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Leaves are one of the parts of plants that can be applied in the process of identifying species images of leaves. The process of classification of leaf imagery can be done by identifying the image of the leaf shape that can be done by identifying the pattern of the leaf by recognizing the structural characteristics of the leaf such as the shape and texture of the leaf. The classification of this leaf image is based on the canny edge detection and gray-level co-occurrence matrix (GLCM) features using the k-Nearest Neighbor (kNN) classification method. The data used is as many as 350 leaf imagery with seven different species. The results show that from the two extraction features used, the Canny feature gets an accuracy of 80% and, the GLCM features gets an accuracy of 93.3%. And the merging of the two features resulted in an increased accuracy of 98%. It can be concluded that this research has produced good accuracy in identifying leaf imagery based on canny edge detection features and Gray-Level Co-occurrence Matrix (GLCM) features and k-Nearest Neighbor classifier method. Key words -- Leaf, canny edge detection, gray-level co-occurrence matrix (GLCM), the k-nearest neighbor (kNN)

The rapid development of artificial intelligence technology makes it widely used in various fields. In order to more scientifically assist teenagers in physical training, this paper develops a set of teenagers’ physical training system based on artificial intelligence technology. Firstly, the experimental platform is built, and the sensor nodes are connected with the test host through the serial port to collect data to the experimental platform. The system consists of target detection module, data analysis module, and human posture estimation module. The background modeling method based on vibe model is used to form the target detection module, and the canny edge detection algorithm is used to form the data analysis module. Finally, the posture auxiliary index is established to estimate the human posture. This paper makes a systematic application test on a youth sports team. The experimental group was trained with artificial intelligence-based physical training system, while the control group was trained with traditional training methods. Before the experiment, the physical fitness of the two groups of subjects were evaluated, including standing long jump, 50 meters sprint, 30 s single swing rope skipping, pull-up, and squat 1RM. After 3 and 6 weeks of training, the physical fitness was evaluated again. The experimental results show that the intelligent assistant system established in this paper can accurately show that the physiological load of the athlete is in line with the law of physiological function change. After six weeks of training, the standing long jump of the experimental group has been improved by 20.97 cm, the 50 meters dash has been accelerated by 1.21 s, the 30 second single swing rope has been increased by 13.76, the pull-up has been increased by 1.41, and the squat 1RM has been increased by 15.16. This shows that the auxiliary training system based on artificial intelligence can help young athletes improve their physical quality and enhance their sports skills.

Saat ini beberapa metode yang peneliti gunakan untuk mempermudah pemrosesan citra yaitu metode filtrasi, penghilangan noise, dan deteksi tepi. Masing-masing memiliki banyak cara khusus untuk menyempurnakan gambar atau membantu pemrosesan langkah selanjutnya. Untuk bidang tertentu seperti kesehatan dan kedokteran atau citra bangunan 2D, beberapa penelitian menggunakan pendekatan morfologi dan non morfologi. Pendekatan morfologi yang digunakan seperti algoritma Dilation, Opening atau Erotion telah menarik perhatian siswa sejak beberapa dekade terakhir. Dalam makalah ini, kami membahas implementasi Pembukaan dan Dilatasi untuk jenis citra tertentu. Menghasilkan judul ini, kami mendapatkan beberapa tingkat perbaikan untuk beberapa tujuan seperti deteksi tepi. Dan pada hasil percobaan didapatkan bahwa operasi morfologi sesuai untuk meningkatkan metode deteksi tepi yang sobel dan cerdik. deteksi tepi banyak digunakan pada bidang penelitian saat ini untuk membantu penelti dalam meneliti ojek 2D berdimensi untuk menghilangkan noise yang timbulkan pada suatu citra digital yang dapat mempengaruhi kualitas citra dengan menggunakan metode morfologi ini dimana citra akan didilasi baik secara opening maupun erosi

An outbreak of Ulva prolifera poses a massive threat to coastal ecology in the Southern Yellow Sea, China (SYS). It is a necessity to extract its area and monitor its development accurately. At present, Ulva prolifera monitoring by remote sensing imagery is mostly based on a fixed threshold or artificial visual interpretation for threshold selection, which has large errors. In this paper, an adaptive threshold model based on Google Earth Engine (GEE) is proposed and applied to extract U. prolifera in the SYS. The model first applies the Floating Algae Index (FAI) or Normalized Difference Vegetation Index (NDVI) algorithm on the preprocessed remote sensing images and then uses the Canny Edge Filter and Otsu threshold segmentation algorithm to extract the threshold automatically. The model is applied to Landsat8/OLI and Sentinel-2/MSI images, and the confusion matrix and cross-sensor comparison are used to evaluate the accuracy and applicability of the model. The verification results show that the model extraction of U. prolifera based on the FAI algorithm has higher accuracy (R2 = 0.99, RMSE = 5.64) and better robustness. However, when the average cloud cover is more than 70% in the image (based on the statistical results of multi-year cloud cover information), the model based on the NDVI algorithm has better applicability and can extract the algae distributed at the edge of the cloud. When the model uses the FAI algorithm, it is named FAI-COM (model based on FAI, the Canny Edge Filter, and Otsu thresholding). And when the model uses the NDVI algorithm, it is named NDVI-COM (model based on NDVI, the Canny Edge Filter, and Otsu thresholding). Therefore, the final extraction results are generated by supplementing NDVI-COM results on the basis of FAI-COM extraction results in this paper. The F1-score of U. prolifera extracted results is above 0.85. The spatiotemporal distribution of U. prolifera in the South Yellow Sea from 2016 to 2020 is obtained through the model calculation. Overall, the coverage area of U. prolifera shows a decreasing trend over the five years. It is found that the delay in recovery time of Porphyra yezoensis culture facilities in the Northern Jiangsu Shoal and the manual salvage and cleaning-up of U. prolifera in May are among the reasons for the smaller interannual scale of algae in 2017 and 2018.

