Enhanced Computer Vision using Automated Optimized Neural Network Image Pre-processing

Software was developed in Python for the digital processing of object images before their detection using the You Only Look Once (YOLO) artificial neural network. Detection was carried out on object images, specifically vehicles and other road users. The preprocessing of images was performed through histogram equalization and local contrast enhancement. The original images were read from video cameras or graphic files. Image detection was performed using the medium-sized YOLO convolutional neural network model, YOLOv8m. As a result of detection, the studied objects, particularly cars, were highlighted with rectangular bounding boxes. To evaluate the accuracy of object detection, several metrics (parameters) were used: Precision, Recall, Intersection over Union (IoU), F1, and Average Precision (AP). The YOLO network was pre-trained on the Common Objects in Context dataset. The YOLO neural network returns the object recognition confidence, the coordinates of its center (x, y) on the image, and its width and height (w, h). The detected objects are marked with rectangular bounding boxes. The positioning of objects in the images was manually performed using the Roboflow tool, which allows for accurate spatial positioning of each object. The study of the impact of image preprocessing on the accuracy of object detection by the YOLO network was conducted using a typical traffic scene image. After histogram equalization, the histogram becomes more symmetrical and uniform, improving the visual quality of both dark and bright areas. This led to improved object detection accuracy across almost all metrics. The increase in detection accuracy by the IoU parameter is 0.169. The summary improvement in detection accuracy by the IoU, F1, AP parameters is 0.502. It was shown that after image preprocessing, detection accuracy increased, and all vehicles were correctly detected. The developed software can be used in various computer vision systems as well as in Internet of Things systems for remote monitoring and control.

Objective: The main objective of this paper is to improve the intrusion detection accuracy of neural networks by hybridized with Jordan network, applying novel Lyapunov function and changing input values as fuzzy values using fuzzy logic. Methods: In this research, the intrusion detection of NSL KDD dataset is carry out by neural network first . The performance of neural networks mainly depends on the parameters like number of hidden layer, no of node in the hidden layer and the no of epoch. Selecting the proper parameters values and weight initialization are the main difficulty in neural network. To overcome this issue a hybrid network by combining neural network with Jordan network is proposed which is highly sensitive to weight convergence. In hybrid network Lyapunov function is used to achieve the stability of equilibrium between two networks. But in this work a novel Lyapunov function is proposed to achieve global robust stability in hybrid network. A novel Lyapunov function uses a class of general activation functions which are not to be differentiable, bounded or monotonically nondecreasing. A set of criteria are derived to guarantee the existence, uniqueness and global robust stability of the equilibrium of hybrid networks with time delays. Then the learning ability of hybrid network is improved by using fuzzy logic. The hybrid network improved by a novel Lyapunov and fuzzy logic is called as Improved Fuzzy Hybrid Jordan network and Artificial Neural Network (IHFJANN). Findings: Artificial Neural Network (ANN), Hybrid Jordan network and Artificial Neural Network (HJANN) and Improved Hybrid Fuzzy Jordan network and Artificial Neural Network (IHFJ ANN) are applied on NSL KDD training dataset to lean the type of available attacks. NSL training dataset contains 2500 instances and 38 attributes where as testing dataset contains 995 instances and 38 attributes. The learned model of three classifiers is used to predict the classes in test dataset. The performance measures are evaluated in terms of accuracy, precision, recall and f-measure values. Improvement: The classification accuracy of Artificial Neural Network (ANN), Hybrid Jordan network and Artificial Neural Network (HJANN) and Improved Hybrid Fuzzy Jordan network and Artificial Neural Network (IHFJANN) are 72%, 80% and 84% respectively. Accuracy is increased by 12% in IHFJANN than the ANN and 4% than the HJANN. The precision value of ANN, HJANN and IHFJANN are 0.46, 0.54 and 0.57 respectively. Precision value is increased by 0.9 in IHFJANN than the ANN and 0.3 than the HJANN. The recall value of ANN, HJANN and IHFJANN are 0.73, 0.81 and 0.84 respectively. Recall value is increased by 0.9 in IHFJANN than the ANN and 0.3 than the HJANN. The F-measure value of ANN, HJANN and IHFJANN are 0.58, 0.65 and 0.68 respectively. F-measure value is increased by 0.10 in IHFJANN than the ANN and 0.3 than the HJANN. The results proved that the proposed IHFJANN provides better performance than ANN and HJANN.

