Edge_Detection
-
Three-dimensional narrow volume reconstruction method with unconditional stability based on a phase-field Lagrange multiplier approach
2025-11-01 11:21:12Renjun Gao, Xiangjie Kong, Dongting Cai, Boyi Fu, Junxiang YangAbstract
Reconstruction of an object from points cloud is essential in prosthetics, medical imaging, computer vision, etc. We present an effective algorithm for an Allen--Cahn-type model of reconstruction, employing the Lagrange multiplier approach. Utilizing scattered data points from an object, we reconstruct a narrow shell by solving the governing equation enhanced with an edge detection function derived from the unsigned distance function. The specifically designed edge detection function ensures the energy stability. By reformulating the governing equation through the Lagrange multiplier technique and implementing a Crank--Nicolson time discretization, we can update the solutions in a stable and decoupled manner. The spatial operations are approximated using the finite difference method, and we analytically demonstrate the unconditional stability of the fully discrete scheme. Comprehensive numerical experiments, including reconstructions of complex 3D volumes such as characters from \textit{Star Wars}, validate the algorithm's accuracy, stability, and effectiveness. Additionally, we analyze how specific parameter selections influence the level of detail and refinement in the reconstructed volumes. To facilitate the interested readers to understand our algorithm, we share the computational codes and data in this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2511.00508
PDF
-
All You Need for Object Detection: From Pixels, Points, and Prompts to Next-Gen Fusion and Multimodal LLMs/VLMs in Autonomous Vehicles
2025-10-30 16:08:25Sayed Pedram Haeri Boroujeni, Niloufar Mehrabi, Hazim Alzorgan, Ahmad Sarlak, Mahlagha Fazeli, Abolfazl RaziAbstract
Autonomous Vehicles (AVs) are transforming the future of transportation through advances in intelligent perception, decision-making, and control systems. However, their success is tied to one core capability, reliable object detection in complex and multimodal environments. While recent breakthroughs in Computer Vision (CV) and Artificial Intelligence (AI) have driven remarkable progress, the field still faces a critical challenge as knowledge remains fragmented across multimodal perception, contextual reasoning, and cooperative intelligence. This survey bridges that gap by delivering a forward-looking analysis of object detection in AVs, emphasizing emerging paradigms such as Vision-Language Models (VLMs), Large Language Models (LLMs), and Generative AI rather than re-examining outdated techniques. We begin by systematically reviewing the fundamental spectrum of AV sensors (camera, ultrasonic, LiDAR, and Radar) and their fusion strategies, highlighting not only their capabilities and limitations in dynamic driving environments but also their potential to integrate with recent advances in LLM/VLM-driven perception frameworks. Next, we introduce a structured categorization of AV datasets that moves beyond simple collections, positioning ego-vehicle, infrastructure-based, and cooperative datasets (e.g., V2V, V2I, V2X, I2I), followed by a cross-analysis of data structures and characteristics. Ultimately, we analyze cutting-edge detection methodologies, ranging from 2D and 3D pipelines to hybrid sensor fusion, with particular attention to emerging transformer-driven approaches powered by Vision Transformers (ViTs), Large and Small Language Models (SLMs), and VLMs. By synthesizing these perspectives, our survey delivers a clear roadmap of current capabilities, open challenges, and future opportunities.
Abstract (translated)
URL
https://arxiv.org/abs/2510.26641
PDF
-
Analysis of the Robustness of an Edge Detector Based on Cellular Automata Optimized by Particle Swarm
2025-10-30 14:03:09Vin\'icius Ferraria, Eurico RuivoAbstract
The edge detection task is essential in image processing aiming to extract relevant information from an image. One recurring problem in this task is the weaknesses found in some detectors, such as the difficulty in detecting loose edges and the lack of context to extract relevant information from specific problems. To address these weaknesses and adapt the detector to the properties of an image, an adaptable detector described by two-dimensional cellular automaton and optimized by meta-heuristic combined with transfer learning techniques was developed. This study aims to analyze the impact of expanding the search space of the optimization phase and the robustness of the adaptability of the detector in identifying edges of a set of natural images and specialized subsets extracted from the same image set. The results obtained prove that expanding the search space of the optimization phase was not effective for the chosen image set. The study also analyzed the adaptability of the model through a series of experiments and validation techniques and found that, regardless of the validation, the model was able to adapt to the input and the transfer learning techniques applied to the model showed no significant improvements.
Abstract (translated)
URL
https://arxiv.org/abs/2510.26509
PDF
-
FPGA-based Lane Detection System incorporating Temperature and Light Control Units
2025-10-25 14:17:38Ibrahim Qamar, Saber Mahmoud, Seif Megahed, Mohamed Khaled, Saleh Hesham, Ahmed Matar, Saif Gebril, Mervat MahmoudAbstract
Intelligent vehicles are one of the most important outcomes gained from the world tendency toward automation. Applications of IVs, whether in urban roads or robot tracks, do prioritize lane path detection. This paper proposes an FPGA-based Lane Detector Vehicle LDV architecture that relies on the Sobel algorithm for edge detection. Operating on 416 x 416 images and 150 MHz, the system can generate a valid output every 1.17 ms. The valid output consists of the number of present lanes, the current lane index, as well as its right and left boundaries. Additionally, the automated light and temperature control units in the proposed system enhance its adaptability to the surrounding environmental conditions.
