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How to Sample High Quality 3D Fractals for Action Recognition Pre-Training?
2026-02-12 10:48:25Marko Putak, Thomas B. Moeslund, Joakim Bruslund HaurumAbstract
Synthetic datasets are being recognized in the deep learning realm as a valuable alternative to exhaustively labeled real data. One such synthetic data generation method is Formula Driven Supervised Learning (FDSL), which can provide an infinite number of perfectly labeled data through a formula driven approach, such as fractals or contours. FDSL does not have common drawbacks like manual labor, privacy and other ethical concerns. In this work we generate 3D fractals using 3D Iterated Function Systems (IFS) for pre-training an action recognition model. The fractals are temporally transformed to form a video that is used as a pre-training dataset for downstream task of action recognition. We find that standard methods of generating fractals are slow and produce degenerate 3D fractals. Therefore, we systematically explore alternative ways of generating fractals and finds that overly-restrictive approaches, while generating aesthetically pleasing fractals, are detrimental for downstream task performance. We propose a novel method, Targeted Smart Filtering, to address both the generation speed and fractal diversity issue. The method reports roughly 100 times faster sampling speed and achieves superior downstream performance against other 3D fractal filtering methods.
Abstract (translated)
合成数据在深度学习领域中被认为是一种有价值的现实标记数据的替代方案。其中一种生成合成数据的方法是公式驱动监督学习（Formula Driven Supervised Learning，FDSL），它可以通过公式驱动的方式，如分形或轮廓，提供无限量的完美标注数据，而无需人工劳动，并且避免了隐私和其他伦理问题。在这项工作中，我们使用3D迭代函数系统（IFS）生成3D分形来预训练动作识别模型。这些分形在时间上变换形成视频，作为下游动作识别任务的预训练数据集。 我们发现传统的生成分形的方法速度慢，并且产生的三维分形质量较差。因此，我们系统地探索了替代方法以生成分形，结果发现过于严格的限制性方法虽然可以产生美观的分形图像，但对下游任务性能有害。为此，我们提出了一种新的方法——目标智能过滤法（Targeted Smart Filtering），旨在同时解决生成速度和分形多样性的问题。该方法报告称采样速度快了大约100倍，并且在与其他3D分形筛选方法相比时，在下游任务中表现出色。
URL
https://arxiv.org/abs/2602.11810
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Quantum walk inspired JPEG compression of images
2026-02-12 05:40:33Abhishek Verma, Sahil Tomar, Sandeep KumarAbstract
This work proposes a quantum inspired adaptive quantization framework that enhances the classical JPEG compression by introducing a learned, optimized Qtable derived using a Quantum Walk Inspired Optimization (QWIO) search strategy. The optimizer searches a continuous parameter space of frequency band scaling factors under a unified rate distortion objective that jointly considers reconstruction fidelity and compression efficiency. The proposed framework is evaluated on MNIST, CIFAR10, and ImageNet subsets, using Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Bits Per Pixel (BPP), and error heatmap visual analysis as evaluation metrics. Experimental results show average gains ranging from 3 to 6 dB PSNR, along with better structural preservation of edges, contours, and luminance transitions, without modifying decoder compatibility. The structure remains JPEG compliant and can be implemented using accessible scientific packages making it ideal for deployment and practical research use.
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https://arxiv.org/abs/2602.12306
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Strategizing at Speed: A Learned Model Predictive Game for Multi-Agent Drone Racing
2026-02-06 18:20:13Andrei-Carlo Papuc, Lasse Peters, Sihao Sun, Laura Ferranti, Javier Alonso-MoraAbstract
Autonomous drone racing pushes the boundaries of high-speed motion planning and multi-agent strategic decision-making. Success in this domain requires drones not only to navigate at their limits but also to anticipate and counteract competitors' actions. In this paper, we study a fundamental question that arises in this domain: how deeply should an agent strategize before taking an action? To this end, we compare two planning paradigms: the Model Predictive Game (MPG), which finds interaction-aware strategies at the expense of longer computation times, and contouring Model Predictive Control (MPC), which computes strategies rapidly but does not reason about interactions. We perform extensive experiments to study this trade-off, revealing that MPG outperforms MPC at moderate velocities but loses its advantage at higher speeds due to latency. To address this shortcoming, we propose a Learned Model Predictive Game (LMPG) approach that amortizes model predictive gameplay to reduce latency. In both simulation and hardware experiments, we benchmark our approach against MPG and MPC in head-to-head races, finding that LMPG outperforms both baselines.