Pak Choy plant is a vegetable plant that can grow with soil and hydroponic media. Plant growth can be monitored by knowing the width of the plant's leaves. This research creates a tool to accurately detect plant leaf width through plant growth monitored by the people-research implementation in the form of a mobile application that can detect the width of plant leaves. Using the canny edge detection method, the results obtained system accuracy of 95.06%, the light intensity of 18.75 lux, angle of 90°, and distance of 30 cm. Pak Choy plant growth was seen for 4 weeks, the best accuracy was obtained at 2nd week with an average accuracy of 99% and an average light intensity value of 22 lux.

In this paper, a novel filter-based model for classification of tobacco leaves for the purpose of harvesting is proposed. The filter-based model relies on estimation of degree of ripeness of a leaf using combination of filters and color models. Degree of ripeness of a leaf is computed using density of maturity spots on a leaf surface and yellowness of a leaf. A new maturity spot detection algorithm based on combination of first order edge extractor (sobel edge detector or canny edge detector) and second order high-pass filtering (Laplacian filter) is proposed to compute the density of maturity spots on a unit area of a leaf. Further, a simple thresholding classifier is designed for the purpose of classification. Superiorities of the proposed model in terms of effectiveness and robustness are established empirically through extensive experiments.

The traditional canny edge detection algorithm has its limitations in the aspect of antinoise interference, and it is susceptible to factors such as light. To solve these defects, the Canny algorithm based on morphological improvement was proposed and applied to the detection of agricultural products. First, the algorithm uses the open and close operation of morphology to form a morphological filter instead of the Gaussian filter, which can remove the image noise and strengthen the protection of image edge. Second, the traditional Canny operator is improved to increase the horizontal and vertical templates to 45° and 135° to improve the edge positioning of the image. Finally, the adaptive threshold segmentation method is used for rough segmentation, and on this basis, double detection thresholds are used for further segmentation to obtain the final edge points. The experimental results show that compared with the traditional algorithm applied to the edge detection of agricultural products, this algorithm can effectively avoid the false contour caused by illumination and other factors and effectively improve the antinoise interference while more accurate and fine detection of the edge of real agricultural products.

Banana leaf disease classification requires the application of machine learning and image processing on the images obtained from the field. A lot of diseases like banana black sigakota (BBS), banana bunchy top virus (BBTV), and banana bacterial wilt (BBW) have been reported on banana plantation. In this paper, the merits of both Gabor filter and 2D log Gabor filter have been used to construct an enhanced Gabor filter, which extracts features from the images of diseased plant. Further, KNN classifier is used in the classification of diseases. Classification results with diseased datasets show that the proposed method achieves better when compared to SIFT and SURF feature descriptors.

Currently, the price of drugs are rising time to time and still growing. The high cost for medical treatment is not compatible compared to the people’s welfare, which is burdens to lives of some people. Indonesian people always look for medicine as the main shortcut, whereas in Indonesia, there are a lot of family medicinal garden (in bahasa – TOGA [Tanaman Obat Keluarga]) which are commonly use as herbs and natural medicine. Most people have a difficulty to identify the type of toga plants and the real efficacy of these plants. This research proposed an identification of toga plants using leaf images. the leaf images features will be extract using Invariant Moment and Canny edge will be used to recognize leaf textures. K-Nearest Neighbor is used for leaf type identification. According to the experiments, this system yields 80% classification accuracy.

An improved method based on edge detection and random Hough transform is proposed for industrial detection of varistors. Firstly, using the improved edge detection to detect the contour, the protruding part can be smoothed effectively, making it more fit to the circular feature without changing the size. Then, the improved random Hough transform is used to accurately locate the circle, the properties of the circle are determined by using three points, three non-collinear pixel points are extracted from the edge point, the circle satisfying the conditions is found, and then the fourth point is randomly taken out. If the difference between the distance and radius from the point to the center of the circle is within the threshold 1, it may be true circle. Then, if the difference between the center and radius of the detected circle is greater than the threshold 2, the detected circle is determined to be true circle. Otherwise, the duplicate circle is deleted.

In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the proposed system consists of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny edge detection algorithm, Connect Component Analysis (CCA) have been exploited for segmenting characters. Finally, a Multi-Layer Perceptron Artificial Neural Network (MLPANN) model is utilized to identify and detect the vehicle license plate characters, and hence the results are displayed as a text on GUI. The proposed system successfully detects LP and recognizes multi-style Arabic characters with rates of 96% and 97.872% respectively under different conditions.

Holy basil (Ocimum basilicum L.) is one of the most vital economic crops that has a significant impact on export earnings. However, the holy basil prices could be dropped due to a curl leaf disease caused by pests. Several previous studies focused on plant leaf disease detection based on the leaf color. Unfortunately, the leaf curl disease sometimes changes a shape of the leaf not the color so it cannot be detected with those schemes. We proposed a novel approach aims to automatically detect a curling leaf on holy basil. This paper presents a Neural Network (NN) model and Logistic Regression (LR) model to automatically detect a curling leaf on holy basil. To be able to detect the infected one not by colors but by its shape, we have applied edge detection algorithms which are Canny and Sobel model. To speed up processing time, images were resized and converted to grayscale before passing them to machine learning models. Moreover, NN and LR were modified with mini-batch technique in order to increase the speed of the processing time. The dataset contains 600 images of holy basil leaves with 300 images of healthy leaves and 300 images of infected leaves. The experimental results indicate that the proposed method effectively detects the curling leaves on holy basil.

