Symmetry

Research

23 pages, 2000 KiB

Open AccessArticle

Blend of Deep Features and Binary Tree Growth Algorithm for Skin Lesion Classification

by Sunil Kumar, Vijay Kumar Nath and Deepika Hazarika

Symmetry 2023, 15(12), 2213; https://doi.org/10.3390/sym15122213 - 18 Dec 2023

Cited by 1 | Viewed by 2793

One of the most frequently identified cancers globally is skin cancer (SC). The computeraided categorization of numerous skin lesions via dermoscopic images is still a complicated problem. Early recognition is crucial since it considerably increases the survival chances. In this study, we introduce [...] Read more.

One of the most frequently identified cancers globally is skin cancer (SC). The computeraided categorization of numerous skin lesions via dermoscopic images is still a complicated problem. Early recognition is crucial since it considerably increases the survival chances. In this study, we introduce an approach for skin lesion categorization where, at first, a powerful hybrid deep-feature set is constructed, and then a binary tree growth (BTG)-based optimization procedure is implemented using a support vector machine (SVM) classifier with an intention to compute the categorizing error and build symmetry between categories, for selecting the most significant features which are finally fed to a multi-class SVM for classification. The hybrid deep-feature set is constructed by utilizing two pre-trained models, i.e., Densenet-201, and Inception-v3, that are fine-tuned on skin lesion data. These two deep-feature models have distinct architectures that characterize dissimilar feature abstraction strengths. This effective deep feature framework has been tested on two publicly available challenging datasets, i.e., ISIC2018 and ISIC2019. The proposed framework outperforms many existing approaches and achieves notable {accuracy, sensitivity, precision, specificity} values of {98.50%, 96.60%, 97.84%, 99.59%} and {96.60%, 94.21%, 96.38%, 99.39%} for the ISIC2018 and ISIC2019 datasets, respectively. The proposed implementation of the BTG-based optimization algorithm performs significantly better on the proposed feature blend for skin lesion classification. Full article

(This article belongs to the Special Issue Symmetry/Asymmetry in Computer Vision and Image Processing)

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12 pages, 1454 KiB

Open AccessArticle

Cross-Camera Tracking Model and Method Based on Multi-Feature Fusion

by Peng Zhang, Siqi Wang, Wei Zhang, Weimin Lei, Xinlei Zhao, Qingyang Jing and Mingxin Liu

Symmetry 2023, 15(12), 2145; https://doi.org/10.3390/sym15122145 - 2 Dec 2023

Cited by 1 | Viewed by 1746

Abstract

Multi-camera video surveillance has been widely applied in crowd statistics and analysis in smart city scenarios. Most existing studies rely on appearance or motion features for cross-camera trajectory tracking, due to the changing asymmetric perspectives of multiple cameras and occlusions in crowded scenes, [...] Read more.

Multi-camera video surveillance has been widely applied in crowd statistics and analysis in smart city scenarios. Most existing studies rely on appearance or motion features for cross-camera trajectory tracking, due to the changing asymmetric perspectives of multiple cameras and occlusions in crowded scenes, resulting in low accuracy and poor tracking performance. This paper proposes a tracking method that fuses appearance and motion features. An implicit social model is used to obtain motion features containing spatio-temporal information and social relations for trajectory prediction. The TransReID model is used to obtain appearance features for re-identification. Fused features are derived by integrating appearance features, spatio-temporal information and social relations. Based on the fused features, multi-round clustering is adopted to associate cross-camera objects. Exclusively employing robust pedestrian reidentification and trajectory prediction models, coupled with the real-time detector YOLOX, without any reliance on supplementary information, an IDF1 score of 70.64% is attained on typical datasets derived from AiCity2023. Full article

(This article belongs to the Special Issue Symmetry/Asymmetry in Computer Vision and Image Processing)

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19 pages, 2921 KiB

Open AccessArticle

Audio-Visual Effects of a Collaborative Robot on Worker Efficiency

by Aljaž Javernik, Klemen Kovič, Iztok Palčič and Robert Ojsteršek

Symmetry 2023, 15(10), 1907; https://doi.org/10.3390/sym15101907 - 12 Oct 2023

Cited by 2 | Viewed by 1183

Abstract

Collaborative workplaces are increasingly used in production systems. The possibility of direct collaboration between robots and humans brings many advantages, as it allows the simultaneous use of human and robotic strengths. However, collaboration between a collaborative robot and a human raises concerns about [...] Read more.

