Journal articles on the topic 'REAL IMAGE PREDICTION'
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1
Takezawa, Takuma, and Yukihiko Yamashita. "Wavelet Based Image Coding via Image Component Prediction Using Neural Networks." International Journal of Machine Learning and Computing 11, no. 2 (March 2021): 137–42. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1026.
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In the process of wavelet based image coding, it is possible to enhance the performance by applying prediction. However, it is difficult to apply the prediction using a decoded image to the 2D DWT which is used in JPEG2000 because the decoded pixels are apart from pixels which should be predicted. Therefore, not images but DWT coefficients have been predicted. To solve this problem, predictive coding is applied for one-dimensional transform part in 2D DWT. Zhou and Yamashita proposed to use half-pixel line segment matching for the prediction of wavelet based image coding with prediction. In this research, convolutional neural networks are used as the predictor which estimates a pair of target pixels from the values of pixels which have already been decoded and adjacent to the target row. It helps to reduce the redundancy by sending the error between the real value and its predicted value. We also show its advantage by experimental results.
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Hong, Yan, Li Niu, and Jianfu Zhang. "Shadow Generation for Composite Image in Real-World Scenes." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 914–22. http://dx.doi.org/10.1609/aaai.v36i1.19974.
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Image composition targets at inserting a foreground object into a background image. Most previous image composition methods focus on adjusting the foreground to make it compatible with background while ignoring the shadow effect of foreground on the background. In this work, we focus on generating plausible shadow for the foreground object in the composite image. First, we contribute a real-world shadow generation dataset DESOBA by generating synthetic composite images based on paired real images and deshadowed images. Then, we propose a novel shadow generation network SGRNet, which consists of a shadow mask prediction stage and a shadow filling stage. In the shadow mask prediction stage, foreground and background information are thoroughly interacted to generate foreground shadow mask. In the shadow filling stage, shadow parameters are predicted to fill the shadow area. Extensive experiments on our DESOBA dataset and real composite images demonstrate the effectiveness of our proposed method. Our dataset and code are available at https://github.com/bcmi/Object-Shadow-Generation- Dataset-DESOBA.
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Sather, A. P., S. D. M. Jones, and D. R. C. Bailey. "Real-time ultrasound image analysis for the estimation of carcass yield and pork quality." Canadian Journal of Animal Science 76, no. 1 (March 1, 1996): 55–62. http://dx.doi.org/10.4141/cjas96-008.
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Average backfat thickness measurements (liveweight of 92.5 kg) were made on 276 pigs using the Krautkramer USK7 ultrasonic machine. Immediately preceding and 1 h after slaughter real-time ultrasonic images were made between the 3rd and 4th last ribs with the Tokyo Keiki LS-1000 (n = 149) and/or CS-3000 (n = 240) machines. Image analysis software was used to measure fat thickness (FT), muscle depth (MD) and area (MA) as well as scoring the number of objects, object area and percentage object area of the loin to be used for predicting meat quality. Carcasses were also graded by the Hennessy Grading Probe (HGP). Prediction equations for lean in the primal cuts based on USK7 and LS-1000 animal fat measurements had R2-values (residual standard deviations, RSD) of 0.62 (27.0) and 0.66 (25.7). Adding MD or MA to LS-1000 FT measurements increased the R2-values to 0.68 and 0.66. Prediction equations using animal fat measurements made by the USK7 and CS-3000 had R2-values (RSD) of 0.66 (26.5) and 0.76 (22.4). Adding MD or MA to CS-3000 FT measurements made no further improvement in the R2-values. Estimation of commercial lean yield from carcass FT and MD measurements made by the HGP and LS-1000 had R2-values (RSD) of 0.58 (1.72) and 0.65 (1.56). Adding MA to LS-1000 measurements made no further improvement in the R2-values. Prediction equations based on carcass FT and MD measurements made by the HGPandCS-3000 had R2-values (RSD) of 0.68 (1.65) and 0.72 (1.54). Adding MA to CS-3000 measurements made no further improvement in the prediction equation. It was concluded that RTU has most value for predicting carcass lean content and further improvements in precision will come from more accurate FT measurements from RTU images made by image analysis software. Correlation of the number of objects, object area and of percent object area of image from RTU images with intramuscular fat or marbling score made on the live pig or carcass were low, and presently do not appear suitable for predicting intramuscular fat. Key words: Carcass composition, meat quality, marbling, intramuscular fat, sex, pigs
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Tham, Hwee Sheng, Razaidi Hussin, and Rizalafande Che Ismail. "A Real-Time Distance Prediction via Deep Learning and Microsoft Kinect." IOP Conference Series: Earth and Environmental Science 1064, no. 1 (July 1, 2022): 012048. http://dx.doi.org/10.1088/1755-1315/1064/1/012048.
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Abstract 3D(Dimension) understanding has become the herald of computer vision and graphics research in the era of technology. It benefits many applications such as autonomous cars, robotics, and medical image processing. The pros and cons of 3D detection bring convenience to the human community instead of 2D detection. The 3D detection consists of RGB (Red, Green and Blue) colour images and depth images which are able to perform better than 2D in real. The current technology is relying on the high costing light detection and ranging (LiDAR). However, the use of Microsoft Kinect has replaced the LiDAR systems for 3D detection gradually. In this project, a Kinect camera is used to extract the depth of image information. From the depth images, the distance can be defined easily. As in the colour scale, the red colour is the nearest and the blue colour is the farthest. The depth image will turn black when reaching the limitation of the Kinect camera measuring range. The depth information collected will be trained with deep learning architecture to perform a real-time distance prediction.
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Pintelas, Emmanuel, Meletis Liaskos, Ioannis E. Livieris, Sotiris Kotsiantis, and Panagiotis Pintelas. "Explainable Machine Learning Framework for Image Classification Problems: Case Study on Glioma Cancer Prediction." Journal of Imaging 6, no. 6 (May 28, 2020): 37. http://dx.doi.org/10.3390/jimaging6060037.
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Image classification is a very popular machine learning domain in which deep convolutional neural networks have mainly emerged on such applications. These networks manage to achieve remarkable performance in terms of prediction accuracy but they are considered as black box models since they lack the ability to interpret their inner working mechanism and explain the main reasoning of their predictions. There is a variety of real world tasks, such as medical applications, in which interpretability and explainability play a significant role. Making decisions on critical issues such as cancer prediction utilizing black box models in order to achieve high prediction accuracy but without provision for any sort of explanation for its prediction, accuracy cannot be considered as sufficient and ethnically acceptable. Reasoning and explanation is essential in order to trust these models and support such critical predictions. Nevertheless, the definition and the validation of the quality of a prediction model’s explanation can be considered in general extremely subjective and unclear. In this work, an accurate and interpretable machine learning framework is proposed, for image classification problems able to make high quality explanations. For this task, it is developed a feature extraction and explanation extraction framework, proposing also three basic general conditions which validate the quality of any model’s prediction explanation for any application domain. The feature extraction framework will extract and create transparent and meaningful high level features for images, while the explanation extraction framework will be responsible for creating good explanations relying on these extracted features and the prediction model’s inner function with respect to the proposed conditions. As a case study application, brain tumor magnetic resonance images were utilized for predicting glioma cancer. Our results demonstrate the efficiency of the proposed model since it managed to achieve sufficient prediction accuracy being also interpretable and explainable in simple human terms.
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Snider, Eric J., Sofia I. Hernandez-Torres, and Ryan Hennessey. "Using Ultrasound Image Augmentation and Ensemble Predictions to Prevent Machine-Learning Model Overfitting." Diagnostics 13, no. 3 (January 23, 2023): 417. http://dx.doi.org/10.3390/diagnostics13030417.
