Recent Trends in Image Processing and Pattern Recognition

Topic Information

Dear Colleagues,

The 5th International Conference on Recent Trends in Image Processing and Pattern Recognition (RTIP2R) aims to attract current and/or advanced research on image processing, pattern recognition, computer vision, and machine learning. The RTIP2R will take place at the Texas A&M University—Kingsville, Texas (USA), on November 22–23, 2022, in collaboration with the 2AI Research Lab—Computer Science, University of South Dakota (USA).

Authors of selected papers from the conference will be invited to submit extended versions of their original papers and contributions under the conference topics (new papers that are closely related to the conference themes are also welcome).

We, however, are not limited to RIP2R 2022 to increase the number of submissions.

Topics of interest include, but are not limited to, the following:

• Signal and image processing.
• Computer vision and pattern recognition: object detection and/or recognition (shape, color, and texture analysis) as well as pattern recognition (statistical, structural, and syntactic methods).
• Machine learning: algorithms, clustering and classification, model selection (machine learning), feature engineering, and deep learning.
• Data analytics: data mining tools and high-performance computing in big data.
• Federated learning: applications and challenges.
• Pattern recognition and machine learning for the Internet of things (IoT).
• Information retrieval: content-based image retrieval and indexing, as well as text analytics.
• Applications (not limited to):
  • Document image analysis and understanding.
  • Forensics.
  • Biometrics: face matching, iris recognition/verification, footprint verification, and audio/speech analysis as well as understanding.
  • Healthcare informatics and (bio)medical imaging as well as engineering.
  • Big data (from document understanding and healthcare to risk management).
  • Cryptanalysis (cryptology and cryptography).

Prof. Dr. KC Santosh
Dr. Ayush Goyal
Dr. Djamila Aouada
Dr. Aaisha Makkar
Dr. Yao-Yi Chiang
Dr. Satish Kumar Singh
Prof. Dr. Alejandro Rodríguez-González
Topic Editors

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Entropy entropy	2.7	4.7	1999	20.8 Days	CHF 2600
Applied Sciences applsci	2.7	4.5	2011	16.9 Days	CHF 2400
Healthcare healthcare	2.8	2.7	2013	19.5 Days	CHF 2700
Journal of Imaging jimaging	3.2	4.4	2015	21.7 Days	CHF 1800
Computers computers	2.8	4.7	2012	17.7 Days	CHF 1800
Big Data and Cognitive Computing BDCC	3.7	4.9	2017	18.2 Days	CHF 1800
AI ai	-	-	2020	20.8 Days	CHF 1600

MDPI Topics is cooperating with Preprints.org and has built a direct connection between MDPI journals and Preprints.org. Authors are encouraged to enjoy the benefits by posting a preprint at Preprints.org prior to publication:

1. Immediately share your ideas ahead of publication and establish your research priority;
2. Protect your idea from being stolen with this time-stamped preprint article;
3. Enhance the exposure and impact of your research;
4. Receive feedback from your peers in advance;
5. Have it indexed in Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (14 papers)

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All Entropy Applied Sciences Healthcare J. Imaging Computers BDCC AI

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18 pages, 1663 KiB  

Open AccessArticle

Automated Optimization-Based Deep Learning Models for Image Classification Tasks

by Daudi Mashauri Migayo, Shubi Kaijage, Stephen Swetala and Devotha G. Nyambo

Computers 2023, 12(9), 174; https://doi.org/10.3390/computers12090174 - 01 Sep 2023

Cited by 1 | Viewed by 1663

Abstract

Applying deep learning models requires design and optimization when solving multifaceted artificial intelligence tasks. Optimization relies on human expertise and is achieved only with great exertion. The current literature concentrates on automating design; optimization needs more attention. Similarly, most existing optimization libraries focus [...] Read more.