Collaborative workplaces are increasingly used in production systems. The possibility of direct collaboration between robots and humans brings many advantages, as it allows the simultaneous use of human and robotic strengths. However, collaboration between a collaborative robot and a human raises concerns about the safety of the interaction, the impact of the robot on human health, human efficiency, etc. Additionally, research is unexplored in the field of the collaborative robot’s audio-visual effects on the worker’s efficiency. Our study results contribute to the field of studying collaborative robots’ audio-visual effects on the worker’s behavior. In this research, we analyze the effect of the changing motion parameters of the collaborative robot (speed and acceleration) on the efficiency of the worker and, consequently, on the production process. Based on the experimental results, we were able to confirm the impact of robot speed and acceleration on the worker’s efficiency in terms of assembly time. We also concluded that the sound level and presence of a visual barrier between the worker and robot by themselves have no effect on the worker’s efficiency. The experimental part of the paper clearly identifies the impact of visualization on work efficiency. According to the results, the robot’s audio-visual effects play a key role in achieving high efficiency and, consequently, justifying the implementation of a collaborative workplace. Full article

(This article belongs to the Special Issue Symmetry/Asymmetry in Computer Vision and Image Processing)

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21 pages, 18458 KiB

Open AccessArticle

Symmetric Enhancement of Visual Clarity through a Multi-Scale Dilated Residual Recurrent Network Approach for Image Deraining

by Jameel Ahmed Bhutto, Ruihong Zhang and Ziaur Rahman

Symmetry 2023, 15(8), 1571; https://doi.org/10.3390/sym15081571 - 11 Aug 2023

Cited by 2 | Viewed by 1244

Abstract

Images captured during rainy days present the challenge of maintaining a symmetrical balance between foreground elements (like rain streaks) and the background scenery. The interplay between these rain-obscured images is reminiscent of the principle of symmetry, where one element, the rain streak, overshadows [...] Read more.

Images captured during rainy days present the challenge of maintaining a symmetrical balance between foreground elements (like rain streaks) and the background scenery. The interplay between these rain-obscured images is reminiscent of the principle of symmetry, where one element, the rain streak, overshadows or disrupts the visual quality of the entire image. The challenge lies not just in eradicating the rain streaks but in ensuring the background is symmetrically restored to its original clarity. Recently, numerous deraining algorithms that employ deep learning techniques have been proposed, demonstrating promising results. Yet, achieving a perfect symmetrical balance by effectively removing rain streaks from a diverse set of images, while also symmetrically restoring the background details, is a monumental task. To address this issue, we introduce an image-deraining algorithm that leverages multi-scale dilated residual recurrent networks. The algorithm begins by utilizing convolutional activation layers to symmetrically process both the foreground and background features. Then, to ensure the symmetrical dissemination of the characteristics of rain streaks and the background, it employs long short-term memory networks in conjunction with gated recurrent units across various stages. The algorithm then incorporates dilated residual blocks (DRB), composed of dilated convolutions with three distinct dilation factors. This integration expands the receptive field, facilitating the extraction of deep, multi-scale features of both the rain streaks and background information. Furthermore, considering the complex and diverse nature of rain streaks, a channel attention (CA) mechanism is incorporated to capture richer image features and enhance the model’s performance. Ultimately, convolutional layers are employed to fuse the image features, resulting in a derained image. An evaluation encompassing seven benchmark datasets, assessed using five quality metrics against various conventional and modern algorithms, confirms the robustness and flexibility of our approach. Full article

(This article belongs to the Special Issue Symmetry/Asymmetry in Computer Vision and Image Processing)

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27 pages, 9831 KiB

Open AccessArticle

A Study on Perception of Visual–Tactile and Color–Texture Features of Footwear Leather for Symmetric Shoes

by Dan-Dan Xu, Chih-Fu Wu and Chung-Shing Wang

Symmetry 2023, 15(7), 1462; https://doi.org/10.3390/sym15071462 - 22 Jul 2023

Cited by 1 | Viewed by 2021

Abstract

The study applies Kansei engineering in analyzing the color and texture of leather footwear, utilizing neural network verification to mirror consumers’ visual and tactile imageries onto varieties of leather. This aids in the development of an advanced system for selecting leather footwear based [...] Read more.

