Edge_Detection
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Evaluation framework for Image Segmentation Algorithms
2025-04-06 10:20:26Tatiana Merkulova, Bharani JayakumarAbstract
This paper presents a comprehensive evaluation framework for image segmentation algorithms, encompassing naive methods, machine learning approaches, and deep learning techniques. We begin by introducing the fundamental concepts and importance of image segmentation, and the role of interactive segmentation in enhancing accuracy. A detailed background theory section explores various segmentation methods, including thresholding, edge detection, region growing, feature extraction, random forests, support vector machines, convolutional neural networks, U-Net, and Mask R-CNN. The implementation and experimental setup are thoroughly described, highlighting three primary approaches: algorithm assisting user, user assisting algorithm, and hybrid methods. Evaluation metrics such as Intersection over Union (IoU), computation time, and user interaction time are employed to measure performance. A comparative analysis presents detailed results, emphasizing the strengths, limitations, and trade-offs of each method. The paper concludes with insights into the practical applicability of these approaches across various scenarios and outlines future work, focusing on expanding datasets, developing more representative approaches, integrating real-time feedback, and exploring weakly supervised and self-supervised learning paradigms to enhance segmentation accuracy and efficiency. Keywords: Image Segmentation, Interactive Segmentation, Machine Learning, Deep Learning, Computer Vision
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本文提出了一种全面评估图像分割算法的框架，涵盖了简单方法、机器学习方法和深度学习技术。文章首先介绍了图像分割的基本概念及其重要性，并探讨了交互式分割在提高准确性方面的作用。详细的背景理论部分探索了各种分割方法，包括阈值处理、边缘检测、区域增长、特征提取、随机森林、支持向量机、卷积神经网络、U-Net和Mask R-CNN。 实施与实验设置被详尽描述，重点介绍了三种主要的方法：算法辅助用户、用户辅助算法以及混合方法。采用交并比（IoU）、计算时间和用户交互时间等评估指标来衡量性能表现。比较分析部分详细展示了各种方法的结果，并强调了每种方法的优势、局限性及权衡因素。 文章最后总结了这些方法在不同场景中的实际应用价值，并展望未来工作，重点关注扩大数据集规模、开发更具代表性的方法、整合实时反馈以及探索弱监督和自监督学习范式以提高分割准确性和效率。关键词包括：图像分割、交互式分割、机器学习、深度学习、计算机视觉。
URL
https://arxiv.org/abs/2504.04435
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Hybrid Multi-Stage Learning Framework for Edge Detection: A Survey
2025-03-26 13:06:31Mark Phil Pacot, Jayno Juventud, Gleen DalaoraoAbstract
Edge detection remains a fundamental yet challenging task in computer vision, especially under varying illumination, noise, and complex scene conditions. This paper introduces a Hybrid Multi-Stage Learning Framework that integrates Convolutional Neural Network (CNN) feature extraction with a Support Vector Machine (SVM) classifier to improve edge localization and structural accuracy. Unlike conventional end-to-end deep learning models, our approach decouples feature representation and classification stages, enhancing robustness and interpretability. Extensive experiments conducted on benchmark datasets such as BSDS500 and NYUDv2 demonstrate that the proposed framework outperforms traditional edge detectors and even recent learning-based methods in terms of Optimal Dataset Scale (ODS) and Optimal Image Scale (OIS), while maintaining competitive Average Precision (AP). Both qualitative and quantitative results highlight enhanced performance on edge continuity, noise suppression, and perceptual clarity achieved by our method. This work not only bridges classical and deep learning paradigms but also sets a new direction for scalable, interpretable, and high-quality edge detection solutions.
Abstract (translated)
边缘检测仍然是计算机视觉中的一个基本且具有挑战性的任务，尤其是在不同的照明条件、噪声以及复杂场景下。本文提出了一种混合多阶段学习框架，该框架结合了卷积神经网络（CNN）的特征提取与支持向量机（SVM）分类器的功能，以提高边缘定位和结构准确性。不同于传统的端到端深度学习模型，我们的方法解耦了特征表示和分类阶段，从而增强了鲁棒性和可解释性。 在BSDS500和NYUDv2等基准数据集上进行的大量实验表明，所提出的框架在最优数据规模（ODS）和最优图像尺度（OIS）方面超过了传统边缘检测器以及近期的学习方法，并且保持了竞争性的平均精度（AP）。无论是定性还是定量结果都显示出了我们的方法在边缘连续性、噪声抑制及感知清晰度方面的性能提升。 这项工作不仅连接了经典学习与深度学习范式，而且还为可扩展的、解释性强和高质量的边缘检测解决方案开辟了一条新的道路。
URL
https://arxiv.org/abs/2503.21827
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Seeing What Matters: Empowering CLIP with Patch Generation-to-Selection
2025-03-21 12:10:38Gensheng Pei, Tao Chen, Yujia Wang, Xinhao Cai, Xiangbo Shu, Tianfei Zhou, Yazhou YaoAbstract
The CLIP model has demonstrated significant advancements in aligning visual and language modalities through large-scale pre-training on image-text pairs, enabling strong zero-shot classification and retrieval capabilities on various domains. However, CLIP's training remains computationally intensive, with high demands on both data processing and memory. To address these challenges, recent masking strategies have emerged, focusing on the selective removal of image patches to improve training efficiency. Although effective, these methods often compromise key semantic information, resulting in suboptimal alignment between visual features and text descriptions. In this work, we present a concise yet effective approach called Patch Generation-to-Selection to enhance CLIP's training efficiency while preserving critical semantic content. Our method introduces a gradual masking process in which a small set of candidate patches is first pre-selected as potential mask regions. Then, we apply Sobel edge detection across the entire image to generate an edge mask that prioritizes the retention of the primary object areas. Finally, similarity scores between the candidate mask patches and their neighboring patches are computed, with optimal transport normalization refining the selection process to ensure a balanced similarity matrix. Our approach, CLIP-PGS, sets new state-of-the-art results in zero-shot classification and retrieval tasks, achieving superior performance in robustness evaluation and language compositionality benchmarks.