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Deep learning predictive models have the potential to simplify and automate medical imaging diagnostics by lowering the skill threshold for image interpretation. However, this requires predictive models that are generalized to handle subject variability as seen clinically. Here, we highlight methods to improve test accuracy of an image classifier model for shrapnel identification using tissue phantom image sets. Using a previously developed image classifier neural network—termed ShrapML—blind test accuracy was less than 70% and was variable depending on the training/test data setup, as determined by a leave one subject out (LOSO) holdout methodology. Introduction of affine transformations for image augmentation or MixUp methodologies to generate additional training sets improved model performance and overall accuracy improved to 75%. Further improvements were made by aggregating predictions across five LOSO holdouts. This was done by bagging confidences or predictions from all LOSOs or the top-3 LOSO confidence models for each image prediction. Top-3 LOSO confidence bagging performed best, with test accuracy improved to greater than 85% accuracy for two different blind tissue phantoms. This was confirmed by gradient-weighted class activation mapping to highlight that the image classifier was tracking shrapnel in the image sets. Overall, data augmentation and ensemble prediction approaches were suitable for creating more generalized predictive models for ultrasound image analysis, a critical step for real-time diagnostic deployment.
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Froning, Dieter, Eugen Hoppe, and Ralf Peters. "The Applicability of Machine Learning Methods to the Characterization of Fibrous Gas Diffusion Layers." Applied Sciences 13, no. 12 (June 9, 2023): 6981. http://dx.doi.org/10.3390/app13126981.
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Porous materials can be characterized by well-trained neural networks. In this study, fibrous paper-type gas diffusion layers were trained with artificial data created by a stochastic geometry model. The features of the data were calculated by means of transport simulations using the Lattice–Boltzmann method based on stochastic micro-structures. A convolutional neural network was developed that can predict the permeability and tortuosity of the material, through-plane and in-plane. The characteristics of real data, both uncompressed and compressed, were predicted. The data were represented by reconstructed images of different sizes and image resolutions. Image artifacts are also a source of potential errors in the prediction. The Kozeny–Carman trend was used to evaluate the prediction of permeability and tortuosity of compressed real data. Using this method, it was possible to decide if the predictions on compressed data were appropriate.
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Moskolaï, Waytehad Rose, Wahabou Abdou, Albert Dipanda, and Kolyang. "Application of Deep Learning Architectures for Satellite Image Time Series Prediction: A Review." Remote Sensing 13, no. 23 (November 27, 2021): 4822. http://dx.doi.org/10.3390/rs13234822.
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Satellite image time series (SITS) is a sequence of satellite images that record a given area at several consecutive times. The aim of such sequences is to use not only spatial information but also the temporal dimension of the data, which is used for multiple real-world applications, such as classification, segmentation, anomaly detection, and prediction. Several traditional machine learning algorithms have been developed and successfully applied to time series for predictions. However, these methods have limitations in some situations, thus deep learning (DL) techniques have been introduced to achieve the best performance. Reviews of machine learning and DL methods for time series prediction problems have been conducted in previous studies. However, to the best of our knowledge, none of these surveys have addressed the specific case of works using DL techniques and satellite images as datasets for predictions. Therefore, this paper concentrates on the DL applications for SITS prediction, giving an overview of the main elements used to design and evaluate the predictive models, namely the architectures, data, optimization functions, and evaluation metrics. The reviewed DL-based models are divided into three categories, namely recurrent neural network-based models, hybrid models, and feed-forward-based models (convolutional neural networks and multi-layer perceptron). The main characteristics of satellite images and the major existing applications in the field of SITS prediction are also presented in this article. These applications include weather forecasting, precipitation nowcasting, spatio-temporal analysis, and missing data reconstruction. Finally, current limitations and proposed workable solutions related to the use of DL for SITS prediction are also highlighted.
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Rajesh, E., Shajahan Basheer, Rajesh Kumar Dhanaraj, Soni Yadav, Seifedine Kadry, Muhammad Attique Khan, Ye Jin Kim, and Jae-Hyuk Cha. "Machine Learning for Online Automatic Prediction of Common Disease Attributes Using Never-Ending Image Learner." Diagnostics 13, no. 1 (December 28, 2022): 95. http://dx.doi.org/10.3390/diagnostics13010095.
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The rapid increase in Internet technology and machine-learning devices has opened up new avenues for online healthcare systems. Sometimes, getting medical assistance or healthcare advice online is easier to understand than getting it in person. For mild symptoms, people frequently feel reluctant to visit the hospital or a doctor; instead, they express their questions on numerous healthcare forums. However, predictions may not always be accurate, and there is no assurance that users will always receive a reply to their posts. In addition, some posts are made up, which can misdirect the patient. To address these issues, automatic online prediction (OAP) is proposed. OAP clarifies the idea of employing machine learning to predict the common attributes of disease using Never-Ending Image Learner with an intelligent analysis of disease factors. Never-Ending Image Learner predicts disease factors by selecting from finite data images with minimum structural risk and efficiently predicting efficient real-time images via machine-learning-enabled M-theory. The proposed multi-access edge computing platform works with the machine-learning-assisted automatic prediction from multiple images using multiple-instance learning. Using a Never-Ending Image Learner based on Machine Learning, common disease attributes may be predicted online automatically. This method has deeper storage of images, and their data are stored per the isotropic positioning. The proposed method was compared with existing approaches, such as Multiple-Instance Learning for automated image indexing and hyper-spectrum image classification. Regarding the machine learning of multiple images with the application of isotropic positioning, the operating efficiency is improved, and the results are predicted with better accuracy. In this paper, machine-learning performance metrics for online automatic prediction tools are compiled and compared, and through this survey, the proposed method is shown to achieve higher accuracy, proving its efficiency compared to the existing methods.
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Bhimte, Sumit, Hrishikesh hasabnis, Rohit Shirsath, Saurabh Sonar, and Mahendra Salunke. "Severity Prediction System for Real Time Pothole Detection." Journal of University of Shanghai for Science and Technology 23, no. 07 (July 29, 2021): 1328–34. http://dx.doi.org/10.51201/jusst/21/07356.
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Pothole Detection System using Image Processing or using Accelerometer is not a new normal. But there is no real time application which utilizes both techniques to provide us with efficient solution. We present a system which can be useful for the drivers to determine the intensity of Pothole using both Image Processing Technology and Accelerometer device-based Algorithm. The challenge in building this system was to efficiently detect a Pothole present in roads, to analyze the severity of Pothole and to provide users with information like Road Quality and best possible route. We have used various algorithms for frequency-based pothole detection. We compared the results. Apart from that, we selected the best approach suitable for achieving the project goals. We have used a Simple Differentiation-based Edge Detection Algorithm for Image Processing. The system has been built on Map Interfaces for Android devices using Android Studio, which consists of usage of Image Processing Algorithm based Python frameworks which is a sub field of Machine Learning. It is backed by powerful DBMS. This project facilitates use of most efficient technology tools to provide a good user experience, real time application, reliability and improved efficiency.
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Caesarendra, Wahyu, Taufiq Aiman Hishamuddin, Daphne Teck Ching Lai, Asmah Husaini, Lisa Nurhasanah, Adam Glowacz, and Gusti Ahmad Fanshuri Alfarisy. "An Embedded System Using Convolutional Neural Network Model for Online and Real-Time ECG Signal Classification and Prediction." Diagnostics 12, no. 4 (March 24, 2022): 795. http://dx.doi.org/10.3390/diagnostics12040795.