The study applies Kansei engineering in analyzing the color and texture of leather footwear, utilizing neural network verification to mirror consumers’ visual and tactile imageries onto varieties of leather. This aids in the development of an advanced system for selecting leather footwear based on such impressions. Initially, representative word pairs denoting consumers’ visual and tactile perceptions of leather footwear were delineated. Post-evaluation of these perceptions through a sensibility assessment questionnaire was administered, using 54 samples of leather footwear provided by manufacturers, with each leather type codified in terms of visual and tactile sensibilities. Subsequently, a customized software algorithm was crafted to isolate the primary color and adhesiveness as color features from the leather sample images. Analyzing grayscale values of the images and using pixel neighborhood as a base, the associated calculation methods, such as LBP, SCOV, VAR, SAC, etc., were proposed to extract texture features from the images. The derived color and texture feature values were used as the input layer and the sensory vocabulary quantified values as the output layer. Backpropagation neural network training was conducted on 49 leather samples, with five leather samples used for testing, culminating in the verification of neural network training for three types and 17 combinations. The outcome was an optimal method for leather footwear Kansei engineering and neural network training, establishing a design process for leather footwear characteristics assisted by sensory vocabulary and a backpropagation neural network. Additionally, a computer-aided system for selecting leather footwear, based on these impressions, was designed and validated through footwear design. This study utilized symmetry in footwear design. By using the design of a single shoe to represent the imagery of a pair of symmetrical shoes, we verified whether the leather samples recommended by the leather imagery selection query system met the expected system input settings. Full article

(This article belongs to the Special Issue Symmetry/Asymmetry in Computer Vision and Image Processing)

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23 pages, 7593 KiB

Open AccessArticle

Anomaly Detection in Chest X-rays Based on Dual-Attention Mechanism and Multi-Scale Feature Fusion

by Dong Liu, Shuzhen Lu, Lingrong Zhang and Yaohui Liu

Symmetry 2023, 15(3), 668; https://doi.org/10.3390/sym15030668 - 7 Mar 2023

Cited by 2 | Viewed by 2915

Abstract

The efficient and automatic detection of chest abnormalities is vital for the auxiliary diagnosis of medical images. Many studies utilize computer vision and deep learning approaches involving symmetry and asymmetry concepts to detect chest abnormalities, and achieve promising findings. However, an accurate instance-level [...] Read more.

The efficient and automatic detection of chest abnormalities is vital for the auxiliary diagnosis of medical images. Many studies utilize computer vision and deep learning approaches involving symmetry and asymmetry concepts to detect chest abnormalities, and achieve promising findings. However, an accurate instance-level and multi-label detection of abnormalities in chest X-rays remains a significant challenge. Here, a novel anomaly detection method for symmetric chest X-rays using dual-attention and multi-scale feature fusion is proposed. Three aspects of our method should be noted in comparison with the previous approaches. We improved the deep neural network with channel-dimensional and spatial-dimensional attention to capture the abundant contextual features. We then used an optimized multi-scale learning framework for feature fusion to adapt to the scale variation in the abnormalities. Considering the influence of the data imbalance and other factors, we introduced a seesaw loss function to flexibly adjust the sample weights and enhance the model learning efficiency. The rigorous experimental evaluation of a public chest X-ray dataset with fourteen different types of abnormalities demonstrates that our model has a mean average precision of 0.362 and outperforms existing methods. Full article

(This article belongs to the Special Issue Symmetry/Asymmetry in Computer Vision and Image Processing)

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20 pages, 8168 KiB

Open AccessArticle

An Efficient Machine Learning-Based Model to Effectively Classify the Type of Noises in QR Code: A Hybrid Approach

by Jawad Rasheed, Ahmad B. Wardak, Adnan M. Abu-Mahfouz, Tariq Umer, Mirsat Yesiltepe and Sadaf Waziry

Symmetry 2022, 14(10), 2098; https://doi.org/10.3390/sym14102098 - 8 Oct 2022

Cited by 10 | Viewed by 2520

Abstract

Granting smart device consumers with information, simply and quickly, is what drives quick response (QR) codes and mobile marketing to go hand in hand. It boosts marketing campaigns and objectives and allows one to approach, engage, influence, and transform a wider target audience [...] Read more.