Applying deep learning models requires design and optimization when solving multifaceted artificial intelligence tasks. Optimization relies on human expertise and is achieved only with great exertion. The current literature concentrates on automating design; optimization needs more attention. Similarly, most existing optimization libraries focus on other machine learning tasks rather than image classification. For this reason, an automated optimization scheme of deep learning models for image classification tasks is proposed in this paper. A sequential-model-based optimization algorithm was used to implement the proposed method. Four deep learning models, a transformer-based model, and standard datasets for image classification challenges were employed in the experiments. Through empirical evaluations, this paper demonstrates that the proposed scheme improves the performance of deep learning models. Specifically, for a Virtual Geometry Group (VGG-16), accuracy was heightened from 0.937 to 0.983, signifying a 73% relative error rate drop within an hour of automated optimization. Similarly, training-related parameter values are proposed to improve the performance of deep learning models. The scheme can be extended to automate the optimization of transformer-based models. The insights from this study may assist efforts to provide full access to the building and optimization of DL models, even for amateurs. Full article

(This article belongs to the Topic Recent Trends in Image Processing and Pattern Recognition)

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14 pages, 3216 KiB  

Open AccessArticle

NRVC: Neural Representation for Video Compression with Implicit Multiscale Fusion Network

by Shangdong Liu, Puming Cao, Yujian Feng, Yimu Ji, Jiayuan Chen, Xuedong Xie and Longji Wu

Entropy 2023, 25(8), 1167; https://doi.org/10.3390/e25081167 - 04 Aug 2023

Viewed by 1131

Abstract

Recently, end-to-end deep models for video compression have made steady advancements. However, this resulted in a lengthy and complex pipeline containing numerous redundant parameters. The video compression approaches based on implicit neural representation (INR) allow videos to be directly represented as a function [...] Read more.

Recently, end-to-end deep models for video compression have made steady advancements. However, this resulted in a lengthy and complex pipeline containing numerous redundant parameters. The video compression approaches based on implicit neural representation (INR) allow videos to be directly represented as a function approximated by a neural network, resulting in a more lightweight model, whereas the singularity of the feature extraction pipeline limits the network’s ability to fit the mapping function for video frames. Hence, we propose a neural representation approach for video compression with an implicit multiscale fusion network (NRVC), utilizing normalized residual networks to improve the effectiveness of INR in fitting the target function. We propose the multiscale representations for video compression (MSRVC) network, which effectively extracts features from the input video sequence to enhance the degree of overfitting in the mapping function. Additionally, we propose the feature extraction channel attention (FECA) block to capture interaction information between different feature extraction channels, further improving the effectiveness of feature extraction. The results show that compared to the NeRV method with similar bits per pixel (BPP), NRVC has a 2.16% increase in the decoded peak signal-to-noise ratio (PSNR). Moreover, NRVC outperforms the conventional HEVC in terms of PSNR. Full article

(This article belongs to the Topic Recent Trends in Image Processing and Pattern Recognition)

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15 pages, 13897 KiB  

Open AccessArticle

YOLOv5-FF: Detecting Floating Objects on the Surface of Fresh Water Environments

by Xiaohong Zhang, Changzhuo Min, Junwei Luo and Zhiying Li

Appl. Sci. 2023, 13(13), 7367; https://doi.org/10.3390/app13137367 - 21 Jun 2023

Viewed by 1403

Abstract

Real-time detection and timely treatment of floating objects on rivers, lakes and reservoirs is very essential to protect water environment and maintain the safety of navigation and water projects. YOLOv5, as a one-stage object detection solution, is very suitable for real-time floating object [...] Read more.

Real-time detection and timely treatment of floating objects on rivers, lakes and reservoirs is very essential to protect water environment and maintain the safety of navigation and water projects. YOLOv5, as a one-stage object detection solution, is very suitable for real-time floating object detection. However, it suffers from the problem of the false detection and missed detection of floating objects especially of small floating objects. In this paper, we conducts a series of improvements on YOLOv5 to alleviate the problem. Concretely, we propose a hybrid attention mechanism supporting the interaction among channels over a long distance while preserving the direct correspondence between channels and their weights. Base on the attention mechanism, we propose an adaptive feature extraction module to capture the feature information of objects in the case of the feature loss caused by downsampling operations. Based on the attention mechanism and dilated encoder, we construct a feature expression enhancement module to cover large objects while not losing small objects in the same certain scale range. We also add a detection layer for small objects to improve the performance in detecting small floating objects. The experiments on the data set verify the usefulness and effectiveness of our work. Full article