Abstract (translated)
URL
https://arxiv.org/abs/2510.24778
PDF
-
Hierarchical Spatial Algorithms for High-Resolution Image Quantization and Feature Extraction
2025-10-09 16:56:24Noor Islam S. MohammadAbstract
This study introduces a modular framework for spatial image processing, integrating grayscale quantization, color and brightness enhancement, image sharpening, bidirectional transformation pipelines, and geometric feature extraction. A stepwise intensity transformation quantizes grayscale images into eight discrete levels, producing a posterization effect that simplifies representation while preserving structural detail. Color enhancement is achieved via histogram equalization in both RGB and YCrCb color spaces, with the latter improving contrast while maintaining chrominance fidelity. Brightness adjustment is implemented through HSV value-channel manipulation, and image sharpening is performed using a 3 * 3 convolution kernel to enhance high-frequency details. A bidirectional transformation pipeline that integrates unsharp masking, gamma correction, and noise amplification achieved accuracy levels of 76.10% and 74.80% for the forward and reverse processes, respectively. Geometric feature extraction employed Canny edge detection, Hough-based line estimation (e.g., 51.50° for billiard cue alignment), Harris corner detection, and morphological window localization. Cue isolation further yielded 81.87\% similarity against ground truth images. Experimental evaluation across diverse datasets demonstrates robust and deterministic performance, highlighting its potential for real-time image analysis and computer vision.
Abstract (translated)
这项研究介绍了一个用于空间图像处理的模块化框架，集成了灰度量化、颜色和亮度增强、图像锐化、双向转换管道以及几何特征提取。该框架通过逐步强度变换将灰度图像量化为八个离散级别，产生一种海报化效果，在简化表示的同时保持结构细节。颜色增强是通过对RGB和YCrCb色彩空间的直方图均衡实现的，后者提高了对比度并保留了色度保真度。亮度调整是通过HSV值通道操作来实施的，并且图像锐化采用3x3卷积核进行高频细节的增强。 双向转换管道整合了不清晰掩模、伽马校正和噪声放大技术，在前向和反向过程中分别达到了76.10%和74.80%的精度水平。几何特征提取使用Canny边缘检测、基于霍夫变换的直线估计（例如，台球杆对齐角度为51.50°）、Harris角点检测以及形态学窗口定位。 台球杆隔离进一步与地面真实图像相比达到了81.87%的相似度。跨多种数据集进行实验评估证明了其稳健和确定性的性能表现，突显出其在实时图像分析和计算机视觉领域的潜在应用价值。
URL
https://arxiv.org/abs/2510.08449
PDF
-
Advances in Medical Image Segmentation: A Comprehensive Survey with a Focus on Lumbar Spine Applications
2025-10-01 01:34:38Ahmed Kabil, Ghada Khoriba, Mina Yousef, Essam A. RashedAbstract
Medical Image Segmentation (MIS) stands as a cornerstone in medical image analysis, playing a pivotal role in precise diagnostics, treatment planning, and monitoring of various medical conditions. This paper presents a comprehensive and systematic survey of MIS methodologies, bridging the gap between traditional image processing techniques and modern deep learning approaches. The survey encompasses thresholding, edge detection, region-based segmentation, clustering algorithms, and model-based techniques while also delving into state-of-the-art deep learning architectures such as Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), and the widely adopted U-Net and its variants. Moreover, integrating attention mechanisms, semi-supervised learning, generative adversarial networks (GANs), and Transformer-based models is thoroughly explored. In addition to covering established methods, this survey highlights emerging trends, including hybrid architectures, cross-modality learning, federated and distributed learning frameworks, and active learning strategies, which aim to address challenges such as limited labeled datasets, computational complexity, and model generalizability across diverse imaging modalities. Furthermore, a specialized case study on lumbar spine segmentation is presented, offering insights into the challenges and advancements in this relatively underexplored anatomical region. Despite significant progress in the field, critical challenges persist, including dataset bias, domain adaptation, interpretability of deep learning models, and integration into real-world clinical workflows.
Abstract (translated)
医学图像分割（MIS）在医学影像分析中占据核心地位，对于精准诊断、治疗规划以及各种医疗状况的监测至关重要。本文对MIS方法进行了全面而系统的综述，旨在弥合传统图像处理技术和现代深度学习方法之间的差距。该文献涵盖了阈值法、边缘检测、基于区域分割、聚类算法和模型驱动技术等传统方法，并深入探讨了当前最先进的深度学习架构，包括卷积神经网络（CNN）、全卷积网络（FCN）以及广受欢迎的U-Net及其变体。此外，还详细探究了注意力机制、半监督学习、生成对抗网络（GANs）及基于Transformer的模型等领域的集成应用。 除了涵盖已确立的方法之外，本综述还强调了一些新兴趋势，例如混合架构、跨模态学习、联邦和分布式学习框架以及主动学习策略。这些方法旨在解决有限标记数据集、计算复杂性以及在不同成像模式中实现模型泛化等问题带来的挑战。此外，本文还提供了一个关于腰椎分割的专门案例研究，提供了对这一相对未被充分探索解剖区域中的难题与进展的独特见解。 尽管该领域取得了显著进步，但仍存在一些关键性的挑战，包括数据集偏差、领域适应性、深度学习模型的可解释性和将其融入实际临床工作流程的问题。
URL
https://arxiv.org/abs/2510.03318
PDF
-
Flight Dynamics to Sensing Modalities: Exploiting Drone Ground Effect for Accurate Edge Detection
2025-09-25 12:37:54Chenyu Zhao, Jingao Xu, Ciyu Ruan, Haoyang Wang, Shengbo Wang, Jiaqi Li, Jirong Zha, Weijie Hong, Zheng Yang, Yunhao Liu, Xiao-Ping Zhang, Xinlei ChenAbstract
Drone-based rapid and accurate environmental edge detection is highly advantageous for tasks such as disaster relief and autonomous navigation. Current methods, using radars or cameras, raise deployment costs and burden lightweight drones with high computational demands. In this paper, we propose AirTouch, a system that transforms the ground effect from a stability "foe" in traditional flight control views, into a "friend" for accurate and efficient edge detection. Our key insight is that analyzing drone basic attitude sensor readings and flight commands allows us to detect ground effect changes. Such changes typically indicate the drone flying over a boundary of two materials, making this information valuable for edge detection. We approach this insight through theoretical analysis, algorithm design, and implementation, fully leveraging the ground effect as a new sensing modality without compromising drone flight stability, thereby achieving accurate and efficient scene edge detection. We also compare this new sensing modality with vision-based methods to clarify its exclusive advantages in resource efficiency and detection capability. Extensive evaluations demonstrate that our system achieves a high detection accuracy with mean detection distance errors of 0.051m, outperforming the baseline method performance by 86%. With such detection performance, our system requires only 43 mW power consumption, contributing to this new sensing modality for low-cost and highly efficient edge detection.
