Quantum circuit design for several morphological image processing methods

Defected woods always influence wood processing. If they are not chosen precisely, the woods will be inferior in quality. In order to obtain contours and internal information of defected woods, they are detected. Images of them are processed for the scientific usage of woods and guarantee of the increasing using rate of woods. Mathematical morphology is a new subject established based on rigorous mathematical theories. In the basis of set theory, mathematical morphology is used in image processing, analysing and comprehending. It is a powerful tool in the geometric morphological analysis and description. Based on the study of mathematical morphology, a new mathematical morphological optimization algorithm is proposed, and it is used in edge detection of defected wood images. For the purpose of suppressing noises and being adapted to different edges of defected wood images, Structuring elements of smooth diamond are chosen appropriately. Mathematical morphological optimization algorithm is constructed by weight adding combination of erosion, dilation, opening and closing operations. The results of simulation in defected wood image processing demonstrate that the method performs better in noise-suppression and edge detection than conventional edge detection operations and morphological gradient method, which also verifies its feasibility and validity. It can be extendedly used in many other fields such as furniture market, security sector and timber manufacturing industry.

Quantum image processing has recently received much attention due to the opportunity to place image data in quantum mechanics systems. The literature survey has shown that the complexity of preparing image data raises the question of how and in what efficient way a medium- and high-size image is represented inside the quantum domain, which is still a big question. Quantum properties such as entanglement, superposition, and parallelism make the quantum system faster and more robust. Qubit is the central basic unit that maps the image data in the quantum processor, which propagates from left to right. The number of qubits and their connection determine the efficiency of the circuit architecture. This chapter aims to investigate the performance of the transformed coefficient-based quantum image representation and compression approaches in terms of the required number of gates and PSNR values. Moreover, the challenges and prospects of quantum image compression and processing will be discussed as a future research direction.

Fully automated snow depth measurements from time-lapse images applying a convolutional neural network

Blood is a red-colored fluid in the human body that plays a crucial role in maintaining the immune system. According to the ABO system, blood is classified into four main types: A, AB, B, and O. This classification is essential for facilitating blood transfusions. Currently, blood type determination is still performed manually by healthcare professionals, who observe the presence or absence of clumping (agglutination) in the blood when it reacts with specific antigens.Numerous studies have been conducted to support and enhance healthcare services, particularly as technological advancements continue to grow rapidly across various fields. In the medical field, these advancements have led to the development of increasingly sophisticated medical devices, including blood type detection tools. These devices typically use manual optical sensors to read blood agglutination by detecting changes in light intensity. However, such devices are not fully automated and still require human intervention, making them prone to human error. Today, automated blood type detection systems utilizing cameras and smartphones—integrated with various image processing methods and Artificial Intelligence (AI) are being increasingly developed. Therefore, this study focuses on the development of a blood type detection model that combines image processing and Deep Learning (DL) to support an intelligent, fast, and efficient healthcare system, achieving a detection accuracy of 98%.

Mechanical parts often have surface defects such as scratches, cracks and pits during the production process, which seriously affect product quality and service life. To address this challenge, this paper proposes a method for detecting and quantifying surface defects of mechanical parts based on image processing. First, this paper uses high-resolution image acquisition technology to obtain surface images of mechanical parts, and improves image quality through preprocessing steps. Then, this paper uses edge detection, morphological operations, and image segmentation algorithms to extract the features of surface defects, thereby realizing automatic identification of defective areas. Finally, this paper combines the Faster R-CNN (Faster Region-Based Convolutional Neural Network) algorithm to further refine the classification and quantification of defects and obtain the specific location, size and type of defects. Experimental results show that among the 500 mechanical parts samples tested, the proposed method has a mean average precision (mAP) of 0.93 in surface defect detection. Compared with other image processing methods, this method has significantly improved defect detection accuracy and speed. Especially when processing higher-resolution images, the Faster R-CNN model can achieve efficient defect classification and quantification to meet the needs of real-time detection in industrial production.

A deep analysis of the image and video processing techniques using nanoscale quantum-dots cellular automata

Digital radiography incresingly is being applied in the fabrication industry. Compared to film- based radiography, digitally radiographed images can be acquired with less time and fewer exposures. However, noises can simply occur on the digital image resulting in a low-quality result. Due to this and the system’s complexity, parameters’ sensitivity, and environmental effects, the results can be difficult to interpret, even for a radiographer. Therefore, the need of an application tool to improve and evaluate the image is becoming urgent. In this research, a user-friendly tool for image processing and image quality measurement was developed. The resulting tool contains important components needed by radiograph inspectors in analyzing defects and recording the results. This tool was written by using image processing and the graphical user interface development environment and compiler (GUIDE) toolbox available in Matrix Laboratory (MATLAB) R2008a. In image processing methods, contrast adjustment, and noise removal, edge detection was applied. In image quality measurement methods, mean square error (MSE), peak signal-to-noise ratio (PSNR), modulation transfer function (MTF), normalized signal-to-noise ratio (SNRnorm), sensitivity and unsharpness were used to measure the image quality. The graphical user interface (GUI) wass then compiled to build a Windows, stand-alone application that enables this tool to be executed independently without the installation of MATLAB.