(This article belongs to the Topic Recent Trends in Image Processing and Pattern Recognition)

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28 pages, 6954 KiB  

Open AccessArticle

Analysis of 2D and 3D Convolution Models for Volumetric Segmentation of the Human Hippocampus

by You Sheng Toh and Carol Anne Hargreaves

Big Data Cogn. Comput. 2023, 7(2), 82; https://doi.org/10.3390/bdcc7020082 - 23 Apr 2023

Viewed by 1853

Abstract

Extensive medical research has revealed evidence of a strong association between hippocampus atrophy and age-related diseases such as Alzheimer’s disease (AD). Therefore; segmentation of the hippocampus is an important task that can help clinicians and researchers in diagnosing cognitive impairment and uncovering the [...] Read more.

Extensive medical research has revealed evidence of a strong association between hippocampus atrophy and age-related diseases such as Alzheimer’s disease (AD). Therefore; segmentation of the hippocampus is an important task that can help clinicians and researchers in diagnosing cognitive impairment and uncovering the mechanisms behind hippocampal changes and diseases of the brain. The main aim of this paper was to provide a fair comparison of 2D and 3D convolution-based architectures for the specific task of hippocampus segmentation from brain MRI volumes to determine whether 3D convolution models truly perform better in hippocampus segmentation and also to assess any additional costs in terms of time and computational resources. Our optimized model, which used 50 epochs and a mini-batch size of 2, achieved the best validation loss and Dice Similarity Score (DSC) of 0.0129 and 0.8541, respectively, across all experiment runs. Based on the model comparisons, we concluded that 2D convolution models can surpass their 3D counterparts in terms of both hippocampus segmentation performance and training efficiency. Our automatic hippocampus segmentation demonstrated potential savings of thousands of clinician person-hours spent on manually analyzing and segmenting brain MRI scans Full article

(This article belongs to the Topic Recent Trends in Image Processing and Pattern Recognition)

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22 pages, 43762 KiB  

Open AccessArticle

Feature Correspondences Increase and Hybrid Terms Optimization Warp for Image Stitching

by Yizhi Cong, Yan Wang, Wenju Hou and Wei Pang

Entropy 2023, 25(1), 106; https://doi.org/10.3390/e25010106 - 04 Jan 2023

Cited by 1 | Viewed by 1392

Abstract

Feature detection and correct matching are the basis of the image stitching process. Whether the matching is correct and the number of matches directly affect the quality of the final stitching results. At present, almost all image stitching methods use SIFT+RANSAC pattern to [...] Read more.

Feature detection and correct matching are the basis of the image stitching process. Whether the matching is correct and the number of matches directly affect the quality of the final stitching results. At present, almost all image stitching methods use SIFT+RANSAC pattern to extract and match feature points. However, it is difficult to obtain sufficient correct matching points in low-textured or repetitively-textured regions, resulting in insufficient matching points in the overlapping region, and this further leads to the warping model being estimated erroneously. In this paper, we propose a novel and flexible approach by increasing feature correspondences and optimizing hybrid terms. It can obtain sufficient correct feature correspondences in the overlapping region with low-textured or repetitively-textured areas to eliminate misalignment. When a weak texture and large parallax coexist in the overlapping region, the alignment and distortion often restrict each other and are difficult to balance. Accurate alignment is often accompanied by projection distortion and perspective distortion. Regarding this, we propose hybrid terms optimization warp, which combines global similarity transformations on the basis of initial global homography and estimates the optimal warping by adjusting various term parameters. By doing this, we can mitigate projection distortion and perspective distortion, while effectively balancing alignment and distortion. The experimental results demonstrate that the proposed method outperforms the state-of-the-art in accurate alignment on images with low-textured areas in the overlapping region, and the stitching results have less perspective and projection distortion. Full article

(This article belongs to the Topic Recent Trends in Image Processing and Pattern Recognition)

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20 pages, 5271 KiB  

Open AccessArticle

Three-Dimensional Face Recognition Using Solid Harmonic Wavelet Scattering and Homotopy Dictionary Learning

by Yi He, Peng Cheng, Shanmin Yang and Jianwei Zhang

Entropy 2022, 24(11), 1646; https://doi.org/10.3390/e24111646 - 13 Nov 2022

Viewed by 1369

Abstract

Data representation has been one of the core topics in 3D graphics and pattern recognition in high-dimensional data. Although the high-resolution geometrical information of a physical object can be well preserved in the form of metrical data, e.g., point clouds/triangular meshes, from a [...] Read more.

