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The purpose of this paper is to present a new geometric model based on the mathematical morphology paradigm, specialized to provide determinism to the classic morphological operations. The determinism is needed to model dynamic processes that require an order of application, as is the case for designing and manufacturing objects in CAD/CAM environments.

The basic trajectory-based operation is the basis of the proposed morphological specialization. This operation allows the definition of morphological operators that obtain sequentially ordered sets of points from the boundary of the target objects, inexistent determinism in the classical morphological paradigm. From this basic operation, the complete set of morphological operators is redefined, incorporating the concept of boundary and determinism: trajectory-based erosion and dilation, and other morphological filtering operations.

This new morphological framework allows the definition of complex three-dimensional objects, providing arithmetical support to generating machining trajectories, one of the most complex problems currently occurring in CAD/CAM.

The model proposes the integration of the processes of design and manufacture, so that it avoids the problems of accuracy and integrity that present other classic geometric models that divide these processes in two phases. Furthermore, the morphological operative is based on points sets, so the geometric data structures and the operations are intrinsically simple and efficient. Another important value that no excessive computational resources are needed, because only the points in the boundary are processed.

In this paper, we will introduce charm pansystems and provide mathematical models for panweighted field‐network. Various mathematical models of pansystems will be discussed. Some traditional mathematical concepts such as topology space and rough sets theory will be analyzed within this framework.

The purpose of this paper is to detect the existence of unknown wireless devices which could result negative means to the privacy. The perceptual layer of internet of things (IoTs) suffers the most significant privacy disclosing because of limited hardware resources, huge quantity and wide varieties of sensing equipment. Determining whether there are unknown wireless devices in the communicating environment is an effective method to implement the privacy protection for the perceptual layer of IoTs.

The authors use horizontal hierarchy slicing (HHS) algorithm to extract the morphology feature of signals. Meanwhile, partitioning around medoids algorithm is used to cluster the HHS curves and agglomerative hierarchical clustering algorithm is utilized to distinguish final results. Link quality indicator (LQI) data are chosen as the network parameters in this research.

Nowadays data encryption and anonymization are the most common methods to protect private information for the perceptual layer of IoTs. However, these efforts are ineffective to avoid privacy disclosure if the communication environment exists unknown wireless nodes which could be malicious devices. How to detect these unknown wireless devices in the communication environment is a valuable topic in the further research.

The authors derive an innovative and passive unknown wireless devices detection method based on the mathematical morphology and machine learning algorithms to detect the existence of unknown wireless devices which could result negative means to the privacy. The simulation results show their effectiveness in privacy protection.

If an autonomous vehicle is working in an image‐based system which needs real‐time answers and whose response is critical, it will be very important to reduce computation times and, as we know, this could be performed by increasing the system parallelism. Since morphological filtering is the origin of several applications in computer vision, in this paper we are going to describe some new features to implement morphological operations by using digital signal processors. After that, an application to path planning is proposed. The standard shortest path planning problem determines a collision‐free path of shortest distance between two distinct locations in an environment scattered with obstacles. Consequently, a path planning algorithm which uses morphological operations and a DSP to process images is then described.

Surveillance is the emerging concept in the current technology, as it plays a vital role in monitoring keen activities at the nooks and corner of the world. Among which moving object identifying and tracking by means of computer vision techniques is the major part in surveillance. If we consider moving object detection in video analysis is the initial step among the various computer applications. The main drawbacks of the existing object tracking method is a time-consuming approach if the video contains a high volume of information. There arise certain issues in choosing the optimum tracking technique for this huge volume of data. Further, the situation becomes worse when the tracked object varies orientation over time and also it is difficult to predict multiple objects at the same time. In order to overcome these issues here, we have intended to propose an effective method for object detection and movement tracking. In this paper, we proposed robust video object detection and tracking technique. The proposed technique is divided into three phases namely detection phase, tracking phase and evaluation phase in which detection phase contains Foreground segmentation and Noise reduction. Mixture of Adaptive Gaussian (MoAG) model is proposed to achieve the efficient foreground segmentation. In addition to it the fuzzy morphological filter model is implemented for removing the noise present in the foreground segmented frames. Moving object tracking is achieved by the blob detection which comes under tracking phase. Finally, the evaluation phase has feature extraction and classification. Texture based and quality based features are extracted from the processed frames which is given for classification. For classification we are using J48 ie, decision tree based classifier. The performance of the proposed technique is analyzed with existing techniques k-NN and MLP in terms of precision, recall, f-measure and ROC.

This paper aims to demonstrate the principle and practical applications of hyperspectral object detection, carry out the problem we now face and the possible solution. Also some challenges in this field are discussed.

