Contour Flow Constraint: Preserving Global Shape Similarity for Deep Learning based Image Segmentation

• URL: http://arxiv.org/abs/2504.09384v1
• Date: Sun, 13 Apr 2025 00:34:47 GMT
• Title: Contour Flow Constraint: Preserving Global Shape Similarity for Deep Learning based Image Segmentation
• Authors: Shengzhe Chen, Zhaoxuan Dong, Jun Liu,
• Abstract summary: We propose a concept of global shape similarity based on the premise that two shapes exhibit comparable contours.<n>We propose two implementations to integrate the constraint with deep neural networks.<n> CFSSnet shows robustness in segmenting noise-contaminated images, and inherent capability to preserve global shape similarity.
• Score: 3.581887371751499
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For effective image segmentation, it is crucial to employ constraints informed by prior knowledge about the characteristics of the areas to be segmented to yield favorable segmentation outcomes. However, the existing methods have primarily focused on priors of specific properties or shapes, lacking consideration of the general global shape similarity from a Contour Flow (CF) perspective. Furthermore, naturally integrating this contour flow prior image segmentation model into the activation functions of deep convolutional networks through mathematical methods is currently unexplored. In this paper, we establish a concept of global shape similarity based on the premise that two shapes exhibit comparable contours. Furthermore, we mathematically derive a contour flow constraint that ensures the preservation of global shape similarity. We propose two implementations to integrate the constraint with deep neural networks. Firstly, the constraint is converted to a shape loss, which can be seamlessly incorporated into the training phase for any learning-based segmentation framework. Secondly, we add the constraint into a variational segmentation model and derive its iterative schemes for solution. The scheme is then unrolled to get the architecture of the proposed CFSSnet. Validation experiments on diverse datasets are conducted on classic benchmark deep network segmentation models. The results indicate a great improvement in segmentation accuracy and shape similarity for the proposed shape loss, showcasing the general adaptability of the proposed loss term regardless of specific network architectures. CFSSnet shows robustness in segmenting noise-contaminated images, and inherent capability to preserve global shape similarity.

Related papers

• Segmenting objects with Bayesian fusion of active contour models and convnet priors [0.729597981661727]
  We propose a novel instance segmentation method geared towards Natural Resource Monitoring (NRM) imagery. We formulate the problem as Bayesian maximum a posteriori inference which, in learning the individual object contours, incorporates shape, location, and position priors. In experiments, we tackle the challenging, real-world problem of segmenting individual dead tree crowns and precise contours.
  arXiv  Detail & Related papers  (2024-10-09T20:36:43Z)
• Physically Feasible Semantic Segmentation [58.17907376475596]
  State-of-the-art semantic segmentation models are typically optimized in a data-driven fashion, minimizing solely per-pixel or per-segment classification objectives on their training data.<n>This purely data-driven paradigm often leads to absurd segmentations, especially when the domain of input images is shifted from the one encountered during training.<n>Our method, Physically Feasible Semantic (PhyFea), first extracts explicit constraints that govern spatial class relations from the semantic segmentation training set at hand in an offline data-driven fashion, and then enforces a morphological yet differentiable loss that penalizes violations of these constraints during
  arXiv  Detail & Related papers  (2024-08-26T22:39:08Z)
• Early Fusion of Features for Semantic Segmentation [10.362589129094975]
  This paper introduces a novel segmentation framework that integrates a classifier network with a reverse HRNet architecture for efficient image segmentation. Our methodology is rigorously tested across several benchmark datasets including Mapillary Vistas, Cityscapes, CamVid, COCO, and PASCAL-VOC2012. The results demonstrate the effectiveness of our proposed model in achieving high segmentation accuracy, indicating its potential for various applications in image analysis.
  arXiv  Detail & Related papers  (2024-02-08T22:58:06Z)
• Self-supervised Semantic Segmentation: Consistency over Transformation [3.485615723221064]
  We propose a novel self-supervised algorithm, textbfS$3$-Net, which integrates a robust framework based on the proposed Inception Large Kernel Attention (I-LKA) modules. We leverage deformable convolution as an integral component to effectively capture and delineate lesion deformations for superior object boundary definition. Our experimental results on skin lesion and lung organ segmentation tasks show the superior performance of our method compared to the SOTA approaches.
  arXiv  Detail & Related papers  (2023-08-31T21:28:46Z)
• RecRecNet: Rectangling Rectified Wide-Angle Images by Thin-Plate Spline Model and DoF-based Curriculum Learning [62.86400614141706]
  We propose a new learning model, i.e., Rectangling Rectification Network (RecRecNet) Our model can flexibly warp the source structure to the target domain and achieves an end-to-end unsupervised deformation. Experiments show the superiority of our solution over the compared methods on both quantitative and qualitative evaluations.
  arXiv  Detail & Related papers  (2023-01-04T15:12:57Z)
• Denoise and Contrast for Category Agnostic Shape Completion [48.66519783934386]
  We present a deep learning model that exploits the power of self-supervision to perform 3D point cloud completion. A denoising pretext task provides the network with the needed local cues, decoupled from the high-level semantics. contrastive learning maximizes the agreement between variants of the same shape with different missing portions.
  arXiv  Detail & Related papers  (2021-03-30T20:33:24Z)
• Commonality-Parsing Network across Shape and Appearance for Partially Supervised Instance Segmentation [71.59275788106622]
  We propose to learn the underlying class-agnostic commonalities that can be generalized from mask-annotated categories to novel categories. Our model significantly outperforms the state-of-the-art methods on both partially supervised setting and few-shot setting for instance segmentation on COCO dataset.
  arXiv  Detail & Related papers  (2020-07-24T07:23:44Z)
• Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images [24.216869988183092]
  We propose a shapeaware semi-supervised segmentation strategy to leverage abundant unlabeled data and to enforce a geometric shape constraint on the segmentation output. We develop a multi-task deep network that jointly predicts semantic segmentation and signed distance mapDM) of object surfaces. Experiments show that our method outperforms current state-of-the-art approaches with improved shape estimation.
  arXiv  Detail & Related papers  (2020-07-21T11:44:52Z)
• Neural Subdivision [58.97214948753937]
  This paper introduces Neural Subdivision, a novel framework for data-driven coarseto-fine geometry modeling. We optimize for the same set of network weights across all local mesh patches, thus providing an architecture that is not constrained to a specific input mesh, fixed genus, or category. We demonstrate that even when trained on a single high-resolution mesh our method generates reasonable subdivisions for novel shapes.
  arXiv  Detail & Related papers  (2020-05-04T20:03:21Z)
• Deep Semantic Matching with Foreground Detection and Cycle-Consistency [103.22976097225457]
  We address weakly supervised semantic matching based on a deep network. We explicitly estimate the foreground regions to suppress the effect of background clutter. We develop cycle-consistent losses to enforce the predicted transformations across multiple images to be geometrically plausible and consistent.
  arXiv  Detail & Related papers  (2020-03-31T22:38:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.