Granting smart device consumers with information, simply and quickly, is what drives quick response (QR) codes and mobile marketing to go hand in hand. It boosts marketing campaigns and objectives and allows one to approach, engage, influence, and transform a wider target audience by connecting from offline to online platforms. However, restricted printing technology and flexibility in surfaces introduce noise while printing QR code images. Moreover, noise is often unavoidable during the gathering and transmission of digital images. Therefore, this paper proposed an automatic and accurate noise detector to identify the type of noise present in QR code images. For this, the paper first generates a new dataset comprising 10,000 original QR code images of varying sizes and later introduces several noises, including salt and pepper, pepper, speckle, Poisson, salt, local var, and Gaussian to form a dataset of 80,000 images. We perform extensive experiments by reshaping the generated images to uniform size for exploiting Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Logistic Regression (LG) to classify the original and noisy images. Later, the analysis is further widened by incorporating histogram density analysis to trace and target highly important features by transforming images of varying sizes to obtain 256 features, followed by SVM, LG, and Artificial Neural Network (ANN) to identify the noise type. Moreover, to understand the impact of symmetry of noises in QR code images, we trained the models with combinations of 3-, 5-, and 7-noise types and analyzed the classification performance. From comparative analyses, it is noted that the Gaussian and Localvar noises possess symmetrical characteristics, as all the classifiers did not perform well to segregate these two noises. The results prove that histogram analysis significantly improves classification accuracy with all exploited models, especially when combined with SVM, it achieved maximum accuracy for 4- and 6-class classification problems. Full article

(This article belongs to the Special Issue Symmetry/Asymmetry in Computer Vision and Image Processing)

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28 pages, 4991 KiB

Open AccessArticle

Machine Vision Approach for Diagnosing Tuberculosis (TB) Based on Computerized Tomography (CT) Scan Images

by Inayatul Haq, Tehseen Mazhar, Qandeel Nasir, Saqib Razzaq, Syed Agha Hassnain Mohsan, Mohammed H. Alsharif, Hend Khalid Alkahtani, Ayman Aljarbouh and Samih M. Mostafa

Symmetry 2022, 14(10), 1997; https://doi.org/10.3390/sym14101997 - 23 Sep 2022

Cited by 7 | Viewed by 3623

Abstract

Tuberculosis is curable, still the world’s second inflectional murderous disease, and ranked 13th (in 2020) by the World Health Organization on the list of leading death causes. One of the reasons for its fatality is the unavailability of modern technology and human experts [...] Read more.

Tuberculosis is curable, still the world’s second inflectional murderous disease, and ranked 13th (in 2020) by the World Health Organization on the list of leading death causes. One of the reasons for its fatality is the unavailability of modern technology and human experts for early detection. This study represents a precise and reliable machine vision-based approach for Tuberculosis detection in the lung through Symmetry CT scan images. TB spreads irregularly, which means it might not affect both lungs equally, and it might affect only some part of the lung. That’s why regions of interest (ROI’s) from TB infected and normal CT scan images of lungs were selected after pre-processing i.e., selection/cropping, grayscale image conversion, and filtration, Statistical texture features were extracted, and 30 optimized features using F (Fisher) + PA (probability of error + average correlation) + MI (mutual information) were selected for final optimization and only 6 most optimized features were selected. Several supervised learning classifiers were used to classify between normal and infected TB images. Artificial Neural Network (ANN: n class) based classifier Multi-Layer Perceptron (MLP) showed comparatively better and probably best accuracy of 99% with execution time of less than a second, followed by Random Forest 98.83%, J48 98.67%, Log it Boost 98%, AdaBoostM1 97.16% and Bayes Net 96.83%. Full article

(This article belongs to the Special Issue Symmetry/Asymmetry in Computer Vision and Image Processing)

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