Abstract (translated)
基于无人机的快速且准确的环境边缘检测对于诸如灾害救援和自主导航等任务非常有利。目前使用雷达或相机的方法会增加部署成本，并给轻型无人机带来高计算需求负担。在本文中，我们提出了AirTouch系统，该系统将地面效应从传统飞行控制视角中的稳定性“敌人”转变为精确高效边缘检测的“朋友”。我们的关键洞察是分析无人机基本姿态传感器读数和飞行指令可以让我们检测到地面效应的变化。这种变化通常表明无人机飞越了两种材料之间的边界，因此这些信息对于边缘检测非常有价值。我们通过理论分析、算法设计和实施来实现这一见解，并充分利用地面效应作为新的感测模式，同时不损害无人机的飞行稳定性，从而实现了精确高效的场景边缘检测。 此外，我们将这种方法与基于视觉的方法进行比较，以阐明其在资源效率和检测能力方面的独特优势。广泛的评估表明，我们的系统能够实现高精度的检测，平均检测距离误差仅为0.051米，优于基准方法86%。凭借这种检测性能，我们的系统仅需消耗43毫瓦功率，为低成本且高效的边缘检测提供了新的感测模式。
URL
https://arxiv.org/abs/2509.21085
PDF
-
A Real-Time On-Device Defect Detection Framework for Laser Power-Meter Sensors via Unsupervised Learning
2025-09-25 09:29:20Dongqi Zheng, Wenjin Fu, Guangzong ChenAbstract
We present an automated vision-based system for defect detection and classification of laser power meter sensor coatings. Our approach addresses the critical challenge of identifying coating defects such as thermal damage and scratches that can compromise laser energy measurement accuracy in medical and industrial applications. The system employs an unsupervised anomaly detection framework that trains exclusively on ``good'' sensor images to learn normal coating distribution patterns, enabling detection of both known and novel defect types without requiring extensive labeled defect datasets. Our methodology consists of three key components: (1) a robust preprocessing pipeline using Laplacian edge detection and K-means clustering to segment the area of interest, (2) synthetic data augmentation via StyleGAN2, and (3) a UFlow-based neural network architecture for multi-scale feature extraction and anomaly map generation. Experimental evaluation on 366 real sensor images demonstrates $93.8\%$ accuracy on defective samples and $89.3\%$ accuracy on good samples, with image-level AUROC of 0.957 and pixel-level AUROC of 0.961. The system provides potential annual cost savings through automated quality control and processing times of 0.5 seconds per image in on-device implementation.
Abstract (translated)
我们提出了一种基于视觉的自动化系统，用于检测和分类激光功率计传感器涂层中的缺陷。该方法旨在解决在医疗和工业应用中识别热损伤、划痕等可能导致激光能量测量精度降低的关键挑战。我们的系统采用了一个无监督异常检测框架，仅使用“良好”的传感器图像进行训练，以学习正常涂层分布模式，并能够不依赖大量标记的缺陷数据集来检测已知和新型缺陷类型。 该方法由三个关键组成部分构成：（1）一个稳健的预处理管道，利用拉普拉斯边缘检测和K-means聚类对感兴趣的区域进行分割；（2）通过StyleGAN2实现的合成数据增强；以及（3）基于UFlow的神经网络架构用于多尺度特征提取及异常图生成。 在包含366张真实传感器图像的数据集上，实验评估表明，在有缺陷样本上的准确率为93.8%，良好样本上的准确率为89.3%，整体AUROC值为0.957（图像级别）和0.961（像素级别）。该系统通过自动化的质量控制流程有望节省年度成本，并且在设备内实现时每张图片的处理时间仅为0.5秒。
URL
https://arxiv.org/abs/2509.20946
PDF
-
Adaptive Guidance Semantically Enhanced via Multimodal LLM for Edge-Cloud Object Detection
2025-09-24 08:25:37Yunqing Hu, Zheming Yang, Chang Zhao, Wen JiAbstract
Traditional object detection methods face performance degradation challenges in complex scenarios such as low-light conditions and heavy occlusions due to a lack of high-level semantic understanding. To address this, this paper proposes an adaptive guidance-based semantic enhancement edge-cloud collaborative object detection method leveraging Multimodal Large Language Models (MLLM), achieving an effective balance between accuracy and efficiency. Specifically, the method first employs instruction fine-tuning to enable the MLLM to generate structured scene descriptions. It then designs an adaptive mapping mechanism that dynamically converts semantic information into parameter adjustment signals for edge detectors, achieving real-time semantic enhancement. Within an edge-cloud collaborative inference framework, the system automatically selects between invoking cloud-based semantic guidance or directly outputting edge detection results based on confidence scores. Experiments demonstrate that the proposed method effectively enhances detection accuracy and efficiency in complex scenes. Specifically, it can reduce latency by over 79% and computational cost by 70% in low-light and highly occluded scenes while maintaining accuracy.