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CLIP模型通过在图像-文本对上进行大规模预训练，在视觉和语言模式的对齐方面取得了显著进展，从而能够在各种领域中实现强大的零样本分类和检索能力。然而，CLIP的训练仍具有较高的计算需求，特别是在数据处理和内存使用方面。为了应对这些挑战，最近出现了一些掩码策略，通过选择性地移除图像补丁来提高训练效率。尽管这些方法有效，但它们通常会牺牲关键的语义信息，导致视觉特征与文本描述之间的对齐效果不佳。 在本文中，我们提出了一种简洁而有效的称为Patch Generation-to-Selection的方法，旨在提升CLIP的训练效率的同时保留重要的语义内容。我们的方法引入了逐步掩码过程，在此过程中，首先从图像中选取一小部分候选补丁作为潜在的掩膜区域。然后，我们在整个图像上应用Sobel边缘检测算法来生成一个边缘掩模，优先保持主要物体区域。最后，计算候选掩模补丁与其邻近补丁之间的相似度分数，并通过最优传输归一化对选择过程进行优化，以确保相似性矩阵的平衡。 我们的方法CLIP-PGS在零样本分类和检索任务中取得了新的最先进成果，在鲁棒性评估和语言组合性基准测试中也表现出优越性能。
URL
https://arxiv.org/abs/2503.17080
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Polygonizing Roof Segments from High-Resolution Aerial Images Using Yolov8-Based Edge Detection
2025-03-12 09:29:10Qipeng Mei, Dimitri Bulatov, Dorota IwaszczukAbstract
This study presents a novel approach for roof detail extraction and vectorization using remote sensing images. Unlike previous geometric-primitive-based methods that rely on the detection of corners, our method focuses on edge detection as the primary mechanism for roof reconstruction, while utilizing geometric relationships to define corners and faces. We adapt the YOLOv8 OBB model, originally designed for rotated object detection, to extract roof edges effectively. Our method demonstrates robustness against noise and occlusion, leading to precise vectorized representations of building roofs. Experiments conducted on the SGA and Melville datasets highlight the method's effectiveness. At the raster level, our model outperforms the state-of-the-art foundation segmentation model (SAM), achieving a mIoU between 0.85 and 1 for most samples and an ovIoU close to 0.97. At the vector level, evaluation using the Hausdorff distance, PolyS metric, and our raster-vector-metric demonstrates significant improvements after polygonization, with a close approximation to the reference data. The method successfully handles diverse roof structures and refines edge gaps, even on complex roof structures of new, excluded from training datasets. Our findings underscore the potential of this approach to address challenges in automatic roof structure vectorization, supporting various applications such as urban terrain reconstruction.
Abstract (translated)
这项研究提出了一种使用遥感图像提取和矢量化屋顶细节的新方法。与以往基于几何原语的方法依赖于角点检测不同，我们的方法主要侧重于边缘检测作为屋顶重建的主要机制，并利用几何关系来定义角点和面。我们对 YOLOv8 OBB 模型进行了改进，该模型最初是为旋转物体检测设计的，以有效地提取屋顶边缘。我们的方法展示了在噪声和遮挡情况下的鲁棒性，从而能够生成精确的建筑屋顶矢量表示。我们在 SGA 和 Melville 数据集上进行的实验突显了这种方法的有效性。 在栅格层面，我们的模型优于最先进的基础分割模型（SAM），大多数样本的 mIoU 在 0.85 到 1 之间，并且 ovIoU 接近 0.97。在矢量层面上，使用 Hausdorff 距离、PolyS 指标以及我们提出的栅格-矢量指标进行评估，在多边形化后表现出显著改进，接近参考数据。 该方法成功处理了各种屋顶结构，并且即使对于从未参与训练的数据集中的复杂屋顶结构也能精炼边缘缺口。我们的研究结果强调了此方法在自动屋顶结构矢量化挑战中应用的潜力，支持诸如城市地形重建等各种应用场景。
URL
https://arxiv.org/abs/2503.09187
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Robust Detection of Extremely Thin Lines Using 0.2mm Piano Wire
2025-03-04 07:05:33Jisoo Hong, Youngjin Jung, Jihwan Bae, Seungho Song, Sung-Woo KangAbstract
This study developed an algorithm capable of detecting a reference line (a 0.2 mm thick piano wire) to accurately determine the position of an automated installation robot within an elevator shaft. A total of 3,245 images were collected from the experimental tower of H Company, the leading elevator manufacturer in South Korea, and the detection performance was evaluated using four experimental approaches (GCH, GSCH, GECH, FCH). During the initial image processing stage, Gaussian blurring, sharpening filter, embossing filter, and Fourier Transform were applied, followed by Canny Edge Detection and Hough Transform. Notably, the method was developed to accurately extract the reference line by averaging the x-coordinates of the lines detected through the Hough Transform. This approach enabled the detection of the 0.2 mm thick piano wire with high accuracy, even in the presence of noise and other interfering factors (e.g., concrete cracks inside the elevator shaft or safety bars for filming equipment). The experimental results showed that Experiment 4 (FCH), which utilized Fourier Transform in the preprocessing stage, achieved the highest detection rate for the LtoL, LtoR, and RtoL datasets. Experiment 2(GSCH), which applied Gaussian blurring and a sharpening filter, demonstrated superior detection performance on the RtoR dataset. This study proposes a reference line detection algorithm that enables precise position calculation and control of automated robots in elevator shaft installation. Moreover, the developed method shows potential for applicability even in confined working spaces. Future work aims to develop a line detection algorithm equipped with machine learning-based hyperparameter tuning capabilities.