Data representation has been one of the core topics in 3D graphics and pattern recognition in high-dimensional data. Although the high-resolution geometrical information of a physical object can be well preserved in the form of metrical data, e.g., point clouds/triangular meshes, from a regular data (e.g., image/audio) processing perspective, they also bring excessive noise in the course of feature abstraction and regression. For 3D face recognition, preceding attempts focus on treating the scan samples as signals laying on an underlying discrete surface (mesh) or morphable (statistic) models and by embedding auxiliary information, e.g., texture onto the regularized local planar structure to obtain a superior expressive performance to registration-based methods, but environmental variations such as posture/illumination will dissatisfy the integrity or uniform sampling condition, which holistic models generally rely on. In this paper, a geometric deep learning framework for face recognition is proposed, which merely requires the consumption of raw spatial coordinates. The non-uniformity and non-grid geometric transformations in the course of point cloud face scanning are mitigated by modeling each identity as a stochastic process. Individual face scans are considered realizations, yielding underlying inherent distributions under the appropriate assumption of ergodicity. To accomplish 3D facial recognition, we propose a windowed solid harmonic scattering transform on point cloud face scans to extract the invariant coefficients so that unrelated variations can be encoded into certain components of the scattering domain. With these constructions, a sparse learning network as the semi-supervised classification backbone network can work on reducing intraclass variability. Our framework obtained superior performance to current competing methods; without excluding any fragmentary or severely deformed samples, the rank-1 recognition rate (RR1) achieved was $99.84\%$ on the Face Recognition Grand Challenge (FRGC) v2.0 dataset and $99.90\%$ on the Bosphorus dataset. Full article

(This article belongs to the Topic Recent Trends in Image Processing and Pattern Recognition)

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14 pages, 10747 KiB  

Open AccessArticle

Text-Guided Customizable Image Synthesis and Manipulation

by Zhiqiang Zhang, Chen Fu, Wei Weng and Jinjia Zhou

Appl. Sci. 2022, 12(20), 10645; https://doi.org/10.3390/app122010645 - 21 Oct 2022

Viewed by 1735

Abstract

Due to the high flexibility and conformity to people’s usage habits, text description has been widely used in image synthesis research recently and has achieved many encouraging results. However, the text can only determine the basic content of the generated image and cannot [...] Read more.

Due to the high flexibility and conformity to people’s usage habits, text description has been widely used in image synthesis research recently and has achieved many encouraging results. However, the text can only determine the basic content of the generated image and cannot determine the specific shape of the synthesized object, which leads to poor practicability. More importantly, the current text-to-image synthesis research cannot use new text descriptions to further modify the synthesis result. To solve these problems, this paper proposes a text-guided customizable image synthesis and manipulation method. The proposed method synthesizes the corresponding image based on the text and contour information at first. It then modifies the synthesized content based on the new text to obtain a satisfactory result. The text and contour information in the proposed method determine the specific content and object shape of the desired composite image, respectively. Aside from that, the input text, contour, and subsequent new text for content modification can be manually input, which significantly improves the artificial controllability in the image synthesis process, making the entire method superior to other methods in flexibility and practicability. Experimental results on the Caltech-UCSD Birds-200-2011 (CUB) and Microsoft Common Objects in Context (MS COCO) datasets demonstrate our proposed method’s feasibility and versatility. Full article

(This article belongs to the Topic Recent Trends in Image Processing and Pattern Recognition)

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21 pages, 2860 KiB  

Open AccessArticle

Deep Matrix Factorization Based on Convolutional Neural Networks for Image Inpainting

by Xiaoxuan Ma, Zhiwen Li and Hengyou Wang

Entropy 2022, 24(10), 1500; https://doi.org/10.3390/e24101500 - 20 Oct 2022

Viewed by 1823

Abstract

In this work, we formulate the image in-painting as a matrix completion problem. Traditional matrix completion methods are generally based on linear models, assuming that the matrix is low rank. When the original matrix is large scale and the observed elements are few, [...] Read more.

