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Journal articles on the topic 'REAL IMAGE PREDICTION'

Author: Grafiati

Published: 11 September 2023

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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 (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 th

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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 (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 o

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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 (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 Grad

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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 (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 th

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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 (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 pred

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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 (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 determi

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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 (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

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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 (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

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Rajesh, E., Shajahan Basheer, Rajesh Kumar Dhanaraj, et al. "Machine Learning for Online Automatic Prediction of Common Disease Attributes Using Never-Ending Image Learner." Diagnostics 13, no. 1 (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.

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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 (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 vari

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Caesarendra, Wahyu, Taufiq Aiman Hishamuddin, Daphne Teck Ching Lai, et al. "An Embedded System Using Convolutional Neural Network Model for Online and Real-Time ECG Signal Classification and Prediction." Diagnostics 12, no. 4 (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 include

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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 (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 opt

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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 distanc

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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 (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 im

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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 (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 esti

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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 (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

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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, w

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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 (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

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Zhang, Lei, and Shaofeng Shao. "Image-based machine learning for materials science." Journal of Applied Physics 132, no. 10 (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 id

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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 (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

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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 (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

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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 (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 fundu

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Rubel, Lukin, Rubel, and Egiazarian. "NN-Based Prediction of Sentinel-1 SAR Image Filtering Efficiency." Geosciences 9, no. 7 (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 simp

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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 (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 di

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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 Laplaci

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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 (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 attent

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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 (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 (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 (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

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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 (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 di

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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 (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 wit

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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 (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 im

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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 (l

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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 (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 bec

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Guo, Yiming, Xiaoqing Wu, Chun Qing, et al. "Blind Restoration of a Single Real Turbulence-Degraded Image Based on Self-Supervised Learning." Remote Sensing 15, no. 16 (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 t

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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 (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 be

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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

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Kim, Jie-Hyun, Sang-Il Oh, So-Young Han, et al. "An Optimal Artificial Intelligence System for Real-Time Endoscopic Prediction of Invasion Depth in Early Gastric Cancer." Cancers 14, no. 23 (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 th

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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), contex

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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 (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

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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 (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 met

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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 quantif

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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 (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 recognitio

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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 (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 (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 (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 (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 efficienc

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Wu, Wang, Rigall, et al. "ECNet: Efficient Convolutional Networks for Side Scan Sonar Image Segmentation." Sensors 19, no. 9 (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

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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 (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

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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.

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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 gene

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