Edge_Detection
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D2-CDIG: Controlled Diffusion Remote Sensing Image Generation with Dual Priors of DEM and Cloud-Fog
2026-05-14 03:41:46Zuopeng Zhao, Ying Liu, Kanyaphakphachsorn Pharksuwan, Su Luo, Xiaoyu Li, Maocai NingAbstract
Remote sensing image generation provides a reliable data foundation for remote sensing large models and downstream tasks. However, existing controllable remote sensing image generation methods typically rely on traditional techniques such as segmentation and edge detection, which do not fully leverage terrain or atmospheric conditions. As a result, the generated images often lack accuracy and naturalness when dealing with complex terrains and atmospheric phenomena. In this paper, we propose a novel remote sensing image generation framework, D2-CDIG, which integrates diffusion models with a dual-prior control mechanism. By incorporating both Digital Elevation Model (DEM) and cloud-fog information as dual prior knowledge, D2-CDIG precisely controls ground features and atmospheric phenomena within the generated images. Specifically, D2-CDIG decouples the terrain and atmospheric generation processes through independent control of ground and atmospheric branches. Additionally, a refined cloud-fog slider is introduced to flexibly adjust cloud thickness and distribution. During training, ground and atmospheric control signals are injected in layers to ensure a seamless transition within the images. Compared to traditional methods based on segmentation or edge detection, D2-CDIG shows significant improvements in image quality, detail richness, and realism. D2-CDIG offers a flexible and precise solution for remote sensing image generation, providing high-quality data for training large remote sensing models and downstream tasks.
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URL
https://arxiv.org/abs/2605.14326
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$mathcal{B}^{3}$-Net: Controlled Posterior Bridge Learning for Multi-Task Dense Prediction
2026-05-07 06:11:12Meihua Zhou, Li YangAbstract
Multi-task dense prediction solves complementary pixel-level tasks in a unified model, such as semantic segmentation, depth estimation, surface normal estimation, and edge detection. Existing decoder-side interactions use attention, prompts, routing, diffusion, Mamba, or bridge features to exchange task evidence, but most of them organize this evidence implicitly. They usually fuse task features by similarity or affinity, without explicitly modeling that evidence reliability varies across tasks and spatial locations. As a result, unreliable evidence may contaminate the shared representation and intensify negative transfer. We propose $\mathcal{B}^{3}$-Net, a controlled posterior bridge learning framework for multi-task dense prediction. Our method decomposes decoder-side interaction into reliability estimation, posterior bridge construction, and bounded redistribution. The Precision Field Estimator estimates patch-wise evidence precision from task-reference alignment and local variation. The Posterior Bridge Operator builds a precision-weighted posterior bridge through heteroscedastic evidence fusion, yielding a shared state more reliable than uniform or heuristic mixtures. The Contractive Dispatch Operator redistributes the bridge to each task branch through a bounded update, reducing uncontrolled feature injection. Experiments on NYUD-v2, PASCAL-Context, and Cityscapes show that $\mathcal{B}^{3}$-Net achieves competitive or superior trade-offs over representative CNN-, Transformer-, diffusion-, Mamba-, and bridge-feature-based methods. Backbone-matched comparisons and extensive analyses further verify that the gains arise from controlled posterior bridge learning rather than backbone capacity or decoder scale.
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URL
https://arxiv.org/abs/2605.05722
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SpecEdit: Training-Free Acceleration for Diffusion based Image Editing via Semantic Locking
2026-05-04 02:30:24Zhengan Yan, Shikang Zheng, Haoran Qin, Xiaobing Tu, Yinggui Wang, Jiacheng Liu, Jiaxuan Ren, Yuqi Lin, Peiliang Cai, Jinkui Ren, Xiantao Zhang, Linfeng ZhangAbstract
Diffusion-based image editing offers strong semantic controllability, but remains computationally expensive due to iterative high-resolution denoising over all spatial tokens. Dynamic-resolution sampling reduces this cost by performing early steps at reduced resolution. However, existing approaches prioritize upsampling using low-level heuristics such as edge detection or channel variance, which are weakly aligned with editing semantics and may lead to structural inconsistency. Moreover, spatial regions are often upsampled without verifying whether semantic modification is actually required, resulting in redundant high-resolution computation and accumulated errors. Therefore, we propose SpecEdit, a training-free dynamic-resolution framework tailored for diffusion-based image editing. SpecEdit follows a draft-and-verify scheme: a low-resolution draft first estimates the semantic outcome, after which token-level discrepancies are used to identify edit-relevant tokens for high-resolution denoising, while the remaining tokens stay at a coarse resolution. Experiments on Qwen-Image-Edit and FLUX.1-Kontext-dev demonstrate up to 10x and 7x acceleration, while maintaining strong quality. SpecEdit is complementary to step distillation and other acceleration techniques, achieving up to 13x speedup when combined with existing methods. Our code is in supplementary material and will be released on GitHub.