In this paper, we focus on the safety supervision of inland vessels. This paper especially aims at studying the vessel target detection and dynamic tracking algorithm based on computer vision and the target fusion algorithm based on multisensor. For the vessel video target detection and tracking, this paper analyzes the current widely used methods and theories. Additionally, facing the application scenarios and characteristics of inland vessels, a comprehensive vessel video target detection algorithm is proposed in this paper. It is combined with a three-frame difference method based on Canny edge detection and a background subtraction method based on mixed Gaussian background modeling. Besides, for the multisensor target fusion, the processing method of laser point cloud data and automatic identification system (AIS) data is analyzed in this paper. Based on the idea of fuzzy mathematics, this paper proposes a method for calculating the fuzzy correlation matrix with normal membership function, which realizes the fusion of vessel track features of laser point cloud data and AIS data under dynamic video correction. Finally, through this method, a set of vessel situation active intelligent perception systems based on multisensor fusion was developed. Experiments show that this method has better environmental applicability and detection accuracy than traditional manual detection and any single monitoring method.

Now a day's image processing is used to solve different types of problems. One of the problems is plant disease detection. However, image processing is considered as difficult and challenging task to apply. The first challenging task is a different feature extraction. This paper presents two novel approaches to extract different types of features like color, shape, and texture from plant leaves to detect different types of diseases. To extract the color feature, the mean value of the pixels of the diseased area is calculated. To perform this operation, masking is applied on RGB images to mask the green pixels by black pixels and then the mean value of three base color band namely red, green and blue are calculated separately. We used the Canny edge detector to present edges which mainly contains texture and shape information of an image. Then inter-edge distances are calculated in the unit of pixels. These edge distances are calculated from row and column separately of an image and obtained two arrays. Histogram analysis is applied to these two arrays for calculating frequency and found another two array which contains the frequency of distances. After that, normalization is done to obtain texture features, and the ratio of elements of the two frequency arrays is calculated for obtaining shape features.

This paper presents a novel approach to classify the leaf shape and to identify plant species using venation detection. The proposed approach consists of five main steps to extract the leaf venation including canny edge detection, remove leaf boundary, extract curve, and produce hue normalization image and image fusion. Moreover, to localize the edge direction efficiently, the lines that extracted from pre-processing, are further divided into smaller segments. Thirty-two leaf images of Malaysian plants are analysed and evaluated with two different datasets, Flavia and Acer. The best accuracy is obtained by 99.3% and 91.06% for Flavia and Acer datasets respectively. Experimental results show the effectiveness of the proposed approach for shape recognition with high accuracy.\ud Keywords: Leaf Venation; plant species; features extraction; features selection; classification.

In order to improve the image segmentation performance of cotton leaves in natural environment, an automatic segmentation model of diseased leaf with active gradient and local information is proposed. Firstly, a segmented monotone decreasing edge composite function is proposed to accelerate the evolution of the level set curve in the gradient smooth region. Secondly, canny edge detection operator gradient is introduced into the model as the global information. In the process of the evolution of the level set function, the guidance information of the energy function is used to guide the curve evolution according to the local information of the image, and the smooth contour curve is obtained. And the main direction of the evolution of the level set curve is controlled according to the global gradient information, which effectively overcomes the local minima in the process of the evolution of the level set function. Finally, the Heaviside function is introduced into the energy function to smooth the contours of the motion and to increase the penalty function φ ( x ) to calibrate the deviation of the level set function so that the level set is smooth and closed. The results showed that the model of cotton leaf edge profile curve could be obtained in the model of cotton leaf covered by bare soil, straw mulching and plastic film mulching, and the ideal edge of the ROI could be realized when the light was not uniform. In the complex background, the model can segment the leaves of the cotton with uneven illumination, shadow and weed background, and it is better to realize the ideal extraction of the edge of the blade. Compared with the Geodesic Active Contour (GAC) algorithm, Chan-Vese (C-V) algorithm and Local Binary Fitting (LBF) algorithm, it is found that the model has the advantages of segmentation accuracy and running time when processing seven kinds of cotton disease leaves images, including uneven lighting, leaf disease spot blur, adhesive diseased leaf, shadow, complex background, unclear diseased leaf edges, and staggered condition. This model can not only conduct image segmentation of cotton leaves under natural conditions, but also provide technical support for the accurate identification and diagnosis of cotton diseases.

Soybeans are an extremely important crop in the United States, providing the largest source of animal protein feed and a main source of vegetable oil in the country. The goal of this research is to identify methodologies to estimate percent defoliation of the soybean canopy and leaves using RGB images taken in the field. The Mahalanobis distance classification method was used to process sets of images and calculate leaf area (number of pixels) corresponding to two classes (leaf and background) with eight different color groups. The Canny edge detection algorithm provided an efficient method for detecting leaf edges, and threshold t2 = 20 was found to be the optimal value for estimating soybean leaf edge. The segmentation results showed a performance of 96% for soybean leaves using Mahalanobis distance classification. Two statistical regression models (polynomial and logistic regression) for defoliation of soybean were developed based on individual images of trifoliate leaves taken from the field. The models both utilized leaf area and edge to provide estimates of soybean defoliation; however, a logistic equation has potential to provide greater understanding and more accurate estimates of defoliation with variations, especially at low defoliation. The R2 and root mean square error (RMSE) of estimated and observed defoliation of trifoliate leaves were 0.90 and 6.16%, respectively. The validation of soybean canopy defoliation and its corresponding trifoliate leaves defoliation also provided reasonable correlation (R2 = 0.96 and RMSE = 1.85%). This approach could lead to use of remotely sensed imagery for estimating defoliation in soybeans and timely intervention with integrated pest management strategies.

This paper presents an R script that quantifies the shape of selected cranial traits and automates sex estimation. The proposed functions were tested on two modern Greek assemblages. The discriminant variables input in the functions are calculated from a digital photograph of the lateral view of the cranium. The cranial outline is determined using the Canny edge detector and discriminant variables that quantify the shape of the glabella/frontal bone, mastoid process, and external occipital protuberance are computed. The best cross-validated results for pooled sexes in the Athens Collection range from 84.2% to 87.3%, and increase up to 93.9% when half of the sample is used for training and the rest for prediction, while correct classification for the Cretan material is 80-90% for optimum combinations of discriminant variables. The greatest advantage of the proposed method is its straightforward and time-efficient application.

