Watershed-based Image Segmentation with Region Merging and Edge Detection

The clustering technique is used to examine each pixel in the image which assigned to one of the clusters depending on the minimum distance to obtain primary classified image into different intensity regions. A watershed transformation technique is then employes. This includes: gradient of the classified image, dividing the image into markers, checking the Marker Image to see if it has zero points (watershed lines). The watershed lines are then deleted in the Marker Image created by watershed algorithm. A Region Adjacency Graph (RAG) and Region Adjacency Boundary (RAB) are created between two regions from Marker Image. Finally region merging is done according to region average intensity and two edge strengths (T1, T2). The approach of the authors is tested on remote sensing and brain MR medical images. The final segmentation result is one closed boundary per actual region in the image.

Image processing of weld pool and keyhole in Nd：YAG laser welding based on edge predicting

Laser welding is one of high efficiency, high energy density welding methods. Quality control should be applied to ensure good welding quality. Weld pool and keyhole contains abundant information of welding quality. Good image processing algorithm is necessary in quality control system based on visual sensing. Aiming at the image captured by a coaxial visual sensing system for laser welding, an image processing algorithm is designed. An edge predicting method is proposed in image processing algorithm which is based on the fact that the local shape of weld pool can be fitted to a circle. The results show that the algorithm works well. It lays solid foundation for further quality control in laser welding.

Edge detection of potential field data based on image processing methods

The conventional methods of edge detection can roughly delineate edge position of geological bodies,but there are still some problems such as low detection accuracy and being susceptible to noise interference.In this paper,three image processing methods,Canny,Lo G and Sobel operators are briefly introduced,and applied to edge detection to determine the edge of geological bodies.Furthermore,model data is built to analyze the edge detection ability of this image processing methods,and compare with conventional methods.Combined with gravity anomaly of Sichuan basin and magnetic anomaly of Zhurihe area,the detection effect of image processing methods is further verified in real data.The results show that image processing methods can be applied to effectively identify the edge of geological bodies.Moreover,when both positive and negative anomalies exist and noise is abundant,fake edge can be avoided and edge division is clearer,and satisfactory results of edge detection are obtained.

Enhancing Building Facade Image Segmentation via Object-Wise Processing and Cascade U-Net

The growing demand for energy-efficient solutions has led to increased interest in analyzing building facades,as buildings contribute significantly to energy consumption in urban environments.However,conventional image segmentation methods often struggle to capture fine details such as edges and contours,limiting their effectiveness in identifying areas prone to energy loss.To address this challenge,we propose a novel segmentation methodology that combines object-wise processing with a two-stage deep learning model,Cascade U-Net.Object-wise processing isolates components of the facade,such as walls and windows,for independent analysis,while Cascade U-Net incorporates contour information to enhance segmentation accuracy.The methodology involves four steps:object isolation,which crops and adjusts the image based on bounding boxes;contour extraction,which derives contours;image segmentation,which modifies and reuses contours as guide data in Cascade U-Net to segment areas;and segmentation synthesis,which integrates the results obtained for each object to produce the final segmentation map.Applied to a dataset of Korean building images,the proposed method significantly outperformed traditional models,demonstrating improved accuracy and the ability to preserve critical structural details.Furthermore,we applied this approach to classify window thermal loss in real-world scenarios using infrared images,showing its potential to identify windows vulnerable to energy loss.Notably,our Cascade U-Net,which builds upon the relatively lightweight U-Net architecture,also exhibited strong performance,reinforcing the practical value of this method.Our approach offers a practical solution for enhancing energy efficiency in buildings by providing more precise segmentation results.

UDT:U-shaped deformable transformer for subarachnoid haemorrhage image segmentation

Subarachnoid haemorrhage(SAH),mostly caused by the rupture of intracranial aneu-rysm,is a common disease with a high fatality rate.SAH lesions are generally diffusely distributed,showing a variety of scales with irregular edges.The complex characteristics of lesions make SAH segmentation a challenging task.To cope with these difficulties,a u-shaped deformable transformer(UDT)is proposed for SAH segmentation.Specifically,first,a multi-scale deformable attention(MSDA)module is exploited to model the diffuseness and scale-variant characteristics of SAH lesions,where the MSDA module can fuse features in different scales and adjust the attention field of each element dynamically to generate discriminative multi-scale features.Second,the cross deformable attention-based skip connection(CDASC)module is designed to model the irregular edge char-acteristic of SAH lesions,where the CDASC module can utilise the spatial details from encoder features to refine the spatial information of decoder features.Third,the MSDA and CDASC modules are embedded into the backbone Res-UNet to construct the proposed UDT.Extensive experiments are conducted on the self-built SAH-CT dataset and two public medical datasets(GlaS and MoNuSeg).Experimental results show that the presented UDT achieves the state-of-the-art performance.