Abstract (translated)
传统的目标检测方法在低光条件和严重遮挡等复杂场景中面临性能下降的挑战，因为这些方法缺乏高层次的语义理解。为了解决这个问题，本文提出了一种基于自适应指导的语义增强边缘-云端协同目标检测方法，该方法利用多模态大型语言模型（MLLM），实现了准确性和效率之间的有效平衡。 具体而言，该方法首先采用指令微调来使 MLLM 生成结构化的场景描述。然后设计了一个动态映射机制，可以将语义信息转换为边缘检测器的参数调整信号，从而实现实时语义增强。在一个边缘-云端协同推理框架内，系统根据置信度分数自动选择是在云端调用语义指导还是直接输出边缘检测结果。 实验表明，所提出的方法在复杂场景中有效地提高了检测准确性和效率。具体来说，在低光和高度遮挡的场景下，它可以将延迟降低79%以上，并减少计算成本达70%，同时保持高准确性。
URL
https://arxiv.org/abs/2509.19875
PDF
-
A Mutual Learning Method for Salient Object Detection with intertwined Multi-Supervision--Revised
2025-09-21 22:30:32Runmin Wu, Mengyang Feng, Wenlong Guan, Dong Wang, Huchuan Lu, Errui DingAbstract
Though deep learning techniques have made great progress in salient object detection recently, the predicted saliency maps still suffer from incomplete predictions due to the internal complexity of objects and inaccurate boundaries caused by strides in convolution and pooling operations. To alleviate these issues, we propose to train saliency detection networks by exploiting the supervision from not only salient object detection, but also foreground contour detection and edge detection. First, we leverage salient object detection and foreground contour detection tasks in an intertwined manner to generate saliency maps with uniform highlight. Second, the foreground contour and edge detection tasks guide each other simultaneously, thereby leading to precise foreground contour prediction and reducing the local noises for edge prediction. In addition, we develop a novel mutual learning module (MLM) which serves as the building block of our method. Each MLM consists of multiple network branches trained in a mutual learning manner, which improves the performance by a large margin. Extensive experiments on seven challenging datasets demonstrate that the proposed method has delivered state-of-the-art results in both salient object detection and edge detection.
Abstract (translated)
尽管深度学习技术在显著物体检测方面最近取得了巨大进展，预测的显著性图仍然由于物体内部复杂性和卷积及池化操作中的步长导致边界不准确而存在不完整预测的问题。为了缓解这些问题，我们提出通过利用不仅来自显著物体检测、而且来自前景轮廓检测和边缘检测的任务监督来训练显著性检测网络。 首先，我们将显著物体检测和前景轮廓检测任务以交织的方式结合使用，生成具有均匀高亮的显著性图。其次，前景轮廓检测和边缘检测任务相互指导，从而实现精确的前景轮廓预测，并减少边缘预测中的局部噪声。此外，我们开发了一种新颖的互学习模块（MLM），这是方法的基础构建块。每个MLM包含多个以互学习方式训练的网络分支，显著提高了性能。 在七个具有挑战性的数据集上进行的广泛实验表明，所提出的方法在显著物体检测和边缘检测方面均达到了最先进的结果。
URL
https://arxiv.org/abs/2509.21363
PDF
-
Energy Efficient Exact and Approximate Systolic Array Architecture for Matrix Multiplication
2025-08-31 10:15:35Pragun Jaswal, L. Hemanth Krishna, B. SrinivasuAbstract
Deep Neural Networks (DNNs) require highly efficient matrix multiplication engines for complex computations. This paper presents a systolic array architecture incorporating novel exact and approximate processing elements (PEs), designed using energy-efficient positive partial product and negative partial product cells, termed as PPC and NPPC, respectively. The proposed 8-bit exact and approximate PE designs are employed in a 8x8 systolic array, which achieves a energy savings of 22% and 32%, respectively, compared to the existing design. To demonstrate their effectiveness, the proposed PEs are integrated into a systolic array (SA) for Discrete Cosine Transform (DCT) computation, achieving high output quality with a PSNR of 38.21,dB. Furthermore, in an edge detection application using convolution, the approximate PE achieves a PSNR of 30.45,dB. These results highlight the potential of the proposed design to deliver significant energy efficiency while maintaining competitive output quality, making it well-suited for error-resilient image and vision processing applications.
Abstract (translated)
深度神经网络（DNN）需要高效的矩阵乘法引擎来进行复杂的计算。本文提出了一种采用新型精确和近似处理单元（PEs）的 systolic 数组架构，这些单元使用节能的正部分积和负部分积细胞设计，分别称为 PPC 和 NPPC。提出的 8 位精确和近似 PE 设计被应用于一个 8x8 的 systolic 数组，在与现有设计相比时，前者在能量节省方面达到了22%和32%。 为了证明其有效性，所提出的 PEs 被集成到用于离散余弦变换（DCT）计算的 systolic 数组中，实现了高达 38.21 dB 的 PSNR 输出质量。此外，在使用卷积进行边缘检测的应用中，近似 PE 实现了 30.45 dB 的 PSNR。 这些结果突显了所提出的架构在保持竞争力输出质量的同时能够实现显著的能源效率，并且该设计非常适合于具有错误容忍能力的图像和视觉处理应用。
URL
https://arxiv.org/abs/2509.00778
PDF
-
VibES: Induced Vibration for Persistent Event-Based Sensing
2025-08-26 14:58:51Vincenzo Polizzi, Stephen Yang, Quentin Clark, Jonathan Kelly, Igor Gilitschenski, David B. LindellAbstract
Event cameras are a bio-inspired class of sensors that asynchronously measure per-pixel intensity changes. Under fixed illumination conditions in static or low-motion scenes, rigidly mounted event cameras are unable to generate any events, becoming unsuitable for most computer vision tasks. To address this limitation, recent work has investigated motion-induced event stimulation that often requires complex hardware or additional optical components. In contrast, we introduce a lightweight approach to sustain persistent event generation by employing a simple rotating unbalanced mass to induce periodic vibrational motion. This is combined with a motion-compensation pipeline that removes the injected motion and yields clean, motion-corrected events for downstream perception tasks. We demonstrate our approach with a hardware prototype and evaluate it on real-world captured datasets. Our method reliably recovers motion parameters and improves both image reconstruction and edge detection over event-based sensing without motion induction.