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This paper presents an automatic ECG signal classification system that applied the Deep Learning (DL) model to classify four types of ECG signals. In the first part of our work, we present the model development. Four different classes of ECG signals from the PhysioNet open-source database were selected and used. This preliminary study used a Deep Learning (DL) technique namely Convolutional Neural Network (CNN) to classify and predict the ECG signals from four different classes: normal, sudden death, arrhythmia, and supraventricular arrhythmia. The classification and prediction process includes pulse extraction, image reshaping, training dataset, and testing process. In general, the training accuracy achieved up to 95% after 100 epochs. However, the prediction of each ECG single type shows a differentiation. Among the four classes, the results show that the predictions for sudden death ECG waveforms are the highest, i.e., 80 out of 80 samples are correct (100% accuracy). In contrast, the lowest is the prediction for normal sinus ECG waveforms, i.e., 74 out of 80 samples are correct (92.5% accuracy). This is due to the image features of normal sinus ECG waveforms being almost similar to the image features of supraventricular arrhythmia ECG waveforms. However, the model has been tuned to achieve an optimal prediction. In the second part, we presented the hardware implementation with the predictive model embedded in an NVIDIA Jetson Nanoprocessor for the online and real-time classification of ECG waveforms.
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Wang, Fan, Jia Chen, Haonan Zhong, Yibo Ai, and Weidong Zhang. "No-Reference Image Quality Assessment Based on Image Multi-Scale Contour Prediction." Applied Sciences 12, no. 6 (March 10, 2022): 2833. http://dx.doi.org/10.3390/app12062833.
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Accurately assessing image quality is a challenging task, especially without a reference image. Currently, most of the no-reference image quality assessment methods still require reference images in the training stage, but reference images are usually not available in real scenes. In this paper, we proposed a model named MSIQA inspired by biological vision and a convolution neural network (CNN), which does not require reference images in the training and testing phases. The model contains two modules, a multi-scale contour prediction network that simulates the contour response of the human optic nerve to images at different distances, and a central attention peripheral inhibition module inspired by the receptive field mechanism of retinal ganglion cells. There are two steps in the training stage. In the first step, the multi-scale contour prediction network learns to predict the contour features of images in different scales, and in the second step, the model combines the central attention peripheral inhibition module to learn to predict the quality score of the image. In the experiments, our method has achieved excellent performance. The Pearson linear correlation coefficient of the MSIQA model test on the LIVE database reached 0.988.
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Arubai, Nadim, Omar Hamdoun, and Assef Jafar. "Building a Real-Time 2D Lidar Using Deep Learning." Journal of Robotics 2021 (February 5, 2021): 1–7. http://dx.doi.org/10.1155/2021/6652828.
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Applying deep learning methods, this paper addresses depth prediction problem resulting from single monocular images. A vector of distances is predicted instead of a whole image matrix. A vector-only prediction decreases training overhead and prediction periods and requires less resources (memory, CPU). We propose a module which is more time efficient than the state-of-the-art modules ResNet, VGG, FCRN, and DORN. We enhanced the network results by training it on depth vectors from other levels (we get a new level by changing the Lidar tilt angle). The predicted results give a vector of distances around the robot, which is sufficient for the obstacle avoidance problem and many other applications.
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Sekrecka, Aleksandra. "Application of the XBoost Regressor for an A Priori Prediction of UAV Image Quality." Remote Sensing 13, no. 23 (November 24, 2021): 4757. http://dx.doi.org/10.3390/rs13234757.
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In general, the quality of imagery from Unmanned Aerial Vehicles (UAVs) is evaluated after the flight, and then a decision is made on the further value and use of the acquired data. In this paper, an a priori (preflight) image quality prediction methodology is proposed to estimate the preflight image quality and to avoid unfavourable flights, which is extremely important from a time and cost management point of view. The XBoost Regressor model and cross-validation were used for machine learning of the model and image quality prediction. The model was learned on a rich database of real-world images acquired from UAVs under conditions varying in both sensor type, UAV type, exposure parameters, weather, topography, and land cover. Radiometric quality indices (SNR, Entropy, PIQE, NIQE, BRISQUE, and NRPBM) were calculated for each image to train and test the model and to assess the accuracy of image quality prediction. Different variants of preflight parameter knowledge were considered in the study. The proposed methodology offers the possibility of predicting image quality with high accuracy. The correlation coefficient between the actual and predicted image quality, depending on the number of parameters known a priori, ranged from 0.90 to 0.96. The methodology was designed for data acquired from a UAV. Similar prediction accuracy is expected for other low-altitude or close-range photogrammetric data.
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Wang, Jian, and Miaomiao Zhang. "Deep Learning for Regularization Prediction in Diffeomorphic Image Registration." Machine Learning for Biomedical Imaging 1, February 2022 (February 13, 2022): 1–20. http://dx.doi.org/10.59275/j.melba.2021-77df.
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This paper presents a predictive model for estimating regularization parameters of diffeomorphic image registration. We introduce a novel framework that automatically determines the parameters controlling the smoothness of diffeomorphic transformations. Our method significantly reduces the effort of parameter tuning, which is time and labor-consuming. To achieve the goal, we develop a predictive model based on deep convolutional neural networks (CNN) that learns the mapping between pairwise images and the regularization parameter of image registration. In contrast to previous methods that estimate such parameters in a high-dimensional image space, our model is built in an efficient bandlimited space with much lower dimensions. We demonstrate the effectiveness of our model on both 2D synthetic data and 3D real brain images. Experimental results show that our model not only predicts appropriate regularization parameters for image registration, but also improving the network training in terms of time and memory efficiency.
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Zha, Daolu, Xi Jin, Rui Shang, and Pengfei Yang. "A Real-Time Learning-Based Super-Resolution System on FPGA." Parallel Processing Letters 30, no. 04 (December 2020): 2050011. http://dx.doi.org/10.1142/s0129626420500115.
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This paper proposes a real-time super-resolution (SR) system. The proposed system performs a fast SR algorithm that generates a high-resolution image from a low-resolution image using direct regression functions with an up-scaling factor of 2. This algorithm contained two processes: feature learning and SR image prediction. The feature learning stage is performed offline, in which several regression functions were trained. The SR image prediction stage is implemented on the proposed system to generate high-resolution image patches. The system implemented on a Xilinx Virtex 7 field-programmable gate array achieves output resolution of [Formula: see text] (UHD) at 85 fps and 700Mpixels/s throughput. Structure similarity (SSIM) is measured for image quality. Experimental results show that the proposed system provides high image quality for real-time applications. And the proposed system possesses high scalability for resolution.
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Wang, Li, Wenhao Li, Xiaoyi Wang, and Jiping Xu. "Remote sensing image analysis and prediction based on improved Pix2Pix model for water environment protection of smart cities." PeerJ Computer Science 9 (April 26, 2023): e1292. http://dx.doi.org/10.7717/peerj-cs.1292.
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Background As an important part of smart cities, smart water environmental protection has become an important way to solve water environmental pollution problems. It is proposed in this article to develop a water quality remote sensing image analysis and prediction method based on the improved Pix2Pix (3D-GAN) model to overcome the problems associated with water environment prediction of smart cities based on remote sensing image data having low accuracy in predicting image information, as well as being difficult to train. Methods Firstly, due to inversion differences and weather conditions, water quality remote sensing images are not perfect, which leads to the creation of time series data that cannot be used directly in prediction modeling. Therefore, a method for preprocessing time series of remote sensing images has been proposed in this article. The original remote sensing image was unified by pixel substitution, the image was repaired by spatial weight matrix, and the time series data was supplemented by linear interpolation. Secondly, in order to enhance the ability of the prediction model to process spatial-temporal data and improve the prediction accuracy of remote sensing images, the convolutional gated recurrent unit network is concatenated with the U-net network as the generator of the improved Pix2Pix model. At the same time, the channel attention mechanism is introduced into the convolutional gated recurrent unit network to enhance the ability of extracting image time series information, and the residual structure is introduced into the downsampling of the U-net network to avoid gradient explosion or disappearance. After that, the remote sensing images of historical moments are superimposed on the channels as labels and sent to the discriminator for adversarial training. The improved Pix2Pix model no longer translates images, but can predict two dimensions of space and one dimension of time, so it is actually a 3D-GAN model. Third, remote sensing image inversion data of chlorophyll-a concentrations in the Taihu Lake basin are used to verify and predict the water environment at future moments. Results The results show that the mean value of structural similarity, peak signal-to-noise ratio, cosine similarity, and mutual information between the predicted value of the proposed method and the real remote sensing image is higher than that of existing methods, which indicates that the proposed method is effective in predicting water environment of smart cities.