SEGMENTATION ALGORITHM BASED ON EDGE-SEARCHING FOR MULTI-LINEAR STRUCTURED LIGHT IMAGES

Aiming at the problem that the existence of disturbances on the edges of light-stripe makes the segmentation of the light-stripes images difficult, a new segmentation algorithm based on edge-searching is presented. It firstly calculates every edge pixel＇s horizontal coordinate grads to produce the corresponding grads-edge, then uses a designed length-variable l D template to scan the light-stripes＇ grads-edges. The template is able to find the disturbances with different width utilizing the distributing character of the edge disturbances. The found disturbances are eliminated finally. The algorithm not only can smoothly segment the light-stripes images, but also eliminate most disturbances on the light-stripes＇ edges without damaging the light-stripes images＇ 3D information. A practical example of using the proposed algorithm is given in the end. It is proved that the efficiency of the algorithm has been improved obviously by comparison.

Image processing of weld pool and keyhole in Nd:YAG laser welding of stainless steel based on visual sensing

In order to obtain good welding quality, it is necessary to apply quality control because there are many influencing factors in laser welding process. The key to realize welding quality control is to obtain the quality information. Abundant weld quality information is contained in weld pool and keyhole. Aiming at Nd：YAG laser welding of stainless steel, a coaxial visual sensing system was constructed. The images of weld pool and keyhole were obtained. Based on the gray character of weld pool and keyhole in images, an image processing algorithm was designed. The search start point and search criteria of weld pool and keyhole edge were determined respectively.

EFFECTIVE IMAGE SEGMENTATION FRAMEWORK FOR GAUSSIAN MIXTURE MODEL INCORPORATING LOCAL INFORMATION

A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the sec- ond step, the discriminant-based methods or clustering-based methods are performed on the reformed distribution. It is focused on the typical clustering methods-Gaussian mixture model （GMM） and its variant to demonstrate the feasibility of the framework. Due to the independence of the first step in its second step, it can be integrated into the pixel-based and the histogram-based methods to improve their segmentation quality. The experiments on artificial and real images show that the framework can achieve effective and robust segmentation results.

Real-time image processing and display in object size detection based on VC++

Real-time detection for object size has now become a hot topic in the testing field and image processing is the core algorithm. This paper focuses on the processing and display of the collected dynamic images to achieve a real-time image pro- cessing for the moving objects. Firstly, the median filtering, gain calibration, image segmentation, image binarization, cor- ner detection and edge fitting are employed to process the images of the moving objects to make the image close to the real object. Then, the processed images are simultaneously displayed on a real-time basis to make it easier to analyze, understand and identify them, and thus it reduces the computation complexity. Finally, human-computer interaction （HCI）-friendly in- terface based on VC ＋＋ is designed to accomplish the digital logic transform, image processing and real-time display of the objects. The experiment shows that the proposed algorithm and software design have better real-time performance and accu- racy which can meet the industrial needs.

Feature fusion method for edge detection of color images

A novel feature fusion method is proposed for the edge detection of color images. Except for the typical features used in edge detection, the color contrast similarity and the orientation consistency are also selected as the features. The four features are combined together as a parameter to detect the edges of color images. Experimental results show that the method can inhibit noisy edges and facilitate the detection for weak edges. It has a better performance than conventional methods in noisy environments.

Review of Theory and Methods of Image Segmentation

Image segmentation refers to the technique and process of partitioning a digital image into multiple segments based on image characteristics so as to extract the object of interest from it. It is a key step from image processing to image analysis. In the mid-1950s, people began to study image segmentation. For decades, various methods for image segmentation have been proposed. In this paper, traditional image segmentation methods and some new methods appearing in recent years were reviewed. Thresholding segmentation methods, region-based, edge detection-based and segmentation methods based on specific theoretical tools were introduced in detail.

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）.

Novel welding image processing method based on fractal theory

Computer vision has come into used in the fields of welding process control and automation. In order to improve precision and rapidity of welding image processing, a novel method based on fractal theory has been put forward in this paper. Compared with traditional methods, the image is preliminarily processed in the macroscopic regions then thoroughly analyzed in the microscopic regions in the new method. With which, an image is divided up to some regions according to the different fractal characters of image edge, and the fuzzy regions including image edges are detected out, then image edges are identified with Sobel operator and curved by LSM (Lease Square Method). Since the data to be processed have been decreased and the noise of image has been reduced, it has been testified through experiments that edges of weld seam or weld pool could be recognized correctly and quickly.

