Canny edge detector and its application to analyze the brazed joint interfaces

Image processing is widely used as a method for visnal inspection in industry. Analyzing the microstructure image of a brazed joint is a very important part of the quality control in products related to brazing aUoys. Edge detection techniques are introduced to analyze the bonding strength of the brazing alloys in this paper. Gaussian filter is used for image smoothing. The sharp edge map produced by the Canny edge detector is added to the smoothed noisy image to generate the edge image, which can show the brazing elements clearly. Using the Canny edge detector as a tool to analyze the bonding strength of brazed joint, the experiment results are robust with a very high level. Therefore, the Canny edge detector can be reliably used to analyze the brazed joint interfaces.

SAR image despeckling based on edge detection and nonsubsampled second generation bandelets

To preserve the sharp features and details of the synthetic aperture radar （SAR） image effectively when despeckling, a despeckling algorithm with edge detection in nonsubsampled second generation bandelet transform （NSBT） domain is proposed. First, the Canny operator is utilized to detect and remove edges from the SAR image. Then the NSBT which has an optimal approximation to the edges of images and a hard thresholding rule are used to approximate the details while despeckling the edge-removed image. Finally, the removed edges are added to the reconstructed image. As the edges axe detected and protected, and the NSBT is used, the proposed algorithm reaches the state-of-the-art effect which realizes both despeckling and preserving edges and details simultaneously. Experimental results show that both the subjective visual effect and the mainly objective performance indexes of the proposed algorithm outperform that of both Bayesian wavelet shrinkage with edge detection and Bayesian least square-Gaussian scale mixture （BLS-GSM）.

Localized Model and Arithmetic System Based on Two Image Sensors Under Complex Circumstance

Two image sensors simulate directly the way of disposing images with the human＇s two eyes, so it has important value to apply in many domains, such as object identification, small unmaned aerial vehicle （UAV）, workpiece localization, robot navigation and so on. The object localization based on two image sensots is studied in this paper. It concentrates on how to apply two charge coupled device （CCD） image sensors to object localization of sphere in complex environments. At first a space model of the two image sensors is set up, then Hough transformation is adopted to get localizated model and arithmetic system. An experiment platform is built in order to prove the correctness and feasibility of that localization algorithm.

Handwritten billet number recognition algorithm based on edge extraction

Character recognition has always been a hot topic in the field of computer vision.However,it is often difficult to obtain high-precision results in the actual scene owing to factors such as lighting conditions and imaging angle.Aiming at the problem of handwritten billet identification in the steel industry,this paper proposes the use of the canny edge extraction method to enhance the contour characteristics of characters.This technique is combined with the object detection network to achieve the automatic identification of blank square numbers and solve the problem of automatic tracking of billet logistics in the production process.The proposed algorithm is applied to the site with more than 2019 images containing characters in the test set.Results show that the proposed algorithm has good practical application potential.

Depth recovery for unstructured farmland road image using an improved SIFT algorithm

Road visual navigation relies on accurate road models.This study was aimed at proposing an improved scale-invariant feature transform(SIFT)algorithm for recovering depth information from farmland road images,which would provide a reliable path for visual navigation.The mean image of pixel value in five channels(R,G,B,S and V)were treated as the inspected image and the feature points of the inspected image were extracted by the Canny algorithm,for achieving precise location of the feature points and ensuring the uniformity and density of the feature points.The mean value of the pixels in 5×5 neighborhood around the feature point at an interval of 45ºin eight directions was then treated as the feature vector,and the differences of the feature vectors were calculated for preliminary matching of the left and right image feature points.In order to achieve the depth information of farmland road images,the energy method of feature points was used for eliminating the mismatched points.Experiments with a binocular stereo vision system were conducted and the results showed that the matching accuracy and time consuming for depth recovery when using the improved SIFT algorithm were 96.48%and 5.6 s,respectively,with the accuracy for depth recovery of-7.17%-2.97%in a certain sight distance.The mean uniformity,time consuming and matching accuracy for all the 60 images under various climates and road conditions were 50%-70%,5.0-6.5 s,and higher than 88%,respectively,indicating that performance for achieving the feature points(e.g.,uniformity,matching accuracy,and algorithm real-time)of the improved SIFT algorithm were superior to that of conventional SIFT algorithm.This study provides an important reference for navigation technology of agricultural equipment based on machine vision.