Abstract (translated)
URL
https://arxiv.org/abs/2605.02152
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Almost for Free: Crafting Adversarial Examples with Convolutional Image Filters
2026-05-01 21:08:09Alexander Warnecke, Konrad RieckAbstract
Adversarial examples in machine learning are typically generated using gradients, obtained either directly through access to the model or approximated via queries to it. In this paper, we propose a much simpler approach to craft adversarial examples, drawing inspiration from insights of explainable machine learning. In particular, we design \emph{adversarial image filters} that are based on classic edge detection algorithms but optimized to deceive learning models. The resulting untargeted attacks are transferable and require only a single pass over the input. Empirically, we find that 3x3 filters already enable success rates between 30% and 80% on different neural networks. Compared to related approaches using generative models for crafting adversarial examples, we reduce the number of parameters by five orders of magnitude, resulting in a very efficient attack. When investigating the parameters of the learned filters, we observe interesting properties such as a high transferability between models and structures common to classic image filters. Our results provide further insights into the vulnerability of neural networks and their fragility to malicious noise.
Abstract (translated)
URL
https://arxiv.org/abs/2605.01098
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Quantum Gradient-Based Approach for Edge and Corner Detection Using Sobel Kernels
2026-05-01 15:48:20Mohammad Aamir Sohail, Gabriela Pinheiro, Yasemin Poyraz Kocak, Batuhan Hangun, Emre Camkerten, Simge Yigit, Hafize Asude ErtanAbstract
Edge detection refers to identifying points in a digital image where intensity changes sharply, indicating object boundaries or structural features. Corners are locations where gray-level intensity changes abruptly in multiple directions and are widely used in feature extraction, object tracking, and 3D modeling. In this study, we present a quantum implementation of Sobel-based edge detection and Harris-style corner detection. Two quantum image encoding methods - Flexible Representation of Quantum Images (FRQI) and Quantum Probability Image Encoding (QPIE) - are used to encode the input data and are comparatively analyzed. The proposed approach introduces a quantum gradient computation scheme based on lag-2 differences, enabling the evaluation of gradient-like features in superposition. To improve detection quality and reduce false positives, a classical post-processing step is applied to candidate corner points identified by the quantum circuit. Results show that the proposed quantum circuits produce outputs consistent with classical Sobel and Harris operators. Furthermore, the QPIE-based configuration yields more stable and coherent results than FRQI, especially under limited measurement shots. While gradient computation can be performed efficiently at the circuit level, the overall cost remains dominated by state preparation, measurement, and classical post-processing. All experiments are conducted under noiseless simulation, and performance on NISQ hardware may be affected by noise and measurement limitations. Therefore, this work demonstrates a functional and scalable quantum realization of classical edge and corner detection methods rather than an end-to-end speedup.
Abstract (translated)
URL
https://arxiv.org/abs/2605.00744
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Graph-augmented Segmentation of Complex Shapes in Laser Powder bed Fusion for Enhanced In Situ Inspection
2026-04-27 09:43:10Stefano Raimondo (Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy), Matteo Bugatti (Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy), Marco Grasso (Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy)Abstract
The technological maturity of in situ inspection and monitoring methods in additive manufacturing is steadily increasing, enabling more efficient and practical qualification procedures. In this context, image segmentation of powder bed images in Laser Powder Bed Fusion (L-PBF) has been investigated by various authors, leveraging both edge detection and machine learning approaches to identify deviations from nominal geometry. Despite these developments, several challenges remain, including the sensitivity of segmentation performance to industrial illumination conditions and layer-to-layer variability in pixel intensity patterns. The study addresses these limitations by proposing a graph-augmented segmentation approach. The underlying principle consists of preserving the geometrical information at a global level rather than at pixel-wise level, modeling dependencies and relational information among spatial regions with a Graph Neural Network bottleneck embedded into a U-Net architecture. This allows enhancing the consistency and accuracy of the geometry reconstruction in the presence of spatial and layer-wise photometric variability systematically faced in real data. The method is evaluated against benchmark techniques for the in situ reconstruction of lattice structures produced by L-PBF, demonstrating its potential as a scalable solution for robust in situ inspection and geometric verification in industrial environments.
