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Process of Canny Edge Detection Algorithm
2021-11-14
Digah Company
17
Process of Canny edge detection algorithm
The Process of Canny edge detection algorithm can be broken down to 5 different steps: Apply Gaussian filter to smooth the image in order to remove the noise Find the intensity gradients of the image Apply non-maximum suppression to get rid of spurious response to edge detection Apply double threshold to determine potential edges Track edge by hysteresis: Finalize the detection of edges by suppressing all the other edges that are weak and not connected to strong edges.Gaussian filterSince all edge detection results are easily affected by the noise in the image, it is essential to filter out the noise to prevent false detection caused by it. To smooth the image, a Gaussian filter kernel is convolved with the image. This step will slightly smooth the image to reduce the effects of obvious noise on the edge detector. The equation for a Gaussian filter kernel of size (2k1)(2k1) is given by: H i j = 1 2 2 exp ( ( i ( k 1 ) ) 2 ( j ( k 1 ) ) 2 2 2 ) ; 1 i , j ( 2 k 1 ) displaystyle H_ij=frac 12pi sigma ^2exp left(-frac (i-(k1))^2(j-(k1))^22sigma ^2
ight);1leq i,jleq (2k1) Here is an example of a 55 Gaussian filter, used to create the adjacent image, with displaystyle sigma = 1. (The asterisk denotes a convolution operation.) B = 1 159 [ 2 4 5 4 2 4 9 12 9 4 5 12 15 12 5 4 9 12 9 4 2 4 5 4 2 ] A . displaystyle mathbf B =frac 1159beginbmatrix2&4&5&4&24&9&12&9&45&12&15&12&54&9&12&9&42&4&5&4&2endbmatrix*mathbf A . It is important to understand that the selection of the size of the Gaussian kernel will affect the performance of the detector. The larger the size is, the lower the detector's sensitivity to noise. Additionally, the localization error to detect the edge will slightly increase with the increase of the Gaussian filter kernel size. A 55 is a good size for most cases, but this will also vary depending on specific situations. Finding the intensity gradient of the imageAn edge in an image may point in a variety of directions, so the Canny algorithm uses four filters to detect horizontal, vertical and diagonal edges in the blurred image. The edge detection operator (such as Roberts, Prewitt, or Sobel) returns a value for the first derivative in the horizontal direction (Gx) and the vertical direction (Gy). From this the edge gradient and direction can be determined: G = G x 2 G y 2 displaystyle mathbf G =sqrt mathbf G _x^2mathbf G _y^2 = atan2 ( G y , G x ) displaystyle mathbf Theta =operatorname atan2 left(mathbf G _y,mathbf G _x
ight) ,where G can be computed using the hypot function and atan2 is the arctangent function with two arguments. The edge direction angle is rounded to one of four angles representing vertical, horizontal and the two diagonals (0, 45, 90 and 135). An edge direction falling in each color region will be set to a specific angle values, for instance in , 22.5] or .5, 180] maps to 0. Non-maximum suppressionNon-maximum suppression is an edge thinning technique. Non-maximum suppression is applied to find "the largest" edge. After applying gradient calculation, the edge extracted from the gradient value is still quite blurred. With respect to criterion 3, there should only be one accurate response to the edge. Thus non-maximum suppression can help to suppress all the gradient values (by setting them to 0) except the local maxima, which indicate locations with the sharpest change of intensity value. The algorithm for each pixel in the gradient image is: Compare the edge strength of the current pixel with the edge strength of the pixel in the positive and negative gradient directions. If the edge strength of the current pixel is the largest compared to the other pixels in the mask with the same direction (e.g., a pixel that is pointing in the y-direction will be compared to the pixel above and below it in the vertical axis), the value will be preserved. Otherwise, the value will be suppressed.In some implementations, the algorithm categorizes the continuous gradient directions into a small set of discrete directions, and then moves a 3x3 filter over the output of the previous step (that is, the edge strength and gradient directions). At every pixel, it suppresses the edge strength of the center pixel (by setting its value to 0) if its magnitude is not greater than the magnitude of the two neighbors in the gradient direction. For example, if the rounded gradient angle is 0 (i.e. the edge is in the north-south direction) the point will be considered to be on the edge if its gradient magnitude is greater than the magnitudes at pixels in the east and west directions, if the rounded gradient angle is 90 (i.e. the edge is in the east-west direction) the point will be considered to be on the edge if its gradient magnitude is greater than the magnitudes at pixels in the north and south directions, if the rounded gradient angle is 135 (i.e. the edge is in the northeast-southwest direction) the point will be considered to be on the edge if its gradient magnitude is greater than the magnitudes at pixels in the north west and south-east directions, if the rounded gradient angle is 45 (i.e. the edge is in the north west-south east direction) the point will be considered to be on the edge if its gradient magnitude is greater than the magnitudes at pixels in the north east and south west directions.In more accurate implementations, linear interpolation is used between the two neighbouring pixels that straddle the gradient direction. For example, if the gradient angle is between 89 and 180, interpolation between gradients at the north and north east pixels will give one interpolated value, and interpolation between the south and south west pixels will give the other (using the conventions of the last paragraph). The gradient magnitude at the central pixel must be greater than both of these for it to be marked as an edge. Note that the sign of the direction is irrelevant, i.e. north-south is the same as south-north and so on. Double thresholdAfter application of non-maximum suppression, remaining edge pixels provide a more accurate representation of real edges in an image. However, some edge pixels remain that are caused by noise and color variation. In order to account for these spurious responses, it is essential to filter out edge pixels with a weak