Abstract (translated)
这项研究开发了一种能够检测基准线（一根0.2毫米厚的钢琴弦）的算法，以准确确定电梯井中自动安装机器人位置。总共从韩国领先的电梯制造商H公司的实验塔楼收集了3,245张图像，并通过四种实验方法（GCH、GSCH、GECH和FCH）评估了检测性能。在初始图像处理阶段，采用了高斯模糊、锐化滤镜、浮雕滤镜以及傅里叶变换，并随后应用Canny边缘检测和霍夫变换。 特别值得注意的是，该方法通过平均霍夫变换中检测到的线段的x坐标来准确提取基准线。这种方法能够在存在噪声和其他干扰因素（如电梯井内的混凝土裂缝或用于拍摄设备的安全栏杆）的情况下，以高精度识别出0.2毫米厚的钢琴弦。实验结果显示，在预处理阶段使用傅里叶变换的实验4（FCH），在LtoL、LtoR和RtoL数据集上的检测率最高；而应用了高斯模糊与锐化滤镜的实验2（GSCH）则在RtoR数据集中表现出优越的检测性能。 本研究提出了一种基准线检测算法，可以实现电梯井安装中自动机器人精确位置计算和控制。此外，所开发的方法还显示出了适用于狭小工作空间的应用潜力。未来的研究将致力于开发一种具备基于机器学习的超参数调优能力的线条检测算法。
URL
https://arxiv.org/abs/2503.13473
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STAR-Edge: Structure-aware Local Spherical Curve Representation for Thin-walled Edge Extraction from Unstructured Point Clouds
2025-03-02 08:51:13Zikuan Li, Honghua Chen, Yuecheng Wang, Sibo Wu, Mingqiang Wei, Jun WangAbstract
Extracting geometric edges from unstructured point clouds remains a significant challenge, particularly in thin-walled structures that are commonly found in everyday objects. Traditional geometric methods and recent learning-based approaches frequently struggle with these structures, as both rely heavily on sufficient contextual information from local point neighborhoods. However, 3D measurement data of thin-walled structures often lack the accurate, dense, and regular neighborhood sampling required for reliable edge extraction, resulting in degraded performance. In this work, we introduce STAR-Edge, a novel approach designed for detecting and refining edge points in thin-walled structures. Our method leverages a unique representation-the local spherical curve-to create structure-aware neighborhoods that emphasize co-planar points while reducing interference from close-by, non-co-planar surfaces. This representation is transformed into a rotation-invariant descriptor, which, combined with a lightweight multi-layer perceptron, enables robust edge point classification even in the presence of noise and sparse or irregular sampling. Besides, we also use the local spherical curve representation to estimate more precise normals and introduce an optimization function to project initially identified edge points exactly on the true edges. Experiments conducted on the ABC dataset and thin-walled structure-specific datasets demonstrate that STAR-Edge outperforms existing edge detection methods, showcasing better robustness under various challenging conditions.
Abstract (translated)
从非结构化点云中提取几何边缘仍然是一个重大挑战，尤其是在日常物体中常见的薄壁结构。传统几何方法和近期基于学习的方法在处理这些结构时经常遇到困难，因为它们都严重依赖于局部点邻域提供的充足上下文信息。然而，由于3D测量数据中的薄壁结构通常缺乏精确、密集且规则的局部采样，这导致了可靠的边缘提取性能下降。 为此，我们提出了一种名为STAR-Edge的新方法，专门用于检测和细化薄壁结构中的边缘点。该方法利用一种独特的表示方式——局部球面曲线（local spherical curve），以此来创建具有结构意识的邻域，突出共面点的同时减少与非共平面表面带来的干扰。此表示进一步被转换为旋转不变描述符，并结合轻量级多层感知器，即使在存在噪声和稀疏或不规则采样情况下也能实现鲁棒边缘点分类。 此外，我们还利用局部球面曲线来估计更精确的法线，并引入优化函数将初始识别的边缘点准确投影到真正的边缘上。实验结果显示，在ABC数据集以及针对薄壁结构专门的数据集中，STAR-Edge在各种挑战性条件下均优于现有的边缘检测方法，表现出更强的鲁棒性。
URL
https://arxiv.org/abs/2503.00801
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Theory-guided Pseudo-spectral Full Waveform Inversion via Deep Neural Networks
2025-02-24 20:18:55Christopher Zerafa, Pauline Galea, Cristiana SebuAbstract
Full-Waveform Inversion seeks to achieve a high-resolution model of the subsurface through the application of multi-variate optimization to the seismic inverse problem. Although now a mature technology, FWI has limitations related to the choice of the appropriate solver for the forward problem in challenging environments requiring complex assumptions, and very wide angle and multi-azimuth data necessary for full reconstruction are often not available. Deep Learning techniques have emerged as excellent optimization frameworks. Data-driven methods do not impose a wave propagation model and are not exposed to modelling errors. On the contrary, deterministic models are governed by the laws of physics. Seismic FWI has recently started to be investigated as a Deep Learning framework. Focus has been on the time-domain, while the pseudo-spectral domain has not been yet explored. However, classical FWI experienced major breakthroughs when pseudo-spectral approaches were employed. This work addresses the lacuna that exists in incorporating the pseudo-spectral approach within Deep Learning. This has been done by re-formulating the pseudo-spectral FWI problem as a Deep Learning algorithm for a theory-driven pseudo-spectral approach. A novel Recurrent Neural Network framework is proposed. This is qualitatively assessed on synthetic data, applied to a two-dimensional Marmousi dataset and evaluated against deterministic and time-based approaches. Pseudo-spectral theory-guided FWI using RNN was shown to be more accurate than classical FWI with only 0.05 error tolerance and 1.45\% relative percent-age error. Indeed, this provides more stable convergence, able to identify faults better and has more low frequency content than classical FWI. Moreover, RNN was more suited than classical FWI at edge detection in the shallow and deep sections due to cleaner receiver residuals.