In this paper, we present an energy-efficient architecture of the Canny edge detector for advanced mobile vision applications. Three key techniques for reducing computational complexity of the Canny edge detector are presented. First, by exploiting the rank characteristic of the convolution kernel of Gaussian smoothing and Sobel gradient filters, common computations are identified and shared in the image filter design to reduce the number of additions and multiplications. For the gradient magnitude/direction computation, only three directions of neighboring pixels are considered to reduce computation energy with minor degradation on conformance performance (CP). For the adaptive threshold selections, an interesting observation is that the mean values of gradient magnitudes show small variations depending on the classified block types. Thus, the threshold selection process can be simplified as multiplying the mean value of the local block with predecided constants. The proposed low complexity Canny edge detector has been implemented using both field-programmable gate arrays (FPGAs) and a 65-nm standard-cell library. The FPGA implementation with Xilinx Virtex-V (XC5VSX240T) shows that our edge detector achieves 48% of area and 73% of execution time savings over the conventional architecture without seriously sacrificing the detection performance. The proposed edge detector implemented with 65-nm standard-cell library can easily support real-time ultrahigh definition video data processing (50 frames/s) with the power consumption of 5.48 mW (108.84 $\mu \text{J}$ /frame).

We developed a robust, quasi high-pass filter for edge detection in medical images. The kernel-based algorithm of our detector was similar to that of conventional edge detectors. The proposed edge detector has a mathematical form of local variance and is adaptive in nature. The mathematical formulation of the detector was exploited and re-expressed as a quadratic form of the Toeplitz matrix. The detector has a highly structured internal architecture with abundant spatial isotropic symmetricity. The proposed operator can greatly reduce problems frequently encountered in edge detection including fragmentation, position dislocation, and thinness loss. The detector is robust to noise and can efficiently extract crucial edge features. We named this new operator as the WL operator (Wang and Lin). The performance of the WL operator was compared to that of other edge detectors by using Pratt’s figure of merits. In addition, the performance was confirmed with experts by using visual analog scale scores. The results obtained using the WL operator for different medical imaging modalities including X-ray, CT, and MRI are promising. Therefore, the WL operator warrants further investigation.

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.

We introduce an edge detection and recovery framework based on open active contour models (snakelets). This is motivated by the noisy or broken edges output by standard edge detection algorithms, like Canny. The idea is to utilize the local continuity and smoothness cues provided by strong edges and grow them to recover the missing edges. This way, the strong edges are used to recover weak or missing edges by considering the local edge structures, instead of blindly linking them if gradient magnitudes are above some threshold. We initialize short snakelets on the gradient magnitudes or binary edges automatically and then deform and grow them under the influence of gradient vector flow. The output snakelets are able to recover most of the breaks or weak edges, and they provide a smooth edge representation of the image; they can also be used for higher level analysis, like contour segmentation.

The ratio of averages is a robust edge detector which provides the property of constant false alarm rate for synthetic aperture radar (SAR) imagery. However, the rectangular window used in the calculation of local mean may cause numerous false maxima. The size of the processing window also has a significant effect on the detection performance, but it is difficult to determine the optimum window size. In this letter, we first propose a new ratio-based detector that is constructed by the Gabor odd filter. The scale of the proposed detector is related to the size of the processing window. Then, edge strength maps extracted by multiscale detectors are combined using an edge tracking algorithm to form a final response. We used the receiver operating characteristic curves to evaluate the performance of the proposed detector. The experimental results on simulated and real-world SAR images show that the proposed multiscale edge detector yields an accurate and consecutive edge response.

Features of G-lets Edge Operator: Geometry of objects in the image are preserved.Edges are continuous.Tracking outlines of small objects in cluttered images are possible.Edges in noisy images without losing continuity is shown to be possible.Computations are light due to sparse operations. A new edge detection technique using transformation groups based G-lets filters is proposed in this paper. Discretizing gradients seem to produce discontinuity in classic edge detectors. No particular filter is capable of identifying meaningful edges at all scales and it increases computations with a multiscale approach. It is a challenge to get localized edges without spurious ones due to noise and integrate the obtained edges into meaningful object boundaries. Without breaking edge continuity and strictly localizing edges requires that filters do not blur the image during preprocessing. G-lets filters are found to be capable of performing well in most type of images including natural, noisy, low resolution and synthetic. In this paper, an edge detection algorithm using G-lets filters which are built by direct factorization of linear transformation matrices using irreducible representations is proposed. A multiresolution approach is shown to enhance the possibility of detecting faint edges. An edge tracing algorithm is presented to produce the edge image. The computational cost involved is comparatively lesser than existing filters. It is found that the geometries in the original image are preserved in the edge image. The edge tracing algorithm is capable of constructing object boundaries without the inner textures in a way that is not completely dependent on intensity thresholding. G-lets filters and the edge operator is found to be a promising algorithm for drastically bringing down the computations needed for realtime applications. The results are compared with BSDS500 boundary detection dataset using pb and global pb detectors.

To solve the problem of long computing time in present target matching algorithm and incorrect positioning on rotating and zooming target, the Paper has proposed a screw target matching algorithm based on Pyramid image structure and Hu moments. First, image pyramid model is built, where the resolutions of test image and template image are proportionately reduced to reduce the ergodic calculation amount of template. And then, template image is moved in per pixel in test image, and the matching coordinate is got through calculations of square error normalization matching-degree function. At last, based on Canny edge detection, the template target profile and suspected screw target edge profile in test image are got, besides, the Hu moments of the two profiles are calculated, of which the characteristic against zoom and rotation interference is used to complete shape matching. The experimental test result shows that: compared with present matching algorithm, the proposed algorithm in the Paper owns a higher real time and accuracy in screw matching positioning.