gradient value and preserve edge pixels with a high gradient value. This is accomplished by selecting high and low threshold values. If an edge pixel's gradient value is higher than the high threshold value, it is marked as a strong edge pixel. If an edge pixel's gradient value is smaller than the high threshold value and larger than the low threshold value, it is marked as a weak edge pixel. If an edge pixel's value is smaller than the low threshold value, it will be suppressed. The two threshold values are empirically determined and their definition will depend on the content of a given input image. Edge tracking by hysteresisSo far, the strong edge pixels should certainly be involved in the final edge image, as they are extracted from the true edges in the image. However, there will be some debate on the weak edge pixels, as these pixels can either be extracted from the true edge, or the noise/color variations. To achieve an accurate result, the weak edges caused by the latter reasons should be removed. Usually a weak edge pixel caused from true edges will be connected to a strong edge pixel while noise responses are unconnected. To track the edge connection, blob analysis is applied by looking at a weak edge pixel and its 8-connected neighborhood pixels. As long as there is one strong edge pixel that is involved in the blob, that weak edge point can be identified as one that should be preserved.
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Although GPR has been widely used in hydrology, engineering, environment and other fields, many basic theoretical and technical problems have not been fundamentally solved, so the real advantages of GPR have not been brought into full play.The main problems existing in GPR technology include:1) The detection depth is shallow, and the contradiction between detection depth and resolution cannot be overcome. Increasing detection depth means sacrificing detection resolution;2) Multiple spread and other clutter jamming are serious, and there has been no good elimination method, which exists in radars at home and abroad;3) The influence of medium unevenness is great and can not be eliminated, resulting in difficulty in obtaining necessary velocity data;4) The data collection method of single sending and single receiving can provide limited information for post-processing and interpretation.The above problems are fatal defects for GPR. Although many geophysicists, electromagnetic experts and geophysical workers have done a lot of research and improvement on radar antenna design, signal processing and underground target imaging, these works are only partial modifications to the existing GPR system. In order to develop GPR technology, we must update our ideas and solve the problems from the fundamental principle.In view of this situation, experts proposed to develop a new GPR system - phased array GPR detection system in 1999.The basic research idea is to replace the current monopole radar antenna with the phased array radar antenna by using the relatively mature military phased array radar technology. Its purpose is to gather the electromagnetic wave into a narrow beam to transmit underground (or detection object) through the phased array technology, and receive the radar echo signal reflected by the target by using the multi-channel acquisition technology, The advanced data processing is carried out, and finally the three-dimensional image of the internal structure of the detection object is given.Development prospect of ground penetrating radar technologyIt is worth noting that at present, similar products have appeared in the market, such as RIS antenna array series of a company, but these products simply combine multiple monopole antennas into array antennas, which is essentially different from the idea of phased array ground penetrating radar.Because the phased array radar converges the electromagnetic wave into a narrow beam by controlling the phase delay of each channel, the energy is concentrated and the wave front diffusion is small. Therefore, the detection depth of the phased array radar is much larger under the condition of the same frequency and transmission power; On the contrary, under the same detection depth, phased array radar can improve the transmission power, so its resolution is much higher than the existing radar. In addition, since the spherical wave transmission is changed to beam transmission, the influence of medium heterogeneity is much smaller.Secondly, the phased array radar works in a continuous scanning mode and can scan in multiple directions. Therefore, the amount of information is much larger than that of the existing ground penetrating radar. For some special detection work, such as the quality detection of embankment cut-off wall, its role is unmatched by the existing ground penetrating radar (monopole antenna radar can not detect the joints, forks and other defects of embankment cut-off wall at all).Because phased array radar is a multi-channel received signal, multi-channel superposition can be carried out, just like the multiple coverage technology of reflection seismic exploration. Therefore, multiple interference can be greatly eliminated, which is difficult for existing radars. The antenna of high frequency (600mhz-1ghz) phased array ground penetrating radar can be made smaller, and its advantages are unmatched by the existing ground penetrating radar in shallow detection.At present, the system prototype has been completed. The carrier free pulse working system with center frequency of 900MHz is adopted, and the transmitting and receiving antennas are separated. 16 (4 × 4) Transmit channel forming, beam aggregation and scanning 16 (4) × 4) The channel receives the echo, and the optional scanning angles are - 36 °, - 24 °, - 12 °, 0 °, 12 °, 24 ° and 36 °.The software part of the system has rich data processing functions. The main conventional processing includes filtering, gain adjustment, static and dynamic correction, deconvolution, complex signal analysis, time-frequency analysis, etc., and multi-channel data processing, such as velocity analysis, superposition technology, coherence analysis technology, array signal processing, etc. And weak signal extraction, target automatic recognition and inversion interpretation under various clutter interference. A large number of field experiments on concrete detection in Yichang Three Gorges dam are carried out. The experimental results show that the spotlight scanning function of phased array radar has been realized, the penetration depth is greater than 1.5m and the resolution is higher than that of ordinary radar.Editing: hfy