Abstract (translated)
全波形反演（Full-Waveform Inversion，FWI）旨在通过多变量优化方法解决地震逆问题，从而获得地下高分辨率的模型。尽管FWI现在是一项成熟的技术，但在复杂环境中选择合适的正向问题求解器时仍存在局限性，并且需要非常宽的角度和多方位的数据才能进行完整的重建，而这些数据通常不可用。深度学习技术已经作为优秀的优化框架出现。数据驱动的方法不依赖于波传播模型，因此不受建模误差的影响。相反，确定性的模型受物理定律的约束。最近，地震FWI开始被研究为一种深度学习框架。目前的研究主要集中在时间域上，而伪谱域尚未得到探索。然而，传统的FWI在采用伪谱方法时取得了重大突破。 这项工作旨在填补将伪谱方法整合到深度学习中的空白。通过重新表述基于理论的伪谱FWI问题为深度学习算法，实现了这一目标，并提出了一种新的递归神经网络框架。该方案已在合成数据上进行了定性评估，并应用于二维Marmousi数据集，同时与确定性和时间基方法进行了对比。结果表明，在仅0.05误差容限和1.45%相对百分比误差的情况下，使用RNN的伪谱理论指导下的FWI比传统FWI更准确。此外，这种方法能够提供更为稳定的收敛性，更好地识别断层，并且具有比传统FWI更多的低频成分。更重要的是，在浅层和深层区域边缘检测方面，RNN方法的表现优于传统FWI，这得益于接收器残差的清洁度。 总之，该研究展示了将深度学习技术应用于地震全波形反演中的伪谱理论指导策略的有效性，并为未来的相关应用提供了新的可能性。
URL
https://arxiv.org/abs/2502.17624
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Subpixel Edge Localization Based on Converted Intensity Summation under Stable Edge Region
2025-02-23 08:52:42Yingyuan Yang, Guoyuan Liang, Xianwen Wang, Kaiming Wang, Can Wang, Xiaojun WuAbstract
To satisfy the rigorous requirements of precise edge detection in critical high-accuracy measurements, this article proposes a series of efficient approaches for localizing subpixel edge. In contrast to the fitting based methods, which consider pixel intensity as a sample value derived from a specific model. We take an innovative perspective by assuming that the intensity at the pixel level can be interpreted as a local integral mapping in the intensity model for subpixel localization. Consequently, we propose a straightforward subpixel edge localization method called Converted Intensity Summation (CIS). To address the limited robustness associated with focusing solely on the localization of individual edge points, a Stable Edge Region (SER) based algorithm is presented to alleviate local interference near edges. Given the observation that the consistency of edge statistics exists in the local region, the algorithm seeks correlated stable regions in the vicinity of edges to facilitate the acquisition of robust parameters and achieve higher precision positioning. In addition, an edge complement method based on extension-adjustment is also introduced to rectify the irregular edges through the efficient migration of SERs. A large number of experiments are conducted on both synthetic and real image datasets which cover common edge patterns as well as various real scenarios such as industrial PCB images, remote sensing and medical images. It is verified that CIS can achieve higher accuracy than the state-of-the-art method, while requiring less execution time. Moreover, by integrating SER into CIS, the proposed algorithm demonstrates excellent performance in further improving the anti-interference capability and positioning accuracy.
Abstract (translated)
为了满足在高精度测量中精确边缘检测的严格要求，本文提出了一系列用于亚像素级边缘定位的有效方法。与基于拟合的方法不同，后者将像素强度视为来自特定模型的样本值，我们采取了一个创新的角度，假设在像素级别的强度可以解释为局部积分映射，在该映射下进行亚像素定位。因此，我们提出了一种简单的称为转换强度累加（Converted Intensity Summation, CIS）的亚像素边缘定位方法。 为了克服仅关注单一边缘点定位所导致的有限鲁棒性问题，本文还提出了一种基于稳定边缘区域（Stable Edge Region, SER）的方法，以减轻边缘附近的局部干扰。鉴于边缘统计在局部区域内的一致性存在，该算法寻找边缘附近的相关稳定区域，以便获取稳健参数并实现更精确的位置定位。 此外，我们还引入了一种基于扩展调整的边缘补充方法，通过有效迁移SER来纠正不规则边缘。 在包含常见边缘模式及各种实际场景（如工业PCB图像、遥感和医学图像）的人工合成与真实图像数据集上进行了大量实验。结果验证了CIS方法比当前最优方法具有更高的精度，并且需要更少的执行时间。此外，通过将SER集成到CIS中，所提出的算法在进一步提升抗干扰能力和定位准确性方面表现出了卓越性能。
URL
https://arxiv.org/abs/2502.16502
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An ocean front detection and tracking algorithm
2025-02-21 07:00:09Yishuo Wang, Feng ZhouAbstract
Ocean front is defined as the interface between different water masses and plays a vital role in the evolution of many physical phenomena. Previous detection methods are based on histogram, Lyapunov exponent, gradient and machine learning. These algorithms, however, introduce discontinuity, inaccuracy, use less information or just approaching traditional results. Moreover, automatic front tracking algrorithm is not open source in preceding studies. This paper foucuses on large-scale ocean fronts and proposes an automatic front detection and tracking algorithm based on Bayesian decision and metric space. In this, front merging, filling and ring deletion are put forward to enhance continuity. The distance between fronts in different days is firstly defined and is well-defined in metric space for functional analysis. These technologies can be migrated to other areas of computer vision such as edge detection and tracking.
Abstract (translated)
海洋前沿被定义为不同水体之间的界面，在许多物理现象的演化中起着至关重要的作用。以往的检测方法基于直方图、李雅普诺夫指数、梯度和机器学习等技术，然而这些算法引入了不连续性、不准确性或信息利用不足的问题，甚至有些方法只是接近传统结果。此外，在先前的研究中，自动前沿追踪算法未开源。 本文重点关注大规模海洋前沿，并提出了一种基于贝叶斯决策和度量空间的自动前沿检测与跟踪算法。文中提出了前沿合并、填充和环形删除等技术以增强连续性。首先定义了不同日期间前沿之间的距离，并在度量空间中进行了良好的定义，以便于函数分析。 这些技术可以迁移到计算机视觉领域的其他方面，例如边缘检测和追踪。
URL
https://arxiv.org/abs/2502.15250
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Trust Me, I'm Wrong: High-Certainty Hallucinations in LLMs
2025-02-18 15:46:31Adi Simhi, Itay Itzhak, Fazl Barez, Gabriel Stanovsky, Yonatan BelinkovAbstract
Large Language Models (LLMs) often generate outputs that lack grounding in real-world facts, a phenomenon known as hallucinations. Prior research has associated hallucinations with model uncertainty, leveraging this relationship for hallucination detection and mitigation. In this paper, we challenge the underlying assumption that all hallucinations are associated with uncertainty. Using knowledge detection and uncertainty measurement methods, we demonstrate that models can hallucinate with high certainty even when they have the correct knowledge. We further show that high-certainty hallucinations are consistent across models and datasets, distinctive enough to be singled out, and challenge existing mitigation methods. Our findings reveal an overlooked aspect of hallucinations, emphasizing the need to understand their origins and improve mitigation strategies to enhance LLM safety. The code is available at this https URL .
