A Multilevel Image Thresholding Based on Hybrid Jaya Algorithm and Simulated Annealing

Thresholding is a method for region-based image segmentation, which is important in image processing applications such as object recognition Multilevel. Thresholding is used to find multiple threshold values. Image segmentation plays a significant role in image analysis and pattern recognition. While threshold techniques traditionally are quite well for bi-level thresholding algorithms, multilevel thresholding for color images may have too much processing complexity. Swarm intelligence methods are frequently employed to minimize the complexity of constrained optimization problems applicable to multilevel thresholding and segmentation of color (RGB) images; In this paper, the hybrid Jaya algorithm with the SA algorithm was proposed to solve the problem of computational complexity in multilevel thresholding. This work uses Otsu method, Kapur entropy and Tsallis method as techniques to find optimal values of thresholds at different levels of color images as the target Tasks Experiments were performed on 5 standardized color images and 3 grayscale images as far as optimal threshold values are concerned, Statistical methods were used to measure the performance of the threshold methods and to select the better threshold, namely, PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error), SSIM (Structural Similarity Index), FSIM (Feature Similarity Index) and values of objective at many levels. The experimental results indicate that the presenter's Jaya and Simulated Annealing (JSA) method is better than other methods for segmenting color (RGB) images with multiple threshold levels. On the other hand, the Tsallis entropy of the cascade was found to be more robust and accurate in segmenting color images at multiple levels

Rancang Bangun Sistem Pengenalan Plat Nomor Kendaraan Menggunakan Jaringan Saraf Tiruan Backpropagation

Sistem parkir manual memungkinkan adanya kesalahan dalam pencatatan, pelayanan yang membutuhkan waktu lama, dan tidak adanya riwayat pengguna kendaraan. Sistem pengenalan plat motor kendaraan dirancang sebagai alternatif sistem parkir yang lebih akurat dalam pencatatan, pelayanan yang cepat, dan adanya data pengguna kendaraan. Sistem pengenalan plat motor kendaraan telah dirancang dilengkapi dengan Jaringan Saraf Tiruan (JST) Backpropagation. Sistem pengenalan yang telah dirancang terlebih dahulu melalui proses pengolahan citra dengan tahap grayscale, black and white, segmentasi, dan principal component analysis (PCA). Sistem diintegrasikan dengan JST backpropagation dengan multi layer network dengan hasil terbaik pada layer1 550 dan layer2 500. Sistem pengenalan plat nomor dilengkapi dengan dengan Graphycal User Interface (GUI) untuk menampilkan hasil identifikasi pengenalan plat nomor kendaraan. Hasil identifikasi pengenalan plat nomor menghasilkan akurasi pelatihan 100% dan akurasi pengujian 97,95918367% pengujian.The manual parking system allows for errors in recording, the service takes a long time, and there is no history of vehicle users. The vehicle license plate recognition system is designed as an alternative parking system that is more accurate in recording, fast service, and the presence of vehicle user data. Vehicle license plate recognition system has been designed equipped with Backpropagation Artificial Neural Network (ANN). The recognition system that has been designed in advance goes through an image processing process with grayscale, black and white, segmentation, and principal component analysis (PCA) stages. The system is integrated with backpropagation ANN with multi layer network with the best results at layer1 550 and layer2 500. The number plate recognition system is equipped with a Graphycal User Interface (GUI) to display the identification results of vehicle number plate identification. The results of identification number plate recognition resulted in 100% training accuracy and 97.95918367% testing accuracy

Deep Learning Applications in Medical Image and Shape Analysis

Deep learning is one of the most rapidly growing fields in computer and data science in the past few years. It has been widely used for feature extraction and recognition in various applications. The training process as a black-box utilizes deep neural networks, whose parameters are adjusted by minimizing the difference between the predicted feedback and labeled data (so-called training dataset). The trained model is then applied to unknown inputs to predict the results that mimic human\u27s decision-making. This technology has found tremendous success in many fields involving data analysis such as images, shapes, texts, audio and video signals and so on. In medical applications, images have been regularly used by physicians for diagnosis of diseases, making treatment plans, and tracking progress of patient treatment. One of the most challenging and common problems in image processing is segmentation of features of interest, so-called feature extraction. To this end, we aim to develop a deep learning framework in the current thesis to extract regions of interest in wound images. In addition, we investigate deep learning approaches for segmentation of 3D surface shapes as a potential tool for surface analysis in our future work. Experiments are presented and discussed for both 2D image and 3D shape analysis using deep learning networks

Deep Learning Applications in Medical Image and Shape Analysis

Deep learning is one of the most rapidly growing fields in computer and data science in the past few years. It has been widely used for feature extraction and recognition in various applications. The training process as a black-box utilizes deep neural networks, whose parameters are adjusted by minimizing the difference between the predicted feedback and labeled data (so-called training dataset). The trained model is then applied to unknown inputs to predict the results that mimic human\u27s decision-making. This technology has found tremendous success in many fields involving data analysis such as images, shapes, texts, audio and video signals and so on. In medical applications, images have been regularly used by physicians for diagnosis of diseases, making treatment plans, and tracking progress of patient treatment. One of the most challenging and common problems in image processing is segmentation of features of interest, so-called feature extraction. To this end, we aim to develop a deep learning framework in the current thesis to extract regions of interest in wound images. In addition, we investigate deep learning approaches for segmentation of 3D surface shapes as a potential tool for surface analysis in our future work. Experiments are presented and discussed for both 2D image and 3D shape analysis using deep learning networks

Optimization Methods for Image Thresholding: A review

Setting a border with the proper gray level in processing images to separate objects from their backgrounds is crucial. One of the simplest and most popular methods of segmenting pictures is histogram-based thresholding. Thresholding is a common technique for image segmentation because of its simplicity. Thresholding is used to separate the Background of the image from the Foreground. There are many methods of thresholding. This paper aims to review many previous studies and mention the types of thresholding. It includes two types: the global and local thresholding methods and each type include a group of methods. The global thresholding method includes (the Otsu method, Kapur's entropy method, Tsallis entropy method, Hysteresis method, and Fuzzy entropy method), and the local thresholding method includes ( Ni-Black method and Bernsen method). The optimization algorithms(Genetic Algorithm, Particle Swarm Optimization, Bat Algorithm, Modified Grasshopper Optimization, Firefly Algorithm, Cuckoo Search, Tabu Search Algorithm, Simulated Annealing, and Jaya Algorithm) used along with thresholding methods are also illustrated

A comprehensive review of fruit and vegetable classification techniques

Recent advancements in computer vision have enabled wide-ranging applications in every field of life. One such application area is fresh produce classification, but the classification of fruit and vegetable has proven to be a complex problem and needs to be further developed. Fruit and vegetable classification presents significant challenges due to interclass similarities and irregular intraclass characteristics. Selection of appropriate data acquisition sensors and feature representation approach is also crucial due to the huge diversity of the field. Fruit and vegetable classification methods have been developed for quality assessment and robotic harvesting but the current state-of-the-art has been developed for limited classes and small datasets. The problem is of a multi-dimensional nature and offers significantly hyperdimensional features, which is one of the major challenges with current machine learning approaches. Substantial research has been conducted for the design and analysis of classifiers for hyperdimensional features which require significant computational power to optimise with such features. In recent years numerous machine learning techniques for example, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Trees, Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) have been exploited with many different feature description methods for fruit and vegetable classification in many real-life applications. This paper presents a critical comparison of different state-of-the-art computer vision methods proposed by researchers for classifying fruit and vegetable