Because the paper quality of papermaking production is very important, but in the process of papermaking production, there are many aspects that will cause paper defects. Therefore, it is urgent to adopt advanced technical solutions to solve the existing problems. The SIMV paper surface defect detection system based on machine vision technology scans the paper surface through a linear array camera, cooperates with the SIMV paper surface defect detection system independently developed by semefi to quickly identify defects, automatically classify defects, find defects, alarm and mark, realize 100% paper defect detection, and greatly improve production efficiency.Wuxi semefi Technology Co., Ltd. is located in the International Innovation Park of China sensor network and in Wuxi, the core area of the Yangtze River Delta with beautiful scenery and developed economy. It is a scientific and technological enterprise specializing in machine vision automatic detection equipment. At the same time, it is also an earlier and technologically leading machine vision solution provider in China.Since its establishment, the company has been focusing on the field of machine vision detection, aiming to help enterprises improve product quality, find bad products, save labor and reduce manufacturing costs. Relying on its strong talent, technology and other resource advantages, the company has achieved great success in the application field of machine vision detection. SIMV machine vision inspection system independently developed by the company is widely used in surface quality inspection of steel strip, film, non-woven fabric, paper and other industries.For normal paper, the whiteness is more than 70%, while the whiteness of rotten pulp is generally less than 30%. The whiteness detection can be measured by various optical detection instruments. Because the current paper measurement requires dynamic detection in high-speed operation, a fast detection device must be used, and the application system in the existing technical scheme is the most ideal choice. The SIMV paper surface defect detection system based on machine vision technology scans the paper surface through a linear array camera, cooperates with the SIMV paper surface defect detection system independently developed by semefi to quickly identify defects, automatically classify defects, find defects, alarm and mark, realize 100% paper defect detection, and greatly improve production efficiency.Detection principle: during normal production of the production line, the bright LED linear condensing cold light source irradiates the product surface based on the transmission principle (for paper products with thick paper and high surface defect detection angle, the reflection detection principle is adopted), and the linear array camera erected on the production line performs real-time synchronous scanning, At the same time, the paper defect image collected by the camera is segmented by SIMV image processing unit. Because the gray scale of defect image is obviously different from that of normal products, the system can find defects and effectively judge and classify defects at the same time.Technical parameters:Test objects: newsprint, special paper, coated paper, whiteboard, art paper, cultural paper, cigarette paper, laser paper, etcTypical defects: scratches, bright spots, damages, black spots, holes, folds, oil spots, etcDetection width: any width (different width can be met through the combination of cameras)Detection speed: max2000m / minDetection accuracy: 0.2mm 2mm (depending on detection speed and number of cameras)Defect identification: audible and visual alarm, real-time labeling, real-time display of defect coordinate information distribution mapSystem advantages:1. Accurate positioning and display of defect coordinates (x, y coordinates);2. Real time defect calibration, recording defect diameter, coordinates, type and other information;3. Monitor meters in real time and display alarm information;4. Realize automatic defect classification according to the characteristics of defect shape, area size and gray difference;5. Automatically generate the product defect record of each roll of products, and the user can query directly according to the batch number;6. The product basic information module records the product batch, model, specification, shift number, operator and other information;7. Automatic statistics, storage, query, call and print of detection history;8. Automatically change the volume and automatically print the quality report of each volume of product information;9. Automatic audible and visual alarm, and display the current and front and rear 10 defect images;10. Long time and large capacity history storage;11. Automatic linkage of products, remote technical services, etc.fqj
What is the key technology of PCB defect detectionPCB, that is, printed circuit board, is the carrier connecting various electronic components in electronic products. It is one of the important components of the electronic industry. In this article, I will share the common defect detection technology of PCB, hoping to be helpful to users.The common defect detection technologies of PCB circuit board include automatic optical detection technology, machine vision detection technology, computer vision detection technology and so on.1. Artificial visual subjective judgment methodThe operator needs to determine the qualification rate of PCB with the help of electron microscope or magnifying glass according to the subjective experience and visual measurement of the operator. The budget cost of this technology is low, the required equipment is cheap and easy to operate, but the accuracy is low, the defect detection efficiency is low, and the data collection analysis is difficult.2. Instrument online detection methodOperators can check the open circuit, short circuit and other faults of circuit board welding and the function detection of components through simulation test experiment and electrical performance test. They can also detect the electronic components with excessive density on PCB through boundary scanning technology, and collectively detect the