This paper introduces the fundamental theory of Canny operator and carries on its analysis and evaluation.On this foundation,an improved Canny edge detection algorithm based on adaptive smooth filtering is proposed.According to the revulsion characteristic of image pixels gray scale,this algorithm adaptively changes the coefficients of the filter.The results of the experiment pictures indicate that the improved algorithm has better accuracy and precision in the edge detection.

An improved Canny edge detection algorithm

Image segmentation is an important preprocessing operation in image recognition and computer vision. This paper proposes an adaptive K-means image segmentation method, which generates accurate segmentation results with simple operation and avoids the interactive input of K value. This method transforms the color space of images into LAB color space firstly. And the value of luminance components is set to a particular value, in order to reduce the effect of light on image segmentation. Then, the equivalent relation between K values and the number of connected domains after setting threshold is used to segment the image adaptively. After morphological processing, maximum connected domain extraction and matching with the original image, the final segmentation results are obtained. Experiments proof that the method proposed in this paper is not only simple but also accurate and effective.

/pdf/image-segmentation-based-on-adaptive-k-means-algorithm-b68gg58et9.pdf

Image segmentation based on adaptive K -means algorithm

As a traditional edge detection algorithm, Canny operator is widely used and improved. Canny adopts hysteresis threshold, the different values of threshold have great influence on the detection result, but the number can not reflect the detection result intuitively. Four of algorithms are introduced through the use of Open CV programming, the programming can visually display the detection results under different thresholds, which is convenient for image edge detection.

Canny edge detection based on Open CV

Although a frequency response analysis (FRA) technique has been extensively used to detect mechanical deformation within power transformers, interpretation of FRA signature still needs a high level of expertise to identify and quantify faults as there is no FRA interpretation code widely accepted yet. All commercial frequency response analyzers can measure the magnitude and phase angle of the impedance, admittance, or transfer function of each phase in a wide frequency range; however, only magnitude is currently used for fault identification and quantification. This paper presents a novel approach for FRA signature interpretation by incorporating the FRA magnitude and phase plots into one polar plot that captures more features of the measured signal than the magnitude plot. Digital image processing-based techniques are employed to automate the fault identification and quantification process. To investigate the impact of transformer rating and size on the proposed technique, two transformers of different ratings and physical geometrical dimensions are simulated using 3-D finite-element analysis to emulate transformer real operation. Short-circuit of various fault levels is simulated at various locations within the high-voltage and low-voltage windings of the two transformer models and the obtained FRA polar plot signature for each case study is analyzed and compared with the healthy signature. Also, practical measurement is conducted to validate the simulation results. Results show that fault level along with fault location can be easily identified using the proposed polar plot signature along with the developed digital image processing technique.

Application of Digital Image Processing to Detect Short-Circuit Turns in Power Transformers Using Frequency Response Analysis

Breast Cancer is the most commonly diagnosed form of cancer in women. Infrared Thermography is a promising technology for breast cancer detection. But analysis of Breast thermograms has often been subjective and has resulted in inconsistency in the diagnosis of breast diseases by thermography. The fast growing tumor has a higher metabolic rate and associated increase in local vascularisation. It will cause the occurrence of some asymmetric heat patterns. Clinical interpretation of a breast thermogram is primarily based on the asymmetry analysis of these heat patterns visually and subjectively. In this paper, a new approach for automatic segmentation of Region of Interest and asymmetry analysis of breast thermograms is implemented. Canny edge detection operator and gradient operator are used to first segment the region of interest. Further asymmetry analysis is performed according to seven extracted features. The abnormality of a breast thermogram is clearly indicated by the features. A GUI is further created in Matlab to make the approach effectual, feasible and for Real Time analysis. 31 thermograms of normal and 11 thermograms of abnormal volunteers were taken.