Abstract (translated)
自主无人机竞速推动了高速运动规划和多智能体策略决策的边界。在这个领域取得成功需要无人机不仅要在极限条件下导航，还要能够预测并应对竞争对手的动作。本文研究了一个在该领域中出现的基本问题：一个智能体在采取行动之前应深入思考到什么程度？为此，我们比较了两种规划范式：模型预测博弈（Model Predictive Game, MPG），它以更长的计算时间为代价找到考虑到互动策略的方法；以及轮廓模型预测控制（contouring Model Predictive Control, MPC），这种方法可以快速制定策略但不考虑交互。通过广泛的实验来研究这一权衡，我们发现MPG在中等速度下优于MPC，但在高速时由于延迟问题而失去优势。为解决这个问题，我们提出了一种学习模型预测博弈（Learned Model Predictive Game, LMPG）方法，该方法将模型预测游戏进行分摊处理以减少延迟。通过模拟和硬件实验，在一对一竞速中将我们的方法与MPG和MPC进行了基准测试，发现LMPG优于两个基线方法。 这段翻译阐明了自主无人机竞速中的策略制定问题，并比较了几种不同的规划方法的效果，突出了所提出的LMPG方法在减少延迟的同时维持性能方面的优势。
URL
https://arxiv.org/abs/2602.06925
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Contour Refinement using Discrete Diffusion in Low Data Regime
2026-02-05 16:55:08Fei Yu Guan, Ian Keefe, Sophie Wilkinson, Daniel D. B. Perrakis, Steven WaslanderAbstract
Boundary detection of irregular and translucent objects is an important problem with applications in medical imaging, environmental monitoring and manufacturing, where many of these applications are plagued with scarce labeled data and low in situ computational resources. While recent image segmentation studies focus on segmentation mask alignment with ground-truth, the task of boundary detection remains understudied, especially in the low data regime. In this work, we present a lightweight discrete diffusion contour refinement pipeline for robust boundary detection in the low data regime. We use a Convolutional Neural Network(CNN) architecture with self-attention layers as the core of our pipeline, and condition on a segmentation mask, iteratively denoising a sparse contour representation. We introduce multiple novel adaptations for improved low-data efficacy and inference efficiency, including using a simplified diffusion process, a customized model architecture, and minimal post processing to produce a dense, isolated contour given a dataset of size <500 training images. Our method outperforms several SOTA baselines on the medical imaging dataset KVASIR, is competitive on HAM10K and our custom wildfire dataset, Smoke, while improving inference framerate by 3.5X.
Abstract (translated)
不规则和透明物体的边界检测是一个重要的问题，它在医学成像、环境监测及制造业中有着广泛的应用。然而，在这些领域中，许多应用由于缺乏标注数据且现场计算资源有限而面临挑战。尽管最近的图像分割研究主要集中在与地面真实值的分割掩码对齐上，但低数据环境下边界检测的任务仍然较少被探索。在这项工作中，我们提出了一种轻量级离散扩散轮廓细化流水线，用于在低数据情况下进行稳健的边界检测。 我们的方法采用带有自注意力层的卷积神经网络（CNN）架构作为核心，并基于分割掩码条件，迭代地去噪稀疏轮廓表示。为了提高低数据下的有效性和推理效率，我们引入了多种新颖的适应性改进措施，包括简化扩散过程、定制模型架构以及最小化后处理步骤，在仅使用少于500张训练图像的数据集上产生稠密且独立的轮廓。 在医学成像数据集KVASIR上，我们的方法优于多个最先进的基线。同时，在HAM10K和我们自定义的野火数据集Smoke上的性能也具有竞争力，并且推理帧率提高了3.5倍。
URL
https://arxiv.org/abs/2602.05880
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Disco: Densely-overlapping Cell Instance Segmentation via Adjacency-aware Collaborative Coloring
2026-02-05 08:05:48Rui Sun, Yiwen Yang, Kaiyu Guo, Chen Jiang, Dongli Xu, Zhaonan Liu, Tan Pan, Limei Han, Xue Jiang, Wu Wei, Yuan ChengAbstract
Accurate cell instance segmentation is foundational for digital pathology analysis. Existing methods based on contour detection and distance mapping still face significant challenges in processing complex and dense cellular regions. Graph coloring-based methods provide a new paradigm for this task, yet the effectiveness of this paradigm in real-world scenarios with dense overlaps and complex topologies has not been verified. Addressing this issue, we release a large-scale dataset GBC-FS 2025, which contains highly complex and dense sub-cellular nuclear arrangements. We conduct the first systematic analysis of the chromatic properties of cell adjacency graphs across four diverse datasets and reveal an important discovery: most real-world cell graphs are non-bipartite, with a high prevalence of odd-length cycles (predominantly triangles). This makes simple 2-coloring theory insufficient for handling complex tissues, while higher-chromaticity models would cause representational redundancy and optimization difficulties. Building on this observation of complex real-world contexts, we propose Disco (Densely-overlapping Cell Instance Segmentation via Adjacency-aware COllaborative Coloring), an adjacency-aware framework based on the "divide and conquer" principle. It uniquely combines a data-driven topological labeling strategy with a constrained deep learning system to resolve complex adjacency conflicts. First, "Explicit Marking" strategy transforms the topological challenge into a learnable classification task by recursively decomposing the cell graph and isolating a "conflict set." Second, "Implicit Disambiguation" mechanism resolves ambiguities in conflict regions by enforcing feature dissimilarity between different instances, enabling the model to learn separable feature representations.