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Pintelas, Emmanuel, Ioannis E. Livieris, and Panagiotis Pintelas. "Explainable Feature Extraction and Prediction Framework for 3D Image Recognition Applied to Pneumonia Detection." Electronics 12, no. 12 (June 14, 2023): 2663. http://dx.doi.org/10.3390/electronics12122663.
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Explainable machine learning is an emerging new domain fundamental for trustworthy real-world applications. A lack of trust and understanding are the main drawbacks of deep learning models when applied to real-world decision systems and prediction tasks. Such models are considered as black boxes because they are unable to explain the reasons for their predictions in human terms; thus, they cannot be universally trusted. In critical real-world applications, such as in medical, legal, and financial ones, an explanation of machine learning (ML) model decisions is considered crucially significant and mandatory in order to acquire trust and avoid fatal ML bugs, which could disturb human safety, rights, and health. Nevertheless, explainable models are more than often less accurate; thus, it is essential to invent new methodologies for creating interpretable predictors that are almost as accurate as black-box ones. In this work, we propose a novel explainable feature extraction and prediction framework applied to 3D image recognition. In particular, we propose a new set of explainable features based on mathematical and geometric concepts, such as lines, vertices, contours, and the area size of objects. These features are calculated based on the extracted contours of every 3D input image slice. In order to validate the efficiency of the proposed approach, we apply it to a critical real-world application: pneumonia detection based on CT 3D images. In our experimental results, the proposed white-box prediction framework manages to achieve a performance similar to or marginally better than state-of-the-art 3D-CNN black-box models. Considering the fact that the proposed approach is explainable, such a performance is particularly significant.
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Zhang, Lei, and Shaofeng Shao. "Image-based machine learning for materials science." Journal of Applied Physics 132, no. 10 (September 14, 2022): 100701. http://dx.doi.org/10.1063/5.0087381.
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Materials research studies are dealing with a large number of images, which can now be facilitated via image-based machine learning techniques. In this article, we review recent progress of machine learning-driven image recognition and analysis for the materials and chemical domains. First, the image-based machine learning that facilitates the property prediction of chemicals or materials is discussed. Second, the analysis of nanoscale images including those from a scanning electron microscope and a transmission electron microscope is discussed, which is followed by the discussion about the identification of molecular structures via image recognition. Subsequently, the image-based machine learning works to identify and classify various practical materials such as metal, ceramics, and polymers are provided, and the image recognition for a range of real-scenario device applications such as solar cells is provided in detail. Finally, suggestions and future outlook for image-based machine learning for classification and prediction tasks in the materials and chemical science are presented. This article highlights the importance of the integration of the image-based machine learning method into materials and chemical science and calls for a large-scale deployment of image-based machine learning methods for prediction and classification of images in materials and chemical science.
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Zhu, Jinsong, Wei Li, Da Lin, and Ge Zhao. "Real-Time Monitoring of Jet Trajectory during Jetting Based on Near-Field Computer Vision." Sensors 19, no. 3 (February 8, 2019): 690. http://dx.doi.org/10.3390/s19030690.
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A novel method of near-field computer vision (NFCV) was developed to monitor the jet trajectory during the jetting process, which was used to precisely predict the falling point position of the jet trajectory. By means of a high-resolution webcam, the NFCV sensor device collected near-field images of the jet trajectory. Preprocessing of collected images was carried out, which included squint image correction, noise elimination, and jet trajectory extraction. The features of the jet trajectory in the processed image were extracted, including: start-point slope (SPS), end-point slope (EPS), and overall trajectory slope (OTS) based on the proposed mean position method. A multiple regression jet trajectory range prediction model was established based on these trajectory characteristics and the reliability of the model was verified. The results show that the accuracy of the prediction model is not less than 94% and the processing time is less than 0.88s, which satisfy the requirements of real-time online jet trajectory monitoring.
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Dong, Huihui, Wenping Ma, Yue Wu, Jun Zhang, and Licheng Jiao. "Self-Supervised Representation Learning for Remote Sensing Image Change Detection Based on Temporal Prediction." Remote Sensing 12, no. 11 (June 9, 2020): 1868. http://dx.doi.org/10.3390/rs12111868.
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Traditional change detection (CD) methods operate in the simple image domain or hand-crafted features, which has less robustness to the inconsistencies (e.g., brightness and noise distribution, etc.) between bitemporal satellite images. Recently, deep learning techniques have reported compelling performance on robust feature learning. However, generating accurate semantic supervision that reveals real change information in satellite images still remains challenging, especially for manual annotation. To solve this problem, we propose a novel self-supervised representation learning method based on temporal prediction for remote sensing image CD. The main idea of our algorithm is to transform two satellite images into more consistent feature representations through a self-supervised mechanism without semantic supervision and any additional computations. Based on the transformed feature representations, a better difference image (DI) can be obtained, which reduces the propagated error of DI on the final detection result. In the self-supervised mechanism, the network is asked to identify different sample patches between two temporal images, namely, temporal prediction. By designing the network for the temporal prediction task to imitate the discriminator of generative adversarial networks, the distribution-aware feature representations are automatically captured and the result with powerful robustness can be acquired. Experimental results on real remote sensing data sets show the effectiveness and superiority of our method, improving the detection precision up to 0.94–35.49%.
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Garg, Meenu, Sheifali Gupta, Rakesh Ahuja, and Deepali Gupta. "Diabetic Retinopathy Prediction Device System." Journal of Computational and Theoretical Nanoscience 16, no. 10 (October 1, 2019): 4266–70. http://dx.doi.org/10.1166/jctn.2019.8511.
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The present study relates to diagnostic devices, and more specifically, to a diabetic retinopathy prediction device, system and method for early prediction of diabetic retinopathy with application of deep learning. The device includes an image capturing device, a memory coupled to processor. The image capturing device obtains a retinal fundus image from the user. The memory comprising executable instructions which upon execution by the processor configures the device to obtain physiological parameters of the user in real-time from the image capturing device, retrieve the obtained retinal fundus image and the one or more obtained physiological parameters and compare the one or more extracted features with at least one pre-stored feature in a database to generate at least a prediction result indicative of detection of the presence, the progression or the treatment effect of the disease in the user.
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Rubel, Lukin, Rubel, and Egiazarian. "NN-Based Prediction of Sentinel-1 SAR Image Filtering Efficiency." Geosciences 9, no. 7 (June 29, 2019): 290. http://dx.doi.org/10.3390/geosciences9070290.
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Images acquired by synthetic aperture radars are degraded by speckle that prevents efficient extraction of useful information from radar remote sensing data. Filtering or despeckling is a tool often used to improve image quality. However, depending upon image and noise properties, the quality of improvement can vary. Besides, a quality can be characterized by different criteria or metrics, where visual quality metrics can be of value. For the case study of discrete cosine transform (DCT)based filtering, we show that improvement of radar image quality due to denoising can be predicted in a simple and fast way, especially if one deals with particular type of radar data such as images acquired by Sentinel-1. Our approach is based on application of a trained neural network that, in general, might have a different number of inputs (features). We propose a set of features describing image and noise statistics from different viewpoints. From this set, that contains 28 features, we analyze different subsets and show that a subset of the 13 most important and informative features leads to a very accurate prediction. Test image generation and network training peculiarities are discussed. The trained neural network is then tested using different verification strategies. The results of the network application to test and real-life radar images are presented, demonstrating good performance for a wide set of quality metrics.