Abstract (translated)
URL
https://arxiv.org/abs/2604.24234
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GAZE: Grounded Agentic Zero-shot Evaluation with Viewer-Level Tools and Literature Retrieval on Rare Brain MRI
2026-04-25 13:38:49Duaa Alim, Mogtaba Alim, Liam ChalcroftAbstract
Vision-language models (VLMs) read an image and produce text in a single forward pass, whereas radiologists typically inspect an image several times and consult the literature before writing a report. We introduce GAZE (Grounded Agentic Zero-shot Evaluation), a framework that lets a medical VLM work in this iterative way by calling viewer-level tools (zoom, windowing, contrast, edge detection) and two retrieval tools backed by the U.S. National Library of Medicine (PubMed for medical literature, Open-i for radiological images), with structured outputs validated against a schema and full tool-call traces recorded for auditability. On NOVA, a benchmark of 906 brain MRI cases covering 281 rare neurological conditions, GAZE reaches 58.2 mean average precision (mAP) at intersection-over-union (IoU) 0.3 for lesion localisation and 34.9% Top-1 diagnostic accuracy under a joint protocol that scores captioning, diagnosis, and localisation from the image alone, without task-specific fine-tuning. Before any tool is used, structured prompting and schema-validated outputs already improve over the published Gemini 2.0 Flash baseline (20.2 to 29.4 mAP@0.3), so framework design is itself an experimental variable. Tool use helps rare pathologies disproportionately: the fraction of cases with IoU > 0.3 rises from 17% to 58% for diagnoses with three or fewer examples versus 25% to 68% for common conditions ($\geq$10 cases), with gains tracking engagement (Gemini 3 Flash: Cohen's d = 0.79, 11.8 tool calls per case; Gemini 2.0 Flash: tools used in 8.2% of cases, no significant benefit). Retrieval ablations additionally reveal a model-dependent trade-off in which gains in diagnosis can coincide with losses in localisation, reinforcing the case for joint evaluation of diagnosis, localisation, and captioning in medical VLMs.
Abstract (translated)
URL
https://arxiv.org/abs/2605.00876
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One Shot Learning for Edge Detection on Point Clouds
2026-04-24 08:42:47Zhikun Tu, Yuhe Zhang, Yiou Jia, Kang Li, Daniel Cohen-OrAbstract
Each scanner possesses its unique characteristics and exhibits its distinct sampling error distribution. Training a network on a dataset that includes data collected from different scanners is less effective than training it on data specific to a single scanner. Therefore, we present a novel one-shot learning method allowing for edge extraction on point clouds, by learning the specific data distribution of the target point cloud, and thus achieve superior results compared to networks that were trained on general data distributions. More specifically, we present how to train a lightweight network named OSFENet (One-Shot edge Feature Extraction Network), by designing a filtered-KNN-based surface patch representation that supports a one-shot learning framework. Additionally, we introduce an RBF_DoS module, which integrates Radial Basis Function-based Descriptor of the Surface patch, highly beneficial for the edge extraction on point clouds. The advantage of the proposed OSFENet is demonstrated through comparative analyses against 7 baselines on the ABC dataset, and its practical utility is validated by results across diverse real-scanned datasets, including indoor scenes like S3DIS dataset, and outdoor scenes such as the Semantic3D dataset and UrbanBIS dataset.
Abstract (translated)
URL
https://arxiv.org/abs/2604.22354
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EdgeFormer: local patch-based edge detection transformer on point clouds
2026-04-23 07:57:04Yifei Xie, Zhikun Tu, Tong Yang, Yuhe Zhang, Xinyu ZhouAbstract
Edge points on 3D point clouds can clearly convey 3D geometry and surface characteristics, therefore, edge detection is widely used in many vision applications with high industrial and commercial demands. However, the fine-grained edge features are difficult to detect effectively as they are generally densely distributed or exhibit small-scale surface gradients. To address this issue, we present a learning-based edge detection network, named EdgeFormer, which mainly consists of two stages. Based on the observation that spatially neighboring points tend to exhibit high correlation, forming the local underlying surface, we convert the edge detection of the entire point cloud into a point classification based on local patches. Therefore, in the first stage, we construct local patch feature descriptors that describe the local neighborhood around each point. In the second stage, we classify each point by analyzing the local patch feature descriptors generated in the first stage. Due to the conversion of the point cloud into local patches, the proposed method can effectively extract the finer details. The experimental results show that our model demonstrates competitive performance compared to six baselines.