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准确的细胞实例分割是数字病理学分析的基础。现有的基于轮廓检测和距离映射的方法在处理复杂且密集的细胞区域时仍然面临重大挑战。基于图着色的方法为这一任务提供了一种新的范式，但在现实场景中，尤其是在存在大量重叠和复杂拓扑结构的情况下，这种方法的有效性尚未得到验证。 为了应对这一问题，我们发布了大规模数据集GBC-FS 2025，该数据集中包含了高度复杂的密集亚细胞核排列。我们在四个不同的数据集上进行了首次关于细胞邻接图的色度特性的系统分析，并揭示了一个重要的发现：大多数现实世界的细胞图是非二部图，且含有大量的奇数长度循环（主要是三角形）。这表明简单的二着色理论不足以处理复杂组织的问题，而更高色度的模型会导致表示冗余和优化困难。 基于对这种复杂现实环境的理解，我们提出了一种新的方法Disco（密集重叠细胞实例分割通过邻接感知协作着色），这是一种基于“分治”原则并利用邻接信息的框架。这种方法独特地结合了数据驱动拓扑标签策略与受约束的深度学习系统，以解决复杂的邻接冲突问题。 首先，“显式标记”策略通过递归分解细胞图并将“冲突集”隔离出来，将拓扑挑战转化为可学习分类任务。 其次，“隐式消歧机制”通过强制不同实例之间的特征差异性来解析冲突区域中的模糊性，使模型能够学习到分离的特征表示。
URL
https://arxiv.org/abs/2602.05420
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Multi-head automated segmentation by incorporating detection head into the contextual layer neural network
2026-02-02 18:51:25Edwin Kys, Febian FebianAbstract
Deep learning based auto segmentation is increasingly used in radiotherapy, but conventional models often produce anatomically implausible false positives, or hallucinations, in slices lacking target structures. We propose a gated multi-head Transformer architecture based on Swin U-Net, augmented with inter-slice context integration and a parallel detection head, which jointly performs slice-level structure detection via a multi-layer perceptron and pixel-level segmentation through a context-enhanced stream. Detection outputs gate the segmentation predictions to suppress false positives in anatomically invalid slices, and training uses slice-wise Tversky loss to address class imbalance. Experiments on the Prostate-Anatomical-Edge-Cases dataset from The Cancer Imaging Archive demonstrate that the gated model substantially outperforms a non-gated segmentation-only baseline, achieving a mean Dice loss of $0.013 \pm 0.036$ versus $0.732 \pm 0.314$, with detection probabilities strongly correlated with anatomical presence, effectively eliminating spurious segmentations. In contrast, the non-gated model exhibited higher variability and persistent false positives across all slices. These results indicate that detection-based gating enhances robustness and anatomical plausibility in automated segmentation applications, reducing hallucinated predictions without compromising segmentation quality in valid slices, and offers a promising approach for improving the reliability of clinical radiotherapy auto-contouring workflows.
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URL
https://arxiv.org/abs/2602.02471
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SPARK: Stochastic Propagation via Affinity-guided Random walK for training-free unsupervised segmentation
2026-01-31 05:12:17Kunal Mahatha, Jose Dolz, Christian DesrosiersAbstract
We argue that existing training-free segmentation methods rely on an implicit and limiting assumption, that segmentation is a spectral graph partitioning problem over diffusion-derived affinities. Such approaches, based on global graph partitioning and eigenvector-based formulations of affinity matrices, suffer from several fundamental drawbacks, they require pre-selecting the number of clusters, induce boundary oversmoothing due to spectral relaxation, and remain highly sensitive to noisy or multi-modal affinity distributions. Moreover, many prior works neglect the importance of local neighborhood structure, which plays a crucial role in stabilizing affinity propagation and preserving fine-grained contours. To address these limitations, we reformulate training-free segmentation as a stochastic flow equilibrium problem over diffusion-induced affinity graphs, where segmentation emerges from a stochastic propagation process that integrates global diffusion attention with local neighborhoods extracted from stable diffusion, yielding a sparse yet expressive affinity structure. Building on this formulation, we introduce a Markov propagation scheme that performs random-walk-based label diffusion with an adaptive pruning strategy that suppresses unreliable transitions while reinforcing confident affinity paths. Experiments across seven widely used semantic segmentation benchmarks demonstrate that our method achieves state-of-the-art zero-shot performance, producing sharper boundaries, more coherent regions, and significantly more stable masks compared to prior spectral-clustering-based approaches.
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URL
https://arxiv.org/abs/2602.00516
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Advanced Geometric Correction Algorithms for 3D Medical Reconstruction: Comparison of Computed Tomography and Macroscopic Imaging
2026-01-30 17:16:17Tomasz Les, Tomasz Markiewicz, Malgorzata Lorent, Miroslaw Dziekiewicz, Krzysztof SiwekAbstract
This paper introduces a hybrid two-stage registration framework for reconstructing three-dimensional (3D) kidney anatomy from macroscopic slices, using CT-derived models as the geometric reference standard. The approach addresses the data-scarcity and high-distortion challenges typical of macroscopic imaging, where fully learning-based registration (e.g., VoxelMorph) often fails to generalize due to limited training diversity and large nonrigid deformations that exceed the capture range of unconstrained convolutional filters. In the proposed pipeline, the Optimal Cross-section Matching (OCM) algorithm first performs constrained global alignment: translation, rotation, and uniform scaling to establish anatomically consistent slice initialization. Next, a lightweight deep-learning refinement network, inspired by VoxelMorph, predicts residual local deformations between consecutive slices. The core novelty of this architecture lies in its hierarchical decomposition of the registration manifold. This hybrid OCM+DL design integrates explicit geometric priors with the flexible learning capacity of neural networks, ensuring stable optimization and plausible deformation fields even with few training examples. Experiments on an original dataset of 40 kidneys demonstrated better results compared to single-stage baselines. The pipeline maintains physical calibration via Hough-based grid detection and employs Bezier-based contour smoothing for robust meshing and volume estimation. Although validated on kidney data, the proposed framework generalizes to other soft-tissue organs reconstructed from optical or photographic cross-sections. By decoupling interpretable global optimization from data-efficient deep refinement, the method advances the precision, reproducibility, and anatomical realism of multimodal 3D reconstructions for surgical planning, morphological assessment, and medical education.