Monitoring various Fish Species and its distribution of the species obtains a primary significance in receiving the insights to marine ecological-system. After this, visual classification of those species would aid in tracing out the movement and yield the patterns and trends in fish activities, which provides in depth knowledge of the species. Unconstrained under-water images pose highly variations because of the fish orientation changes, Light-intensities, similarity in fish patterns and fish shapes. This would create the greater challenge for Image-processing techniques in accurate classification of Fish species or the Fish classes. Hence, for this reason, Underwater Image Enhancement is implemented in combination of Morphological-operations in pre-processing method. The pre-processed image is then subjected to feature extraction process by using Speed-up Robust Feature algorithm. This is followed by Firefly Algorithm, applied for optimization of Region of interest selection in the selected-features. For the categorization of Fish-species, PatternNet is a technique which is employed, in classifying 10,000 marine fish-images to five categories (Dascyllus reticulatus, Plectroglyphidodon dickii, Chromis chrysura, Amphiprion clarkii, and Chaetodon lunulatus). The Efficiency of the proposed-framework is performed in terms of Classification accuracy, execution time, precision value, F-measure and recall factors with respect to various categories of fish species. The comparison of the proposed-framework is also assessed with the other existing methods. 98% of accuracy rate in classification was produced by the evaluation results of the proposed framework with a lesser average computation time of 3.64 s upon different tested images. Thus, the higher efficiency of the proposed framework is proved by the outcomes of the study.

This paper proposes a three pieces game device based on machine vision and intelligent control for the limitations of traditional three pieces game. The device uses an integrated OpenART camera for image acquisition and LAB color space for accurate recognition of the chess board and pieces. First, the camera is calibrated to ensure the accuracy of image acquisition; subsequently, the coordinates of the chessboard grid, the border, and the color and position of the pieces are identified by image preprocessing and target detection algorithms. On this basis, the device adopts a geometric method to solve the robotic arm to realize the accurate picking up and placing of chess pieces. Experiments show that the device can accurately recognize the state of the board, quickly respond to the player's commands, and show good stability and intelligence in the process of the game. The three pieces game device not only provides players with a new game experience, but also demonstrates the prospect of the wide application of machine vision and intelligent control in the game field.

Positioning systems based on the lateration method utilize distance measurements and the knowledge of the location of the beacons to estimate the position of the target object. Although most of the global positioning techniques rely on beacons whose locations are known a priori, miscellaneous factors and disturbances such as obstacles, reflections, signal propagation speed, the orientation of antennas, measurement offsets of the beacons hardware, electromagnetic noise, or delays can affect the measurement accuracy. In this paper, we propose a novel hybrid calibration method based on Neural Networks (NN) and Apparent Beacon Position Estimation (ABPE) to improve the accuracy of a lateration positioning system. The main idea of the proposed method is to use a two-step position correction pipeline that first performs the ABPE step to estimate the perceived positions of the beacons that are used in the standard position estimation algorithm and then corrects these initial estimates by filtering them with a multi-layer feed-forward neural network in the second step. In order to find an optimal neural network, 16 NN architectures with 10 learning algorithms and 12 different activation functions for hidden layers were implemented and tested in the MATLAB environment. The best training outcomes for NNs were then employed in two real-world indoor scenarios: without and with obstacles. With the aim to validate the proposed methodology in a scenario where a fast set-up of the system is desired, we tested eight different uniform sampling patterns to establish the influence of the number of the training samples on the accuracy of the system. The experimental results show that the proposed hybrid NN-ABPE method can achieve a high level of accuracy even in scenarios when a small number of calibration reference points are measured.

The paper investigates computer diagnostic systems, their architectures, methods, and algorithms used in their work to diagnose cancer, including breast, lung, brain, and other tumors. Traditional and neural network methods for image segmentation and classification are analyzed and compared, and diagnostic tools in medicine are analyzed. The key approaches to medical image processing are investigated, in particular, the analysis of segmentation methods based on U-Net networks and classification using convolutional neural networks. It is established that neural network methods outperform traditional approaches in terms of accuracy and efficiency in segmentation and classification tasks. The main advantages of using neural network architectures in computer diagnostic systems are revealed, in particular, the possibility of automating the diagnostic process and improving the accuracy of the results. Neural network-based solutions provide a more adaptive and scalable approach that can be trained and improved as new data becomes available, making them highly suitable for rapidly evolving industries such as medical diagnostics. Computer experiments on image preprocessing and segmentation were conducted. Thus, the effectiveness of U-Net networks for image segmentation tasks was established. An automatic diagnostic method based on U-Net and convolutional neural networks has been developed that reduces the diagnostic time due to parallel image processing. The paper presents a detailed scheme of the developed method. It includes the segmentation of immunohistochemical images, after which the quantitative characteristics and classification of histological images will be calculated, followed by the combination of all the results obtained to make a diagnosis. This approach provides a comprehensive analysis that combines structural and quantitative data, which helps to increase the reliability of diagnostic results The scientific novelty of the developed method of automatic diagnosis based on neural networks is the use of a parallel approach to performing segmentation and classification. The developed method can be used in computer diagnostic systems in medicine. The use of the developed method gives an increase in the speed of data processing and, accordingly, diagnosis. In addition, parallel execution contributes to more efficient use of computing resources.