Abstract (translated)
大型语言模型（LLMs）经常生成缺乏现实世界事实依据的输出，这种现象被称为幻觉。先前的研究将幻觉与模型不确定性联系起来，并利用这一关系进行幻觉检测和缓解。在本文中，我们挑战了所有幻觉都与不确定性相关的基本假设。通过知识检测和不确定度测量方法，我们证明即使拥有正确的知识，模型也可以以高度确信的方式产生幻觉。此外，我们还表明，高确定性的幻觉在不同的模型和数据集中是一致且独特的，并对现有的缓解方法提出了挑战。我们的发现揭示了幻觉的一个被忽视的方面，强调了理解其起源并改进缓解策略以增强LLM安全性的必要性。代码可在[此处](https://example.com)获取。
URL
https://arxiv.org/abs/2502.12964
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Enhancing Transparent Object Pose Estimation: A Fusion of GDR-Net and Edge Detection
2025-02-17 16:59:37Tessa Pulli, Peter H\"onig, Stefan Thalhammer, Matthias Hirschmanner, Markus VinczeAbstract
Object pose estimation of transparent objects remains a challenging task in the field of robot vision due to the immense influence of lighting, background, and reflections. However, the edges of clear objects have the highest contrast, which leads to stable and prominent features. We propose a novel approach by incorporating edge detection in a pre-processing step for the tasks of object detection and object pose estimation. We conducted experiments to investigate the effect of edge detectors on transparent objects. We examine the performance of the state-of-the-art 6D object pose estimation pipeline GDR-Net and the object detector YOLOX when applying different edge detectors as pre-processing steps (i.e., Canny edge detection with and without color information, and holistically-nested edges (HED)). We evaluate the physically-based rendered dataset Trans6D-32 K of transparent objects with parameters proposed by the BOP Challenge. Our results indicate that applying edge detection as a pre-processing enhances performance for certain objects.
Abstract (translated)
透明物体的姿态估计在机器人视觉领域中仍然是一个具有挑战性的任务，这主要是由于光照、背景和反射等因素的显著影响。然而，清晰物体的边缘部分对比度最高，从而提供了稳定且突出的特征。为此，我们提出了一种新的方法，在对象检测和姿态估计任务之前引入边缘检测作为预处理步骤。 我们在实验中研究了不同的边缘检测器对透明物体的影响，并评估了几种先进的算法：6D物体姿态估计流水线GDR-Net及目标检测器YOLOX在应用不同边缘检测器（如Canny边缘检测是否结合颜色信息，以及全卷积网络生成的holistically-nested edges (HED)）作为预处理步骤时的表现。我们使用了BOP Challenge提出的物理基础渲染数据集Trans6D-32K来评估透明物体。 实验结果表明，在特定的对象上应用边缘检测作为预处理可以增强性能。
URL
https://arxiv.org/abs/2502.12027
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Agricultural Field Boundary Detection through Integration of 'Simple Non-Iterative Clustering Super Pixels' and 'Canny Edge Detection Method'
2025-02-06 22:00:41Artughrul Gayibov (Baku Engineering University)Abstract
Efficient use of cultivated areas is a necessary factor for sustainable development of agriculture and ensuring food security. Along with the rapid development of satellite technologies in developed countries, new methods are being searched for accurate and operational identification of cultivated areas. In this context, identification of cropland boundaries based on spectral analysis of data obtained from satellite images is considered one of the most optimal and accurate methods in modern agriculture. This article proposes a new approach to determine the suitability and green index of cultivated areas using satellite data obtained through the "Google Earth Engine" (GEE) platform. In this approach, two powerful algorithms, "SNIC (Simple Non-Iterative Clustering) Super Pixels" and "Canny Edge Detection Method", are combined. The SNIC algorithm combines pixels in a satellite image into larger regions (super pixels) with similar characteristics, thereby providing better image analysis. The Canny Edge Detection Method detects sharp changes (edges) in the image to determine the precise boundaries of agricultural fields. This study, carried out using high-resolution multispectral data from the Sentinel-2 satellite and the Google Earth Engine JavaScript API, has shown that the proposed method is effective in accurately and reliably classifying randomly selected agricultural fields. The combined use of these two tools allows for more accurate determination of the boundaries of agricultural fields by minimizing the effects of outliers in satellite images. As a result, more accurate and reliable maps can be created for agricultural monitoring and resource management over large areas based on the obtained data. By expanding the application capabilities of cloud-based platforms and artificial intelligence methods in the agricultural field.
Abstract (translated)
高效利用耕种区域是农业可持续发展和保障粮食安全的一个重要因素。随着发达国家卫星技术的迅速发展，人们正在寻找准确且操作性强的方式来识别耕地。在这种背景下，基于从卫星图像获取的数据进行光谱分析以确定耕地边界的方法被认为是现代农业中最为优化和精确的方法之一。本文提出了一种新方法，利用通过“Google Earth Engine”（GEE）平台获得的卫星数据来确定耕种区域的适宜性和绿色指数。该方法结合了两个强大的算法：“SNIC（Simple Non-Iterative Clustering）超像素”算法以及“Canny边缘检测法”。 SNIC算法将卫星图像中的像素组合成具有类似特性的较大区域（即超像素），从而提供更好的图像分析能力。而Canny边缘检测法则用于识别图像中急剧变化的边界，以确定农业用地的确切边界。这项研究使用了来自Sentinel-2卫星的高分辨率多光谱数据以及Google Earth Engine JavaScript API，并表明所提出的方法在准确且可靠地分类随机选择的农田方面非常有效。 通过结合这两种工具的使用，可以更精确地确定农业土地的边界，减少卫星图像中异常值的影响。基于获得的数据，可以在大面积范围内创建更加准确和可靠的农业监测与资源管理地图。这扩展了云计算平台及人工智能方法在农业领域的应用能力。
URL
https://arxiv.org/abs/2502.04529
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Training-free Quantum-Inspired Image Edge Extraction Method
2025-01-31 07:24:38Arti Jain, Pradeep SinghAbstract
Edge detection is a cornerstone of image processing, yet existing methods often face critical limitations. Traditional deep learning edge detection methods require extensive training datasets and fine-tuning, while classical techniques often fail in complex or noisy scenarios, limiting their real-world applicability. To address these limitations, we propose a training-free, quantum-inspired edge detection model. Our approach integrates classical Sobel edge detection, the Schrödinger wave equation refinement, and a hybrid framework combining Canny and Laplacian operators. By eliminating the need for training, the model is lightweight and adaptable to diverse applications. The Schrödinger wave equation refines gradient-based edge maps through iterative diffusion, significantly enhancing edge precision. The hybrid framework further strengthens the model by synergistically combining local and global features, ensuring robustness even under challenging conditions. Extensive evaluations on datasets like BIPED, Multicue, and NYUD demonstrate superior performance of the proposed model, achieving state-of-the-art metrics, including ODS, OIS, AP, and F-measure. Noise robustness experiments highlight its reliability, showcasing its practicality for real-world scenarios. Due to its versatile and adaptable nature, our model is well-suited for applications such as medical imaging, autonomous systems, and environmental monitoring, setting a new benchmark for edge detection.