welding points and connections of circuit board. It is a detection technology based on electrical signal as the medium. The advantages of this technology are high conversion rate, low cost and large detection coverage, but the operation is difficult and the operation time is long.3. Function test methodOperators comprehensively test the functional modules of the circuit board on the production line with the help of special test equipment to confirm the good or bad state of the circuit board, but they can not provide in-depth data such as improved electronic components and pin diagnosis. They need to use different test equipment according to different needs, so it is difficult to write the program.4. Visual inspection technologyVisual inspection technology has covered many fields such as electronics, computer, artificial intelligence and image processing. It has become the mainstream research method of PCB defect detection in today's society. It is mainly divided into:1) Automatic optical inspection technology (AOI):Integrated with image processing, automatic control and other computer technologies, PCB welding defects are detected and processed based on optical principle. Mainly scan the PCB through the camera and obtain the image of the PCB solder joint area. Automatically detect the PCB defects through the visual processing technology, extract the characteristic data of each solder joint, contact the database for comparison, determine the types of solder joint defects, identify them, and give the number of detection results.2) Machine vision inspection technology (MVI):Combined with CMOS, digital image sensor, DSP, FPGA and other technologies, the machine is used to replace the naked eye for a variety of PCB measurement and judgment. Because it uses a variety of machine technologies, it is very popular in the field of precision testing and is often used in industrial PCB testing. It has the advantages of fast speed, non-contact and good flexibility, and is easy to avoid huge losses of PCB production line, It has high economic value.3) Computer vision inspection technology (AVI):This technology is developed on the basis of computer vision. It is an emerging high-tech industrial detection technology. It realizes three-dimensional measurement of the size and spatial position of the measured object through the image sensor. The obtained data is compared or extracted with the fault image data in the computer, and guides the action of electronic equipment according to the detection parameters. It has high timeliness and Strong resistance.The above is an overview of PCB defect detection technology.This article is integrated from CSDN and Baidu EncyclopediaEditor ajx
Lingxiang Residual Pressure Monitoring System Is an Intelligent Measurement and Control Equipment fo
On the basis of fully understanding GB code for fire protection design of high rise civil buildings, the bypass valve controls the positive pressure value of pressurized air supply. Firstly, the corridor pressure measurement and control device realizes the accurate monitoring of differential pressure, controls the positive pressure value of pressurized air supply through the bypass valve, and carries out residual pressure control with the positive pressure air supply equipment. Its key core is differential pressure monitoring. In case of fire, it can ensure that the residual pressure value of smoke proof staircase is 40pa-50pa, and that of front room, shared front room, front room of fire elevator room and closed refuge floor (room) is 25pa-30pa. It can not only ensure that smoke and heat can not be invaded, but also ensure that people in the fire area can easily open the door to enter the smoke prevention area, so as to create a smoke-free area and smoke-free passage for people who escape.At present, some systems in the market realize differential pressure monitoring through residual pressure micro differential pressure sensor monitoring module, pressure transmitter, pressure switch and other equipment, but there are the following problems:The monitoring module of residual pressure micro differential pressure sensor is an integrated circuit. It is the original Zui device for pressure measurement. It can only be combined with other peripheral circuits and computer chips to form products with measurement function. Therefore, the monitoring module of residual pressure micro differential pressure sensor can not be directly applied to the measurement and control of residual pressure.The pressure transmitter is a component level product developed by using the residual pressure micro differential pressure sensor monitoring module. Its function is to convert the measured value of differential pressure into 4-20mA standard current signal output. The pressure transmitter cannot directly drive the bypass valve of the positive pressure forced draft fan. Therefore, the pressure transmitter is not directly applicable to the control of residual pressure range.Mechanical pressure switch has low measurement accuracy and large measurement error due to its working principle. Because it can only act based on one differential pressure setting point, it can not realize the differential pressure "hysteresis" function, and can not ensure that the differential pressure between the front room and corridor is controlled at 25Pa 30Pa, and the differential pressure between the staircase and corridor is controlled at 40Pa 50pA. Therefore, the differential pressure switch cannot measure and control the residual pressure range.Lingxiang residual pressure monitoring system is an intelligent measurement and control equipment specially designed for residual pressure detection and control in smoke proof staircase, front room, shared front room, front room of fire elevator room, closed refuge floor (room), etc.Lingxiang residual pressure monitoring systemThe system, also known as residual pressure control system, is composed of lx-jkq fire emergency evacuation residual pressure monitor, lx-kzq fire emergency evacuation residual pressure controller, lx-cgq fire emergency residual pressure sensor and lx-xyf fire emergency evacuation electric pressure relief air valve actuator through computer data bus. The residual pressure of positive pressure air supply is detected through the residual pressure monitoring modules of each floor working independently, Send the control command to the residual pressure controller, and then control the working state of the control module through the residual pressure controller to realize the corresponding spatial differential pressure control, so as to maintain the reasonable range of residual pressure value.Product standards and specifications involved in the equipment; Engineering standards and specifications; Acceptance standards and specifications must fully meet all current regulations and specifications of the people's Republic of China, including but not limited to:JGJ code for electrical design of civil buildingsGB intelligent building design standardGB code for fire protection design of buildingsGB technical standard for smoke control and smoke exhaust system of buildings LW