Abstract (translated)
URL
https://arxiv.org/abs/2604.21387
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SynthPID: P&ID digitization from Topology-Preserving Synthetic Data
2026-04-15 09:14:44Suraj Prasad, Pinak MahapatraAbstract
Automating the digitization of Piping and Instrumentation Diagrams (P&IDs) into structured process graphs would unlock significant value in plant operations, yet progress is bottlenecked by a fundamental data problem: engineering drawings are proprietary, and the entire community shares a single public benchmark of just 12 annotated images. Prior attempts at synthetic augmentation have fallen short because template-based generators scatter symbols at random, producing graphs that bear little resemblance to real process plants and, accordingly, yield only approximately 33% edge detection accuracy under synth-only training. We argue the failure is structural rather than visual and address it by introducing SynthPID, a corpus of 665 synthetic P&IDs whose pipe topology is seeded directly from real drawings. Paired with a patch-based Relationformer adapted for high-resolution diagrams, a model trained on SynthPID alone achieves 63.8 +/- 3.1% edge mAP on PID2Graph OPEN100 without seeing a single real P&ID during training, closing within 8 pp of the real-data oracle. These gains hold up under a controlled comparison against the template-based regime, confirming that generation quality drives performance rather than model choice. A scaling study reveals that gains flatten beyond roughly 400 synthetic images, pointing to seed diversity as the binding constraint.
Abstract (translated)
URL
https://arxiv.org/abs/2604.16513
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FlowExtract: Procedural Knowledge Extraction from Maintenance Flowcharts
2026-04-08 07:38:43Guillermo Gil de Avalle, Laura Maruster, Eric Sloot, Christos EmmanouilidisAbstract
Maintenance procedures in manufacturing facilities are often documented as flowcharts in static PDFs or scanned images. They encode procedural knowledge essential for asset lifecycle management, yet inaccessible to modern operator support systems. Vision-language models, the dominant paradigm for image understanding, struggle to reconstruct connection topology from such diagrams. We present FlowExtract, a pipeline for extracting directed graphs from ISO 5807-standardized flowcharts. The system separates element detection from connectivity reconstruction, using YOLOv8 and EasyOCR for standard domain-aligned node detection and text extraction, combined with a novel edge detection method that analyzes arrowhead orientations and traces connecting lines backward to source nodes. Evaluated on industrial troubleshooting guides, FlowExtract achieves very high node detection and substantially outperforms vision-language model baselines on edge extraction, offering organizations a practical path toward queryable procedural knowledge representations. The implementation is available athttps://github.com/guille-gil/FlowExtract.
Abstract (translated)
URL
https://arxiv.org/abs/2604.06770
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Optimized Weighted Voting System for Brain Tumor Classification Using MRI Images
2026-03-30 12:24:54Ha Anh VuAbstract
The accurate classification of brain tumors from MRI scans is essential for effective diagnosis and treatment planning. This paper presents a weighted ensemble learning approach that combines deep learning and traditional machine learning models to improve classification performance. The proposed system integrates multiple classifiers, including ResNet101, DenseNet121, Xception, CNN-MRI, and ResNet50 with edge-enhanced images, SVM, and KNN with HOG features. A weighted voting mechanism assigns higher influence to models with better individual accuracy, ensuring robust decision-making. Image processing techniques such as Balance Contrast Enhancement, K-means clustering, and Canny edge detection are applied to enhance feature extraction. Experimental evaluations on the Figshare and Kaggle MRI datasets demonstrate that the proposed method achieves state-of-the-art accuracy, outperforming existing models. These findings highlight the potential of ensemble-based learning for improving brain tumor classification, offering a reliable and scalable framework for medical image analysis.