Abstract (translated)
事件相机是一种受生物启发的传感器类，能够异步地测量每个像素的强度变化。在固定照明条件下的静态或低动态场景中，刚性安装的事件相机无法生成任何事件，从而不适合大多数计算机视觉任务。为了解决这一限制，最近的研究探讨了通过复杂硬件或附加光学组件来诱导运动以刺激事件的方法。相比之下，我们提出了一种轻量级方法，即使用一个简单的旋转不平衡质量来持续地产生周期性振动，从而维持持久的事件生成。这种方法结合了一个运动补偿流水线，该流水线可以消除注入的运动，并为下游感知任务提供干净、经过修正的事件数据。 我们在硬件原型中演示了这一方法，并在实际捕获的数据集上对其进行了评估。我们的方法能够可靠地恢复运动参数，在没有引入运动的情况下改善基于事件感应的图像重建和边缘检测性能。
URL
https://arxiv.org/abs/2508.19094
PDF
-
A biological vision inspired framework for machine perception of abutting grating illusory contours
2025-08-24 08:45:06Xiao Zhang, Kai-Fu Yang, Xian-Shi Zhang, Hong-Zhi You, Hong-Mei Yan, Yong-Jie LiAbstract
Higher levels of machine intelligence demand alignment with human perception and cognition. Deep neural networks (DNN) dominated machine intelligence have demonstrated exceptional performance across various real-world tasks. Nevertheless, recent evidence suggests that DNNs fail to perceive illusory contours like the abutting grating, a discrepancy that misaligns with human perception patterns. Departing from previous works, we propose a novel deep network called illusory contour perception network (ICPNet) inspired by the circuits of the visual cortex. In ICPNet, a multi-scale feature projection (MFP) module is designed to extract multi-scale representations. To boost the interaction between feedforward and feedback features, a feature interaction attention module (FIAM) is introduced. Moreover, drawing inspiration from the shape bias observed in human perception, an edge detection task conducted via the edge fusion module (EFM) injects shape constraints that guide the network to concentrate on the foreground. We assess our method on the existing AG-MNIST test set and the AG-Fashion-MNIST test sets constructed by this work. Comprehensive experimental results reveal that ICPNet is significantly more sensitive to abutting grating illusory contours than state-of-the-art models, with notable improvements in top-1 accuracy across various subsets. This work is expected to make a step towards human-level intelligence for DNN-based models.
Abstract (translated)
更高的机器智能需要与人类感知和认知相匹配。深度神经网络（DNN）主导的机器智能在各种现实世界任务中表现出卓越性能。然而，最近的研究表明，DNN无法识别像相邻条栅这样的错觉轮廓，这种差异与人类感知模式不符。不同于以往的工作，我们提出了一种新的深度网络——错觉轮廓感知网络（ICPNet），它受到了视觉皮层回路的启发。在ICPNet中，设计了一个多尺度特征投影（MFP）模块来提取多尺度表示。为了增强前馈和反馈特征之间的交互作用，引入了特征互动注意模块（FIAM）。此外，借鉴人类感知中的形状偏好的观察结果，我们通过边缘融合模块（EFM）执行的边缘检测任务注入了形状约束，引导网络专注于前景。 我们在现有的AG-MNIST测试集以及本研究构建的AG-Fashion-MNIST测试集上评估了我们的方法。全面的实验结果显示，ICPNet比现有最先进的模型对相邻条栅错觉轮廓更敏感，在各种子集上的顶级准确率都有显著提高。这项工作有望使基于DNN的模型向人类级别的智能迈出一步。
URL
https://arxiv.org/abs/2508.17254
PDF
-
PEdger++: Practical Edge Detection via Assembling Cross Information
2025-08-16 07:48:36Yuanbin Fu, Liang Li, Xiaojie GuoAbstract
Edge detection serves as a critical foundation for numerous computer vision applications, including object detection, semantic segmentation, and image editing, by extracting essential structural cues that define object boundaries and salient edges. To be viable for broad deployment across devices with varying computational capacities, edge detectors shall balance high accuracy with low computational complexity. While deep learning has evidently improved accuracy, they often suffer from high computational costs, limiting their applicability on resource-constrained devices. This paper addresses the challenge of achieving that balance: \textit{i.e.}, {how to efficiently capture discriminative features without relying on large-size and sophisticated models}. We propose PEdger++, a collaborative learning framework designed to reduce computational costs and model sizes while improving edge detection accuracy. The core principle of our PEdger++ is that cross-information derived from heterogeneous architectures, diverse training moments, and multiple parameter samplings, is beneficial to enhance learning from an ensemble perspective. Extensive experimental results on the BSDS500, NYUD and Multicue datasets demonstrate the effectiveness of our approach, both quantitatively and qualitatively, showing clear improvements over existing methods. We also provide multiple versions of the model with varying computational requirements, highlighting PEdger++'s adaptability with respect to different resource constraints. Codes are accessible at this https URL.