In recent years, with the rapid development of industrial modernization and intelligence, modern glass, such as panel glass, automobile glass, architectural glass, optical glass, mobile phone cover glass, rear shell glass, 2D glass, 2.5D curved glass, 3D curved glass and other application fields are becoming more and more extensive. Among them, the covering glass industry has developed rapidly, with an annual shipment of about 3 billion pieces. The automatic detection technology of covering glass is called Mount Everest in the field of industrial detection. The quality of glass surface directly affects the quality of products and the later use of users. More and more enterprises begin to pay attention to the detection and control of surface defects in the process of glass online production.Due to the influence of production process, environment and other factors, defects such as crystal spots, scratches, stains, impurities, black spots and clots are easy to appear on the glass surface during the production process. Therefore, the production process of glass cover plate is more complex. Especially after entering the era of 2.5D and 3D cover plate, due to the introduction of more and more complex process and the continuous improvement of final customers' appearance requirements, in the production process, Inevitably, there will be various defects with different characteristics, such as scratches, pits, poor silk screen printing, impurities, different colors and other appearance defects. Defects with various features require the inspector to constantly turn the cover plate, and can be captured at a specific illumination angle or angle of view. This puts forward high requirements for the design and manufacture of automatic detection equipment for glass cover plate to replace manual visual detection. The previous detection methods rely on human eye manual, the program is complex, the efficiency is low, and the output is difficult to guarantee. Upstream and downstream enterprises in the industry need a huge manual inspection team. It is estimated that more than 150000 people are engaged in such work in the Pearl River Delta alone. The inspection work under strong light not only causes great damage to the vision of quality inspection, but also brings heavy labor cost and operation cost to the enterprise.At present, the world's leading manufacturer of glass coverage testing equipment is German equipment. Some domestic glass plate manufacturers have purchased their equipment, but there are still many problems. First, the price is expensive. Second, the equipment can only detect ordinary glass (white), while the radian of 2.5D or 3D glass cover plate cannot be detected. Therefore, the equipment can not meet all the needs of the current market. At present, China has made some technological breakthroughs in glass cover testing, but there is no particularly important enterprise to replace foreign equipment. With the improvement of production capacity and the integration of supply chain in China, especially in the Pearl River Delta, the glass coverage testing industry will usher in explosive growth. At the same time, there are many problems in the field of glass cover detection at home and abroad, and the industry urgently needs innovative mobile phone glass cover detection equipment.The glass surface defect online detection system independently developed by Jingxin technology can completely replace the artificial naked eye for high-speed and accurate 7 * 24-hour real-time online detection of glass production, automatically alarm and display the specific information of current defects (picture display, diameter, coordinate distribution, defect type, etc.), so that workers can deal with defects in time. At the same time, the system automatically prints and saves the test report of each volume of products according to the product batch number, which is convenient for users to query, call and print in the later stage, so as to realize the rating and classification of product quality and the traceability of customer complaints, effectively save labor costs and improve product quality. Therefore, the on-line detection system of glass surface defects has become an indispensable part of the automatic glass production line.[technical parameters]Test objects: float glass, coated glass, tempered glass, optical glass, automotive glass, liquid crystal glass plate, etc;Test contents: pits, scratches, dust, black spots, stains, nodes, mosquitoes, foreign matters, oil stains, folds, fibers, etc;Etc;Detection speed: determined according to the actual production line speed;Inspection width: determined according to the actual width of the production line;Detection accuracy: 0.1mm * 0.1mm (determined according to the configuration of the camera);Treatment methods: screenshots, audible and visual alarm, defect classification, automatic recording location, picture preservation and retrieval;Detection method: select positive or back lighting according to different light transmittance;Other configurations: marking machine (optional);Precautions: edge problems of glass inspection;Typical defect: detection width: any width (various widths are met through the combination of camera types and quantities);Detection speed: maximum 800m / min;After the use of the equipment, the single link detection efficiency of mobile phone glass cover plate is more than 20 times higher than that in the past, and the yield is also greatly improved. The full leakage detection rate of the product is less than 1%, the over inspection rate is less than 2%, which is far lower than the error rate of 8-10% of the full manual inspection in the industry.