Abstract (translated)
边缘检测是图像处理中的核心环节，然而现有的方法往往面临着关键的局限性。传统的深度学习边缘检测方法需要大量的训练数据集和精细调整，而经典技术在复杂或噪声环境中常常表现不佳，限制了其实际应用效果。为了克服这些局限性，我们提出了一种无需训练、受量子启发的边缘检测模型。我们的方法结合了经典的Sobel边缘检测算法、薛定谔波动方程细化以及将Canny和Laplacian算子相结合的混合框架。 通过消除对训练的需求，该模型变得轻量且易于适应各种应用场景。薛定谔波动方程通过对基于梯度的边缘图进行迭代扩散来精炼其细节，显著提高了边缘检测的精确度。混合框架进一步增强了模型，通过协同结合局部和全局特征，在挑战性条件下确保了其鲁棒性。 在BIPED、Multicue和NYUD等数据集上的广泛评估显示，所提出的模型实现了卓越的表现，并达到了包括ODS（Object Detection Score）、OIS（Object Integrity Score）、AP（Average Precision）以及F-measure在内的各项指标的最新水平。噪声鲁棒性的实验强调了该模型的可靠性，展示了其在实际场景中的实用性。 鉴于其灵活和适应性强的特点，我们的模型非常适合医疗成像、自主系统和环境监测等应用领域，并为边缘检测设定了新的基准标准。
URL
https://arxiv.org/abs/2501.18929
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Autonomous Horizon-based Asteroid Navigation With Observability-constrained Maneuvers
2025-01-27 06:28:29Aditya Arjun Anibha, Kenshiro OguriAbstract
Asteroid exploration is a pertinent challenge due to the varying complexity of their dynamical environments, shape and communication delays due to distance. Thus, autonomous navigation methods are continually being developed and improved in current research to enable their safe exploration. These methods often involve using horizon-based Optical Navigation (OpNav) to determine the spacecraft's location, which is reliant on the visibility of the horizon. It is critical to ensure the reliability of this measurement such that the spacecraft may maintain an accurate state estimate throughout its mission. This paper presents an algorithm that generates control maneuvers for spacecraft to follow trajectories that allow continuously usable optical measurements to maintain system observability for safe navigation. This algorithm improves upon existing asteroid navigation capabilities by allowing the safe and robust autonomous targeting of various trajectories and orbits at a wide range of distances within optical measurement range. It is adaptable to different asteroid scenarios. Overall, the approach develops an all-encompassing system that simulates the asteroid dynamics, synthetic image generation, edge detection, horizon-based OpNav, filtering and observability-enhancing control.
Abstract (translated)
小行星探测面临的一大挑战在于它们动态环境的复杂性、形状各异以及由于距离造成的通信延迟。因此，目前的研究不断开发和改进自主导航方法，以确保安全地进行探测。这些方法通常涉及使用基于地平线的光学导航（OpNav）来确定航天器的位置，这种方法依赖于地平线的可见度。为了保证这一测量的可靠性并使航天器在整个任务期间能够保持准确的状态估计，至关重要的是要开发出一种算法：该算法生成控制指令，以引导航天器沿着允许持续进行光学测量的轨迹行进，从而维持系统的可观测性，实现安全导航。 本文提出了一种算法，它通过设计航天器可以遵循的轨迹来确保连续可用的光学测量数据，进而保持系统可观测性，以实现安全导航。这种算法改进了现有的小行星导航能力，使在广泛的光度测量范围内进行安全且稳健的自主目标定位成为可能，并适用于各种不同的小行星情况。 总的来说，该方法建立了一个全面的系统，模拟小行星的动力学特性、合成图像生成、边缘检测、基于地平线的光学导航、滤波以及增强可观测性的控制策略。
URL
https://arxiv.org/abs/2501.15806
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Snapshot Compressed Imaging Based Single-Measurement Computer Vision for Videos
2025-01-25 08:20:30Fengpu Pan, Jiangtao Wen, Yuxing HanAbstract
Snapshot compressive imaging (SCI) is a promising technique for capturing high-speed video at low bandwidth and low power, typically by compressing multiple frames into a single measurement. However, similar to traditional CMOS image sensor based imaging systems, SCI also faces challenges in low-lighting photon-limited and low-signal-to-noise-ratio image conditions. In this paper, we propose a novel Compressive Denoising Autoencoder (CompDAE) using the STFormer architecture as the backbone, to explicitly model noise characteristics and provide computer vision functionalities such as edge detection and depth estimation directly from compressed sensing measurements, while accounting for realistic low-photon conditions. We evaluate the effectiveness of CompDAE across various datasets and demonstrated significant improvements in task performance compared to conventional RGB-based methods. In the case of ultra-low-lighting (APC $\leq$ 20) while conventional methods failed, the proposed algorithm can still maintain competitive performance.
Abstract (translated)
快照压缩成像（SCI）是一种有前景的技术，可在低带宽和低功耗条件下捕捉高速视频，通常通过将多个帧压缩为单个测量值来实现。然而，与传统的基于CMOS图像传感器的成像系统类似，SCI在光线不足、光子数量有限以及信噪比低的图像条件下也面临挑战。本文中，我们提出了一种使用STFormer架构作为骨干的新颖压缩去噪自动编码器（CompDAE），该编码器能够明确地建模噪声特征，并直接从压缩感知测量值提供计算机视觉功能，例如边缘检测和深度估计，在同时考虑现实中的低光子条件时也是如此。我们在多个数据集上评估了CompDAE的有效性，并展示了与传统基于RGB的方法相比在任务性能上的显著改进。即使在极低光照（APC ≤ 20）的情况下，常规方法失效，所提出的算法仍能保持竞争力的性能。
URL
https://arxiv.org/abs/2501.15122
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Stroke classification using Virtual Hybrid Edge Detection from in silico electrical impedance tomography data
2025-01-24 18:29:34Juan Pablo Agnelli, Fernando S. Moura, Siiri Rautio, Melody Alsaker, Rashmi Murthy, Matti Lassas, Samuli SiltanenAbstract
Electrical impedance tomography (EIT) is a non-invasive imaging method for recovering the internal conductivity of a physical body from electric boundary measurements. EIT combined with machine learning has shown promise for the classification of strokes. However, most previous works have used raw EIT voltage data as network inputs. We build upon a recent development which suggested the use of special noise-robust Virtual Hybrid Edge Detection (VHED) functions as network inputs, although that work used only highly simplified and mathematically ideal models. In this work we strengthen the case for the use of EIT, and VHED functions especially, for stroke classification. We design models with high detail and mathematical realism to test the use of VHED functions as inputs. Virtual patients are created using a physically detailed 2D head model which includes features known to create challenges in real-world imaging scenarios. Conductivity values are drawn from statistically realistic distributions, and phantoms are afflicted with either hemorrhagic or ischemic strokes of various shapes and sizes. Simulated noisy EIT electrode data, generated using the realistic Complete Electrode Model (CEM) as opposed to the mathematically ideal continuum model, is processed to obtain VHED functions. We compare the use of VHED functions as inputs against the alternative paradigm of using raw EIT voltages. Our results show that (i) stroke classification can be performed with high accuracy using 2D EIT data from physically detailed and mathematically realistic models, and (ii) in the presence of noise, VHED functions outperform raw data as network inputs.