We describe a subpixel edge detection approach in images. The proposed approach is based on the algebraic moments of the brightness function of halftone images. For an ideal two-dimensional edge, we consider a model with the following four parameters: the edge orientation, the distance from the edge to the center of the mask, and the brightness values from both sides of the edge. To obtain all subpixel parameters of the edge, six algebraic moments are used. To compute the moments rapidly, masks are used. The specificity of the proposed approach is as follows: masks of almost all sizes can be used and they are computed by means of explicit relations provided in the present paper as well. Increasing mask sizes, one can increase the accuracy of the detection of subpixel edge parameters, which is especially important for high-definition images. We present experiments displaying the efficiency of the proposed approach.

Postimplant validation is an indispensable part in the brachytherapy technique. It provides the necessary feedback to ensure the quality of operation. The ability to pick implanted seed relates directly to the accuracy of validation. To address it, an automatic approach is proposed for picking implanted brachytherapy seeds in 3D CT images. In order to pick seed configuration (location and orientation) efficiently, the approach starts with the segmentation of seed from CT images using a thresholding filter which based on gray-level histogram. Through the process of filtering and denoising, the touching seed and single seed are classified. The true novelty of this approach is found in the application of the canny edge detection and improved concave points matching algorithm to separate touching seeds. Through the computation of image moments, the seed configuration can be determined efficiently. Finally, two different experiments are designed to verify the performance of the proposed approach: (1) physical phantom with 60 model seeds, and (2) patient data with 16 cases. Through assessment of validated results by a medical physicist, the proposed method exhibited promising results. Experiment on phantom demonstrates that the error of seed location and orientation is within ( $$0.6\, \pm \, 0.38$$ ) mm and ( $$2.4 \pm 1.2$$ ) $${^{\circ }}$$ , respectively. In addition, the most seed location and orientation error is controlled within 0.8 mm and 3.5 $${^{\circ }}$$ in all cases, respectively. The average process time of seed picking is 8.7 s per 100 seeds. In this paper, an automatic, efficient and robust approach, performed on CT images, is proposed to determine the implanted seed location as well as orientation in a 3D workspace. Through the experiments with phantom and patient data, this approach also successfully exhibits good performance.

WOS: 000425293800016, We present a high-speed method for triangular object detection. The proposed method utilizes the recently developed, real-time edge segment detection algorithm, Edge Drawing; hence, the name EDTriangles, which consists of a detection stage and a validation stage. In the detection stage, EDTriangles extracts edge segments from the image using Edge Drawing and converts these edge segments into line segments, which are then converted into line pairs according to the angles between the line segments and the distance between their endpoints. Next, the line pairs are combined together using some heuristics to generate many triangle candidates, some of which are valid detections and some invalid. Finally, in the validation stage the candidate triangles are validated using the Helmholtz principle and number of false alarms computation to eliminate false detections. Experimental results show that EDTriangles runs very fast, detects various types of triangular objects ranging from narrow to wide-angled triangles and offers a higher detection performance compared to some of the well-known triangle detection algorithms found in the literature.

The objective of this study is to develop an image processing algorithm to determine the average grain size in a metallic microstructure by counting the number of grains using support vector regression (SVR). Automatic grain size measurement algorithm is implemented in the microstructural analysis to attain high speed along with accuracy than manual methods The grain boundaries of various metals were determined using Otsu and canny edge detection techniques. The edge detected image is divided into blocks and the number of white to black transitions in each block is used as feature. The extracted features are used to train the support vector machine in regression mode. The number of grains in test image is determined using support vector regression. The accuracy of developed algorithm is verified using the manual intercept method. The experimental results show that the canny edge detection based feature extraction achieves low root mean square error (RMSE) when compared to Otsu method for detecting the grain count.

Orthogonal polynomials (OPs) are beneficial for image processing. OPs are used to reflect an image or a scene to a moment domain, and moments are subsequently used to extract object contours utilised in various applications. In this study, OP-based edge detection operators are introduced to replace traditional convolution-based and block processing methods with direct matrix multiplication. A mathematical model with empirical study results is established to investigate the performance of the proposed detectors compared with that of traditional algorithms, such as Sobel and Canny operators. The proposed operators are then evaluated by using entire images from a well-known data set. Experimental results reveal that the proposed operator achieves a more favourable interpretation, especially for images distorted by motion effects, than traditional methods do.

WOS: 000395355600008, We extend our recent edge and segment detector, Edge Drawing (GrayED), to detect edge segments in color images. Edge Drawing for color images, named ColorED, takes in a color image, and outputs a set of edge segments, each a contiguous, 1-pixel wide chain of pixels. Detected edge segments are then passed through an 'a contrario' validation step due to the Helmholtz principle to eliminate perceptually invalid detections. We quantitatively evaluate ColorED with different colorspaces and vector gradient operators within the precision-recall framework of the widely-used Berkeley Segmentation Dataset and Benchmark (BSDS300), and compare its results with those of GrayED and a color version of the Canny edge detector named ColorCanny. We conclude that color edge detection is in general superior to grayscale edge detection, and that ColorED with edge segment validation (ColorEDV) greatly outperforms GrayED, ColorED, and ColorCanny, producing an F-score of 0.6593 with DiZenzo and 0.6747 with the Compass operator while taking an average time of 31.5 ms for BSDS300

In this paper, we propose a new synthetic aperture radar (SAR) image detection algorithm based on the de-noising algorithm via the sparse representation and a new morphology edge detector. Firstly, we apply the Shearlet transform to the SAR image to get the sparse representation of it. Then, morphological edge detector with direction is applied to directional sub-band coefficients of the Shearlet which are recovered by the iterative de-noising process. Finally, the completed SAR image edge is obtained by merging each sub-band edge using Dempster–Shafer evidence theory. By completely using the directional sub-bands of the Shearlet transform, the proposed algorithm overcomes the disadvantages of transform detection algorithms which are very unrobust to noise and can also generate inaccurate edges. The experimental results demonstrate the effectiveness and superiority of our proposed algorithm in terms of the edge positioning accuracy, integrity, and the number of false edge points.