Anomaly detection (also known as outlier detection) is a task to detect abnormal instances, which are very different from conventional instances. These instances are called outliers or outliers, while normal instances are called internal values.Anomaly detection can be used in a variety of applications, such as:â‘ Fraud identificationâ‘¡ Detect defective products in manufacturingâ‘¢ Data cleansing -- removing outliers from a data set before training another model.You may have noticed that some unbalanced classification problems are often solved by anomaly detection algorithms. For example, spam detection task can be considered as a classification task (spam is much less than ordinary e-mail), but we can use exception detection to achieve this task.A related task is singular value detection. It differs from anomaly detection in that it is assumed that the algorithm is trained on a clean data set (no outliers). It is widely used in online learning when it is necessary to identify whether a new instance is an outlier.Another related task is density estimation. It is the task of estimating the probability density function of the random process generated by the data set. Density estimation is usually used for anomaly detection (instances located in low-density areas are likely to be anomalies) and data analysis. It is usually solved by clustering algorithm based on density (Gaussian mixture model or DBSCAN).statistical methodThe easiest way to detect outliers is to try statistical methods, which were developed a long time ago. One of the most popular methods is called outlier detection Tukey method (or quartile distance IQR).Its essence is to calculate the range between percentile and quartile. Data points before q1-1.5 * IQR and after Q3 1.5 * IQR are considered outliers. Below you can see an example of using a person's height data set. Heights below 54.95 inches (139 cm) and above 77.75 inches (197 cm) are considered outliers.This and other statistical methods (Z-score method for detecting outliers, etc.) are usually used for data cleaning.Clustering and dimensionality reduction algorithmAnother simple, intuitive and usually effective anomaly detection method is to use some clustering algorithms (such as Gaussian mixture model and DBSCAN) to solve the task of density estimation. Then, any instance located in the low-density area can be considered as an exception. We only need to set some density thresholds.In addition, any with inverse can be used_ The dimension reduction algorithm of transform() method. This is because the abnormal reconstruction error is always much larger than that of the normal example.Isolated forest and SVMSome supervised learning algorithms can also be used for anomaly detection, of which the two most popular are isolated forest and SVM. These algorithms are more suitable for singular value detection, but they are usually also suitable for anomaly detection.The isolated forest algorithm constructs a random forest, in which each decision tree grows randomly. With each step, the forest isolates more and more points until all points become isolated. Because exceptions are located far from the usual data points, they are usually isolated in fewer steps than normal instances. The algorithm performs well for high-dimensional data, but needs a larger data set than SVM.SVM (a kind of SVM in our example) is also widely used in anomaly detection. Kernel SVM can construct an effective "constraint hyperplane", which separates normal points from abnormal points. Like any SVM modification, it can handle high-dimensional or sparse data well, but it is only suitable for small and medium-sized data sets.Local anomaly factorThe local outlier factor (LOF) algorithm is based on the assumption that the anomaly is located in a low-density region. It not only sets the density threshold (as we can do with DBSCAN), but compares the density of a point with the density of K of its nearest neighbor. If the density of this particular point is much lower than that of its neighbors (which means it is far from them), it is considered an anomaly.The algorithm can be used for both anomaly detection and singular value detection. Because of its simple calculation and good quality, it will be often used.Minimum covariance determinantThe minimum covariance determinant (MCD or its modified fast MCD) can be used for outlier detection, especially in data cleaning. It assumes that interior points are generated from a single Gaussian distribution, while outliers are not generated from this distribution. Because many data have normal distribution (or can be simplified to normal distribution), the algorithm usually performs well. In sklearn, the ellipticenvelope class is its implementation.How to select anomaly detection algorithm?If you need to clean up the dataset, you should first try classical statistical methods, such as Tukey method for outlier detection. If you know that the data distribution is Gaussian, you can use fast MCD,.If you don't do exception detection for data cleaning, first try a simple and fast lof. If it doesn't work well (or if you need to separate hyperplanes for some reason) - try other algorithms based on your task and dataset:Single class SVM for sparse high-dimensional data or isolated forest for continuous high-dimensional dataIf you can assume that the data is generated by the mixture of multiple Gaussian distributions, you can try the Gaussian mixture model
Product Counting Reverse Counting Product 3D Scanning Detection Blu Ray High Precision 3D Scanner Ha
3D measurement scanning service descriptionWhen users need to control the quality of their own production, improve the design, or 3D print the designed objects before making samples, or oil mud modeling to form a comparable point cloud data triangle (. ASC;. STL), high-precision portable laser 3D scanning is the basis to ensure the smooth progress of users' work. For any development or production, it is necessary to conduct 3D measurement on the surface of the real object and obtain point cloud data, such as 3C product plastic parts, automobile sludge, sheet metal, mold, gypsum model, etc. the users with the above requirements can entrust us to provide professional 3D scanning services for customers. The service items include:
Detection method of precision spectrum measurement and control melt blown non-woven fabric defect detection system - at present, the detection of non-woven fabric defects is mainly solved by manual detection. However, this method has a slow detection and processing speed, and will cause relatively high labor intensity to workers. At the same time, the final detection effect will be greatly affected by the work experience of inspectors Due to the influence of many factors such as familiarity with process operation, the test results are not reliable. With the continuous development of textile industry and the continuous expansion of production scale, it is more and more urgent to develop a non-woven fabric defect on-line detection system to realize stable, fast and accurate detection.The precision spectrum measurement and control melt blown non-woven fabric defect detection system integrates the current leading machine image vision acquisition technology, photoelectric identification technology, as well as the supporting powerful and complete computer machine vision image software and hardware combination suitable for the production of domestic non-woven fabric products, so as to accurately, accurately and quickly detect the surface defects (defects) of on-line non-woven fabrics at a high speed High resolution industrial camera image display defects are intuitive and super clear. Abandon the "original" online quality inspection relying on cumbersome and low-quality manual naked eyes. Because machine vision inspection adopts unified standards, it is not affected by artificial fatigue / emotion / misjudgment and other factors, which can greatly improve the production efficiency and yield, reduce the labor cost and return rework cost, and realize safety, speed, convenience, quality and quantity.Detection principle of precision spectrum measurement and control melt blown non-woven fabric defect detection system:In view of the uneven thickness, sparse and uneven structure of non-woven fabric and the randomness of defect distribution (front, back and middle of non-woven fabric), the traditional transmission lighting method is easy to cause systematic missed detection and false alarm. The non-woven stain detector adopts a shadowless bright field to form the detection environment, that is, one light source transmits below the material and the other light source reflects above the material. When the production line starts running, the CCD camera scans and shoots synchronously according to the speed signal collected by the encoder, and the collected image is dynamically segmented through the image analysis software algorithm, Through the gray-scale difference between the defect image and the normal product, we can find the defect and carry out alarm, statistics, classification, recording and other operations.Functions of precision spectrum measurement and control melt blown non-woven fabric defect detection system:1. Real time display of defect location distribution map;2. Real time defect image specific information display;3. Automatically generate the product batch number, and manually modify the product batch number during production;4. Establish a complete historical database query according to the product batch number;5. The history record automatically generates the defect position distribution map in reverse order corresponding to the batch number, which is convenient for later volume division;6. Statistical generation and printing of information report;7. Real time capture and display of defect pictures;8. Long time and large capacity history storage;9. Remote technical services;
So I am thinking of a possible answer to my own question:Build my own journal note entry app linked to a wiki.Zim Wiki uses a file based system for wiki. Maybe I could write app to export/sync entries in its files. Or WikkaWikki uses a MySQL (which is probably what I would use for journal entries anyway) so perhaps use same DB for Wiki and journal, with Journal app "doing right thing" to enter/sync Entries in wiki.Does this seem like a good approach?Or maybe I can customize a wiki with existing plugins - "All" I really need is good journaling / date feature, time tracking, checklist, and tags. Perhaps there already are plugins for this?Please comment if you have feedback on this idea.1. Circular tag wiki excerptsTag excerpts should at least try and give a concise definition as to the subject, and provided any usage guidance if necessary.Therefore, you need to make sure to address a set of key points:In general, excerpts should provide at least some guidance, even if it may appear to be ridiculously basic. Therefore, interpretation of the rejection reason is critical:That's like saying, [abs]: For questions about [abs] filaments. That should probably be rejected. This is better: [abs]: For questions about [abs] filaments - filaments that are used with blah blah printers, and are not toxic for use.Or even better: [abs]: For questions about [abs] filaments - filaments that are used with blah blah printers, and are not toxic for use. Not to be confused with [pla] filaments. Do not use this tag if your question does not concern this filament specifically. Obviously, I have no idea if abs is even a thing. Anyways, I hope this helps :)2. Why is this answer a community wiki?Apparently the answer under scrutiny was made CW because it drew on an answer from another Stack site, and the answerer wanted to share it without being associated with it for good or for ill.But community wiki is not a tool for reputation denial (or for dodging the repercussions of questionable-quality answers) and practically speaking I see no difference between quoting a different Stack and quoting a blog or a book. We would never expect someone to eschew rep for quoting a blog or a book. The answerer went to the trouble of tracking down the information and sharing it; why should not rep gains should reflect that?The moderation team is under no obligation to revert the CW in this case, nor are they obligated to leave it be, but I would lean toward reverting it myself, for reasons which follow.We've talked about posting answers from other sites, and it's pretty clear this is not cheese.Community wiki used to be massively overused. Changes to the editing system rendered its original purpose largely moot, and there's now a lot of confusion about CW's role in the Stack mechanics. These days there are three basic reasons to use CW:I do not see this particular answer needing CW to make it "easier to edit and maintain by a wider group of users," so I do not see any reason for it to be a community wiki. Community wiki is a tool with a specific set of uses, and CW rollbacks are left to mod discretion. One of the responsibilities of our moderators is to help the community use the right tools for the job at hand.As for the answer itself--it's not very good by lit.se standards, because it was written for a different site with different priorities. And the question itself is under a tag whose implementation is still being debated, so quality there is... in flux, I suppose we could say. We need to bring in our own expertise and tailor the answer to meet the expectations of our own Stack.3. Need to Change Edit Approval Limits for Tag Wiki EditorsI just upped it from 3 to 5. Hopefully this alleviates the issue a bit. If we still notice a pattern of users being blocked we can look at adjusting further4. Difference between Wiki Library and Document LibraryYes there is difference, Below might be helpful to understand.Document library can contains documents, it can be any document like office documetnts doc,xls,ppt, js, css, jpg, png etc...any file extension you can think of. Main purpose of document library is to store documents. wiki library is a kind of document library which contains wiki pages, Wiki pages are html pages with rich text editing capabilities so that users can create pages using Rich Text Editor without knowing html in detail. So when wiki library template is selected it comes with some default columns created based on content type, this columns are useful to create wiki page. Making changes and edits in a wiki page is incredibly easy. Just click on the edit button (at the top of the page) and immediately the page appears as an editor's version of the page. You can then make edits straight away and simply hit save. The page will instantly be up and running with your changes in place.