Statistical techniques for digital pre-processing of computed tomography medical images: A current review

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

With several applications in various different fields, computer vision can be found everywhere in our society. A fundamental problem of artificial intelligence (AI) is to give automatic description of the content in an image. In this paper, we focus on one of the visual recognition facets of computer vision, i.e., image captioning. To give the description for visual data has been studied for a long time but in the field of videos. In the recent few years, emphasis has been lead on still image description with natural text. Due to the recent advancements in the field of object detection, the task of scene description in an image has become easier. The aim of the paper was to train convolutional neural networks (CNN) with several hundreds of hyperparameters and apply it on a huge dataset of images (ImageNet) and combine the results of this image classifier with a recurrent neural network to create a caption for the classified picture. In this paper, we systematically analyze a deep neural networks-based image caption generation method. With an image as the input, the method can output an English sentence describing the content in the image. We analyze three components of the method: convolutional neural network (CNN), recurrent neural network (RNN) and sentence generation. By replacing the CNN part with three state-of-the-art architectures, we find the VGGNet which performs best according to the BLEU score. In this paper, we present the detailed architecture of the model used by us. We achieved a BLEU score of 56 on the Flickr8k dataset while the state-of-the-art results rest at 66 on the dataset.

Neural networks and image pyramids are massively parallel processing structures. In this paper we exploit the similarities as well as the differences between these structures. The general goal is to exchange knowledge between these two fields. After introducing the basic concepts of neural networks and image pyramids we give a translation table of the vocabulary used in image pyramids and those used in neural networks. Image pyramids which store and process numerical information (e.g. grey values of pixels) are very similar to neural networks. Therefore we concentrate on ”symbolic pyramids”. The main idea is to replace a cell of the pyramid by a small neural network, in order to represent and process symbolic information. We will consider local as well as distributed representations for symbolic information. In particular we present a neural implementation of the 2 x 2/2 curve pyramid. We derive some general rules for implementing symbolic pyramids by neural networks. Finally we briefly discuss the role of learning in image pyramids.

Cardiovascular Disease (CVD) is one of the primary diseases that causes death every year. An approximation of roughly about 17.5 million people dies due to CVD, signifying about 31% of global deaths. Based on the statistics, every 34 seconds, a person dies due to heart disease. Various classification algorithms have been developed and utilized as classifiers to support doctors who are ineffectually diagnosed with heart disease. The main aim of this work is to improve the performance of the heart disease approach using image processing algorithm. To improve the effectiveness and efficiency of classification performance for heart disease diagnosis, an optimized neural network was proposed based on the feature extraction and selection approach for handling features. The objective of this investigation is to diagnose heart diseases using feature extraction, reduction based classification and image processing methods. The proposed model comprises of two subsets: Feature extraction using gray scale properties and Moth flame optimization (MFO) for effectual feature selection, followed by a classification technique using Generalized Regression Neural Network. The first system includes three stages: (i) Pre-processing of the dataset (ii) feature extraction (iii) performing MFO for efficient selection. In the second method, GRNN is proposed. The heart data set obtained from ACDC Challenge, was utilized for performing the computation. The image obtained from the MRI-scanner is in the NIfTI image format. The pre-process step used in this is to convert the image type from INT16 to uint8 to improve the quality of image viewing and for feature extraction process. In this phase, the texture properties from the pre-processed image are calculated and the value is in the numeric format. These values are the feature attributes of the dataset. The feature attributes of the image are given as input for the moth flame optimization process and output is the feature selected from the optimization process. The whole process is performed on the feature attributes of the image by determining the optimal feature for the classifier by reducing its error rate. The optimal feature from the moth flame optimization is used for training and testing the network. The classifier used in this approach is a single neural network classifier with a regression nature. Due to the regression property, the network is well trained with the feature. The Generalized regression neural network is used for classifying heart diseases. The proposed method achieves the accuracy of 96.23%, sensitivity of 95.41% and specificity of 96.75%. These values are calculated based on the confusion matrix of the classifier. In this, the feature extraction using the gray scale properties plays an important role to determine the feature attributes from the MRI heart images. The Moth flame optimization able to produce an accuracy of 97.23% using GRNN for classification with minimum single attribute mean of the image. It also outperformed the other methods, either the feature extraction based classification or the feature reduction based classification.

