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28 pages, 73215 KB

Open AccessArticle

Linear-Region-Based Contour Tracking for Edge Images

by Erick Huitrón-Ramírez, Leonel G. Corona-Ramírez and Diego Jiménez-Badillo

Appl. Sci. 2026, 16(1), 509; https://doi.org/10.3390/app16010509 - 4 Jan 2026

Viewed by 1084

This work presents the Linear-Region-Based Contour Tracking (LRCT) method for extracting external contours in images, designed to achieve an accurate and efficient description of shapes, particularly useful for archaeological materials with irregular geometries. The approach treats the contour as a discrete signal and [...] Read more.

This work presents the Linear-Region-Based Contour Tracking (LRCT) method for extracting external contours in images, designed to achieve an accurate and efficient description of shapes, particularly useful for archaeological materials with irregular geometries. The approach treats the contour as a discrete signal and analyzes image regions containing edge segments. From these regions, a local linear model is estimated to guide the selection and chaining of representative pixels, yielding a continuous perimeter trajectory. This strategy reduces the amount of data required to describe the contour without compromising shape fidelity. As a case study, the method was applied to images of replicas of archaeological materials exhibiting substantial variations in color and morphology. The results show that the obtained trajectories are comparable in quality to those obtained using classical pipelines based on Canny edge detection followed by Moore tracing, while providing more compact representations well suited for subsequent analyses. Consequently, the method offers an efficient and reproducible alternative for documentation, recording, and morphological comparison, strengthening data-driven approaches in archaeological research. Full article

(This article belongs to the Special Issue Applications in Computer Vision and Image Processing)

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15 pages, 1730 KB

Open AccessArticle

Research on Printed Circuit Board (PCB) Defect Detection Algorithm Based on Convolutional Neural Networks (CNN)

by Zhiduan Ni and Yeonhee Kim

Appl. Sci. 2025, 15(24), 13115; https://doi.org/10.3390/app152413115 - 12 Dec 2025

Viewed by 2427

Abstract

Printed Circuit Board (PCB) defect detection is critical for quality control in electronics manufacturing. Traditional manual inspection and classical Automated Optical Inspection (AOI) methods face challenges in speed, consistency, and flexibility. This paper proposes a CNN-based approach for automatic PCB defect detection using [...] Read more.

Printed Circuit Board (PCB) defect detection is critical for quality control in electronics manufacturing. Traditional manual inspection and classical Automated Optical Inspection (AOI) methods face challenges in speed, consistency, and flexibility. This paper proposes a CNN-based approach for automatic PCB defect detection using the YOLOv5 model. The method leverages a Convolutional Neural Network to identify various PCB defect types (e.g., open circuits, short circuits, and missing holes) from board images. In this study, a model was trained on a PCB image dataset with detailed annotations. Data augmentation techniques, such as sharpening and noise filtering, were applied to improve robustness. The experimental results showed that the proposed approach could locate and classify multiple defect types on PCBs, with overall detection precision and recall above 90% and 91%, respectively, enabling reliable automated inspection. A brief comparison with the latest YOLOv8 model is also presented, showing that the proposed CNN-based detector offers competitive performance. This study shows that deep learning-based defect detection can improve the PCB inspection efficiency and accuracy significantly, paving the way for intelligent manufacturing and quality assurance in PCB production. From a sensing perspective, we frame the system around an industrial RGB camera and controlled illumination, emphasizing how imaging-sensor choices and settings shape defect visibility and model robustness, and sketching future sensor-fusion directions. Full article

(This article belongs to the Special Issue Applications in Computer Vision and Image Processing)

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29 pages, 5173 KB

Open AccessArticle

A Quantitative Evaluation of UAV Flight Parameters for SfM-Based 3D Reconstruction of Buildings

by Inho Jo, Yunku Lee, Namhyuk Ham, Juhyung Kim and Jae-Jun Kim

Appl. Sci. 2025, 15(13), 7196; https://doi.org/10.3390/app15137196 - 26 Jun 2025

Viewed by 1725

Abstract

This study aims to address the critical lack of standardized guidelines for unmanned aerial vehicle (UAV) image acquisition strategies utilizing structure-from-motion (SfM) by focusing on 3D building exterior modeling. A comprehensive experimental analysis was conducted to systematically investigate and quantitatively evaluate the effects [...] Read more.

