at the same time to avoid obstacle. Detecting objects here including the shape, size, and color of object. Various implementations of robot detection have been made in various fields, especially in industrial field. In this study, a circular object detection application system was developed using the Hough Circle Transformation method. Circle object detection system developed in the following stages: Image processing (capturing video from the camera, BRG color conversion to Gray scale, and Gaussian Blur); the application of Hough Circle Transformation; and calculation of diameter and distance of circle objects. The test results show that the system works well to detect circle objects in various variations of the object given. The system can also detect the distance of circle objects with an average error of 8.17%. By contrast, upon measuring circle object diameter, the system can measure with an average error of 5.55%.

Tracking and recognition algorithm for a robot harvesting oscillating apples

Apple fruits on trees tend to swing because of wind or other natural causes, therefore reducing the accuracy of apple picking by robots. To increase the accuracy and to speed up the apple tracking and identifying process, tracking and recognition method combined with an affine transformation was proposed. The method can be divided into three steps. First, the initial image was segmented by Otsu’s thresholding method based on the two times Red minus Green minus Blue (2R-G-B) color feature; after improving the binary image, the apples were recognized with a local parameter adaptive Hough circle transformation method, thus improving the accuracy of recognition and avoiding the long, time-consuming process and excessive fitted circles in traditional Hough circle transformation. The process and results were verified experimentally. Second, the Shi-Tomasi corners detected and extracted from the first frame image were tracked, and the corners with large positive and negative optical flow errors were removed. The affine transformation matrix between the two frames was calculated based on the Random Sampling Consistency algorithm (RANSAC) to correct the scale of the template image and predict the apple positions. Third, the best positions of the target apples within 1.2 times of the prediction area were searched with a de-mean normalized cross-correlation template matching algorithm. The test results showed that the running time of each frame was 25 ms and 130 ms and the tracking error was more than 8% and 20% in the absence of template correction and apple position prediction, respectively. In comparison, the running time of our algorithm was 25 ms, and the tracking error was less than 4%. Therefore, test results indicate that speed and efficiency can be greatly improved by using our method, and this strategy can also provide a reference for tracking and recognizing other oscillatory fruits. Keywords: apple picking robot, tracking and recognition algorithm, oscillating apple, Hough transform, pyramid LK optical flow algorithm, affine transform, template matching DOI: 10.25165/j.ijabe.20201305.5520 Citation: Yang Q H, Chen C, Dai J Y, Xun Y, Bao G J. Tracking and recognition algorithm for a robot harvesting oscillating apples. Int J Agric & Biol Eng, 2020; 13(5): 163–170.

Real-Time Traffic Light Detection and Interpretation using Circle Centroid

The paper proposes a new method to recognize the sequence of a traffic light using image processing algorithm. Invariant in factor lightning and weather condition that lead to misinterpret the color of traffic light is one of the factors of accident at traffic light conjunction besides the behavior of the driver itself. The process to identify the color and shape of traffic light are Image Acquisition, Pre-Processing, Detection, Feature Extraction and Interpretation. RGB normalization is performed and simple thresholding method that acts as color segmentation provides a better division of the traffic light colors. Circle Hough Transform and HSV color features based on the traffic light aspect are used to decide whether the spots on the frames are likely to be traffic lights’ color and shape. The detection of traffic light will be obtained after identifying the feature such as the centroid of the Circle Hough Transform that need to be extracted at the end of the result. The research has been improved by focusing on detection and interpretation of traffic light based on real time video rather than image sample as the input. The proposed algorithm can detect the green color accurately with maximum accuracy of 83.8%, yellow color of 75.6% and red color with minimum accuracy of 70.19%. This indicates that there is a possibility to use the proposed algorithm to detect the three different color of green, yellow and red color of traffic lights’ colour.