First, the paper summarized the current research status of the hyperspectral techniques. Then, the paper demonstrated the development of underwater hyperspectral techniques from three major aspects, which are UHI preprocess, unmixing and applications. Finally, the paper presents a conclusion of applications of hyperspectral imaging and future research directions.

Various methods and scenarios for underwater object detection with hyperspectral imaging are compared, which include preprocessing, unmixing and classification. A summary is made to demonstrate the application scope and results of different methods, which may play an important role in the application of underwater hyperspectral object detection in the future.

This paper introduced several methods of hyperspectral image process, give out the conclusion of the advantages and disadvantages of each method, then demonstrated the challenges we face and the possible way to deal with them.

Classically, the mathematical morphology operators (i.e. erosion, dilation and its combinations) have not been implemented when working in real‐time constraints, as its computation time is often too high for this kind of applications. There are currently some approaches in order to solve this problem. The purpose of this paper is to present an alternative for this classical operators so as to speed up effectively their running time.

The paper shows the algorithm, calculate the computational complexity and finally, implement the fast operators to compare the increasing velocity of this choice using different kind of gray‐scale images.

The experimental results verify the theoretical results, as is pointed out throughout this work.

Highlights two techniques for image processing using mathematical morphology.

This paper aims to develop a method to improve the accuracy of tolerance analysis considering the spatial distribution characteristics of part surface morphology (SDCPSM) and local surface deformations (LSD) of planar mating surfaces during the assembly process.

First, this paper proposes a skin modeling method considering SDCPSM based on Non-Gaussian random field. Second, based on the skin model shapes, an improved boundary element method is adopted to solve LSD of nonideal planar mating surfaces, and the progressive contact method is adopted to obtain relative positioning deviation of mating surfaces. Finally, the case study is given to verify the proposed approach.

Through the case study, the results show that different SDCPSM have different influences on tolerance analysis, and LSD have nonnegligible and different influence on tolerance analysis considering different SDCPSM. In addition, the LSD have a greater influence on translational deviation along the z-axis than rotational deviation around the x- and y-axes.

The surface morphology with different spatial distribution characteristics leads to different contact behavior of planar mating surfaces, especially when considering the LSD of mating surfaces during the assembly process, which will have further influence on tolerance analysis. To address the above problem, this paper proposes a tolerance analysis method with skin modeling considering SDCPSM and LSD of mating surfaces, which can help to improve the accuracy of tolerance analysis.

The purpose of this paper is to develop a new method for detection and classification of power quality disturbances such as transients, waveform distortions, sags, swells and interruptions.

For the purposes of the proposed method, the power quality disturbances are divided into two groups. Different algorithms are applied to detect and classify the disturbances from each of the two groups. For the processing of transients and waveform distortions, digital high‐pass filter and the mathematical morphology closing are used. Calculation of the RMS value is used for detection of sags, swells and interruptions.

The proposed method was implemented in a PC‐based measuring setup. The measuring setup was used in a seven‐months‐long monitoring of a single‐phase power system. In the course of the monitoring, the proposed method was verified on over 19,000 transients, 3,500 waveform distortions, 77 sags and 18 interruptions.

The classification stage of the proposed method does not differentiate between individual types of waveform distortions (harmonics, interharmonics, noise…).

The described approach is simpler and more reliable than, for example, methods based solely on wavelet transform. The proposed method is suitable for real‐time monitoring of power systems.

The paper describes a new and efficient way of detection and classification of disturbances (especially of transients and waveform distortions). It shows that mathematical morphology operations, which are normally used in image processing, represent a useful tool also in the field of power quality measurements.

The purpose of this paper is to present a detection method based on computer vision for automatic flexible printed circuit (FPC) defect detection.

This paper proposes a new method of watershed segmentation based on morphology. A dimensional increment matrix calculation method and an image segmentation method combined with a fuzzy clustering algorithm are provided. The visibility of the segmented image and the segmentation accuracy of a defective image are guaranteed.

Compared with the traditional one, the segmentation result obtained in this study is superior in aspects of noise control and defect segmentation. It completely proves that the segmentation method proposed in this study is better matches the requirements of FPC defect extraction and can more effectively provide the segmentation result. Compared with traditional human operators, this system ensures greater accuracy and more objective detection results.

The extraction of FPC defect characteristics contains some obvious characteristics as well as many implied characteristics. These characteristics can be extracted through specific space conversion and arithmetical operation. Therefore, more images are required for analysis and foresight to establish a more widely used FPC defect detection sorting algorithm.

This paper proposes a new method of watershed segmentation based on morphology. It combines a traditional edge detection algorithm and mathematical morphology. The FPC surface defect detection system can meet the requirements of online detection through constant design and improvement. Therefore, human operators will be replaced by machine vision, which can preferably reduce the production costs and improve the efficiency of FPC production.