In this work, we formulate the image in-painting as a matrix completion problem. Traditional matrix completion methods are generally based on linear models, assuming that the matrix is low rank. When the original matrix is large scale and the observed elements are few, they will easily lead to over-fitting and their performance will also decrease significantly. Recently, researchers have tried to apply deep learning and nonlinear techniques to solve matrix completion. However, most of the existing deep learning-based methods restore each column or row of the matrix independently, which loses the global structure information of the matrix and therefore does not achieve the expected results in the image in-painting. In this paper, we propose a deep matrix factorization completion network (DMFCNet) for image in-painting by combining deep learning and a traditional matrix completion model. The main idea of DMFCNet is to map iterative updates of variables from a traditional matrix completion model into a fixed depth neural network. The potential relationships between observed matrix data are learned in a trainable end-to-end manner, which leads to a high-performance and easy-to-deploy nonlinear solution. Experimental results show that DMFCNet can provide higher matrix completion accuracy than the state-of-the-art matrix completion methods in a shorter running time. Full article

(This article belongs to the Topic Recent Trends in Image Processing and Pattern Recognition)

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19 pages, 15280 KiB  

Open AccessArticle

Exploration of the X-ray Dark-Field Signal in Mineral Building Materials

by Benjamin K. Blykers, Caori Organista, Matias Kagias, Federica Marone, Marco Stampanoni, Matthieu N. Boone, Veerle Cnudde and Jan Aelterman

J. Imaging 2022, 8(10), 282; https://doi.org/10.3390/jimaging8100282 - 14 Oct 2022

Cited by 2 | Viewed by 1827

Abstract

Mineral building materials suffer from weathering processes such as salt efflorescence, freeze–thaw cycling, and microbial colonization. All of these processes are linked to water (liquid and vapor) in the pore space. The degree of damage following these processes is heavily influenced by pore [...] Read more.

Mineral building materials suffer from weathering processes such as salt efflorescence, freeze–thaw cycling, and microbial colonization. All of these processes are linked to water (liquid and vapor) in the pore space. The degree of damage following these processes is heavily influenced by pore space properties such as porosity, pore size distribution, and pore connectivity. X-ray computed micro-tomography (µCT) has proven to be a valuable tool to non-destructively investigate the pore space of stone samples in 3D. However, a trade-off between the resolution and field-of-view often impedes reliable conclusions on the material’s properties. X-ray dark-field imaging (DFI) is based on the scattering of X-rays by sub-voxel-sized features, and as such, provides information on the sample complementary to that obtained using conventional µCT. In this manuscript, we apply X-ray dark-field tomography for the first time on four mineral building materials (quartzite, fired clay brick, fired clay roof tile, and carbonated mineral building material), and investigate which information the dark-field signal entails on the sub-resolution space of the sample. Dark-field tomography at multiple length scale sensitivities was performed at the TOMCAT beamline of the Swiss Light Source (Villigen, Switzerland) using a Talbot grating interferometer. The complementary information of the dark-field modality is most clear in the fired clay brick and roof tile; quartz grains that are almost indistinguishable in the conventional µCT scan are clearly visible in the dark-field owing to their low dark-field signal (homogenous sub-voxel structure), whereas the microporous bulk mass has a high dark-field signal. Large (resolved) pores on the other hand, which are clearly visible in the absorption dataset, are almost invisible in the dark-field modality because they are overprinted with dark-field signal originating from the bulk mass. The experiments also showed how the dark-field signal from a feature depends on the length scale sensitivity, which is set by moving the sample with respect to the grating interferometer. Full article

(This article belongs to the Topic Recent Trends in Image Processing and Pattern Recognition)