Abstract (translated)
边缘检测是众多计算机视觉应用的基础，包括物体检测、语义分割和图像编辑，它通过提取定义对象边界和显著边界的结构线索来发挥作用。为了在计算能力各异的设备上广泛部署，边缘检测器需要在高精度与低计算复杂度之间取得平衡。虽然深度学习明显提高了准确性，但由于其高昂的计算成本，限制了它们在资源受限设备上的应用。本文解决了这一挑战：即如何高效地捕捉鉴别特征而不依赖于大型且复杂的模型。我们提出了PEdger++，这是一个协作学习框架，旨在减少计算成本和模型尺寸的同时提高边缘检测精度。我们的PEdger++的核心理念是，来自异构架构、多样化训练时刻以及多个参数采样的交叉信息，在集合视角上对提升学习效果是有益的。 在BSDS500、NYUD和Multicue数据集上的大量实验结果表明了我们方法的有效性，无论是定量还是定性的评估都显示了相对于现有方法的明显改进。我们也提供了多种版本的模型，适应不同的计算需求，突显出PEdger++在面对不同资源约束时的灵活性。代码可以在提供的链接中获取。
URL
https://arxiv.org/abs/2508.11961
PDF
-
Stable Diffusion Models are Secretly Good at Visual In-Context Learning
2025-08-13 17:08:22Trevine Oorloff, Vishwanath Sindagi, Wele Gedara Chaminda Bandara, Ali Shafahi, Amin Ghiasi, Charan Prakash, Reza ArdekaniAbstract
Large language models (LLM) in natural language processing (NLP) have demonstrated great potential for in-context learning (ICL) -- the ability to leverage a few sets of example prompts to adapt to various tasks without having to explicitly update the model weights. ICL has recently been explored for computer vision tasks with promising early outcomes. These approaches involve specialized training and/or additional data that complicate the process and limit its generalizability. In this work, we show that off-the-shelf Stable Diffusion models can be repurposed for visual in-context learning (V-ICL). Specifically, we formulate an in-place attention re-computation within the self-attention layers of the Stable Diffusion architecture that explicitly incorporates context between the query and example prompts. Without any additional fine-tuning, we show that this repurposed Stable Diffusion model is able to adapt to six different tasks: foreground segmentation, single object detection, semantic segmentation, keypoint detection, edge detection, and colorization. For example, the proposed approach improves the mean intersection over union (mIoU) for the foreground segmentation task on Pascal-5i dataset by 8.9% and 3.2% over recent methods such as Visual Prompting and IMProv, respectively. Additionally, we show that the proposed method is able to effectively leverage multiple prompts through ensembling to infer the task better and further improve the performance.
Abstract (translated)
在自然语言处理（NLP）领域，大型语言模型（LLM）已经展示了在上下文学习（ICL）中的巨大潜力——即利用少量示例提示来适应各种任务的能力，而无需显式更新模型权重。最近有研究将ICL应用于计算机视觉任务，并取得了初步的积极成果。然而，这些方法通常需要专门的训练和/或额外的数据支持，这使得整个过程复杂且限制了其普适性。 在这项工作中，我们展示了现成的Stable Diffusion模型可以被重新部署用于视觉上下文学习（V-ICL）。具体来说，我们在Stable Diffusion架构的自注意力层中提出了一种就地注意力重构方法，该方法明确地将查询与示例提示之间的背景信息纳入考虑。无需任何额外微调的情况下，我们证明了这种重新部署后的Stable Diffusion模型可以适应六种不同的任务：前景分割、单对象检测、语义分割、关键点检测、边缘检测和着色。 例如，在Pascal-5i数据集的前景分割任务上，我们的方法提出的方案相对于最近的方法如Visual Prompting和IMProv分别提高了8.9%和3.2%的平均交并比（mIoU）。此外，我们还展示了所提出的方法能够通过集成多个提示来更有效地推断任务，并进一步提高性能。
URL
https://arxiv.org/abs/2508.09949
PDF
-
Edge Detection for Organ Boundaries via Top Down Refinement and SubPixel Upsampling
2025-08-09 03:28:12Aarav Mehta, Priya Deshmukh, Vikram Singh, Siddharth Malhotra, Krishnan Menon Iyer, Tanvi IyerAbstract
Accurate localization of organ boundaries is critical in medical imaging for segmentation, registration, surgical planning, and radiotherapy. While deep convolutional networks (ConvNets) have advanced general-purpose edge detection to near-human performance on natural images, their outputs often lack precise localization, a limitation that is particularly harmful in medical applications where millimeter-level accuracy is required. Building on a systematic analysis of ConvNet edge outputs, we propose a medically focused crisp edge detector that adapts a novel top-down backward refinement architecture to medical images (2D and volumetric). Our method progressively upsamples and fuses high-level semantic features with fine-grained low-level cues through a backward refinement pathway, producing high-resolution, well-localized organ boundaries. We further extend the design to handle anisotropic volumes by combining 2D slice-wise refinement with light 3D context aggregation to retain computational efficiency. Evaluations on several CT and MRI organ datasets demonstrate substantially improved boundary localization under strict criteria (boundary F-measure, Hausdorff distance) compared to baseline ConvNet detectors and contemporary medical edge/contour methods. Importantly, integrating our crisp edge maps into downstream pipelines yields consistent gains in organ segmentation (higher Dice scores, lower boundary errors), more accurate image registration, and improved delineation of lesions near organ interfaces. The proposed approach produces clinically valuable, crisp organ edges that materially enhance common medical-imaging tasks.