This study aims to address the critical lack of standardized guidelines for unmanned aerial vehicle (UAV) image acquisition strategies utilizing structure-from-motion (SfM) by focusing on 3D building exterior modeling. A comprehensive experimental analysis was conducted to systematically investigate and quantitatively evaluate the effects of various shooting patterns and parameters on SfM reconstruction quality and processing efficiency. This study implemented a systematic experimental framework to test various UAV flight patterns, including circular, surface, and aerial configurations. Under controlled environmental conditions on representative building structures, key variables were manipulated, and all collected data were processed through a consistent SfM pipeline based on the SIFT algorithm. Quantitative evaluation results using various analytical methodologies (multiple regression analysis, Kruskal–Wallis test, random forest feature importance, principal component analysis including K-means clustering, response surface methodology (RSM), preference ranking technique based on similarity to the ideal solution (TOPSIS), and Pareto optimization) revealed that the basic shooting pattern ‘type’ has a significant and statistically significant influence on all major SfM performance metrics (reprojection error, final point count, computation time, reconstruction completeness; Kruskal–Wallis p < 0.001). Additionally, within the patterns, clear parameter sensitivity and complex nonlinear relationships were identified (e.g., overlapping variables play a decisive role in determining the point count and completeness of surface patterns, with an adjusted R² ≈ 0.70; the results of circular patterns are strongly influenced by the interaction between radius and tilt angle on reprojection error and point count, with an adjusted R² ≈ 0.80). Furthermore, composite pattern analysis using TOPSIS identified excellent combinations that balanced multiple criteria, and Pareto optimization explicitly quantified the inherent trade-offs between conflicting objectives (e.g., time vs. accuracy, number of points vs. completeness). In conclusion, this study clearly demonstrates that hierarchical strategic approaches are essential for optimizing UAV-SfM data collection. Additionally, it provides important empirical data, a validated methodological framework, and specific quantitative guidelines for standardizing UAV data collection workflows, thereby improving existing empirical or case-specific approaches. Full article

(This article belongs to the Special Issue Applications in Computer Vision and Image Processing)

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20 pages, 8769 KB

Open AccessArticle

Mamba-DQN: Adaptively Tunes Visual SLAM Parameters Based on Historical Observation DQN

by Xubo Ma, Chuhua Huang, Xin Huang and Wangping Wu

Appl. Sci. 2025, 15(6), 2950; https://doi.org/10.3390/app15062950 - 9 Mar 2025

Cited by 1 | Viewed by 3170

Abstract

The parameter configuration of traditional visual SLAM algorithms usually relies on expert experience and extensive experiments, and the parameter configuration needs to be reset as the scene changes, which is a complex and tedious process. To achieve parameter adaptation in visual SLAM, we [...] Read more.

The parameter configuration of traditional visual SLAM algorithms usually relies on expert experience and extensive experiments, and the parameter configuration needs to be reset as the scene changes, which is a complex and tedious process. To achieve parameter adaptation in visual SLAM, we propose the Mamba-DQN method, which transforms complex parameter adjustment tasks into policy learning assignments for the agent. In this paper, we select the key parameters of visual SLAM to construct the agent action space. The reward function is constructed based on the absolute trajectory error (ATE), and the Mamba history observer is built within the agent to learn the observation trajectory, aiming to improve the quality of the agent’s decisions. Finally, the proposed method was experimented on the EuRoc MAV and TUM-VI datasets. The experimental results show that Mamba-DQN not only enhances the positioning accuracy of visual SLAM and demonstrates good real-time performance but also avoids the tedious parameter adjustment process. Full article

(This article belongs to the Special Issue Applications in Computer Vision and Image Processing)

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18 pages, 36707 KB

Open AccessArticle

High-Precision Image Editing via Dual Attention Control in Diffusion Models Without Fine-Tuning

by Zhiqiang Pan, Yingchun Kuang, Jianmei Lan and Lizhuo Zhang

Appl. Sci. 2025, 15(3), 1079; https://doi.org/10.3390/app15031079 - 22 Jan 2025

Cited by 2 | Viewed by 7420

Abstract

Existing diffusion models outperform generative models like Generative Adversarial Networks in image synthesis and editing. However, they struggle with high-precision image editing while preserving image details and the accuracy of editing instructions. To address these challenges, we propose a dual attention control method [...] Read more.

Existing diffusion models outperform generative models like Generative Adversarial Networks in image synthesis and editing. However, they struggle with high-precision image editing while preserving image details and the accuracy of editing instructions. To address these challenges, we propose a dual attention control method to achieve high-precision image editing. Our approach includes two key attention control modules: (1) cross-attention control module, which combines the cross-attention maps of the original and edited images through weighted parameters, ensures that the synthesized edited image retains the structure of the input image. (2) Self-attention control module, which varies based on the editing task, applied at “coarse” and “fine” layers, since the coarse layers help maintain input image details and the fine layers are better suited for style transformations. Experimental evaluations have demonstrated that our approach achieves excellent results in detail preservation, content consistency, visual realism, and semantic understanding, making it especially suitable for tasks requiring high-precision editing. Specifically, compared to the editing outcomes under no control conditions, the introduction of dual visual attention control has led to an increase of 6.19% in CLIP scores, a reduction of 29.3% in LPIPS, and a decrease of 24.7% in FID. These significant improvements not only validate the effectiveness of the dual attention control but also attest to the method’s substantial flexibility and adaptability across different scenarios. Notably, our approach is a zero-shot solution, requiring no user optimization or fine-tuning, facilitating real-world applications. Full article

(This article belongs to the Special Issue Applications in Computer Vision and Image Processing)

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17 pages, 6879 KB

Open AccessArticle

Machine Learning Models for Artist Classification of Cultural Heritage Sketches

by Gianina Chirosca, Roxana Rădvan, Silviu Mușat, Matei Pop and Alecsandru Chirosca

Appl. Sci. 2025, 15(1), 212; https://doi.org/10.3390/app15010212 - 30 Dec 2024

Cited by 5 | Viewed by 3494

Abstract

Modern computer vision algorithms allow researchers and art historians to search for artist-characteristic contour extraction from sketches, thus providing accurate input for artwork analysis, for possible assignments and classifications, and also for the identification of the specific stylistic features. We approach this challenging [...] Read more.