/pdf/image-segmentation-and-asymmetry-analysis-of-breast-2sy89tk62h.pdf

Image Segmentation and Asymmetry Analysis of Breast Thermograms for Tumor Detection

CANNY arithmetic operator has been proved to have good detective effect in the common usage of edge detection However, CANNY operator also has certain deficiencies Based on the analysis of the traditional CANNY algorithm, an improved canny algorithm is proposed in this paper In the algorithm, self-adaptive filter is used to replace the Gaussian filter, morphological thinning is adopted to thin the edge and morphological operator is used to achieved the refining treatment of edge points detection and the single pixel level edge The results of experiment show the improved CANNY algorithm is reasonable

An Improved CANNY Edge Detection Algorithm

Due to rich spectral information, hyperspectral images (HSIs) have been widely used in various fields. However, limited by imaging systems, the low spatial resolution of HSIs has become an important problem. In this article, for enhancing the spatial resolution, Interactformer is proposed to interact with global and local features extracted by transformer and 3-D convolutional neural network (CNN) branches. Within the transformer branch, a separable self-attention module with linear complexity is designed to solve the problem that traditional self-attention mechanisms suffer from large memory costs due to quadratic complexity. In the 3-D CNN branch, the spectral attention module and 3-D convolution are applied jointly to better protect the spectral correlation among spectral bands and facilitate local feature extraction of HSIs. The interactive attention unit between the two parallel branches is designed to interact with local and global feature information adaptively. Compared with state-of-the-art super-resolution (SR) methods, the proposed method reconstructs better HSI in simulated SR experiments, real SR experiments, and classification experiments, which proves that Interactformer can effectively improve the spatial resolution while preserving the spectral information.

Interactformer: Interactive Transformer and CNN for Hyperspectral Image Super-Resolution

Stacked maximal quality-driven autoencoder: Deep feature representation for soft analyzer and its application on industrial processes

With the rapid growth of underground cable trenches, the corresponding inspections become a heavy burden, and an intelligent inspection robot for automatic examinations in underground cable trenches would be a suitable solution. To achieve this, this paper establishes one new navigation methodology for intelligent inspection robots, especially when applied in complex scenarios and the corresponding hardware. Firstly, to map the underground trenches with higher precision, an improved graph optimization cartographer-SLAM algorithm is proposed, which is based on the combination of depth camera and LIDAR. The depth image is converted into pseudo laser data, and fused with LIDAR data for calibration. Secondly, to overcome the low precision of the Laser odometer due to the uneven ground, an adaptive keyframe selection method is designed. Thirdly, the forward A* model is presented, which has been adjusted in three aspects, including the convergence of node searching, the cost function, and the path smoothness, to adapt to the narrow underground environment for global path planning. Fourthly, to realize dynamic obstacle avoidance, an improved fusion scheme is built to integrate the proposed global path planning algorithm and the dynamic window approach (DWA). In the case study, the simulation experiments showed the advantage of the forward A* algorithm over the state-of-the-art algorithm in both time consumption and the number of inflection points generated, the field tests illustrated the effect of the fusion of depth camera images and LIDAR. Hence, the feasibility of this navigation methodology can be verified, and the average length of path and time consumption decreased by 6.5% and 17.8%, respectively, compared with the traditional methods.

/pdf/an-intelligent-inspection-robot-for-underground-cable-ch97dig5.pdf

An Intelligent Inspection Robot for Underground Cable Trenches Based on Adaptive 2D-SLAM

Deep convolutional neural networks (CNNs) have made great progress in the super-resolution (SR) of hyperspectral images (HSIs). However, most methods utilize convolution to explore local features, and global features are ignored. It is expected that combining nonlocal mechanism with CNN will improve the performance of HSI SR. This article presents a multilevel progressive HSI SR network. The dense nonlocal and local block (DNLB) is constructed to combine local and global features, which are used to reconstruct SR images at each level. Due to the high dimension of HSI, original nonlocal methods produce memory-expensive attention maps. We develop a nonlocal channel attention block to extract the global features of HSIs efficiently. Spatial-spectral gradient is injected in the nonlocal attention block to obtain better details. Furthermore, the progressive learning mode-based multilevel network is proposed to reconstruct HSI with fine details. A number of experiments demonstrate that our method can reconstruct HSIs more accurately than existing methods.

ShaoSheng Fan

Papers

An Improved CANNY Edge Detection Algorithm

Interactformer: Interactive Transformer and CNN for Hyperspectral Image Super-Resolution

Stacked maximal quality-driven autoencoder: Deep feature representation for soft analyzer and its application on industrial processes

An Intelligent Inspection Robot for Underground Cable Trenches Based on Adaptive 2D-SLAM

Multilevel Progressive Network With Nonlocal Channel Attention for Hyperspectral Image Super-Resolution