Abstract (translated)
URL
https://arxiv.org/abs/2603.28357
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Amped: Adaptive Multi-stage Non-edge Pruning for Edge Detection
2026-03-29 12:26:35Yuhan Gao, Xinqing Li, Xin He, Bing Li, Xinzhong Zhu, Ming-Ming Cheng, Yun LiuAbstract
Edge detection is a fundamental image analysis task that underpins numerous high-level vision applications. Recent advances in Transformer architectures have significantly improved edge quality by capturing long-range dependencies, but this often comes with computational overhead. Achieving higher pixel-level accuracy requires increased input resolution, further escalating computational cost and limiting practical deployment. Building on the strong representational capacity of recent Transformer-based edge detectors, we propose an Adaptive Multi-stage non-edge Pruning framework for Edge Detection(Amped). Amped identifies high-confidence non-edge tokens and removes them as early as possible to substantially reduce computation, thus retaining high accuracy while cutting GFLOPs and accelerating inference with minimal performance loss. Moreover, to mitigate the structural complexity of existing edge detection networks and facilitate their integration into real-world systems, we introduce a simple yet high-performance Transformer-based model, termed Streamline Edge Detector(SED). Applied to both existing detectors and our SED, the proposed pruning strategy provides a favorable balance between accuracy and efficiency-reducing GFLOPs by up to 40% with only a 0.4% drop in ODS F-measure. In addition, despite its simplicity, SED achieves a state-of-the-art ODS F-measure of 86.5%. The code will be released.
Abstract (translated)
URL
https://arxiv.org/abs/2603.27661
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MEMO: Human-like Crisp Edge Detection Using Masked Edge Prediction
2026-03-21 12:21:29Jiaxin Cheng, Yue Wu, Yicong ZhouAbstract
Learning-based edge detection models trained with cross-entropy loss often suffer from thick edge predictions, which deviate from the crisp, single-pixel annotations typically provided by humans. While previous approaches to achieving crisp edges have focused on designing specialized loss functions or modifying network architectures, we show that a carefully designed training and inference strategy alone is sufficient to achieve human-like edge quality. In this work, we introduce the Masked Edge Prediction MOdel (MEMO), which produces both accurate and crisp edges using only cross-entropy loss. We first construct a large-scale synthetic edge dataset to pre-train MEMO, enhancing its generalization ability. Subsequent fine-tuning on downstream datasets requires only a lightweight module comprising 1.2\% additional parameters. During training, MEMO learns to predict edges under varying ratios of input masking. A key insight guiding our inference is that thick edge predictions typically exhibit a confidence gradient: high in the center and lower toward the boundaries. Leveraging this, we propose a novel progressive prediction strategy that sequentially finalizes edge predictions in order of prediction confidence, resulting in thinner and more precise contours. Our method achieves visually appealing, post-processing-free, human-like edge maps and outperforms prior methods on crispness-aware evaluations.
Abstract (translated)
基于交叉熵损失实现精准锐利的边缘检测，一直是计算机视觉领域的一项挑战。本文提出了MEMO模型，仅通过交叉熵损失即可生成既准确又锐利的边缘。我们首先构建了一个大规模合成边缘数据集用于预训练MEMO，以增强其泛化能力。随后在各类下游数据集上的微调仅需一个轻量级模块，该模块仅增加1.2%的参数。在训练过程中，MEMO学习在不同比例的输入掩码下预测边缘。其推理过程的核心思想是：粗边缘预测通常呈现置信度梯度——中心区域置信度高，向边缘边界逐渐降低。基于此，我们提出一种新颖的渐进式预测策略，该策略按预测置信度顺序逐步确定边缘，从而得到更细、更精确的轮廓。我们的方法能够生成无需后处理、视觉上媲美人工标注的优美边缘图，并在锐利度感知的评估指标上超越了以往方法。
URL
https://arxiv.org/abs/2603.20782
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An approximate graph elicits detonation lattice
2026-03-17 13:46:29Vansh Sharma, Venkat RamanAbstract
This study presents a novel algorithm based on graph theory for the precise segmentation and measurement of detonation cells from 3D pressure traces, termed detonation lattices, addressing the limitations of manual and primitive 2D edge detection methods prevalent in the field. Using a segmentation model, the proposed training-free algorithm is designed to accurately extract cellular patterns, a longstanding challenge in detonations research. First, the efficacy of segmentation on generated data is shown with a prediction error 2%. Next, 3D simulation data is used to establish performance of the graph-based workflow. The results of statistics and joint probability densities show oblong cells aligned with the wave propagation axis with 17% deviation, whereas larger dispersion in volume reflects cubic amplification of linear variability. Although the framework is robust, it remains challenging to reliably segment and quantify highly complex cellular patterns. However, the graph-based formulation generalizes across diverse cellular geometries, positioning it as a practical tool for detonation analysis and a strong foundation for future extensions in triple-point collision studies.