Modern computer vision algorithms allow researchers and art historians to search for artist-characteristic contour extraction from sketches, thus providing accurate input for artwork analysis, for possible assignments and classifications, and also for the identification of the specific stylistic features. We approach this challenging task with three machine learning algorithms and evaluate their performance on a small collection of images from five distinct artists. These algorithms aim to find the most appropriate artist for a sketch (or a contour of a sketch), with promising results that have a higher level of confidence (around 92%). Models start from common Faster R-CNN architectures, reinforcement learning, and vector extraction tools. The proposed tool provides a base for future improvements to create a tool that aids artwork evaluators. Full article

(This article belongs to the Special Issue Applications in Computer Vision and Image Processing)

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23 pages, 106560 KB

Open AccessArticle

RLUNet: Overexposure-Content-Recovery-Based Single HDR Image Reconstruction with the Imaging Pipeline Principle

by Yiru Zheng, Wei Wang, Xiao Wang and Xin Yuan

Appl. Sci. 2024, 14(23), 11289; https://doi.org/10.3390/app142311289 - 3 Dec 2024

Cited by 1 | Viewed by 3121

Abstract

With the popularity of High Dynamic Range (HDR) display technology, consumer demand for HDR images is increasing. Since HDR cameras are expensive, reconstructing High Dynamic Range (HDR) images from traditional Low Dynamic Range (LDR) images is crucial. However, existing HDR image reconstruction algorithms [...] Read more.

With the popularity of High Dynamic Range (HDR) display technology, consumer demand for HDR images is increasing. Since HDR cameras are expensive, reconstructing High Dynamic Range (HDR) images from traditional Low Dynamic Range (LDR) images is crucial. However, existing HDR image reconstruction algorithms often fail to recover fine details and do not adequately address the fundamental principles of the LDR imaging pipeline. To overcome these limitations, the Reversing Lossy UNet (RLUNet) has been proposed, aiming to effectively balance dynamic range expansion and recover overexposed areas through a deeper understanding of LDR image pipeline principles. The RLUNet model comprises the Reverse Lossy Network, which is designed according to the LDR–HDR framework and focuses on reconstructing HDR images by recovering overexposed regions, dequantizing, linearizing the mapping, and suppressing compression artifacts. This framework, grounded in the principles of the LDR imaging pipeline, is designed to reverse the operations involved in lossy image operations. Furthermore, the integration of the Texture Filling Module (TFM) block with the Recovery of Overexposed Regions (ROR) module in the RLUNet model enhances the visual performance and detail texture of the overexposed areas in the reconstructed HDR image. The experiments demonstrate that the proposed RLUNet model outperforms various state-of-the-art methods on different testsets. Full article

(This article belongs to the Special Issue Applications in Computer Vision and Image Processing)

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19 pages, 1669 KB

Open AccessArticle

FETrack: Feature-Enhanced Transformer Network for Visual Object Tracking

by Hang Liu, Detian Huang and Mingxin Lin

Appl. Sci. 2024, 14(22), 10589; https://doi.org/10.3390/app142210589 - 17 Nov 2024

Cited by 1 | Viewed by 3087

Abstract

Visual object tracking is a fundamental task in computer vision, with applications ranging from video surveillance to autonomous driving. Despite recent advances in transformer-based one-stream trackers, unrestricted feature interactions between the template and the search region often introduce background noise into the template, [...] Read more.

Visual object tracking is a fundamental task in computer vision, with applications ranging from video surveillance to autonomous driving. Despite recent advances in transformer-based one-stream trackers, unrestricted feature interactions between the template and the search region often introduce background noise into the template, degrading the tracking performance. To address this issue, we propose FETrack, a feature-enhanced transformer-based network for visual object tracking. Specifically, we incorporate an independent template stream in the encoder of the one-stream tracker to acquire the high-quality template features while suppressing the harmful background noise effectively. Then, we employ a sequence-learning-based causal transformer in the decoder to generate the bounding box autoregressively, simplifying the prediction head network. Further, we present a dynamic threshold-based online template-updating strategy and a template-filtering approach to boost tracking robustness and reduce redundant computations. Extensive experiments demonstrate that our FETrack achieves a superior performance over state-of-the-art trackers. Specifically, the proposed FETrack achieves a 75.1% AO on GOT-10k, 81.2% AUC on LaSOT, and 89.3%

P_{n o r m}

on TrackingNet. Full article

(This article belongs to the Special Issue Applications in Computer Vision and Image Processing)

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