Abstract (translated)
准确地定位器官边界在医学成像中的分割、配准、手术规划和放射治疗等方面至关重要。虽然深度卷积网络（ConvNets）已经将一般用途的边缘检测提升到了接近人类水平的性能，尤其是在自然图像上，但它们的输出往往缺乏精确的定位能力，在要求毫米级精度的医疗应用中尤其有害。基于对ConvNet边缘输出的系统分析，我们提出了一种以医学为重点的清晰边缘探测器，该探测器采用新颖的自顶向下反向细化架构来处理医学影像（二维和体积图像）。我们的方法通过一个反向细化路径逐步上采样并融合高层次语义特征与低层次细节线索，从而产生高分辨率、精确定位的器官边界。进一步地，我们设计了一个扩展方案，以处理各向异性的体素数据，结合了2D切片级别的优化和轻量级3D上下文聚合，保留计算效率的同时改善效果。 在多个CT和MRI器官数据集上的评估表明，与基线ConvNet检测器及当前的医学边缘/轮廓方法相比，在严格的指标（边界F-measure、Hausdorff距离）下，我们的方法显著提高了边界的定位能力。重要的是，将我们提出的清晰边缘图集成到下游管道中，可以一致地提升器官分割性能（Dice分数更高，边界误差更低），提高图像配准的准确性，并改善邻近器官界面处病变区域的描绘精度。 所提出的方法生成了临床有价值的、清晰的器官边缘，显著增强了常见的医学成像任务。
URL
https://arxiv.org/abs/2508.06805
PDF
-
DET-GS: Depth- and Edge-Aware Regularization for High-Fidelity 3D Gaussian Splatting
2025-08-06 05:37:26Zexu Huang, Min Xu, Stuart PerryAbstract
3D Gaussian Splatting (3DGS) represents a significant advancement in the field of efficient and high-fidelity novel view synthesis. Despite recent progress, achieving accurate geometric reconstruction under sparse-view conditions remains a fundamental challenge. Existing methods often rely on non-local depth regularization, which fails to capture fine-grained structures and is highly sensitive to depth estimation noise. Furthermore, traditional smoothing methods neglect semantic boundaries and indiscriminately degrade essential edges and textures, consequently limiting the overall quality of reconstruction. In this work, we propose DET-GS, a unified depth and edge-aware regularization framework for 3D Gaussian Splatting. DET-GS introduces a hierarchical geometric depth supervision framework that adaptively enforces multi-level geometric consistency, significantly enhancing structural fidelity and robustness against depth estimation noise. To preserve scene boundaries, we design an edge-aware depth regularization guided by semantic masks derived from Canny edge detection. Furthermore, we introduce an RGB-guided edge-preserving Total Variation loss that selectively smooths homogeneous regions while rigorously retaining high-frequency details and textures. Extensive experiments demonstrate that DET-GS achieves substantial improvements in both geometric accuracy and visual fidelity, outperforming state-of-the-art (SOTA) methods on sparse-view novel view synthesis benchmarks.
Abstract (translated)
3D高斯点阵（3DGS）在高效且高质量的新视图合成领域中代表了重要进展。尽管最近取得了进步，但在稀疏视角条件下实现精确的几何重建仍然是一个基本挑战。现有方法通常依赖于非局部深度正则化技术，这无法捕捉细微结构，并且极易受到深度估计噪声的影响。此外，传统的平滑方法忽略了语义边界，并无差别地降低了重要的边缘和纹理质量，从而限制了整体重建效果的质量。 在本工作中，我们提出了DET-GS（深度与边缘感知的正则化框架），用于3D高斯点阵中的统一深度和边缘感知处理。DET-GS引入了一个层次化的几何深度监督框架，该框架能够自适应地强制执行多级几何一致性，从而极大地提升了结构保真度，并增强了对深度估计噪声的鲁棒性。为了保留场景边界，我们设计了一种基于语义掩膜（从Canny边缘检测导出）引导的边缘感知深度正则化方法。此外，我们引入了一种RGB指导下的边缘保持总变差损失，这种损失能够在平滑同质区域的同时严格保存高频细节和纹理。 广泛的实验表明，DET-GS在几何精度和视觉保真度方面均实现了显著改进，并且在稀疏视角的新视图合成基准测试中超越了最先进的方法。
URL
https://arxiv.org/abs/2508.04099
PDF
-
Beyond Illumination: Fine-Grained Detail Preservation in Extreme Dark Image Restoration
2025-08-05 11:31:08Tongshun Zhang, Pingping Liu, Zixuan Zhong, Zijian Zhang, Qiuzhan ZhouAbstract
Recovering fine-grained details in extremely dark images remains challenging due to severe structural information loss and noise corruption. Existing enhancement methods often fail to preserve intricate details and sharp edges, limiting their effectiveness in downstream applications like text and edge detection. To address these deficiencies, we propose an efficient dual-stage approach centered on detail recovery for dark images. In the first stage, we introduce a Residual Fourier-Guided Module (RFGM) that effectively restores global illumination in the frequency domain. RFGM captures inter-stage and inter-channel dependencies through residual connections, providing robust priors for high-fidelity frequency processing while mitigating error accumulation risks from unreliable priors. The second stage employs complementary Mamba modules specifically designed for textural structure refinement: (1) Patch Mamba operates on channel-concatenated non-downsampled patches, meticulously modeling pixel-level correlations to enhance fine-grained details without resolution loss. (2) Grad Mamba explicitly focuses on high-gradient regions, alleviating state decay in state space models and prioritizing reconstruction of sharp edges and boundaries. Extensive experiments on multiple benchmark datasets and downstream applications demonstrate that our method significantly improves detail recovery performance while maintaining efficiency. Crucially, the proposed modules are lightweight and can be seamlessly integrated into existing Fourier-based frameworks with minimal computational overhead. Code is available at this https URL.