Recognition and Classification of Pomegranate Leaves Diseases by Image Processing and Machine Learning Techniques

Disease recognition in plants is one of the essential problems in agricultural image processing.This article focuses on designing a framework that can recognize and classify diseases on pomegranate plants exactly.The framework utilizes image processing techniques such as image acquisition,image resizing,image enhancement,image segmentation,ROI extraction(region of interest),and feature extraction.An image dataset related to pomegranate leaf disease is utilized to implement the framework,divided into a training set and a test set.In the implementation process,techniques such as image enhancement and image segmentation are primarily used for identifying ROI and features.An image classification will then be implemented by combining a supervised learning model with a support vector machine.The proposed framework is developed based on MATLAB with a graphical user interface.According to the experimental results,the proposed framework can achieve 98.39%accuracy for classifying diseased and healthy leaves.Moreover,the framework can achieve an accuracy of 98.07%for classifying diseases on pomegranate leaves.

An Improved Algorithm for Image Edge Detection Based on Lifting Scheme

Wavelet transform is an ideal way for edge detection because of its multi-scale property, localization both in time and frequency domain, sensitivity to the abrupt change of signals, and so on. An improved algorithm for image edge detection based on Lifting Scheme is proposed in this paper. The simulation results show that our improved method can better reflect edge information of images.

APPLICATION OF MVP IN REAL TIME IMAGE PROCESSING

MVP is a digital signal processor, which is of MIMD structure and fit for multimedia application. MVP has several processors in it, and its operation is characteristic of parallelism and pipeline; therefore, real-time signal processing can be done on it. This paper presents the image processing system based on MVP, explains the principles of parallel task assignment and hardware pipeline design, and gives out the example of target tracking and edge detection.

Techniques of Image Processing Based on Artificial Neural Networks

This paper presented an online quality inspection system based on artificial neural networks. Chromatism classification and edge detection are two difficult problems in glass steel surface quality inspection. Two artificial neural networks were made and the two problems were solved. The one solved chromatism classification. Hue, saturation and their probability of three colors, whose appearing probabilities were maximum in color histogram, were selected as input parameters, and the number of output node could be adjusted with the change of requirement. The other solved edge detection. In this neutral network, edge detection of gray scale image was able to be tested with trained neural networks for a binary image. It prevent the difficulty that the number of needed training samples was too large if gray scale images were directly regarded as training samples. This system is able to be applied to not only glass steel fault inspection but also other product online quality inspection and classification.

Development and Parallelization of an Improved 2D Moving Window Standard Deviation Python Routine for Image Segmentation Purposes

Two additional features are particularly useful in pixelwise satellite data segmentation using neural networks: one results from local window averaging around each pixel (MWA) and another uses a standard deviation estimator (MWSD) instead of the average. While the former’s complexity has already been solved to a satisfying minimum, the latter did not. This article proposes a new algorithm that can substitute a naive MWSD, by making the complexity of the computational process fall from O(N2n2) to O(N2n), where N is a square input array side, and n is the moving window’s side length. The Numba python compiler was used to make python a competitive high-performance computing language in our optimizations. Our results show efficiency benchmars

A Multi-Classifier Based Prediction Model for Phishing Emails Detection Using Topic Modelling, Named Entity Recognition and Image Processing

Phishing is the act of attempting to steal a user’s financial and personal information, such as credit card numbers and passwords by pretending to be a trustworthy participant, during online communication. Attackers may direct the users to a fake website that could seem legitimate, and then gather useful and confidential information using that site. In order to protect users from Social Engineering techniques such as phishing, various measures have been developed, including improvement of Technical Security. In this paper, we propose a new technique, namely, “A Prediction Model for the Detection of Phishing e-mails using Topic Modelling, Named Entity Recognition and Image Processing”. The features extracted are Topic Modelling features, Named Entity features and Structural features. A multi-classifier prediction model is used to detect the phishing mails. Experimental results show that the multi-classification technique outperforms the single-classifier-based prediction techniques. The resultant accuracy of the detection of phishing e-mail is 99% with the highest False Positive Rate being 2.1%.

Dual-Branch-UNet: A Dual-Branch Convolutional Neural Network for Medical Image Segmentation

In intelligent perception and diagnosis of medical equipment,the visual and morphological changes in retinal vessels are closely related to the severity of cardiovascular diseases(e.g.,diabetes and hypertension).Intelligent auxiliary diagnosis of these diseases depends on the accuracy of the retinal vascular segmentation results.To address this challenge,we design a Dual-Branch-UNet framework,which comprises a Dual-Branch encoder structure for feature extraction based on the traditional U-Net model for medical image segmentation.To be more explicit,we utilize a novel parallel encoder made up of various convolutional modules to enhance the encoder portion of the original U-Net.Then,image features are combined at each layer to produce richer semantic data and the model’s capacity is adjusted to various input images.Meanwhile,in the lower sampling section,we give up pooling and conduct the lower sampling by convolution operation to control step size for information fusion.We also employ an attentionmodule in the decoder stage to filter the image noises so as to lessen the response of irrelevant features.Experiments are verified and compared on the DRIVE and ARIA datasets for retinal vessels segmentation.The proposed Dual-Branch-UNet has proved to be superior to other five typical state-of-the-art methods.