Abstract (translated)
URL
https://arxiv.org/abs/2603.16524
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SPRITETOMESH: Automatic Mesh Generation for 2D Skeletal Animation Using Learned Segmentation and Contour-Aware Vertex Placement
2026-02-24 17:58:31Bastien GimbertAbstract
We present SPRITETOMESH, a fully automatic pipeline for converting 2D game sprite images into triangle meshes compatible with skeletal animation frameworks such as Spine2D. Creating animation-ready meshes is traditionally a tedious manual process requiring artists to carefully place vertices along visual boundaries, a task that typically takes 15-60 minutes per sprite. Our method addresses this through a hybrid learned-algorithmic approach. A segmentation network (EfficientNet-B0 encoder with U-Net decoder) trained on over 100,000 sprite-mask pairs from 172 games achieves an IoU of 0.87, providing accurate binary masks from arbitrary input images. From these masks, we extract exterior contour vertices using Douglas-Peucker simplification with adaptive arc subdivision, and interior vertices along visual boundaries detected via bilateral-filtered multi-channel Canny edge detection with contour-following placement. Delaunay triangulation with mask-based centroid filtering produces the final mesh. Through controlled experiments, we demonstrate that direct vertex position prediction via neural network heatmap regression is fundamentally not viable for this task: the heatmap decoder consistently fails to converge (loss plateau at 0.061) while the segmentation decoder trains normally under identical conditions. We attribute this to the inherently artistic nature of vertex placement - the same sprite can be meshed validly in many different ways. This negative result validates our hybrid design: learned segmentation where ground truth is unambiguous, algorithmic placement where domain heuristics are appropriate. The complete pipeline processes a sprite in under 3 seconds, representing a speedup of 300x-1200x over manual creation. We release our trained model to the game development community.
Abstract (translated)
我们介绍了SPRITETOMESH，这是一种全自动流程，用于将2D游戏精灵图像转换为与Spine2D等骨骼动画框架兼容的三角网格。传统的做法是手动创建适合动画制作的网格，这个过程既耗时又繁琐，需要艺术家仔细地沿着视觉边界放置顶点，通常每个精灵花费15到60分钟的时间。我们的方法通过混合学习算法的方法解决了这个问题。 该流程包括一个经过训练的分割网络（使用EfficientNet-B0编码器和U-Net解码器），它在一个包含超过10万对游戏遮罩图像的数据库上进行训练，涉及来自172个不同游戏的数据集，获得了0.87的交并比（IoU），能够从任意输入图像中生成准确的二值掩膜。基于这些掩膜，我们利用道格拉斯-普克简化算法结合自适应弧细分来提取外部轮廓顶点，并通过双边滤波多通道Canny边缘检测与跟踪放置方法沿着视觉边界找到内部顶点。最后，在使用遮罩中心点过滤的基础上进行Delaunay三角剖分生成最终网格。 在受控实验中，我们证明了直接通过神经网络热图回归预测顶点位置对于此任务是不可行的：在相同的条件下，热图解码器始终无法收敛（损失函数停滞在0.061），而分割解码器正常训练。我们认为这源于顶点放置本质上是一种艺术性的过程——同一个精灵可以通过多种不同的方式被合理地网格化。这一结果验证了我们混合设计的有效性：对于地面实况明确的情况下采用学习分割方法，而对于领域启发式适用的场景则使用算法放置的方法。 整个流程可以在不到3秒内处理完一个精灵图像，相比于手动创建而言速度提高了300到1200倍。我们向游戏开发社区开放了训练好的模型。
URL
https://arxiv.org/abs/2602.21153
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MatchED: Crisp Edge Detection Using End-to-End, Matching-based Supervision
2026-02-24 08:45:49Bedrettin Cetinkaya, Sinan Kalkan, Emre AkbasAbstract
Generating crisp, i.e., one-pixel-wide, edge maps remains one of the fundamental challenges in edge detection, affecting both traditional and learning-based methods. To obtain crisp edges, most existing approaches rely on two hand-crafted post-processing algorithms, Non-Maximum Suppression (NMS) and skeleton-based thinning, which are non-differentiable and hinder end-to-end optimization. Moreover, all existing crisp edge detection methods still depend on such post-processing to achieve satisfactory results. To address this limitation, we propose \MethodLPP, a lightweight, only $\sim$21K additional parameters, and plug-and-play matching-based supervision module that can be appended to any edge detection model for joint end-to-end learning of crisp edges. At each training iteration, \MethodLPP performs one-to-one matching between predicted and ground-truth edges based on spatial distance and confidence, ensuring consistency between training and testing protocols. Extensive experiments on four popular datasets demonstrate that integrating \MethodLPP substantially improves the performance of existing edge detection models. In particular, \MethodLPP increases the Average Crispness (AC) metric by up to 2--4$\times$ compared to baseline models. Under the crispness-emphasized evaluation (CEval), \MethodLPP further boosts baseline performance by up to 20--35\% in ODS and achieves similar gains in OIS and AP, achieving SOTA performance that matches or surpasses standard post-processing for the first time. Code is available at this https URL.