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URL
https://arxiv.org/abs/2602.00220
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Towards Pixel-Level VLM Perception via Simple Points Prediction
2026-01-27 05:50:40Tianhui Song, Haoyu Lu, Hao Yang, Lin Sui, Haoning Wu, Zaida Zhou, Zhiqi Huang, Yiping Bao, Y. Charles, Xinyu Zhou, Limin WangAbstract
We present SimpleSeg, a strikingly simple yet highly effective approach to endow Multimodal Large Language Models (MLLMs) with native pixel-level perception. Our method reframes segmentation as a simple sequence generation problem: the model directly predicts sequences of points (textual coordinates) delineating object boundaries, entirely within its language space. To achieve high fidelity, we introduce a two-stage SF$\to$RL training pipeline, where Reinforcement Learning with an IoU-based reward refines the point sequences to accurately match ground-truth contours. We find that the standard MLLM architecture possesses a strong, inherent capacity for low-level perception that can be unlocked without any specialized architecture. On segmentation benchmarks, SimpleSeg achieves performance that is comparable to, and often surpasses, methods relying on complex, task-specific designs. This work lays out that precise spatial understanding can emerge from simple point prediction, challenging the prevailing need for auxiliary components and paving the way for more unified and capable VLMs. Homepage: this https URL
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URL
https://arxiv.org/abs/2601.19228
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FBSDiff++: Improved Frequency Band Substitution of Diffusion Features for Efficient and Highly Controllable Text-Driven Image-to-Image Translation
2026-01-27 02:39:20Xiang Gao, Yunpeng JiaAbstract
With large-scale text-to-image (T2I) diffusion models achieving significant advancements in open-domain image creation, increasing attention has been focused on their natural extension to the realm of text-driven image-to-image (I2I) translation, where a source image acts as visual guidance to the generated image in addition to the textual guidance provided by the text prompt. We propose FBSDiff, a novel framework adapting off-the-shelf T2I diffusion model into the I2I paradigm from a fresh frequency-domain perspective. Through dynamic frequency band substitution of diffusion features, FBSDiff realizes versatile and highly controllable text-driven I2I in a plug-and-play manner (without need for model training, fine-tuning, or online optimization), allowing appearance-guided, layout-guided, and contour-guided I2I translation by progressively substituting low-frequency band, mid-frequency band, and high-frequency band of latent diffusion features, respectively. In addition, FBSDiff flexibly enables continuous control over I2I correlation intensity simply by tuning the bandwidth of the substituted frequency band. To further promote image translation efficiency, flexibility, and functionality, we propose FBSDiff++ which improves upon FBSDiff mainly in three aspects: (1) accelerate inference speed by a large margin (8.9$\times$ speedup in inference) with refined model architecture; (2) improve the Frequency Band Substitution module to allow for input source images of arbitrary resolution and aspect ratio; (3) extend model functionality to enable localized image manipulation and style-specific content creation with only subtle adjustments to the core method. Extensive qualitative and quantitative experiments verify superiority of FBSDiff++ in I2I translation visual quality, efficiency, versatility, and controllability compared to related advanced approaches.
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URL
https://arxiv.org/abs/2601.19115
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Goal-oriented Communication for Fast and Robust Robotic Fault Detection and Recovery
2026-01-26 18:32:33Shutong Chen, Adnan Aijaz, Yansha DengAbstract
Autonomous robotic systems are widely deployed in smart factories and operate in dynamic, uncertain, and human-involved environments that require low-latency and robust fault detection and recovery (FDR). However, existing FDR frameworks exhibit various limitations, such as significant delays in communication and computation, and unreliability in robot motion/trajectory generation, mainly because the communication-computation-control (3C) loop is designed without considering the downstream FDR goal. To address this, we propose a novel Goal-oriented Communication (GoC) framework that jointly designs the 3C loop tailored for fast and robust robotic FDR, with the goal of minimising the FDR time while maximising the robotic task (e.g., workpiece sorting) success rate. For fault detection, our GoC framework innovatively defines and extracts the 3D scene graph (3D-SG) as the semantic representation via our designed representation extractor, and detects faults by monitoring spatial relationship changes in the 3D-SG. For fault recovery, we fine-tune a small language model (SLM) via Low-Rank Adaptation (LoRA) and enhance its reasoning and generalization capabilities via knowledge distillation to generate recovery motions for robots. We also design a lightweight goal-oriented digital twin reconstruction module to refine the recovery motions generated by the SLM when fine-grained robotic control is required, using only task-relevant object contours for digital twin reconstruction. Extensive simulations demonstrate that our GoC framework reduces the FDR time by up to 82.6% and improves the task success rate by up to 76%, compared to the state-of-the-art frameworks that rely on vision language models for fault detection and large language models for fault recovery.
