Print Defect Mapping with Semantic Segmentation

• URL: http://arxiv.org/abs/2001.10111v1
• Date: Mon, 27 Jan 2020 22:40:09 GMT
• Title: Print Defect Mapping with Semantic Segmentation
• Authors: Augusto C. Valente, Cristina Wada, Deangela Neves, Deangeli Neves, F\'abio V. M. Perez, Guilherme A. S. Megeto, Marcos H. Cascone, Otavio Gomes, Qian Lin
• Abstract summary: We propose the first end-to-end framework to map print defects at pixel level. Our framework uses Convolutional Neural Networks, specifically DeepLab-v3+, and achieves promising results.
• Score: 4.189639503810488
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efficient automated print defect mapping is valuable to the printing industry since such defects directly influence customer-perceived printer quality and manually mapping them is cost-ineffective. Conventional methods consist of complicated and hand-crafted feature engineering techniques, usually targeting only one type of defect. In this paper, we propose the first end-to-end framework to map print defects at pixel level, adopting an approach based on semantic segmentation. Our framework uses Convolutional Neural Networks, specifically DeepLab-v3+, and achieves promising results in the identification of defects in printed images. We use synthetic training data by simulating two types of print defects and a print-scan effect with image processing and computer graphic techniques. Compared with conventional methods, our framework is versatile, allowing two inference strategies, one being near real-time and providing coarser results, and the other focusing on offline processing with more fine-grained detection. Our model is evaluated on a dataset of real printed images.

Related papers

• Topograph: An efficient Graph-Based Framework for Strictly Topology Preserving Image Segmentation [78.54656076915565]
  Topological correctness plays a critical role in many image segmentation tasks. Most networks are trained using pixel-wise loss functions, such as Dice, neglecting topological accuracy. We propose a novel, graph-based framework for topologically accurate image segmentation.
  arXiv  Detail & Related papers  (2024-11-05T16:20:14Z)
• ENTED: Enhanced Neural Texture Extraction and Distribution for Reference-based Blind Face Restoration [51.205673783866146]
  We present ENTED, a new framework for blind face restoration that aims to restore high-quality and realistic portrait images. We utilize a texture extraction and distribution framework to transfer high-quality texture features between the degraded input and reference image. The StyleGAN-like architecture in our framework requires high-quality latent codes to generate realistic images.
  arXiv  Detail & Related papers  (2024-01-13T04:54:59Z)
• Pixel-Inconsistency Modeling for Image Manipulation Localization [63.54342601757723]
  Digital image forensics plays a crucial role in image authentication and manipulation localization. This paper presents a generalized and robust manipulation localization model through the analysis of pixel inconsistency artifacts. Experiments show that our method successfully extracts inherent pixel-inconsistency forgery fingerprints.
  arXiv  Detail & Related papers  (2023-09-30T02:54:51Z)
• WOUAF: Weight Modulation for User Attribution and Fingerprinting in Text-to-Image Diffusion Models [32.29120988096214]
  This paper introduces a novel approach to model fingerprinting that assigns responsibility for the generated images. Our method modifies generative models based on each user's unique digital fingerprint, imprinting a unique identifier onto the resultant content that can be traced back to the user.
  arXiv  Detail & Related papers  (2023-06-07T19:44:14Z)
• Semi-Siamese Network for Robust Change Detection Across Different Domains with Applications to 3D Printing [17.176767333354636]
  We present a novel Semi-Siamese deep learning model for defect detection in 3D printing processes. Our model is designed to enable comparison of heterogeneous images from different domains while being robust against perturbations in the imaging setup. Using our model, defect localization predictions can be made in less than half a second per layer using a standard MacBook Pro while achieving an F1-score of more than 0.9.
  arXiv  Detail & Related papers  (2022-12-16T17:02:55Z)
• Document Image Binarization in JPEG Compressed Domain using Dual Discriminator Generative Adversarial Networks [0.0]
  The proposed model has been thoroughly tested with different versions of DIBCO dataset having challenges like holes, erased or smudged ink, dust, and misplaced fibres. The model proved to be highly robust, efficient both in terms of time and space complexities, and also resulted in state-of-the-art performance in JPEG compressed domain.
  arXiv  Detail & Related papers  (2022-09-13T12:07:32Z)
• Towards Smart Monitored AM: Open Source in-Situ Layer-wise 3D Printing Image Anomaly Detection Using Histograms of Oriented Gradients and a Physics-Based Rendering Engine [0.0]
  This study presents an open source method for detecting 3D printing anomalies by comparing images of printed layers from a stationary monocular camera with G-code-based reference images of an ideal process generated with Blender, a physics rendering engine. Recognition of visual deviations was accomplished by analyzing the similarity of histograms of oriented gradients (HOG) of local image areas. The implementation of this novel method does not require preliminary data for training, and the greatest efficiency can be achieved with the mass production of parts by either additive or subtractive manufacturing of the same geometric shape.
  arXiv  Detail & Related papers  (2021-11-04T09:27:10Z)
• BoundarySqueeze: Image Segmentation as Boundary Squeezing [104.43159799559464]
  We propose a novel method for fine-grained high-quality image segmentation of both objects and scenes. Inspired by dilation and erosion from morphological image processing techniques, we treat the pixel level segmentation problems as squeezing object boundary. Our method yields large gains on COCO, Cityscapes, for both instance and semantic segmentation and outperforms previous state-of-the-art PointRend in both accuracy and speed under the same setting.
  arXiv  Detail & Related papers  (2021-05-25T04:58:51Z)
• High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling [122.06593036862611]
  Existing image inpainting methods often produce artifacts when dealing with large holes in real applications. We propose an iterative inpainting method with a feedback mechanism. Experiments show that our method significantly outperforms existing methods in both quantitative and qualitative evaluations.
  arXiv  Detail & Related papers  (2020-05-24T13:23:45Z)
• Printing and Scanning Attack for Image Counter Forensics [11.193867567895353]
  Examining the authenticity of images has become increasingly important as manipulation tools become more accessible and advanced. Recent work has shown that while CNN-based image manipulation detectors can successfully identify manipulations, they are also vulnerable to adversarial attacks. We explore another method of highly plausible attack: printing and scanning.
  arXiv  Detail & Related papers  (2020-04-27T00:32:15Z)
• Learning End-to-End Lossy Image Compression: A Benchmark [90.35363142246806]
  We first conduct a comprehensive literature survey of learned image compression methods. We describe milestones in cutting-edge learned image-compression methods, review a broad range of existing works, and provide insights into their historical development routes. By introducing a coarse-to-fine hyperprior model for entropy estimation and signal reconstruction, we achieve improved rate-distortion performance.
  arXiv  Detail & Related papers  (2020-02-10T13:13:43Z)

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.