Abstract (translated)
电气阻抗断层成像（EIT）是一种通过电边界测量来恢复物理体内导电性的非侵入性成像方法。结合机器学习，EIT在中风分类方面显示出巨大潜力。然而，大多数以前的工作都使用原始的EIT电压数据作为网络输入。我们基于最近的一项研究发展而来，该研究表明可以使用特殊的抗噪能力强的虚拟混合边缘检测（VHED）函数作为网络输入，尽管这项工作仅使用了高度简化的数学理想模型。 在这项工作中，我们将进一步论证利用EIT和VHED函数在中风分类中的应用。我们设计具有高细节度和数学现实性的模型来测试VHED函数作为输入的效果。通过一个包含已知会导致实际成像场景挑战的物理详细二维头部模型创建虚拟患者，并从统计上真实分布中抽取导电性值，将这些虚拟人设为患有不同形状和大小的出血或缺血性中风。 我们使用现实中的完整电极模型（CEM）而非数学理想的连续体模型来生成包含噪声的EIT电极数据，并通过该数据获取VHED函数。我们将作为输入使用的VHED函数的效果与使用原始EIT电压的替代方案进行了比较。 我们的结果显示，(i) 使用物理详细且数学现实性的二维EIT数据可以以高精度进行中风分类；(ii) 在存在噪声的情况下，VHED函数相较于原始数据能更好地作为网络输入。
URL
https://arxiv.org/abs/2501.14704
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Enhanced Extractor-Selector Framework and Symmetrization Weighted Binary Cross-Entropy for Edge Detections
Abstract
Recent advancements have demonstrated the effectiveness of the extractor-selector (E-S) framework in edge detection (ED) tasks, which achieves state-of-the-art (SOTA) performance in both quantitative metrics and perceptual quality. However, this method still falls short of fully exploiting the potential of feature extractors, as selectors only operate on highly compressed feature maps that lack diversity and suffer from substantial information loss. Additionally, while union training can improve perceptual quality, the highest evaluation scores are typically obtained without it, creating a trade-off between quantitative accuracy and perceptual fidelity. To address these limitations, we propose an enhanced E-S architecture, which utilizes richer, less-loss feature representations and incorporates auxiliary features during the selection process, thereby improving the effectiveness of the feature selection mechanism. Additionally, we introduce a novel loss function, the Symmetrization Weight Binary Cross-Entropy (SWBCE), which simultaneously emphasizes both the recall of edge pixels and the suppression of erroneous edge predictions, thereby enhancing the predictions both in the perceptual quality and the prediction accuracy. The effectiveness and superiority of our approaches over baseline models, the standard E-S framework, and the standard Weight Binary Cross-Entropy (WBCE) loss function are demonstrated by extensive experiments. For example, our enhanced E-S architecture trained with SWBCE loss function achieves average improvements of 8.25$\%$, 8.01$\%$, and 33.25$\%$ in ODS, OIS, and AP, measured on BIPED2 compared with the baseline models, significantly outperforming the standard E-S method. The results set new benchmarks for ED tasks, and highlight the potential of the methods in beyond.
Abstract (translated)
最近的研究进展展示了提取器-选择器（E-S）框架在边缘检测（ED）任务中的有效性，该框架在量化指标和感知质量方面均达到了最先进的性能。然而，此方法仍然未能充分利用特征提取器的全部潜力，因为选择器仅操作于高度压缩且缺乏多样性的特征图上，并因此导致了大量信息损失。此外，尽管联合训练可以提高感知质量，但通常最高评估分数是在没有联合训练的情况下获得的，这就造成了定量准确性与感知保真度之间的权衡。为了解决这些限制，我们提出了一种增强版E-S架构，该架构利用更为丰富、丢失较少的信息特征表示，并在选择过程中加入辅助特征，从而提高了特征选择机制的有效性。此外，我们还引入了一种新的损失函数——对称化权重二元交叉熵（SWBCE），它同时强调了边缘像素的召回率和错误边缘预测的抑制，从而提升了感知质量和预测准确性。 通过广泛的实验，我们的方法在基线模型、标准E-S框架以及标准加权二元交叉熵（WBCE）损失函数方面展示了其有效性和优越性。例如，在BIPED2数据集上与基线模型相比，使用SWBCE损失函数训练的增强版E-S架构分别实现了ODS、OIS和AP指标平均提高了8.25%，8.01% 和33.25%，显著优于标准E-S方法的表现。这些结果为边缘检测任务设立了新的基准，并突显了该方法在更广泛应用中的潜力。
URL
https://arxiv.org/abs/2501.13365
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EDMB: Edge Detector with Mamba
2025-01-08 21:15:14Yachuan Li, Xavier Soria Poma, Yun Bai, Qian Xiao, Chaozhi Yang, Guanlin Li, Zongmin LiAbstract
Transformer-based models have made significant progress in edge detection, but their high computational cost is prohibitive. Recently, vision Mamba have shown excellent ability in efficiently capturing long-range dependencies. Drawing inspiration from this, we propose a novel edge detector with Mamba, termed EDMB, to efficiently generate high-quality multi-granularity edges. In EDMB, Mamba is combined with a global-local architecture, therefore it can focus on both global information and fine-grained cues. The fine-grained cues play a crucial role in edge detection, but are usually ignored by ordinary Mamba. We design a novel decoder to construct learnable Gaussian distributions by fusing global features and fine-grained features. And the multi-grained edges are generated by sampling from the distributions. In order to make multi-granularity edges applicable to single-label data, we introduce Evidence Lower Bound loss to supervise the learning of the distributions. On the multi-label dataset BSDS500, our proposed EDMB achieves competitive single-granularity ODS 0.837 and multi-granularity ODS 0.851 without multi-scale test or extra PASCAL-VOC data. Remarkably, EDMB can be extended to single-label datasets such as NYUDv2 and BIPED. The source code is available at this https URL.