This paper presents a fuzzy system for edge detection, using smallest univalue segment assimilating nucleus (USAN) principle and bacterial foraging algorithm (BFA). The proposed algorithm fuzzifies the USAN area obtained from the original image, using a USAN area histogram-based Gaussian membership function. A parametric fuzzy intensification operator (FINT) is proposed to enhance the weak edge information, which results in another fuzzy set. The fuzzy measures, i.e., fuzzy edge quality factor and sharpness factor, are defined on fuzzy sets. The BFA is used to optimize the parameters involved in the fuzzy membership function and the FINT. The fuzzy edge map is obtained using optimized parameters. The adaptive thresholding is used to defuzzify the fuzzy edge map to obtain a binary edge map. The experimental results are analyzed qualitatively and quantitatively. The quantitative measures, i.e., Pratt's figure of merit, Cohen’ Kappa, Shannon's entropy, and edge strength similarity-based edge quality metric, are used. The quantitative results are statistically analyzed using t-test. The proposed algorithm outperforms many of the traditional and state-of-the-art edge detectors.

This paper takes Quercus mongolica as an example, In order to determine the position of insects inside the Quercus mongolica seed. The seeds of Quercus mongolica were cut at a certain thickness to obtain an internal crosssectional image of the seeds. In this paper, Canny and Hough algorithm are used to perform edge detection and line detection on unprocessed images, and determine the coordinate origin of the study area so that all study areas are in the same coordinate range. In order to obtain the position information of the insect path, based on the RGB principle, this paper designs a screening algorithm for the pixel points in the study area, and obtains the position information of the insect track. In order to observe the insect path more intuitively, this paper establishes a three-dimensional data model, and uses MATLAB to draw a three-dimensional structure diagram of the position information of the insect path, and shields the interference items such as seed germ to obtain a more accurate movement track of the insect.

An adaptive Canny edge detector for gray scale images has been presented in this paper. Initially, gradient of gray scale images were obtained by means of Sobel operator and the entropy of gradient images were calculated. Then the gray level, in which the half of entropy of gradient image is achieved, is selected as the upper threshold of Canny edge detector. Additionally the upper and lower thresholds were also selected by means of median, mean, Kapur’s maximum entropy and Otsu’s between-class variance method and the results have been comparatively discussed.

In the production of steel, lateral bending, also known as Camber, is a major defect. However, to achieve production of desired and high quality steel, the ability to accurately measure the strip and plate width, detect the head tail shape of strip slabs and to remove lateral bending during hot rolling is of essence. This is also paramount in achieving ace outturn. In this study, head tail shape detection and width measurement of slabs using machine vision and image processing techniques is developed. A model was developed to measure the width, head tail shape of an iron slab using machine vision. Firstly, impulse noise was removed by computing local similarity analysis and calculating the neighborhood noise evaluation function Gaussian noise is filtered out by combining the inverse of the gray difference together with the reciprocal of the geometric distance. After which improved canny edge detection is implemented. Midpoint and width is measured estimated by using progressive scanline based on edge points. Lastly, the head tail shape is calculated using the measure of depression on the steel plate along the length of the steel direction. The program computes the distance between the most depressive point and straight line that links the two end of the center line. Four simulated slabs are used to test the proposed method, experimental results proved the effectiveness of the study.

Purpose: To propose a novel method to improve the mega-voltage CT (MVCT) image quality for helical TomoTherapy while maintaining the stability on dose calculation.Materials and Methods: The Block-Matching 3D-transform (BM3D) and Discriminative Feature Representation (DFR) methods were combined into a novel BM3D + DFR method for their respective advantages. A phantom (Catphan504) and three serials of clinical (head & neck, chest, and pelvis) MVCT images from 30 patients were acquired using the helical TomoTherapy system. The contrast-to-noise ratio (CNR) and edge detection algorithm (canny) was employed for image quality comparisons between the original and BM3D + DFR enhanced MVCT. A simulated rectangular field of 6 MV X-ray beams were vertically delivered on the original and post-processed MVCT serials of the same CT density phantom, and the dose curves on both serials were compared to test the effects of image enhancement on dose calculation accuracy.Results: In total, 466 transversal MVCT slices were acquired and processed by both BM3D and the proposed BM3D + DFR methods. Compared to the original MVCT image, the BM3D + DFR method presented a remarkable improvement in terms of the soft tissue contrast and noise reduction. For the phantom image, the CNR of the region of interest (ROI) was improved from 1.70 to 4.03. The average CNR of ROIs for 10 patients from each anatomical group, were increased significantly from 1.45 ± 1.51 to 2.09 ± 1.68 for the head & neck (p < 0.001), from 0.92 ± 0.78 to 1.36 ± 0.85 for the chest (p < 0.001), and from 1.12 ± 1.22 to 1.76 ± 1.31 for the pelvis (p < 0.001), respectively. The canny edge detection operator showed that BM3D + DFR provided clearer organ boundaries with less chaos. The root-mean-square of the dosimetry difference on the iso-center passed horizontal dose profile curves and vertical percentage depth dose curves were only 0.09% and 0.06%, respectively.Conclusions: The proposed BM3D + DFR method is feasible to improve the soft tissue contrast for the original MVCT images with coincidence in dose calculation and without compromising resolution. After integration in clinical workflow, the post-processed MVCT may be better applied on image-guided and adaptive helical TomoTherapy.