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URL
https://arxiv.org/abs/2601.18765
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FlowMorph: Physics-Consistent Self-Supervision for Label-Free Single-Cell Mechanics in Microfluidic Videos
2026-01-25 18:57:36Bora Yimenicioglu, Vishal ManikandenAbstract
Mechanical properties of red blood cells (RBCs) are promising biomarkers for hematologic and systemic disease, motivating microfluidic assays that probe deformability at throughputs of $10^3$--$10^6$ cells per experiment. However, existing pipelines rely on supervised segmentation or hand-crafted kymographs and rarely encode the laminar Stokes-flow physics that governs RBC shape evolution. We introduce FlowMorph, a physics-consistent self-supervised framework that learns a label-free scalar mechanics proxy $k$ for each tracked RBC from short brightfield microfluidic videos. FlowMorph models each cell by a low-dimensional parametric contour, advances boundary points through a differentiable ''capsule-in-flow'' combining laminar advection and curvature-regularized elastic relaxation, and optimizes a loss coupling silhouette overlap, intra-cellular flow agreement, area conservation, wall constraints, and temporal smoothness, using only automatically derived silhouettes and optical flow. Across four public RBC microfluidic datasets, FlowMorph achieves a mean silhouette IoU of $0.905$ on physics-rich videos with provided velocity fields and markedly improves area conservation and wall violations over purely data-driven baselines. On $\sim 1.5\times 10^5$ centered sequences, the scalar $k$ alone separates tank-treading from flipping dynamics with an AUC of $0.863$. Using only $200$ real-time deformability cytometry (RT-DC) events for calibration, a monotone map $E=g(k)$ predicts apparent Young's modulus with a mean absolute error of $0.118$\,MPa on $600$ held-out cells and degrades gracefully under shifts in channel geometry, optics, and frame rate.
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URL
https://arxiv.org/abs/2601.17947
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UCAD: Uncertainty-guided Contour-aware Displacement for semi-supervised medical image segmentation
2026-01-24 08:21:14Chengbo Ding, Fenghe Tang, Shaohua Kevin ZhouAbstract
Existing displacement strategies in semi-supervised segmentation only operate on rectangular regions, ignoring anatomical structures and resulting in boundary distortions and semantic inconsistency. To address these issues, we propose UCAD, an Uncertainty-Guided Contour-Aware Displacement framework for semi-supervised medical image segmentation that preserves contour-aware semantics while enhancing consistency learning. Our UCAD leverages superpixels to generate anatomically coherent regions aligned with anatomy boundaries, and an uncertainty-guided selection mechanism to selectively displace challenging regions for better consistency learning. We further propose a dynamic uncertainty-weighted consistency loss, which adaptively stabilizes training and effectively regularizes the model on unlabeled regions. Extensive experiments demonstrate that UCAD consistently outperforms state-of-the-art semi-supervised segmentation methods, achieving superior segmentation accuracy under limited annotation. The code is available at:this https URL.
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URL
https://arxiv.org/abs/2601.17366
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Filtered 2D Contour-Based Reconstruction of 3D STL Model from CT-DICOM Images
2026-01-21 13:56:43K. Punnam Chandar, Y. Ravi KumarAbstract
Reconstructing a 3D Stereo-lithography (STL) Model from 2D Contours of scanned structure in Digital Imaging and Communication in Medicine (DICOM) images is crucial to understand the geometry and deformity. Computed Tomography (CT) images are processed to enhance the contrast, reduce the noise followed by smoothing. The processed CT images are segmented using thresholding technique. 2D contour data points are extracted from segmented CT images and are used to construct 3D STL Models. The 2D contour data points may contain outliers as a result of segmentation of low resolution images and the geometry of the constructed 3D structure deviate from the actual. To cope with the imperfections in segmentation process, in this work we propose to use filtered 2D contour data points to reconstruct 3D STL Model. The filtered 2D contour points of each image are delaunay triangulated and joined layer-by-layer to reconstruct the 3D STL model. The 3D STL Model reconstruction is verified on i) 2D Data points of basic shapes and ii) Region of Interest (ROI) of human pelvic bone and are presented as case studies. The 3D STL model constructed from 2D contour data points of ROI of segmented pelvic bone with and without filtering are presented. The 3D STL model reconstructed from filtered 2D data points improved the geometry of model compared to the model reconstructed without filtering 2D data points.