Abstract (translated)
基于Transformer的模型在边缘检测方面取得了显著进步，但其高昂的计算成本是一个限制因素。最近的研究显示，视觉Mamba（一种轻量级网络）表现出色，在捕捉长距离依赖关系时非常高效。受此启发，我们提出了一种名为EDMB的新颖边缘探测器，它能够利用Mamba高效生成高质量、多粒度边缘。 在EDMB中，Mamba与全局-局部架构相结合，因此它可以同时关注整体信息和细粒度线索。细粒度线索对于边缘检测至关重要，但在普通Mamba中通常被忽略。我们设计了一种新的解码器来融合全局特征和细粒度特征以构建可学习的高斯分布，并通过从这些分布中采样生成多粒度边缘。 为了使多粒度边缘适用于单标签数据，我们引入了证据下界（Evidence Lower Bound）损失来监督分布的学习过程。在BSDS500这一多标签数据集上，我们的EDMB模型在未使用多尺度测试或额外PASCAL-VOC数据的情况下达到了具有竞争力的单一粒度ODS 0.837和多粒度ODS 0.851。 值得注意的是，EDMB可以扩展应用于NYUDv2和BIPED等单标签数据集。源代码可在以下链接获取：[提供URL]（请将实际链接替换为提供的示例中的占位符）。
URL
https://arxiv.org/abs/2501.04846
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Gaussian Masked Autoencoders
2025-01-06 18:59:57Jathushan Rajasegaran, Xinlei Chen, Rulilong Li, Christoph Feichtenhofer, Jitendra Malik, Shiry GinosarAbstract
This paper explores Masked Autoencoders (MAE) with Gaussian Splatting. While reconstructive self-supervised learning frameworks such as MAE learns good semantic abstractions, it is not trained for explicit spatial awareness. Our approach, named Gaussian Masked Autoencoder, or GMAE, aims to learn semantic abstractions and spatial understanding jointly. Like MAE, it reconstructs the image end-to-end in the pixel space, but beyond MAE, it also introduces an intermediate, 3D Gaussian-based representation and renders images via splatting. We show that GMAE can enable various zero-shot learning capabilities of spatial understanding (e.g., figure-ground segmentation, image layering, edge detection, etc.) while preserving the high-level semantics of self-supervised representation quality from MAE. To our knowledge, we are the first to employ Gaussian primitives in an image representation learning framework beyond optimization-based single-scene reconstructions. We believe GMAE will inspire further research in this direction and contribute to developing next-generation techniques for modeling high-fidelity visual data. More details at this https URL
Abstract (translated)
本文探讨了使用高斯点绘的遮蔽自编码器（Masked Autoencoders，简称MAE）技术。虽然像MAE这样的重建式自监督学习框架能够学到良好的语义抽象，但它并没有针对明确的空间感知进行训练。我们的方法，名为高斯遮蔽自编码器或GMAE，旨在同时学习语义抽象和空间理解。与MAE类似，它在像素空间中端到端地重构图像，但除此之外，还引入了一个基于3D高斯的中间表示，并通过点绘来渲染图像。我们展示了GMAE可以实现各种零样本学习的空间理解能力（例如前景-背景分割、图像分层、边缘检测等），同时保持了来自MAE的自监督表示质量的高级语义信息。据我们所知，这是首次在基于优化方法的单场景重构之外，在图像表示学习框架中使用高斯原语。我们认为GMAE将激发进一步的研究，并为开发用于建模高质量视觉数据的下一代技术做出贡献。更多详情请访问此链接：[https URL]（原文中的URL被替换成了占位符）
URL
https://arxiv.org/abs/2501.03229
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Pixel-Wise Feature Selection for Perceptual Edge Detection without post-processing
Abstract
Although deep convolutional neutral networks (CNNs) have significantly enhanced performance in image edge detection (ED), current models remain highly dependent on post-processing techniques such as non-maximum suppression (NMS), and often fail to deliver satisfactory perceptual results, while the performance will deteriorate significantly if the allowed error toleration distance decreases. These limitations arise from the uniform fusion of features across all pixels, regardless of their specific characteristics, such as the distinction between textural and edge areas. If the features extracted by the ED models are selected more meticulously and encompass greater diversity, the resulting predictions are expected to be more accurate and perceptually meaningful. Motivated by this observation, this paper proposes a novel feature selection paradigm for deep networks that facilitates the differential selection of features and can be seamlessly integrated into existing ED models. By incorporating this additional structure, the performance of conventional ED models is substantially enhanced without post-processing, while simultaneously enhancing the perceptual quality of the predictions. Extensive experimental evaluations validate the effectiveness of the proposed model.
Abstract (translated)
尽管深度卷积神经网络（CNN）在图像边缘检测（ED）方面显著提升了性能，但目前的模型仍然高度依赖于非极大值抑制（NMS）等后处理技术，并且常常无法提供令人满意的感知结果。如果允许的误差容忍距离减小，其性能会明显下降。这些限制源于对所有像素特征进行统一融合，而没有考虑到它们的具体特性，例如纹理区域和边缘区域之间的区别。如果通过ED模型提取的特征被更精心地选择并包含更大的多样性，则预测的结果有望更加准确且具有感知意义。 受此观察结果启发，本文提出了一种新颖的深度网络特征选择范式，该范式可以实现差异化特征的选择，并能够无缝集成到现有的边缘检测模型中。通过引入这种额外的结构，在不进行后处理的情况下显著提升了传统ED模型的性能，并同时提高了预测的质量感知度。广泛的实验评估验证了所提出的模型的有效性。
URL
https://arxiv.org/abs/2501.02534
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