Abstract (translated)
生成清晰的边缘地图（即单像素宽）仍然是边缘检测中的一个基本挑战，影响着传统的和基于学习的方法。为了获取清晰的边缘，现有的大多数方法依赖于两种手工设计的后处理算法：非极大值抑制（NMS）和骨架细化。这些算法是非可微分的，阻碍了端到端优化过程。此外，所有现有的清晰边缘检测方法仍然需要这种后处理才能取得满意的结果。 为了克服这一限制，我们提出了\MethodLPP，这是一种轻量级的方法，大约只需要21K个附加参数，并且可以插入任何边缘检测模型中以进行联合的端到端学习，用于生成清晰的边缘。在每次训练迭代过程中，\MethodLPP 基于空间距离和置信度执行预测边缘与真实边缘之间的一对一匹配操作，确保了训练和测试协议之间的一致性。 我们在四个流行的数据集上进行了广泛的实验，结果表明将\MethodLPP 集成到现有的边缘检测模型中可以显著提高这些模型的性能。特别是在平均清晰度（AC）指标方面，与基线模型相比，\MethodLPP 可以提升2-4倍。在强调清晰度评估（CEval）下，\MethodLPP 还能将基线模型的表现提升最多达20%-35%在开放性数据集上，并且在开放性图像集合和平均精度方面也实现了相似的改进，这是首次达到了与标准后处理相匹配或超越其性能的标准。 代码可在以下链接获取：[提供的链接]。
URL
https://arxiv.org/abs/2602.20689
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Multi-Modal Monocular Endoscopic Depth and Pose Estimation with Edge-Guided Self-Supervision
2026-02-19 19:38:11Xinwei Ju, Rema Daher, Danail Stoyanov, Sophia Bano, Francisco VasconcelosAbstract
Monocular depth and pose estimation play an important role in the development of colonoscopy-assisted navigation, as they enable improved screening by reducing blind spots, minimizing the risk of missed or recurrent lesions, and lowering the likelihood of incomplete examinations. However, this task remains challenging due to the presence of texture-less surfaces, complex illumination patterns, deformation, and a lack of in-vivo datasets with reliable ground truth. In this paper, we propose **PRISM** (Pose-Refinement with Intrinsic Shading and edge Maps), a self-supervised learning framework that leverages anatomical and illumination priors to guide geometric learning. Our approach uniquely incorporates edge detection and luminance decoupling for structural guidance. Specifically, edge maps are derived using a learning-based edge detector (e.g., DexiNed or HED) trained to capture thin and high-frequency boundaries, while luminance decoupling is obtained through an intrinsic decomposition module that separates shading and reflectance, enabling the model to exploit shading cues for depth estimation. Experimental results on multiple real and synthetic datasets demonstrate state-of-the-art performance. We further conduct a thorough ablation study on training data selection to establish best practices for pose and depth estimation in colonoscopy. This analysis yields two practical insights: (1) self-supervised training on real-world data outperforms supervised training on realistic phantom data, underscoring the superiority of domain realism over ground truth availability; and (2) video frame rate is an extremely important factor for model performance, where dataset-specific video frame sampling is necessary for generating high quality training data.