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Shi, Jingmin, Fanhuai Shi, and Xixia Huang. "Prediction of Maturity Date of Leafy Greens Based on Causal Inference and Convolutional Neural Network." Agriculture 13, no. 2 (February 9, 2023): 403. http://dx.doi.org/10.3390/agriculture13020403.
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The prediction of the maturity date of leafy greens in a planting environment is an essential research direction of precision agriculture. Real-time detection of crop growth status and prediction of its maturity for harvesting is of great significance for improving the management of greenhouse crops and improving the quality and efficiency of the greenhouse planting industry. The development of image processing technology provides great help for real-time monitoring of crop growth. However, image processing technology can only obtain the representation information of leafy greens, and it is difficult to describe the causal mechanism of environmental factors affecting crop growth. Therefore, a framework combining an image processing model and a crop growth model based on causal inference was proposed to predict the maturity of leafy greens. In this paper, a deep convolutional neural network was used to classify the growth stages of leafy greens. Then, since some environmental factors have causal effects on the growth rate of leafy greens, the causal effects of various environmental factors on the growth of leafy greens are obtained according to the data recorded by environmental sensors in the greenhouse, and the prediction results of the maturity of leafy greens in the study area are obtained by combining image data. The experiments showed that the root mean square error (RMSE) was 2.49 days, which demonstrated that the method had substantial feasibility in predicting the maturity for harvesting and effectively solved the limitations of poor timeliness of prediction. This model has great application potential in predicting crop maturity in greenhouses.
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Xu, Peng, Man Guo, Lei Chen, Weifeng Hu, Qingshan Chen, and Yujun Li. "No-Reference Stereoscopic Image Quality Assessment Based on Binocular Statistical Features and Machine Learning." Complexity 2021 (January 28, 2021): 1–14. http://dx.doi.org/10.1155/2021/8834652.
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Learning a deep structure representation for complex information networks is a vital research area, and assessing the quality of stereoscopic images or videos is challenging due to complex 3D quality factors. In this paper, we explore how to extract effective features to enhance the prediction accuracy of perceptual quality assessment. Inspired by the structure representation of the human visual system and the machine learning technique, we propose a no-reference quality assessment scheme for stereoscopic images. More specifically, the statistical features of the gradient magnitude and Laplacian of Gaussian responses are extracted to form binocular quality-predictive features. After feature extraction, these features of distorted stereoscopic image and its human perceptual score are used to construct a statistical regression model with the machine learning technique. Experimental results on the benchmark databases show that the proposed model generates image quality prediction well correlated with the human visual perception and delivers highly competitive performance with the typical and representative methods. The proposed scheme can be further applied to the real-world applications on video broadcasting and 3D multimedia industry.
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Vidyapu, Sandeep, Vijaya Saradhi Vedula, and Samit Bhattacharya. "Weighted Voting-Based Effective Free-Viewing Attention Prediction On Web Image Elements." Interacting with Computers 32, no. 2 (March 2020): 170–84. http://dx.doi.org/10.1093/iwcomp/iwaa013.
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Abstract Quantifying and predicting the user attention on web image elements finds applications in synthesis and rendering of elements on webpages. However, the majority of the existing approaches either overlook the visual characteristics of these elements or do not incorporate the users’ visual attention. Especially, obtaining a representative quantified attention (for images) from the attention allocation of multiple users is a challenging task. Toward overcoming the challenge for free-viewing attention, this paper introduces four weighted voting strategies to assign effective visual attention (fixation index (FI)) for web image elements. Subsequently, the prominent image visual features in explaining the assigned attention are identified. Further, the association between image visual features and the assigned attention is modeled as a multi-class prediction problem, which is solved through support vector machine-based classification. The analysis of the proposed approach on real-world webpages reveals the following: (i) image element’s position, size and mid-level color histograms are highly informative for the four weighting schemes; (ii) the presented computational approach outperforms the baseline for four weighted voting schemes with an average accuracy of 85% and micro F1-score of 60%; and (iii) uniform weighting (same weight for all FIs) is adequate for estimating the user’s initial attention while the proportional weighting (weight the FI in proportion to its likelihood of occurrence) extends to the latter attention prediction.
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Verma, Poonam, Huanmei Wu, Mark Langer, Indra Das, and George Sandison. "Survey: Real-Time Tumor Motion Prediction for Image-Guided Radiation Treatment." Computing in Science & Engineering 13, no. 5 (September 2011): 24–35. http://dx.doi.org/10.1109/mcse.2010.99.
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Sharp, Gregory C., Steve B. Jiang, Shinichi Shimizu, and Hiroki Shirato. "Prediction of respiratory tumour motion for real-time image-guided radiotherapy." Physics in Medicine and Biology 49, no. 3 (January 16, 2004): 425–40. http://dx.doi.org/10.1088/0031-9155/49/3/006.
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Yan, Kai, Lanyue Liang, Ziqiang Zheng, Guoqing Wang, and Yang Yang. "Medium Transmission Map Matters for Learning to Restore Real-World Underwater Images." Applied Sciences 12, no. 11 (May 27, 2022): 5420. http://dx.doi.org/10.3390/app12115420.
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Low illumination, light reflections, scattering, absorption, and suspended particles inevitably lead to critically degraded underwater image quality, which poses great challenges for recognizing objects from underwater images. The existing underwater enhancement methods that aim to promote underwater visibility heavily suffer from poor image restoration performance and generalization ability. To reduce the difficulty of underwater image enhancement, we introduce the media transmission map as guidance for image enhancement. Different from the existing frameworks, which also introduce the medium transmission map for better distribution modeling, we formulate the interaction between the underwater visual images and the transmission map explicitly to obtain better enhancement results. At the same time, our network only requires supervised learning of the media transmission map during training, and the corresponding prediction map can be generated in subsequent tests, which reduces the operation difficulty of subsequent tasks. Thanks to our formulation, the proposed method with a very lightweight network configuration can produce very promising results of 22.6 dB on the challenging Test-R90 with an impressive 30.3 FPS, which is faster than most current algorithms. Comprehensive experimental results have demonstrated the superiority on underwater perception.
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Zhao, Qi, Zhichao Xin, Zhibin Yu, and Bing Zheng. "Unpaired Underwater Image Synthesis with a Disentangled Representation for Underwater Depth Map Prediction." Sensors 21, no. 9 (May 9, 2021): 3268. http://dx.doi.org/10.3390/s21093268.
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As one of the key requirements for underwater exploration, underwater depth map estimation is of great importance in underwater vision research. Although significant progress has been achieved in the fields of image-to-image translation and depth map estimation, a gap between normal depth map estimation and underwater depth map estimation still remains. Additionally, it is a great challenge to build a mapping function that converts a single underwater image into an underwater depth map due to the lack of paired data. Moreover, the ever-changing underwater environment further intensifies the difficulty of finding an optimal mapping solution. To eliminate these bottlenecks, we developed a novel image-to-image framework for underwater image synthesis and depth map estimation in underwater conditions. For the problem of the lack of paired data, by translating hazy in-air images (with a depth map) into underwater images, we initially obtained a paired dataset of underwater images and corresponding depth maps. To enrich our synthesized underwater dataset, we further translated hazy in-air images into a series of continuously changing underwater images with a specified style. For the depth map estimation, we included a coarse-to-fine network to provide a precise depth map estimation result. We evaluated the efficiency of our framework for a real underwater RGB-D dataset. The experimental results show that our method can provide a diversity of underwater images and the best depth map estimation precision.
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Chen, Kaimeng, and Chin-Chen Chang. "Real-Time Error-Free Reversible Data Hiding in Encrypted Images Using (7, 4) Hamming Code and Most Significant Bit Prediction." Symmetry 11, no. 1 (January 4, 2019): 51. http://dx.doi.org/10.3390/sym11010051.