Traditional counting of red blood cells (RBC) and white blood cells (WBC) in a urine sample is done manually by a medical technologist. However, this makes the blood cell count subjective to the skill of the laboratory technician and will take much longer time in doing the task. This paper proposes the use of image processing in counting the WBCs and RBCs in a urine sample through use of Canny Edge Detection and Circular Hough Transform algorithm. The process consists of two (2) main parts. First is the Canny Edge Detection and the final part is Circular Hough Transform algorithms. It shows that the proposed system has a percentage accuracy of at least 93.229% in reference to the actual RBC and WBC count result.

Tento článek představuje nové přístupy pro aproximaci výškových 2D dat pomocí radiálních bázových funkcí (RBF). Navržené přístupy respektují místní vlastnosti vstupních dat, tj. Stacionární body, inflexní body, křivost a další důležité vlastnosti dat. Pozice radiálních bázových funkcí pro aproximaci RBF jsou vybírány podle těchto znaků, protože umístění radiálních základních funkcí má významný dopad na konečnou aproximační chybu. Navrhované přístupy byly testovány na několika souborech dat. Testy prokázaly výrazně lepší výsledky aproximace než standardní aproximace RBF s náhodným rozložením umístění radiální bázové funkce. This paper presents new approaches for Radial basis function (RBF) approximation of 2D height data. The proposed approaches respect local properties of the input data, i.e. stationary points, inflection points, the curvature and other important features of the data. Positions of radial basis functions for RBF approximation are selected according to these features, as the placement of radial basis functions has significant impacts on the final approximation error. The proposed approaches were tested on several data sets. The tests proved significantly better approximation results than the standard RBF approximation with the random distribution of placements of radial basis functions.

In this paper, a new algorithm for line detection was proposed. It finds out both ends of a possible straight line by searching the relative maximum points on closed envelope of Canny edge, then obtains the fitting variance of the possible straight lines through least squares fitting, and at last detects the straight lines in images through the ratio between fitting variance and line length as the criteria of straight line determination. Experiments show that compared with traditional methods such as PPHT and LSD, the method has higher precision and faster detection speed.

Whether from the perspective of agricultural production or food safety, potato germination detection is of great significance. Since the features (color, texture and context) of the germination area are similar to those of the non-germination area, the existing vision frameworks are difficult to accurately detect the germinations on the surface of potatoes. In this study, the method for detecting potato germination based on multispectral image combined with supervised multiple threshold segmentation model (SMTSM) and Canny edge detector was proposed. The SMTSM based on Genetic Programming algorithm combined with a hybrid fitness function (HF-GP) was utilized to transform the original multispectral images into multiple 2-D images for improving the contrast between region of interest (ROI) and background. A sub-mask of each transformed image was constructed using optimal segmentation threshold, and all of sub-masks were merged through pixel-multiplication to obtain segmentation mask. Meanwhile, in order to filter out the boundless areas that are misidentified as germinations, Canny edge detector was used on gray image to obtain edge mask. Finally, the segmentation mask and the edge mask were combined to complete the detection of germination of potato. Experimental results shown that the proposed method achieved the TPR of 90.91% and the precision of 89.28% for the edible potatoes, which were 4.17–19.05% and 12.39–24.62% higher than the competitive detectors in TPR and precision respectively. For the breeding potatoes, the proposed method with 89.67% of TPR and 86.37% of precision was 9.74–24.58% and 15.70–20.39% better than the competitors in TPR and precision respectively. The comparison confirms the proposed method has excellent detection effect on potato’s germination.

GGAC is an improvement based on geodesic active contour model (GAC). GGAC model is a widely used method for image segmentation. But, it will be difficult to achieve satisfactory segmentation results to the texture, uneven structure, edge particles, weak edge and other features of the wood surface image. Therefore, the author proposes a segmentation method that integrates the improved Canny edge detection result integrated into the improved GGAC model redrawing boundary stop function, and uses the improved variational level set method to achieve the numerical solution. The algorithm has reduced the choice sensitivity to the initial contour and enhanced the scalability, which can make the profile curve converge to defect edges more rapidly, avoid the local optimum, and improve segmentation effects of weak edges and uneven image. The results are clearer, more consistent and real-time. It has provided a more effective way to segment the wood surface defects, and broadened the application scope of Canny operator and an improved geodesic active contour model.

An image brightness enhancement algorithm for outdoor images was proposed.Images had constant brightness after enhancement regardless of the original brightness.Applied canny edge density was proposed to choose the best citrus-containing circle.The detection algorithm showed different performances depending on size of sub-image.Highest performance was 89.6% for correct identification and 5.0% for false positive. The devastating disease Huanglongbing (HLB) has greatly affected citrus in Florida and other growing regions. Detecting dropped fruit is one method of estimating the presence and severity of the disease. The purpose of this study was to develop a machine vision system that can detect dropped citrus on the ground in varying illumination conditions and identify decaying stages of the dropped fruit. In this paper, a novel method for image brightness correction using a contrast limited adaptive histogram equalization was developed to produce constant image brightness levels between and within images. Objectives of this study were to: (1) solve the varying illumination problem and create a consistent brightness level between and within the images, (2) develop an algorithm to eliminate multiple detections of a single fruit from the circular Hough transform, (3) design an algorithm to evaluate decaying stages of the dropped citrus, and (4) demonstrate ability to create a fruit drop map of citrus at each decaying stage in a commercial citrus grove. The result shows all processed images had desired brightness levels (152 out of 255) with a standard deviation of 1.0. Correct identification of fruit and false positives were measured as 89.6% and 5.0%, respectively. False classifications of decay stages of fruit were as low as 4.2% and 18.5% for recently dropped fruit and rotten fruit, respectively. The techniques developed in this work could be further developed into a commercial machine vision system for a real-time dropped fruit mapping system.