This article aims to test FOS (first-order statistical) in extracting features of embryonated eggs. This test uses the initial step of image processing to get the best input image in feature extraction. The image processing method starts from the image acquisition process, then improves with image preprocessing and segmentation. Image acquisition in this study uses the concept of egg candling in a dark place captured with a smartphone camera. The acquisition results are improved by image preprocessing using gray scaling, image enhancement (by Histogram Equalization), and segmentation of chicken egg image. The segmentation results were extracted using FOS with five parameters: mean, entropy, variance, skewness, and kurtosis. Based on the calculation of these parameters, it is graphed and shows the difference in patterns between fertile and infertile eggs. However, some eggs have a similar pattern, thus affecting the identification process. The identification process used neural networks by the backpropagation method for training and testing. The training results provide an accuracy value of 100% of all training data; however, 80% of the new test data obtained test results at testing. This test is carried out with 100 data, 50 each for training and test data. Based on the test results, which significantly affect the level of accuracy is the feature extraction method. FOS pattern in detecting the fertility of chicken eggs by BP Neural Network is still categorized as low, so it is necessary to improve methods to get maximum results.

Diabetic retinopathy stands as a leading cause of blindness among people. Manual examination of DR images is labor-intensive and prone to error. Existing methods to detect this disease often rely on handcrafted features which limit the adaptability and classification accuracy. Thus, the aim of this research is to develop an automated and efficient system for early detection and accurate grading of diabetic retinopathy severity with less time consumption. In our research, we have developed a deep neural network named RSG-Net (Retinopathy Severity Grading) to classify DR into 4 stages (multi-class classification) and 2 stages (binary classification). The dataset utilized in this study is Messidor-1. In preprocessing, we have used Histogram Equalization to improve image contrast and denoising techniques to remove noise and artifacts which enhanced the clarity of the fundus images. We applied data augmentation techniques to our preprocessed images in order to tackle class imbalance issues. Augmentation techniques involve flipping, rotation, zooming and adjustment of color, contrast and brightness. The proposed RSG-Net model contains convolutional layers to perform automatic feature extraction from the input images and batch normalization layers to improve training speed and performance. The model also contains max pooling, drop out and fully connected layers. Our proposed RSG-Net model achieved a testing accuracy of 99.36%, specificity of 99.79% and a sensitivity of 99.41% in classifying diabetic retinopathy into 4 grades and it achieved 99.37% accuracy, 100% sensitivity and 98.62% specificity in classifying DR into 2 grades. The performance of RSG-Net is also compared with other state-of-the-art methodologies where it outperformed these methods.

In computer vision, classifying facial attributes has attracted deep interest from researchers and corporations. Deep Neural Network based approaches are now widely spread for such tasks and have reached higher detection accuracies than previously manually-designed approaches. Our paper reports how preprocessing and face image alignment influence accuracy scores when detecting face attributes. More importantly it demonstrates how the combination of a representation of the shape of a face and its appearance, organized as a sequence of convolutional neural networks, improves classification scores of facial attributes when compared with previous work on the FER+ dataset. While most studies in the field have tried to improve detection accuracy by averaging multiple very deep networks, exposed work concentrates on building efficient models while maintaining high accuracy scores. By taking advantage of the face shape component and relying on an efficient shallow CNN architecture, we unveil the first available, highly accurate real-time implementation on mobile browsers.

In recent years, deep neural networks (DNNs) have achieved great successes in many areas, such as cognitive computation, pattern recognition, and computer vision. Although many hand-crafted deep networks have been proposed in the literature, designing a well-behaved neural network for a specific application requires high-level expertise yet. Hence, the automatic architecture design of DNNs has become a challenging and important problem. In this paper, we propose a new reinforcement learning method, whose action policy is to select neural blocks and construct deep networks. We define the action search space with three types of neural blocks, i.e., dense block, residual block, and inception-like block. Additionally, we have also designed several variants for the residual and inception-like blocks. The optimal network is automatically learned by a Q-learning agent, which is iteratively trained to generate well-performed deep networks. To evaluate the proposed method, we have conducted experiments on three datasets, MNIST, SVHN, and CIFAR-10, for image classification applications. Compared with existing hand-crafted and auto-generated neural networks, our auto-designed neural network delivers promising results. Moreover, the proposed reinforcement learning algorithm for deep networks design only runs on one GPU, demonstrating much higher efficiency than most of the previous deep network search approaches.