About 8 I think1. where can i get ncaa football 10 rosters with names?For the last two years I got mine from "Pastapadre". From what i can tell they are really pretty accurate. Give it a shot2. where can I watch NCAA football games for free?...?Try channelsurfing.net. They pick up a lot of justin.tv stuff, and they have loads of ncaa football games, free, no spam, etc. I watch all the time with no problems3. Can you download a team on NCAA Football 10's Team Builder using a flash drive?Why would you want to do that? Build your own team. Do not take the wussy way out.4. NCAA Football 13 Heisman Challenge 3 Player Pack?The exclusive Heisman Challenge 3-Player Pack including Mark Ingram, Tim Tebow and Matt Leinart. See if you can match the career accomplishments of these former Heisman trophy winners in the all new Heisman Challenge Mode. Its come with your ordered game copy5. What ncaa football team is your favorite one?Oklahoma SOONERS!6. I want to do a dynasty in NCAA Football 14 what team should I use (I want it to be a challenge like a weaker)?Vanderbilt, they are in a great conference and are already headed in the right direction as far as getting better and it will be a challenge because you will be in the SEC7. does ncaa football deserve a playoffs instead of bowl games being based off of BCS rankings?Whether or not the NCAA deserves a playoff does not matter. They do not want one so they do not have one. Frankly, the national championship playoff debate is a joke. This is something created by sportswriters and tolkshow hosts for the sole benefit of sportswriters and talkshow hosts. The NCAA does not recognize a national champion of the FBS so any discussion about who should or should not be the champion is pointless bloviating. Win your conference. Beat your rivals. Go to a bowl game. Those are the only things that matter in college football.8. What was determining the NCAA football title like in pre-BCS years?Hawaii, is not a very reliable team, yet might desire to win the Sugar Bowl in simple terms as surely as Georgia as a results of fact their offense constantly performs like its at the back of and it wears down the protection whilst they might desire to conceal the whole field. The unfold offense is the excellent offense in college soccer. It supplies much less gifted communities liek Hawaii and Illinois a gamble to beat far extra gifted communities. that's alsovery risky, that's why some exceptionally ranked communities dont run it. Hawaii surely did no longer deserve a identify shot in simple terms as a results of fact Boise State gained The Fiesta Bowl. Boise beat a BCS convention team who complete 10-4(Oregon State who beat USC and Hawaii in Hawaii) Hawaii has no high quality wins and does not deserve a NT shot. ending undefeated might desire to get you a BCS sport nevertheless9. NCAA Football 2009-What Defense to run online?Usually the defense package that comes with the team you are using as the best combo of players are on the field10. Which Championship/Playoff Picture needs more work....the NFL or NCAA Football?everybody keeps blaming the computers, but the computers are pretty much the only good part about the BCS, it only counts as 1/3 anyways. another 1/3 is coaches, former players, former athletic directors and people like that, the other third is the media. a lot of voters admitted to not even watching a utah game until they played alabama, how messed up is that. so lets put the blame where it really belongs, the stupid voters, who do not educate themselves about what or who they vote for11. could an NCAA football team beat a weak pro team?No-NCAA teams always have weak links and for the most part are BOYS.NFL teams have chosen the best of the best,and are MEN12. What teams rush the ball the most in ncaa football?its either navy or army one of them thats pretty much all they do13. What D1-AA teams are in NCAA Football 08?Usually they are. Just check out some of the SEC teams schedules. You find a lot of them there. Looks like the truth hurts down there!
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