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从医学数字成像和通信（DICOM）图像扫描结构的二维轮廓重建三维立体光刻（STL）模型对于理解几何形状和畸形至关重要。计算机断层扫描（CT）图像经过处理以增强对比度，减少噪声，并进行平滑处理。处理后的CT图像使用阈值技术进行分割。从分段CT图像中提取2D轮廓数据点并用于构建3D STL模型。由于低分辨率图像的分割，这些二维轮廓数据点可能包含离群点，导致构造出的三维结构与实际几何形状有所偏差。 为了应对分割过程中的缺陷，我们在此工作中提出使用过滤后的二维轮廓数据点来重建3D STL模型。每个图层上的滤波2D轮廓点进行德劳内三角剖分并层层连接以重构3D STL模型。3D STL模型的重建通过以下案例进行了验证：一是基本形状的2D数据点，二是人体髋骨感兴趣区域（ROI）。论文中展示了从分割后的髋骨ROI二维轮廓数据点构建的3D STL模型，并比较了过滤和未过滤的数据点在建模效果上的差异。结果表明，基于滤波2D数据点重建的3D STL模型比使用未经处理的数据点重构出的模型具有更好的几何形状精度。
URL
https://arxiv.org/abs/2601.14997
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Single-step Controllable Music Bandwidth Extension With Flow Matching
2026-01-20 18:26:49Carlos Hernandez-Olivan, Hendrik Vincent Koops, Hao Hao Tan, Elio QuintonAbstract
Audio restoration consists in inverting degradations of a digital audio signal to recover what would have been the pristine quality signal before the degradation occurred. This is valuable in contexts such as archives of music recordings, particularly those of precious historical value, for which a clean version may have been lost or simply does not exist. Recent work applied generative models to audio restoration, showing promising improvement over previous methods, and opening the door to the ability to perform restoration operations that were not possible before. However, making these models finely controllable remains a challenge. In this paper, we propose an extension of FLowHigh and introduce the Dynamic Spectral Contour (DSC) as a control signal for bandwidth extension via classifier-free guidance. Our experiments show competitive model performance, and indicate that DSC is a promising feature to support fine-grained conditioning.
Abstract (translated)
音频修复是指逆向处理数字音频信号中的退化，以恢复在退化发生前的原始高质量信号。这在诸如音乐录音档案的情境中尤其有价值，特别是在历史价值珍贵的情况下，可能已丢失或根本不存在干净版本的原始录音。近期的研究将生成模型应用于音频修复领域，显示出相对于先前方法的重大改进，并开启了能够执行以前无法实现的修复操作的可能性。然而，使这些模型具有精细控制性仍然是一项挑战。 在本文中，我们提出了对FLowHigh的扩展，并引入了动态频谱轮廓（Dynamic Spectral Contour, DSC）作为带宽扩展中的分类器自由引导的控制信号。我们的实验表明该方法具有竞争力的表现，并且DSC作为一个支持细粒度调节的功能显示出前景。
URL
https://arxiv.org/abs/2601.14356
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Partial Decoder Attention Network with Contour-weighted Loss Function for Data-Imbalance Medical Image Segmentation
2026-01-20 13:21:12Zhengyong Huang, Ning Jiang, Xingwen Sun, Lihua Zhang, Peng Chen, Jens Domke, Yao SuiAbstract
Image segmentation is pivotal in medical image analysis, facilitating clinical diagnosis, treatment planning, and disease evaluation. Deep learning has significantly advanced automatic segmentation methodologies by providing superior modeling capability for complex structures and fine-grained anatomical regions. However, medical images often suffer from data imbalance issues, such as large volume disparities among organs or tissues, and uneven sample distributions across different anatomical structures. This imbalance tends to bias the model toward larger organs or more frequently represented structures, while overlooking smaller or less represented structures, thereby affecting the segmentation accuracy and robustness. To address these challenges, we proposed a novel contour-weighted segmentation approach, which improves the model's capability to represent small and underrepresented structures. We developed PDANet, a lightweight and efficient segmentation network based on a partial decoder mechanism. We evaluated our method using three prominent public datasets. The experimental results show that our methodology excelled in three distinct tasks: segmenting multiple abdominal organs, brain tumors, and pelvic bone fragments with injuries. It consistently outperformed nine state-of-the-art methods. Moreover, the proposed contour-weighted strategy improved segmentation for other comparison methods across the three datasets, yielding average enhancements in Dice scores of 2.32%, 1.67%, and 3.60%, respectively. These results demonstrate that our contour-weighted segmentation method surpassed current leading approaches in both accuracy and robustness. As a model-independent strategy, it can seamlessly fit various segmentation frameworks, enhancing their performance. This flexibility highlighted its practical importance and potential for broad use in medical image analysis.