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13 pages, 5445 KiB  

Open AccessArticle

A Novel Defect Inspection System Using Convolutional Neural Network for MEMS Pressure Sensors

by Mingxing Deng, Quanyong Zhang, Kun Zhang, Hui Li, Yikai Zhang and Wan Cao

J. Imaging 2022, 8(10), 268; https://doi.org/10.3390/jimaging8100268 - 30 Sep 2022

Cited by 5 | Viewed by 1758

Abstract

Defect inspection using imaging-processing techniques, which detects and classifies manufacturing defects, plays a significant role in the quality control of microelectromechanical systems (MEMS) sensors in the semiconductor industry. However, high-precision classification and location are still challenging because the defect images that can be [...] Read more.

Defect inspection using imaging-processing techniques, which detects and classifies manufacturing defects, plays a significant role in the quality control of microelectromechanical systems (MEMS) sensors in the semiconductor industry. However, high-precision classification and location are still challenging because the defect images that can be obtained are small and the scale of the different defects on the picture of the defect is different. Therefore, a simple, flexible, and efficient convolutional neural network (CNN) called accurate-detection CNN (ADCNN) to inspect MEMS pressure-sensor-chip packaging is proposed in this paper. The ADCNN is based on the faster region-based CNN, which improved the performance of the network by adding random-data augmentation and defect classifiers. Specifically, the ADCNN achieved a mean average precision of 92.39% and the defect classifier achieved a mean accuracy of 97.2%. Full article

(This article belongs to the Topic Recent Trends in Image Processing and Pattern Recognition)

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13 pages, 356 KiB  

Open AccessArticle

Model Soups for Various Training and Validation Data

by Kaiyu Suzuki and Tomofumi Matsuzawa

AI 2022, 3(4), 796-808; https://doi.org/10.3390/ai3040048 - 28 Sep 2022

Cited by 1 | Viewed by 2492

Abstract

Model soups synthesize multiple models after fine-tuning them with different hyperparameters based on the accuracy of the validation data. They train different models on the same training and validation data sets. In this study, we maximized the model fine-tuning accuracy using the inference [...] Read more.

Model soups synthesize multiple models after fine-tuning them with different hyperparameters based on the accuracy of the validation data. They train different models on the same training and validation data sets. In this study, we maximized the model fine-tuning accuracy using the inference time and memory cost of a single model. We extended the model soups to create subsets of k training and validation data using a method similar to k-fold cross-validation and trained models on these subsets. First, we showed the correlation between the validation and test data when the models are synthesized, such that their training data contain validation data. Thereafter, we showed that synthesizing k of these models, after synthesizing models based on subsets of the same training and validation data, provides a single model with high test accuracy. This study provides a method for learning models with both high accuracy and reliability for small datasets such as medical images. Full article

(This article belongs to the Topic Recent Trends in Image Processing and Pattern Recognition)

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10 pages, 1188 KiB  

Open AccessArticle

Comparison of INTEGRA and the Manual Method to Determine the Axis for Intraocular Lens Implantation—A Case Series of 60 Eyes

by Marcin Jaworski, Dorota Wyględowska-Promieńska, Piotr Jaworski, Michał Kowalski, Krzysztof Jaskot and Robert Bieda

Healthcare 2022, 10(9), 1773; https://doi.org/10.3390/healthcare10091773 - 14 Sep 2022

Viewed by 1471

Abstract

(1) Background: To compare the results of a new intraoperative contactless device (INTEGRA Optomed, Poland) with the result of a manual method for determining the axis for toric intraocular lens implantation. (2) Material and Methods: This retrospective observational study included 60 eyes of [...] Read more.