In this paper, we propose a boundary-based method for object segmentation by using only the edge information. The proposed method is especially applied to object segmentation of dangerous firearms and knives in the X-ray images of baggage, where no colour or texture features are available to describe the target object. The Canny edge detector is used to extract edge points from the X-ray image. These edges have cluttered backgrounds and may be discontinuous. A fast spiral search is proposed to connect neighbouring points, either continuous or discontinuous, and form closed contours for individual objects. The distance and direction angle of an edge point in the search process can be obtained from a pre-constructed spiral look-up-table. No computation of the geometric features is required. Thus, the search of the coherent neighbouring points for edge connection is very fast. The experimental results have shown that the proposed method can effectively and efficiently segment a variety of firearms and knives...

Edge detection is an integral component of image processing to enhance the clarity of edges in an image. Detection of edges for an image may help for image segmentation, data compression, and image reconstruction. Edges of an image are considered a type of crucial information that can be extracted by applying detectors with different methodologies. Its main purpose is to simplify the image data in order to minimize the amount of data to be processed. There exist many rich classical edge detection techniques which make use of integer-order differentiation operators and can function in both spatial and frequency domains. In the case of integer-order differentiation operators, the gradient operator is identified by order `one' and the Laplacian by order `two.' This paper demonstrates a new kind of edge detector based on the `fractional' (`non-integer')-order differentiation operation and through the usage of the `fractional Fourier transformation' tool, so as to perform it in the fractional Fourier frequency domain, known as the edge detection based on fractional signal processing approach. It is shown through computer simulations that this approach can detect the edges precisely and efficiently. Finally, the performance of the proposed methodology is illustrated from the quantitative aspects of mean square error and peak signal-to-noise ratio through simulations. The experiments show that, for any grayscale image, this method can obtain better edge detection performance to satisfy human visual sense. Moreover, comparisons are also provided to prove that the proposed method outperforms the classical edge detection operators, interpreted in terms of robustness to noise.

Since 3D measurement technologies have been widely used in manufacturing industries edge detection in a depth image plays an important role in computer vision applications. In this paper, we have proposed an edge detection process in a depth image based on the image based smoothing and morphological operations. In this method we have used the principle of Median filtering, which has a renowned feature for edge preservation properties. The edge detection was done based on Canny Edge detection principle and was improvised with morphological operations, which are represented as combinations of erosion and dilation. Later, we compared our results with some existing methods and exhibited that this method produced better results. However, this method works in multiframe applications with effective framerates. Thus this technique will aid to detect edges robustly from depth images and contribute to promote applications in depth images such as object detection, object segmentation, etc. Highlights A method is proposed that can detect edges from depth images more profoundly. We modified the Canny edge detection method using morphological operations. The proposed method works in multi-frames.

In this paper, a new method has been presented for the extraction of edge information by using Differential Search Optimization Algorithm. The proposed method is based on using a new heuristic image thresholding method for edge detection. The success of the proposed method has been examined on fusion of two remote sensed images. The applicability of the proposed method on edge detection and image fusion problems have been analysed in detail and the empirical results exposed that the proposed method is useful for solving the mentioned problems.

Image segmentation is one of the most important and challenging problems in image processing. The main purpose of image segmentation is to partition an image into a set of disjoint regions with uniform attributes. In this study, we propose an improved method for edge detection and image segmentation using fuzzy cellular automata. In the first stage, we introduce a new edge detection method based on fuzzy cellular automata, called the texture histogram, and empirically demonstrate the efficiency of the proposed method and its robustness in denoising images. In the second stage, we propose an edge detection algorithm by considering the mean values of the edges matrix. In this algorithm, we use four fuzzy rules instead of 32 fuzzy rules reported earlier in the literature. In the third and final stage, we use the local edge in the edge detection stage to more accurately accomplish image segmentation. We demonstrate that the proposed method produces better output images in comparison with the separate segmentation and edge detection methods studied in the literature. In addition, we show that the method proposed in this study is more flexible and efficient when noise is added to an image.

In this paper, a noise-robust color edge detector using gradient matrix and anisotropic Gaussian directional derivative (ANDD) matrix is proposed. In order to alleviate the conflict between high edge resolution and noise robustness in the color Canny detector where the isotropic Gaussian kernels and gradient matrix of the R, G, B components are used, the ANDDs of the three components of a color image are arranged into the ANDD matrix. From its singular value decomposition (SVD), the ANDD-based color edge strength map (CESM) is constructed and is relevant to the pixelwise optimal fusion of the ANDDs of the three components in the sense of color edge enhancement. The merits and defects of the ANDD-based CESM and gradient-based CESM are contrasted to show their complementarity in color edge detection. The two CESMs are fused to develop a new color edge detector. It is compared with the color Canny detector and two recent color edge detectors. The results show that the proposed detector attains better detection performance for noiseless and noisy color images corrupted by white Gaussian noise or impulsive noise of small percentage. A color edge detector using gradient matrix and ANDD matrix is proposed.The SVD of the ANDD matrix is used to determine CESM and CESD of a color image.The new detector has both high edge resolution and noise-robustness.It attains better performance than the two existing color edge detectors.

In this paper, a new class of step edge detection IIR filters derived from the Shen–Castan and Deriche edge detection filters is proposed. To avoid the discontinuity drawback of Shen–Castan edge detector, we multiply its impulse response by a proper function. This function exhibits a behavior closed to the sign(.) for large values of x, and similar to the line f(x) = k.x, for small values of x. Hence, the new edge detector preserves good behaviors of both Deriche and Shen–Castan operators, while a detection-localization product larger than 2 is achieved. Furthermore, it is shown that the proposed edge detector is optimal according to Canny’s criteria. In addition, a recursive implementation of the new operator is proposed that provides a fast edge detection algorithm. Experimental results confirm the high performance of the proposed edge detector.