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In this paper, a novel, real-time, error-free, reversible data hiding method for encrypted images has been proposed. Based on the (7, 4) Hamming code, we designed an efficient encoding scheme to embed secret data into the least significant bits (LSBs) of the encrypted image. For reversibility, we designed a most significant bit (MSB) prediction scheme that can recover a portion of the modified MSBs after the image is decrypted. These MSBs can be modified to accommodate the additional information that is used to recover the LSBs. After embedding the data, the original image can be recovered with no error and the secret data can be extracted from both the encrypted image and the decrypted image. The experimental results proved that compared with existing methods, the proposed method can achieve higher embedding rate, better quality of the marked image and less execution time of data embedding. Therefore, the proposed method is suitable for real-time applications in the cloud.
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Siddiqui, Faisal Mubeen. "Chili Leaf Disease Prediction Using CNN." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 4791–97. http://dx.doi.org/10.22214/ijraset.2023.52757.
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Abstract: Chili leaf diseases cause significant damage to chili plants, leading to reduced crop yield and economic losses for farmers. Early detection and diagnosis of these diseases are crucial for effective disease management. In this research paper, we propose a chili leaf disease prediction model using Convolutional Neural Network (CNN). The proposed model utilizes an image dataset collected from different regions ,consisting of chili leaf images infected with common chili leaf diseases, like bacterial leaf spot, leaf Curl , Mosaic virus, etc. We pre-processed the dataset to enhance the image quality and to remove noise. The preprocessed dataset was split into training and validation sets. The CNN model was trained using the training set and validated using the validation set. The proposed model achieved an high accuracy on the validation set. The proposed model can be used to predict the occurrence of chili leaf diseases in real-time, which can help farmers in taking preventive measures to protect their crops
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Liu, Hui, Liangchen Qi, and Mingsong Sun. "Short-Term Stock Price Prediction Based on CAE-LSTM Method." Wireless Communications and Mobile Computing 2022 (June 22, 2022): 1–7. http://dx.doi.org/10.1155/2022/4809632.
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Artificial intelligence methods are important tools for mining information for forecasting in the stock market. Most of the literature related to short-term stock price prediction focuses on the technical data, but in the real market, many individual investors make investment decisions more from stock price shape characteristics rather than specific stock price values. Compared with traditional measurement methods, deep neural networks perform better in processing high-dimensional complex data such as images. This paper proposes a model that combines CAE (convolutional autoencoder) and LSTM (long short-term memory) neural network, uses CAE to extract stock price image feature data, and combines technical data to predict short-term stock prices. The results show that the CAE-LSTM model, based on stock price image morphological feature data and technical data, performs well in short-term stock price prediction and has good generalization ability. The root mean square error of the CAE-LSTM model decreased by about 4% from that of LSTM. CAE-LSTM models have better predictive power than LSTM models that only use technical indicator data as valid inputs.
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Islam, Md Mahbubul, and Joong-Hwan Baek. "A Hierarchical Approach toward Prediction of Human Biological Age from Masked Facial Image Leveraging Deep Learning Techniques." Applied Sciences 12, no. 11 (May 24, 2022): 5306. http://dx.doi.org/10.3390/app12115306.
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The lifestyle of humans has changed noticeably since the contagious COVID-19 disease struck globally. People should wear a face mask as a protective measure to curb the spread of the contagious disease. Consequently, real-world applications (i.e., electronic customer relationship management) dealing with human ages extracted from face images must migrate to a robust system proficient to estimate the age of a person wearing a face mask. In this paper, we proposed a hierarchical age estimation model from masked facial images in a group-to-specific manner rather than a single regression model because age progression across different age groups is quite dissimilar. Our intention was to squeeze the feature space among limited age classes so that the model could fairly discern age. We generated a synthetic masked face image dataset over the IMDB-WIKI face image dataset to train and validate our proposed model due to the absence of a benchmark masked face image dataset with real age annotations. We somewhat mitigated the data sparsity problem of the large public IMDB-WIKI dataset using off-the-shelf down-sampling and up-sampling techniques as required. The age estimation task was fully modeled like a deep classification problem, and expected ages were formulated from SoftMax probabilities. We performed a classification task by deploying multiple low-memory and higher-accuracy-based convolutional neural networks (CNNs). Our proposed hierarchical framework demonstrated marginal improvement in terms of mean absolute error (MAE) compared to the one-off model approach for masked face real age estimation. Moreover, this research is perhaps the maiden attempt to estimate the real age of a person from his/her masked face image.
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Guo, Yiming, Xiaoqing Wu, Chun Qing, Liyong Liu, Qike Yang, Xiaodan Hu, Xianmei Qian, and Shiyong Shao. "Blind Restoration of a Single Real Turbulence-Degraded Image Based on Self-Supervised Learning." Remote Sensing 15, no. 16 (August 18, 2023): 4076. http://dx.doi.org/10.3390/rs15164076.
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Turbulence-degraded image frames are distorted by both turbulent deformations and space–time varying blurs. Restoration of the atmospheric turbulence-degraded image is of great importance in the state of affairs, such as remoting sensing, surveillance, traffic control, and astronomy. While traditional supervised learning uses lots of simulated distorted images for training, it has poor generalization ability for real degraded images. To address this problem, a novel blind restoration network that only inputs a single turbulence-degraded image is presented, which is mainly used to reconstruct the real atmospheric turbulence distorted images. In addition, the proposed method does not require pre-training, and only needs to input a single real turbulent degradation image to output a high-quality result. Meanwhile, to improve the self-supervised restoration effect, Regularization by Denoising (RED) is introduced to the network, and the final output is obtained by averaging the prediction of multiple iterations in the trained model. Experiments are carried out with real-world turbulence-degraded data by implementing the proposed method and four reported methods, and we use four non-reference indicators for evaluation, among which Average Gradient, NIQE, and BRISQUE have achieved state-of-the-art effects compared with other methods. As a result, our method is effective in alleviating distortions and blur, restoring image details, and enhancing visual quality. Furthermore, the proposed approach has a certain degree of generalization, and has an excellent restoration effect for motion-blurred images.
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Shirkande, Dr S. T., Rutuja B. Bhosale, Shweta S. More, and Suyash S. Awate. "Drowsiness Prediction Based on Multiple Aspects Using Image Processing Techniques: A Review." International Journal for Research in Applied Science and Engineering Technology 11, no. 2 (February 28, 2023): 92–94. http://dx.doi.org/10.22214/ijraset.2023.48970.
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Abstract: Clinical depression is a type of soft biometric trait that can be used to characterize a person. Because of its importance in a variety of legal situations, this mood illness can be included in forensic psychological evaluations. In recent years, research into the automatic detection of depression based on real image has yielded a variety of algorithmic approaches and auditory indicators. Machine learning algorithms have recently been used successfully in a variety of image-based applications. Automatic depression recognition - the recognition of facial expressions linked with sad behaviour – is one of the most important applications. Modern algorithms for detecting depression usually look at both geographical and temporal data separately. This method restricts the capacity to capture a wide range of face expressions as well as the use of different facial parts. This research introduces a novel machine learning strategy for accurately representing face information associated to depressive behaviours from real-world images. Our suggested architecture outperforms state-of-the-art algorithms in automatic depression recognition, according to results from two benchmark datasets.
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Chen, Xinru. "Prediction of Electric Load Neural Network Prediction Model for Big Data." BCP Social Sciences & Humanities 21 (February 15, 2023): 549–55. http://dx.doi.org/10.54691/bcpssh.v21i.3640.