(1) Background: To compare the results of a new intraoperative contactless device (INTEGRA Optomed, Poland) with the result of a manual method for determining the axis for toric intraocular lens implantation. (2) Material and Methods: This retrospective observational study included 60 eyes of 40 patients (17 men, 23 women) who had toric intraocular lenses implanted. A video recording of each surgery that used the INTEGRA system was made for the analysis. Two researchers then independently assessed the location of the implant axes determined with both digital and manual slit-lamp methods, and compared the results between methods. (3) Results: The implantation axes suggested through the manual and INTEGRA methods were similar. The median axis disparities were 0.0 degrees for both groups. The standard deviation was 0.63 and 0.75 for researcher 1 and 2, respectively. The dominant value was 0.0 in both groups. The INTEGRA axis designation was statistically significantly different from the manual method for researcher 1 (p < 0.05), but it was statistically insignificant for researcher 2 (p = 0.79). (4) Conclusions: The INTEGRA system is a digital ink-free device for image tracking scleral vessels. It was helpful for determining the implantation axis in a precise manner, and the measurements were comparable with those obtained through a manual technique. Full article

(This article belongs to the Topic Recent Trends in Image Processing and Pattern Recognition)

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16 pages, 2496 KiB  

Open AccessArticle

Working Condition Recognition Based on Transfer Learning and Attention Mechanism for a Rotary Kiln

by Yuchao Hu, Weihua Zheng, Xin Wang and Bin Qin

Entropy 2022, 24(9), 1186; https://doi.org/10.3390/e24091186 - 25 Aug 2022

Cited by 3 | Viewed by 1501

Abstract

It is difficult to identify the working conditions of the rotary kilns due to the harsh environment in the kilns. The flame images of the firing zone in the kilns contain a lot of working condition information, but the flame image data sample [...] Read more.

It is difficult to identify the working conditions of the rotary kilns due to the harsh environment in the kilns. The flame images of the firing zone in the kilns contain a lot of working condition information, but the flame image data sample size is too small to be used to fully extract the key features. In order to solve this problem, a method combining transfer learning and attention mechanism is proposed to extract key features of flame images, in which the deep residual network is used as the backbone network, the coordinate attention module is introduced to capture the position information and channel information on the branch of feature graphs, and the features of flame images obtained are further screened to improve the extraction ability. At the same time, migration learning is performed by the pre-trained ImageNet data set, and feature migration and parameter sharing are realized to cope with the training difficulty of a small sample data size. Moreover, an activation function Mish is introduced to reduce the loss of effective information. The experimental results show that, compared with traditional methods, the working condition recognition accuracy of rotary kilns is improved by about 5% with the proposed method. Full article

(This article belongs to the Topic Recent Trends in Image Processing and Pattern Recognition)

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27 pages, 12451 KiB  

Open AccessArticle

A Novel Image Encryption Algorithm Based on Improved Arnold Transform and Chaotic Pulse-Coupled Neural Network

by Jinhong Ye, Xiangyu Deng, Aijia Zhang and Haiyue Yu

Entropy 2022, 24(8), 1103; https://doi.org/10.3390/e24081103 - 10 Aug 2022

Cited by 10 | Viewed by 1978

Abstract

Information security has become a focal topic in the information and digital age. How to realize secure transmission and the secure storage of image data is a major research focus of information security. Aiming at this hot topic, in order to improve the [...] Read more.

Information security has become a focal topic in the information and digital age. How to realize secure transmission and the secure storage of image data is a major research focus of information security. Aiming at this hot topic, in order to improve the security of image data transmission, this paper proposes an image encryption algorithm based on improved Arnold transform and a chaotic pulse-coupled neural network. Firstly, the oscillatory reset voltage is introduced into the uncoupled impulse neural network, which makes the uncoupled impulse neural network exhibit chaotic characteristics. The chaotic sequence is generated by multiple iterations of the chaotic pulse-coupled neural network, and then the image is pre-encrypted by XOR operation with the generated chaotic sequence. Secondly, using the improved Arnold transform, the pre-encrypted image is scrambled to further improve the scrambling degree and encryption effect of the pre-encrypted image so as to obtain the final ciphertext image. Finally, the security analysis and experimental simulation of the encrypted image are carried out. The results of quantitative evaluation show that the proposed algorithm has a better encryption effect than the partial encryption algorithm. The algorithm is highly sensitive to keys and plaintexts, has a large key space, and can effectively resist differential attacks and attacks such as noise and clipping. Full article

(This article belongs to the Topic Recent Trends in Image Processing and Pattern Recognition)

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