In addition to causing damage to vehicles, road defects are one of the main causes of vehicle accidents which lead to loss of human lives. Many methods of detecting defects have been introduced over the years to reduce the consequences of these defects. One of these methods is image processing. Use of image and video processing has many applications in medicine, science, agriculture, and defect detection in structures. It has been used for defect detection on roads because timely detection and analysis of defect is very important for road serviceability and safety of the people. Detection of a defect by image processing broadly follows some of the basic steps which include feature extraction, edge detection, morphological operators, and training of data. Different approaches are used for various kinds of defect detection and analysis which have replaced the manual inspection method of roads saving time and resources. This chapter discusses the basic steps involved in defect detection using image processing along with existing systems that use machine learning and artificial intelligence for the detection of defects from a distance. To write this chapter, papers on the topic of image and computer vision-based defect detection systems have been consulted.

Within the recent twenty years, the dental X-ray images have widely been employed in forensic odontology for human identification, particularly where mass disasters happen. In this paper, a novel method is proposed for the process of teeth segmentation and individual teeth isolation of Bitewing X-ray radiographs. The main objective of this study is to develop an automatic teeth segmentation approach that can be used in an Automated Dental Identification System (ADIS). The proposed method is based on separating teeth according to edge lines between crowns of teeth. It comprises four phases as image enhancement, edge detection by using wavelet transform, Region of Interest (ROI) definition, and morphological processing. Image enhancement in our case is done by image sharpening using a Butterworth high pass filter. Directional changes of the image and a blurred version of it are obtained by wavelet transform in the second phase. In ROI definition the upper and lower jaws are first separated using the integral intensity projection and then a region containing the desired edge lines are defined. In the final stage, some morphological operations are applied to isolate the teeth based on separating edge lines. The evaluation of the teeth segmentation is measured by isolating accuracy and visual inspection. Experimental results with 90.6% isolation accuracy of total 681 teeth illustrate that the proposed method is more efficient that the existing algorithms.

Hotspot cells can harm the photovoltaic modules because the cells spend or use up energy instead of generating energy. Therefore, the image processing method is applied to allow the hot spot detection automatically. This paper evaluates Multi-level Otsu based approach for image processing to segment and detect hot spot solar photovoltaic cell of photovoltaic module using thermal images. The methodology was completed by performing image segmentation, image binarization, and noise removal with morphological operation. This proposed method was tested by using ten thermal images and evaluated by measuring the accuracy of each image with different threshold level. The highest accuracy achieved 97.22% from thermal image DJI_0898, DJI_0911 and DJI_0918.

Budaya aksara Lampung pada saat ini sudah jarang digunakan sehingga terancam punah. Untuk melestarikan budaya tersebut, maka dilakukan penelitian mengenai huruf aksara Lampung secara digital dengan metode pengolahan citra dan pengenalan pola. Pada penelitian ini, terbagi menjadi dua tahap yaitu tahap pelatihan dan tahap pengujian. Pada tahap pelatihan dilakukan pengolahan citra dan pelatihan jaringan saraf tiruan (JST) backpropagation. Salah satu tahapan dalam pengolahan citra adalah deteksi tepi. Dalam menganalisis karakter huruf aksara Lampung menggunakan metode deteksi tepi untuk melihat pola garis huruf tersebut. Metode pengolahan citra yang digunakan dalam mendeteksi tepi citra adalah metode deteksi tepi Roberts dan metode deteksi tepi Sobel. Hasil pengolahan citra akan masuk ke tahap pelatihan JST. Bobot yang didapat dari pelatihan kemudian akan digunakan pada tahap pengujian. Metode Sobel dapat mengenali karakter aksara Lampung lebih baik, dimana hasil dari pengujian karakter aksara Lampung memberikan nilai presentase error sebesar 28.5% dengan metode deteksi Roberts dan 14.5% dengan metode deteksi Sobel.

RETRACTED: Urban ecological forestry analysis based on FPGA and dynamic image sampling

Mathematical morphological operations are commonly used as a tool in image processing for extracting image components that are useful in the representation and description of region shape. In this paper, six basic morphological operations are investigated to remove noise and enhance the appearance of binary images. Dilation, erosion, opening, closing, fill and majority operations are tested on twenty-five images and subjectively evaluated based on perceived quality of the enhanced images. Results of the experiments showed that noise can be effectively removed from binary images using combinations of erode-dilate operations. Also, the binary images are significantly enhanced using combinations of majority-close operations.

A new type of nonlinear image processor based on morphological processing is demonstrated. Morphological image processing is based on the successive binary convolution of an image with a selectable convolution kernel. Its field of application includes numerical optical computing and binary image processing. Simple serial commands from a small electronic computer control the selection of the convolution kernels for a given iteration and consequently the parallel processing of very large optical data planes. 1.

Mathematical morphology is a powerful tool for image processing and analysis of binary, greyscale and colour images. In the paper, an efficient new fuzzy model for morphological colour image processing is introduced. A new vector-ordering scheme that uses fuzzy if–then rules is proposed, and then the basic morphological operations of erosion and dilation are defined. The morphological operators presented, which are vector preserving, are less sensitive to image distortion and perform significantly better in noise removal problems than other reported morphological operators. Experimental results demonstrate these advantageous characteristics on real images. Additionally, illustrative examples that exhibit the applicability of the proposed framework to edge-detection problems are given.