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Based on the background of the new media era, this paper takes the research on the online advertising marketing characteristics of LEGO “Rebuild the World” series as an example to explore how brands should play the advantages of online marketing under the new internet background, update the creative thinking of online marketing communication, and achieve the purpose of clarifying the target consumer group positioning and optimizing the brand image. By combining the Method of combining observation and content analysis, the author found 3 characteristics of Lego “Rebuild the World” series online advertising, which are: using LEGO bricks to connect the seemingly unconnected toy world with the real world, showing the richness, diversity and plasticity of LEGO bricks; cleverly designed with lots of elements to maximize the imagination of adults and children; closely connected the digital world with the real world, making the online advertising of LEGO “Rebuild the World” timely, progressive and universal. In addition, in view of the updating of online marketing modes of major brands in the era of new media, the author also puts forward corresponding thinking: how to improve the fit between the host broadcast with goods and the brand image, and how to rationally apply the Internet platform to realize the updating of online marketing modes.
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Kim, Jie-Hyun, Sang-Il Oh, So-Young Han, Ji-Soo Keum, Kyung-Nam Kim, Jae-Young Chun, Young-Hoon Youn, and Hyojin Park. "An Optimal Artificial Intelligence System for Real-Time Endoscopic Prediction of Invasion Depth in Early Gastric Cancer." Cancers 14, no. 23 (December 5, 2022): 6000. http://dx.doi.org/10.3390/cancers14236000.
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We previously constructed a VGG-16 based artificial intelligence (AI) model (image classifier [IC]) to predict the invasion depth in early gastric cancer (EGC) using endoscopic static images. However, images cannot capture the spatio-temporal information available during real-time endoscopy—the AI trained on static images could not estimate invasion depth accurately and reliably. Thus, we constructed a video classifier [VC] using videos for real-time depth prediction in EGC. We built a VC by attaching sequential layers to the last convolutional layer of IC v2, using video clips. We computed the standard deviation (SD) of output probabilities for a video clip and the sensitivities in the manner of frame units to observe consistency. The sensitivity, specificity, and accuracy of IC v2 for static images were 82.5%, 82.9%, and 82.7%, respectively. However, for video clips, the sensitivity, specificity, and accuracy of IC v2 were 33.6%, 85.5%, and 56.6%, respectively. The VC performed better analysis of the videos, with a sensitivity of 82.3%, a specificity of 85.8%, and an accuracy of 83.7%. Furthermore, the mean SD was lower for the VC than IC v2 (0.096 vs. 0.289). The AI model developed utilizing videos can predict invasion depth in EGC more precisely and consistently than image-trained models, and is more appropriate for real-world situations.
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Gupta, Ashish, and Rishabh Mehrotra. "Joint Attention Neural Model for Demand Prediction in Online Marketplaces." Proceedings of the Northern Lights Deep Learning Workshop 1 (February 6, 2020): 6. http://dx.doi.org/10.7557/18.5170.
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As an increasing number of consumers rely on online marketplaces to purchase goods from, demand prediction becomes an important problem for suppliers to inform their pricing and inventory management decisions. Business volatility and the complexity of factors influence demand, which makes it a harder quantity to predict. In this paper, we consider the case of an online classified marketplace and propose a joint multi-modal neural model for demand prediction. The proposed neural model incorporates a number of factors including product description information (title, description, images), contextual information (geography, similar products) and historic interest to predict demand. Large-scale experiments on real-world data demonstrate superior performance over established baselines. Our experiments highlight the importance of considering, quantifying and leveraging the textual content of products and image quality for enhanced demand prediction. Finally, we quantify the impact of the different factors in predicting demand.
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Jiang, Shu, and Graham A. Colditz. "Abstract LB161: Whole mammogram image improves breast cancer prediction." Cancer Research 82, no. 12_Supplement (June 15, 2022): LB161. http://dx.doi.org/10.1158/1538-7445.am2022-lb161.
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Abstract To efficiently capture data from mammographic breast images and classify long term risk of breast cancer, we developed methods that use the extensive existing data that are currently ignored in the context of breast cancer risk stratification. More than 20 studies support texture features add value to risk prediction beyond breast density. However, the entire mammogram imaging data has a high dimension of pixels (~13 million per image), greatly exceeding the number of women in a cohort. We apply functional principal component analysis methods to predict 5-years breast cancer incidence using baseline mammograms. We applied these methods onto women participating in the Joanne Knight Breast Health Cohort which is comprised of over 10,000 women undergoing repeated mammography screening at Siteman Cancer Center and followed since 2010. All women had baseline mammogram at entry, provided a blood sample and completed a risk factor questionnaire. Mammograms are all using the same technology (Hologic). During follow-up through October 2020, we identified 246 incident breast cancer cases (pathology confirmed) and matched them to controls from the perspective cohort based on month of mammogram and age at entry. In a baseline model we controlled for age, menopause, BMI, and mammographic breast density (BIRADs). We then added the full image (characterized by the FPC) to the base model and further compared the AUC of the new model vs the base model using the likelihood ratio test. AUC is validated with internal 10-fold cross validation. The AUC for 5-year breast cancer risk classification increased significantly from a median of 0.61 (sd 0.09 for estimated AUCs across 10-fold internal validation) for the baseline model to 0.70 (0.10) when the full image is added, p < 0.001. We conclude that using full mammogram images for breast cancer risk prediction captures additional information on breast tissue characteristics that relate to cancer risk, and improves prediction classification. This prediction algorithm can run efficiently in real time (in seconds) with processing of digital mammograms. Thus, this model can be easily implemented in mammography screening services and other clinical settings to guide real-time risk stratification to improve precision prevention of the leading cancer in women world-wide. Further analysis will quantify the value of adding other breast cancer risk factors, including polygenic risk scores. Addition of repeated mammogram images over time should further increase classification performance. This approach has the potential to improve risk classification by using data already available for the vast majority of women already having repeated screening mammograms. Citation Format: Shu Jiang, Graham A. Colditz. Whole mammogram image improves breast cancer prediction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB161.
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Zhu, Jianchen, Kaixin Han, and Shenlong Wang. "Automobile tire life prediction based on image processing and machine learning technology." Advances in Mechanical Engineering 13, no. 3 (March 2021): 168781402110027. http://dx.doi.org/10.1177/16878140211002727.
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With economic growth, automobiles have become an irreplaceable means of transportation and travel. Tires are important parts of automobiles, and their wear causes a large number of traffic accidents. Therefore, predicting tire life has become one of the key factors determining vehicle safety. This paper presents a tire life prediction method based on image processing and machine learning. We first build an original image database as the initial sample. Since there are usually only a few sample image libraries in engineering practice, we propose a new image feature extraction and expression method that shows excellent performance for a small sample database. We extract the texture features of the tire image by using the gray-gradient co-occurrence matrix (GGCM) and the Gauss-Markov random field (GMRF), and classify the extracted features by using the K-nearest neighbor (KNN) classifier. We then conduct experiments and predict the wear life of automobile tires. The experimental results are estimated by using the mean average precision (MAP) and confusion matrix as evaluation criteria. Finally, we verify the effectiveness and accuracy of the proposed method for predicting tire life. The obtained results are expected to be used for real-time prediction of tire life, thereby reducing tire-related traffic accidents.
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Manickathan, L., C. Mucignat, and I. Lunati. "Higher-Order Accurate Neural Network For Real-Time Fluid Velocimetry." Proceedings of the International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics 20 (July 11, 2022): 1–13. http://dx.doi.org/10.55037/lxlaser.20th.59.
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In the present work, we introduce a novel lightweight neural network for fluid velocimetry called LIMA (Lightweight Image Matching Architecture) designed and optimized for PIV, which can potentially fit on low-cost computer hardware. We compare two versions of the network: LIMA-4, a 4-level architecture focused on fast reconstruction; and LIMA-6, a 6-level architecture focused on maximizing accuracy. We demonstrate the new approach provides more accurate prediction with fewer network parameters and faster inference speed. Furthermore, we quantified the disparity error using uncertainty quantification (UQ) by image matching to assess the prediction accuracy of the network. We assess the performance of a synthetic direct numerical simulation (DNS) dataset and a wind tunnel measurement dataset of flow past a cylinder. In all cases, we validate that the new network shows higher accuracy than the previous state-of-the-art neural network (PWCIRR) and also the classic particle image velocimetry (PIV) approach. In the future, we envision deploying the lightweight architecture on low-cost devices to provide affordable, real-time inference of the flow field during PIV measurements.