Abstract (translated)
图像分割在医学影像分析中至关重要，它有助于临床诊断、治疗计划制定以及疾病评估。深度学习通过提供对复杂结构和细微解剖区域的卓越建模能力，在自动分割方法上取得了显著进展。然而，医学图像常常受到数据不平衡问题的影响，例如不同器官或组织之间的体积差异较大，以及在不同解剖结构之间样本分布不均等。这种不平衡会导致模型偏向于更大的器官或更频繁出现的结构，而忽略较小或较少代表的结构，从而影响分割的准确性和鲁棒性。 为了解决这些问题，我们提出了一种新颖的轮廓加权分割方法，这种方法能够提升模型表示小规模和代表性较弱结构的能力。为此，我们开发了PDANet——一种基于部分解码机制的轻量级高效分割网络。我们在三个著名的公开数据集上评估了我们的方法。实验结果表明，在多器官腹部图像、脑肿瘤以及骨盆骨折碎片的分割这三项不同任务中，我们的方法均优于九种当前最先进的技术。此外，所提出的轮廓加权策略在上述所有对比方法中都带来了显著改进：分别提高了Dice分数平均值2.32%、1.67%和3.60%。这些结果表明，在准确性和鲁棒性方面，我们提出的轮廓加权分割法已经超越了当前的领先方案。 作为一种与模型无关的战略，它可以无缝地应用于各种分割框架中以提升其性能。这种灵活性突显了它在医学影像分析中的实用价值及其广泛的应用潜力。
URL
https://arxiv.org/abs/2601.14338
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Deep Image Prior with L0 Gradient Regularizer for Image Smoothing
2026-01-19 21:10:32Nhat Thanh Tran, Kevin Bui, Jack XinAbstract
Image smoothing is a fundamental image processing operation that preserves the underlying structure, such as strong edges and contours, and removes minor details and textures in an image. Many image smoothing algorithms rely on computing local window statistics or solving an optimization problem. Recent state-of-the-art methods leverage deep learning, but they require a carefully curated training dataset. Because constructing a proper training dataset for image smoothing is challenging, we propose DIP-$\ell_0$, a deep image prior framework that incorporates the $\ell_0$ gradient regularizer. This framework can perform high-quality image smoothing without any training data. To properly minimize the associated loss function that has the nonconvex, nonsmooth $\ell_0$ ``norm", we develop an alternating direction method of multipliers algorithm that utilizes an off-the-shelf $\ell_0$ gradient minimization solver. Numerical experiments demonstrate that the proposed DIP-$\ell_0$ outperforms many image smoothing algorithms in edge-preserving image smoothing and JPEG artifact removal.
Abstract (translated)
图像平滑是一种基本的图像处理操作，它保留了底层结构（如强边缘和轮廓），同时移除图像中的小细节和纹理。许多图像平滑算法依赖于计算局部窗口统计信息或求解优化问题。最近最先进的方法利用深度学习技术，但它们需要精心准备的训练数据集。由于为图像平滑构建合适的训练数据集颇具挑战性，我们提出了一种名为DIP-$\ell_0$（Deep Image Prior）的框架，并在该框架中引入了$\ell_0$梯度正则化器。这个框架能够在无需任何训练数据的情况下实现高质量的图像平滑。 为了有效最小化含有非凸且不光滑的$\ell_0$“范数”的相关损失函数，我们开发了一种交替方向乘子法算法，并利用现成的$\ell_0$梯度最小化求解器。数值实验表明，所提出的DIP-$\ell_0$框架在边缘保护型图像平滑和JPEG伪影去除方面优于许多其他图像平滑算法。
URL
https://arxiv.org/abs/2601.13400
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Diagnosis Support of Sickle Cell Anemia by Classifying Red Blood Cell Shape in Peripheral Blood Images
2026-01-19 13:15:14Wilkie Delgado-Font, Miriela Escobedo-Nicot, Manuel Gonz\'alez-Hidalgo, Silena Herold-Garcia, Antoni Jaume-i-Cap\'o, Arnau MirAbstract
Red blood cell (RBC) deformation is the consequence of several diseases, including sickle cell anemia, which causes recurring episodes of pain and severe pronounced anemia. Monitoring patients with these diseases involves the observation of peripheral blood samples under a microscope, a time-consuming procedure. Moreover, a specialist is required to perform this technique, and owing to the subjective nature of the observation of isolated RBCs, the error rate is high. In this paper, we propose an automated method for differentially enumerating RBCs that uses peripheral blood smear image analysis. In this method, the objects of interest in the image are segmented using a Chan-Vese active contour model. An analysis is then performed to classify the RBCs, also called erythrocytes, as normal or elongated or having other deformations, using the basic shape analysis descriptors: circular shape factor (CSF) and elliptical shape factor (ESF). To analyze cells that become partially occluded in a cluster during sample preparation, an elliptical adjustment is performed to allow the analysis of erythrocytes with discoidal and elongated shapes. The images of patient blood samples used in the study were acquired by a clinical laboratory specialist in the Special Hematology Department of the ``Dr. Juan Bruno Zayas'' General Hospital in Santiago de Cuba. A comparison of the results obtained by the proposed method in our experiments with those obtained by some state-of-the-art methods showed that the proposed method is superior for the diagnosis of sickle cell anemia. This superiority is achieved for evidenced by the obtained F-measure value (0.97 for normal cells and 0.95 for elongated ones) and several overall multiclass performance measures. The results achieved by the proposed method are suitable for the purpose of clinical treatment and diagnostic support of sickle cell anemia.