Abstract (translated)
单目深度和姿态估计在结肠镜辅助导航的发展中扮演着重要角色，因为它们通过减少盲点、降低漏诊或复发病变的风险以及降低检查不完全的可能性来提高筛查效果。然而，由于无纹理表面、复杂的光照模式、变形及缺乏可靠地面真实数据的体内数据集等原因，该任务仍然具有挑战性。本文提出了一种新的方法**PRISM**(基于内在阴影和边缘图的姿态精炼)，这是一种自监督学习框架，利用解剖学和照明先验指导几何学习。我们的方法独特地结合了边缘检测和亮度分离以提供结构引导。具体而言，通过使用训练有素的边缘探测器（如DexiNed或HED）来捕捉薄且高频边界，可以得到边缘图；同时通过内在分解模块获得亮度分离，该模块能够将阴影与反射分离开来，使模型利用阴影线索进行深度估计。在多个真实和合成数据集上的实验结果表明了其优越的性能表现。我们进一步进行了详尽的数据选择消融研究，以建立结肠镜检查中姿态和深度估算的最佳实践。这项分析提供了两个实用见解：（1）基于现实世界数据的自监督训练优于基于逼真仿真数据的监督训练，突显了领域真实性的优越性而非地面真相的可用性；（2）视频帧率是模型性能的一个极其重要的因素，在生成高质量训练数据时需要针对特定的数据集进行视频帧采样。
URL
https://arxiv.org/abs/2602.17785
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EasyControlEdge: A Foundation-Model Fine-Tuning for Edge Detection
2026-02-18 07:28:09Hiroki Nakamura, Hiroto Iino, Masashi Okada, Tadahiro TaniguchiAbstract
We propose EasyControlEdge, adapting an image-generation foundation model to edge detection. In real-world edge detection (e.g., floor-plan walls, satellite roads/buildings, and medical organ boundaries), crispness and data efficiency are crucial, yet producing crisp raw edge maps with limited training samples remains challenging. Although image-generation foundation models perform well on many downstream tasks, their pretrained priors for data-efficient transfer and iterative refinement for high-frequency detail preservation remain underexploited for edge detection. To enable crisp and data-efficient edge detection using these capabilities, we introduce an edge-specialized adaptation of image-generation foundation models. To better specialize the foundation model for edge detection, we incorporate an edge-oriented objective with an efficient pixel-space loss. At inference, we introduce guidance based on unconditional dynamics, enabling a single model to control the edge density through a guidance scale. Experiments on BSDS500, NYUDv2, BIPED, and CubiCasa compare against state-of-the-art methods and show consistent gains, particularly under no-post-processing crispness evaluation and with limited training data.
Abstract (translated)
URL
https://arxiv.org/abs/2602.16238
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A Universal Action Space for General Behavior Analysis
2026-02-10 08:22:17Hung-Shuo Chang, Yue-Cheng Yang, Yu-Hsi Chen, Wei-Hsin Chen, Chien-Yao Wang, James C. Liao, Chien-Chang Chen, Hen-Hsen Huang, Hong-Yuan Mark LiaoAbstract
Analyzing animal and human behavior has long been a challenging task in computer vision. Early approaches from the 1970s to the 1990s relied on hand-crafted edge detection, segmentation, and low-level features such as color, shape, and texture to locate objects and infer their identities-an inherently ill-posed problem. Behavior analysis in this era typically proceeded by tracking identified objects over time and modeling their trajectories using sparse feature points, which further limited robustness and generalization. A major shift occurred with the introduction of ImageNet by Deng and Li in 2010, which enabled large-scale visual recognition through deep neural networks and effectively served as a comprehensive visual dictionary. This development allowed object recognition to move beyond complex low-level processing toward learned high-level representations. In this work, we follow this paradigm to build a large-scale Universal Action Space (UAS) using existing labeled human-action datasets. We then use this UAS as the foundation for analyzing and categorizing mammalian and chimpanzee behavior datasets. The source code is released on GitHub at this https URL.
Abstract (translated)
从20世纪70年代到90年代，分析动物和人类的行为一直是计算机视觉中的一个难题。早期的方法依赖于手工设计的边缘检测、分割以及诸如颜色、形状和纹理等低级特征来定位物体并推断其身份——这是一个本质上难以解决的问题。在那个时代的行为分析通常是通过随时间跟踪已识别的对象，并使用稀疏特征点模型它们的轨迹来进行，这进一步限制了系统的鲁棒性和泛化能力。 随着邓和李在2010年引入ImageNet（一个大型图像数据库），情况发生了重大转变。ImageNet使大规模视觉识别成为可能，促进了深度神经网络的应用，并有效地充当了一个全面的视觉词典。这一发展使得物体识别可以超越复杂的低级处理，转向学习高级表示。在这项工作中，我们遵循这一范式，利用现有的标注人类动作数据集构建一个大规模的通用行为空间（UAS）。然后，我们将使用这个UAS作为分析和分类哺乳动物及黑猩猩行为数据集的基础。源代码可在GitHub上发布，网址为[此处提供链接]。
URL
https://arxiv.org/abs/2602.09518
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