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MANZIUK, E., T. SKRYPNYK, and M. HIRNYI. "DETERMINATION OF RECIPES CONSTITUENT ELEMENTS BASED ON IMAGE." Computer Systems and Information Technologies 1, no. 1 (September 2, 2020): 42–46. http://dx.doi.org/10.31891/csit-2020-1-5.
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Image recognition is used to retrieve, analyse, understand, and process images from the real world to convert them into digital information. In this area involved data mining, machine learning, pattern recognition, knowledge extension. Developments in the image recognition area have resulted in computers and smartphones becoming capable of mimicking human eyesight. Improved cameras in modern devices can take pictures of very high quality, and with the help of new software, they receive the necessary information and on the basis of the received data is processed images. However, food recognition challenges modern computer vision systems and needs to go beyond just an visible image. Compared to understanding the natural image, visual prediction of ingredients requires high-level solutions and previous knowledge. This creates additional problems, because food components have high variability between the class, when cooking, you have to convert components and the ingredients are often included in the cooked dish. The recognition system allows you to take a step toward understanding the food supply systems such as calorie score and create recipes. The recognition system can be used to address wider problems, such as the prediction of the image on the consistency of the folding elements.
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Li Zhou, Tao Sun, Shaotao Sun, and Yuanzhi Zhang. "Real-time Depth Map Prediction and Optimization Based on Adaptive Image Segmentation." International Journal of Advancements in Computing Technology 5, no. 2 (January 31, 2013): 621–31. http://dx.doi.org/10.4156/ijact.vol5.issue2.77.
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Wijaya, I. Made Anorn S., S. Shibusawa, A. Sasao, K. Sakai, and H. Sato. "Soil Parameters Prediction with Soil Image Collected by Real-Time Soil Spectrophotometer." IFAC Proceedings Volumes 34, no. 11 (August 2001): 44–48. http://dx.doi.org/10.1016/s1474-6670(17)34103-4.
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Esteghamatian, Mehdi, Zohreh Azimifar, Perry Radau, and Graham Wright. "Real time cardiac image registration during respiration: a time series prediction approach." Journal of Real-Time Image Processing 8, no. 2 (May 7, 2011): 179–91. http://dx.doi.org/10.1007/s11554-011-0202-0.
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Rubel, Oleksii, Vladimir Lukin, Andrii Rubel, and Karen Egiazarian. "Selection of Lee Filter Window Size Based on Despeckling Efficiency Prediction for Sentinel SAR Images." Remote Sensing 13, no. 10 (May 12, 2021): 1887. http://dx.doi.org/10.3390/rs13101887.
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Radar imaging has many advantages. Meanwhile, SAR images suffer from a noise-like phenomenon called speckle. Many despeckling methods have been proposed to date but there is still no common opinion as to what the best filter is and/or what are its parameters (window or block size, thresholds, etc.). The local statistic Lee filter is one of the most popular and best-known despeckling techniques in radar image processing. Using this filter and Sentinel-1 images as a case study, we show how filter parameters, namely scanning window size, can be selected for a given image based on filter efficiency prediction. Such a prediction can be carried out using a set of input parameters that can be easily and quickly calculated and employing a trained neural network that allows determining one or several criteria of filtering efficiency with high accuracy. The statistical analysis of the obtained results is carried out. This characterizes improvements due to the adaptive selection of the filter window size, both potential and based on prediction. We also analyzed what happens if, due to prediction errors, erroneous decisions are undertaken. Examples for simulated and real-life images are presented.
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Wu, Wang, Rigall, Li, Zhu, He, and Yan. "ECNet: Efficient Convolutional Networks for Side Scan Sonar Image Segmentation." Sensors 19, no. 9 (April 29, 2019): 2009. http://dx.doi.org/10.3390/s19092009.
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This paper presents a novel and practical convolutional neural network architecture to implement semantic segmentation for side scan sonar (SSS) image. As a widely used sensor for marine survey, SSS provides higher-resolution images of the seafloor and underwater target. However, for a large number of background pixels in SSS image, the imbalance classification remains an issue. What is more, the SSS images contain undesirable speckle noise and intensity inhomogeneity. We define and detail a network and training strategy that tackle these three important issues for SSS images segmentation. Our proposed method performs image-to-image prediction by leveraging fully convolutional neural networks and deeply-supervised nets. The architecture consists of an encoder network to capture context, a corresponding decoder network to restore full input-size resolution feature maps from low-resolution ones for pixel-wise classification and a single stream deep neural network with multiple side-outputs to optimize edge segmentation. We performed prediction time of our network on our dataset, implemented on a NVIDIA Jetson AGX Xavier, and compared it to other similar semantic segmentation networks. The experimental results show that the presented method for SSS image segmentation brings obvious advantages, and is applicable for real-time processing tasks.
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Chen, Chi-Chang, and Chien-Hsing Huang. "USING ARTIFICIAL INTELLIGENCE TO ASSESS SOLAR RADIATION FROM THE TOTAL SKY IMAGES." International Journal of Engineering Technologies and Management Research 7, no. 5 (June 3, 2020): 64–71. http://dx.doi.org/10.29121/ijetmr.v7.i5.2020.685.
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Solar power generation converts solar radiation into electrical energy. It is the most environmentally friendly green energy source in modern times, but the solar radiation reception rate is unstable due to weather. The general weather forecast is for the climate of a large area and cannot provide effective real-time prediction to the area where the power plant generating radiant energy from solar radiation. The sky imager can collect the sky image of the location of the solar power panel in real time, which can help to understand the weather conditions in real time, especially the dynamics of the clouds, which is the main reason for affecting the solar power generation. In this study, the optical flow method was used to analyze the motion vectors of clouds in the sky image, thereby estimating the changes of clouds in a short time, and the correlation between the distribution of clouds in the sky and the radiation of the whole sky images was analyzed through a neural network. The change further predicts the change in radiation across the sky, thereby effectively assessing the efficiency of solar power generation.
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Mucignat, C., L. Manickathan, J. Shah, T. Rösgen, and I. Lunati. "Estimating BOS Image Deformation With A Lightweight CNN." Proceedings of the International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics 20 (July 11, 2022): 1–14. http://dx.doi.org/10.55037/lxlaser.20th.60.
Full textAbstract:
We introduce a Convolutional Neural Network (CNN) to post-process recordings obtained by means of Background Oriented Schlieren (BOS), a popular technique to visualize compressible/convective flows. To reconstruct BOS image deformation, we devised a lightweight network (LIMA) that has comparatively fewer parameters to train, allowing the deployment of the network on embedded GPU hardware. To train the CNN, we introduce a novel strategy based on the generation of synthetic images with random, irrotational displacement field that mimic those provided by real BOS recording. This allows us to generate a large number of training examples at minimal computational cost. To assess the accuracy of the reconstructed displacement, we consider test cases consisting of synthetic images with sinusoidal displacement as well as images obtained in a real experimental study of flow past and inside a heated hollow hemisphere. By comparing the prediction of the CNN with conventional post-processing techniques such as Direct Image Correlation (DIC) and Particle Image Velocimetry (PIV) cross-correlation, we show that LIMA gives more accurate and robust results for the synthetic example. When applied to the recordings from the real experiment, all methods provide consistent deformation fields. As they offer similar or better accuracy at a fraction of the computational costs, properly designed CNNs offer a valuable alternative to conventional post-processing techniques for BOS experiments.