Abstract (translated)
URL
https://arxiv.org/abs/2601.17032
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SoundPlot: An Open-Source Framework for Birdsong Acoustic Analysis and Neural Synthesis with Interactive 3D Visualization
2026-01-19 06:17:26Naqcho Ali Mehdi, Mohammad Adeel, Aizaz Ali LarikAbstract
We present SoundPlot, an open-source framework for analyzing avian vocalizations through acoustic feature extraction, dimensionality reduction, and neural audio synthesis. The system transforms audio signals into a multi-dimensional acoustic feature space, enabling real-time visualization of temporal dynamics in 3D using web-based interactive graphics. Our framework implements a complete analysis-synthesis pipeline that extracts spectral features (centroid, bandwidth, contrast), pitch contours via probabilistic YIN (pYIN), and mel-frequency cepstral coefficients (MFCCs), mapping them to a unified timbre space for visualization. Audio reconstruction employs the Griffin-Lim phase estimation algorithm applied to mel spectrograms. The accompanying this http URL-based interface provides dual-viewport visualization comparing original and synthesized audio trajectories with independent playback controls. We demonstrate the framework's capabilities through comprehensive waveform analysis, spectrogram comparisons, and feature space evaluation using Principal Component Analysis (PCA). Quantitative evaluation shows mel spectrogram correlation scores exceeding 0.92, indicating high-fidelity preservation of perceptual acoustic structure. SoundPlot is released under the MIT License to facilitate research in bioacoustics, audio signal processing, and computational ethology.
Abstract (translated)
我们介绍了SoundPlot，这是一个开源框架，用于通过声学特征提取、降维和神经音频合成来分析鸟类鸣叫声。该系统将音频信号转换为一个多维的声学特性空间，从而能够使用基于网络的交互式图形实现实时3D时间动态可视化。我们的框架实现了一个完整的分析-综合管道，从频谱特征（中心频率、带宽、对比度）、通过概率YIN (pYIN) 提取音高轮廓以及梅尔频率倒谱系数（MFCCs），并将它们映射到一个统一的音色空间进行可视化。音频重构采用Griffin-Lim相位估计算法应用于梅尔光谱图。 该框架附带了一个基于Web的界面，提供了双视口可视化功能，可以比较原始和合成音频轨迹，并提供独立的播放控制。我们通过全面的波形分析、频谱图对比以及使用主成分分析（PCA）进行特征空间评估来展示该框架的能力。定量评价显示梅尔光谱相关得分超过0.92，表明感知声学结构的高度保真度保存。 SoundPlot根据MIT许可发布，以促进生物声学、音频信号处理和计算动物行为学的研究。
URL
https://arxiv.org/abs/2601.12752
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Unbounded Harms, Bounded Law: Liability in the Age of Borderless AI
Abstract
The rapid proliferation of artificial intelligence (AI) has exposed significant deficiencies in risk governance. While ex-ante harm identification and prevention have advanced, Responsible AI scholarship remains underdeveloped in addressing ex-post liability. Core legal questions regarding liability allocation, responsibility attribution, and remedial effectiveness remain insufficiently theorized and institutionalized, particularly for transboundary harms and risks that transcend national jurisdictions. Drawing on contemporary AI risk analyses, we argue that such harms are structurally embedded in global AI supply chains and are likely to escalate in frequency and severity due to cross-border deployment, data infrastructures, and uneven national oversight capacities. Consequently, territorially bounded liability regimes are increasingly inadequate. Using a comparative and interdisciplinary approach, this paper examines compensation and liability frameworks from high-risk transnational domains - including vaccine injury schemes, systemic financial risk governance, commercial nuclear liability, and international environmental regimes - to distill transferable legal design principles such as strict liability, risk pooling, collective risk-sharing, and liability channelling, while highlighting potential structural constraints on their application to AI-related harms. Situated within an international order shaped more by AI arms race dynamics than cooperative governance, the paper outlines the contours of a global AI accountability and compensation architecture, emphasizing the tension between geopolitical rivalry and the collective action required to govern transboundary AI risks effectively.
Abstract (translated)
人工智能（AI）的迅速普及揭示了风险管理治理方面的显著缺陷。虽然事前危害识别和预防已经有所进步，但负责任的人工智能研究在处理事后责任方面仍不够充分发展。关于责任分配、责任归属以及补救效果的核心法律问题尚未得到充分理论化和制度化，尤其是在跨国界伤害和超越国家管辖范围的风险领域。基于当代人工智能风险分析，我们认为此类损害结构性地嵌入全球AI供应链中，并且由于跨境部署、数据基础设施以及各国监管能力的不均衡，它们可能会在频率和严重程度上增加。因此，基于领土边界的责任制度变得越来越不足以应对这些挑战。 本文采用比较性和跨学科的方法，审视疫苗伤害计划、系统性金融风险治理、商业核能责任以及国际环境体制中的高风险跨国领域中补偿和责任框架，提炼出可应用于人工智能相关损害的严格责任、风险共担、集体风险管理及责任渠道转移等法律设计原则，并强调将这些原则应用到AI相关危害时可能面临的结构性限制。鉴于当前国际秩序更倾向于由AI军备竞赛动态塑造而非合作治理，在这篇论文中我们勾勒出了一个全球性的AI问责和补偿架构，突出地缘政治竞争与有效管理跨境人工智能风险所需的集体行动之间的紧张关系。
URL
https://arxiv.org/abs/2601.12646
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