Abstract (translated)
在极暗图像中恢复细微细节仍然是一个挑战，因为这会导致严重的结构信息丢失和噪声干扰。现有的增强方法往往无法保留复杂的细节和锐利边缘，从而限制了它们在下游应用（如文本和边缘检测）中的有效性。为了弥补这些缺陷，我们提出了一种基于细节恢复的高效双阶段方法来处理暗图像。 第一阶段引入了一个残差傅里叶引导模块 (RFGM)，该模块能够有效恢复频率域内的全局照明。RFGM 通过残差连接捕捉跨阶段和跨通道依赖性，为高频处理提供稳健先验，并降低由于不可靠先验导致的误差累积风险。 第二阶段采用互补的 Mamba 模块，专门用于纹理结构细化：(1) Patch Mamba 在不进行下采样的通道级联补丁上操作，精细建模像素级别的相关性，增强细微细节而不损失分辨率。(2) Grad Mamba 明确关注高梯度区域，在状态空间模型中缓解了状态衰减问题，并优先重建锐利边缘和边界。 在多个基准数据集和下游应用上的广泛实验表明，我们的方法显著提高了细节恢复性能，同时保持了效率。至关重要的是，所提出的模块是轻量级的，可以无缝集成到现有的傅里叶基础框架中，并且几乎没有计算开销。 代码可在 [此处](https://此链接应由原作者提供，原文中的具体URL未给出) 获取。
URL
https://arxiv.org/abs/2508.03336
PDF
-
A Modified VGG19-Based Framework for Accurate and Interpretable Real-Time Bone Fracture Detection
2025-07-31 19:22:58Md. Ehsanul Haque, Abrar Fahim, Shamik Dey, Syoda Anamika Jahan, S. M. Jahidul Islam, Sakib Rokoni, Md Sakib MorshedAbstract
Early and accurate detection of the bone fracture is paramount to initiating treatment as early as possible and avoiding any delay in patient treatment and outcomes. Interpretation of X-ray image is a time consuming and error prone task, especially when resources for such interpretation are limited by lack of radiology expertise. Additionally, deep learning approaches used currently, typically suffer from misclassifications and lack interpretable explanations to clinical use. In order to overcome these challenges, we propose an automated framework of bone fracture detection using a VGG-19 model modified to our needs. It incorporates sophisticated preprocessing techniques that include Contrast Limited Adaptive Histogram Equalization (CLAHE), Otsu's thresholding, and Canny edge detection, among others, to enhance image clarity as well as to facilitate the feature extraction. Therefore, we use Grad-CAM, an Explainable AI method that can generate visual heatmaps of the model's decision making process, as a type of model interpretability, for clinicians to understand the model's decision making process. It encourages trust and helps in further clinical validation. It is deployed in a real time web application, where healthcare professionals can upload X-ray images and get the diagnostic feedback within 0.5 seconds. The performance of our modified VGG-19 model attains 99.78\% classification accuracy and AUC score of 1.00, making it exceptionally good. The framework provides a reliable, fast, and interpretable solution for bone fracture detection that reasons more efficiently for diagnoses and better patient care.
Abstract (translated)
早期且准确地检测骨骼骨折对于尽早启动治疗以及避免延误患者的治疗结果至关重要。解读X射线图像是一项耗时且容易出错的任务，尤其是在放射学专业知识资源有限的情况下更是如此。此外，目前使用的深度学习方法通常存在误分类问题，并且缺乏可解释性来支持临床应用。为了解决这些挑战，我们提出了一种使用修改后的VGG-19模型进行骨骼骨折自动检测的框架。该框架采用了包括对比限制自适应直方图均衡（CLAHE）、Otsu阈值处理和Canny边缘检测在内的复杂预处理技术，以增强图像清晰度并帮助特征提取。因此，我们采用了一种可解释的人工智能方法——Grad-CAM来生成模型决策过程的可视化热力图，作为提高模型可解释性的手段，以便临床医生理解模型的决策流程。这种方法鼓励信任，并有助于进一步的临床验证。该框架部署在实时网络应用中，医疗专业人员可以上传X射线图像并在0.5秒内获得诊断反馈。 我们修改后的VGG-19模型的性能达到了99.78%的分类准确率和AUC分数为1.00，表现非常优秀。该框架提供了一种可靠、快速且可解释的解决方案，用于骨骼骨折检测，并能够更有效地进行诊断并改善患者的护理质量。
URL
https://arxiv.org/abs/2508.03739
PDF
-
Robust Adverse Weather Removal via Spectral-based Spatial Grouping
2025-07-30 09:08:34Yuhwan Jeong, Yunseo Yang, Youngjo Yoon, Kuk-Jin YoonAbstract
Adverse weather conditions cause diverse and complex degradation patterns, driving the development of All-in-One (AiO) models. However, recent AiO solutions still struggle to capture diverse degradations, since global filtering methods like direct operations on the frequency domain fail to handle highly variable and localized distortions. To address these issue, we propose Spectral-based Spatial Grouping Transformer (SSGformer), a novel approach that leverages spectral decomposition and group-wise attention for multi-weather image restoration. SSGformer decomposes images into high-frequency edge features using conventional edge detection and low-frequency information via Singular Value Decomposition. We utilize multi-head linear attention to effectively model the relationship between these features. The fused features are integrated with the input to generate a grouping-mask that clusters regions based on the spatial similarity and image texture. To fully leverage this mask, we introduce a group-wise attention mechanism, enabling robust adverse weather removal and ensuring consistent performance across diverse weather conditions. We also propose a Spatial Grouping Transformer Block that uses both channel attention and spatial attention, effectively balancing feature-wise relationships and spatial dependencies. Extensive experiments show the superiority of our approach, validating its effectiveness in handling the varied and intricate adverse weather degradations.
Abstract (translated)
恶劣的天气条件会导致各种复杂且多样的图像退化模式，这推动了All-in-One（AiO）模型的发展。然而，最近的AiO解决方案仍然难以捕捉到这些多样化的退化情况，因为像频域直接操作这样的全局滤波方法无法处理高度变化和局部分布的失真。为了解决这些问题，我们提出了一种基于谱分解的空间分组变换器(SSGformer)，这是一种新颖的方法，利用谱分解和分组注意力机制来进行多天气图像恢复。 SSGformer通过传统的边缘检测将图像分解成高频边缘特征，并使用奇异值分解提取低频信息。我们采用多头线性注意机制有效地建模这些特征之间的关系。融合后的特征与输入结合生成一个分组掩码，根据空间相似性和图像纹理对区域进行聚类。为了充分利用这个掩码，我们引入了一种基于分组的注意力机制，从而实现稳健的恶劣天气去除，并确保在各种天气条件下的一致性能。 此外，我们还提出了一种空间分组变换器块（Spatial Grouping Transformer Block），该模块结合了通道注意和空间注意，有效地平衡了特征关系与空间依赖性。广泛的实验表明，我们的方法优于现有技术，在处理复杂且多样的恶劣天气退化方面表现出色。
URL
https://arxiv.org/abs/2507.22498
PDF
