SAFFIRE: System for Autonomous Feature Filtering and Intelligent ROI Estimation

• URL: http://arxiv.org/abs/2012.02502v2
• Date: Thu, 4 Mar 2021 20:29:14 GMT
• Title: SAFFIRE: System for Autonomous Feature Filtering and Intelligent ROI Estimation
• Authors: Marco Boschi, Luigi Di Stefano, Martino Alessandrini
• Abstract summary: This work introduces a new framework, named SAFFIRE, to automatically extract a dominant recurrent image pattern from a set of image samples. The framework is specialized here in the context of a machine vision system for automated product inspection.
• Score: 18.879707999761653
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work introduces a new framework, named SAFFIRE, to automatically extract a dominant recurrent image pattern from a set of image samples. Such a pattern shall be used to eliminate pose variations between samples, which is a common requirement in many computer vision and machine learning tasks. The framework is specialized here in the context of a machine vision system for automated product inspection. Here, it is customary to ask the user for the identification of an anchor pattern, to be used by the automated system to normalize data before further processing. Yet, this is a very sensitive operation which is intrinsically subjective and requires high expertise. Hereto, SAFFIRE provides a unique and disruptive framework for unsupervised identification of an optimal anchor pattern in a way which is fully transparent to the user. SAFFIRE is thoroughly validated on several realistic case studies for a machine vision inspection pipeline.

Related papers

• AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving [68.73885845181242]
  We propose an Automatic Data Engine (AIDE) that automatically identifies issues, efficiently curates data, improves the model through auto-labeling, and verifies the model through generation of diverse scenarios. We further establish a benchmark for open-world detection on AV datasets to comprehensively evaluate various learning paradigms, demonstrating our method's superior performance at a reduced cost.
  arXiv  Detail & Related papers  (2024-03-26T04:27:56Z)
• OCR is All you need: Importing Multi-Modality into Image-based Defect Detection System [7.1083241462091165]
  We introduce an external modality-guided data mining framework, primarily rooted in optical character recognition (OCR), to extract statistical features from images. A key aspect of our approach is the alignment of external modality features, extracted using a single modality-aware model, with image features encoded by a convolutional neural network. Our methodology considerably boosts the recall rate of the defect detection model and maintains high robustness even in challenging scenarios.
  arXiv  Detail & Related papers  (2024-03-18T07:41:39Z)
• Video Anomaly Detection using GAN [0.0]
  This thesis study aims to offer the solution for this use case so that human resources won't be required to keep an eye out for any unusual activity in the surveillance system records. We have developed a novel generative adversarial network (GAN) based anomaly detection model.
  arXiv  Detail & Related papers  (2023-11-23T16:41:30Z)
• Identifying Systematic Errors in Object Detectors with the SCROD Pipeline [46.52729366461028]
  The identification and removal of systematic errors in object detectors can be a prerequisite for their deployment in safety-critical applications. We overcome this limitation by generating synthetic images with fine-granular control. We propose a novel framework that combines the strengths of both approaches.
  arXiv  Detail & Related papers  (2023-09-23T22:41:08Z)
• Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability [70.72426887518517]
  Out-of-distribution (OOD) detection is an indispensable aspect of secure AI when deploying machine learning models in real-world applications. We propose a novel method, Unleashing Mask, which aims to restore the OOD discriminative capabilities of the well-trained model with ID data. Our method utilizes a mask to figure out the memorized atypical samples, and then finetune the model or prune it with the introduced mask to forget them.
  arXiv  Detail & Related papers  (2023-06-06T14:23:34Z)
• Dynamic Prototype Mask for Occluded Person Re-Identification [88.7782299372656]
  Existing methods mainly address this issue by employing body clues provided by an extra network to distinguish the visible part. We propose a novel Dynamic Prototype Mask (DPM) based on two self-evident prior knowledge. Under this condition, the occluded representation could be well aligned in a selected subspace spontaneously.
  arXiv  Detail & Related papers  (2022-07-19T03:31:13Z)
• Toward an ImageNet Library of Functions for Global Optimization Benchmarking [0.0]
  This study proposes to transform the identification problem into an image recognition problem, with a potential to detect conception-free, machine-driven landscape features. We address it as a supervised multi-class image recognition problem and apply basic artificial neural network models to solve it. This evident successful learning is another step toward automated feature extraction and local structure deduction of BBO problems.
  arXiv  Detail & Related papers  (2022-06-27T21:05:00Z)
• A framework for the automation of testing computer vision systems [17.360163137925998]
  We contribute to the area of testing vision software, and present a framework for the automated generation of tests for systems based on vision and image recognition. We show how such a framework can be used for testing a particular industrial application on identifying defects on riblet surfaces.
  arXiv  Detail & Related papers  (2021-05-10T14:02:42Z)
• Anomaly Detection Based on Selection and Weighting in Latent Space [73.01328671569759]
  We propose a novel selection-and-weighting-based anomaly detection framework called SWAD. Experiments on both benchmark and real-world datasets have shown the effectiveness and superiority of SWAD.
  arXiv  Detail & Related papers  (2021-03-08T10:56:38Z)
• MOGAN: Morphologic-structure-aware Generative Learning from a Single Image [59.59698650663925]
  Recently proposed generative models complete training based on only one image. We introduce a MOrphologic-structure-aware Generative Adversarial Network named MOGAN that produces random samples with diverse appearances. Our approach focuses on internal features including the maintenance of rational structures and variation on appearance.
  arXiv  Detail & Related papers  (2021-03-04T12:45:23Z)
• Aurora Guard: Reliable Face Anti-Spoofing via Mobile Lighting System [103.5604680001633]
  Anti-spoofing against high-resolution rendering replay of paper photos or digital videos remains an open problem. We propose a simple yet effective face anti-spoofing system, termed Aurora Guard (AG)
  arXiv  Detail & Related papers  (2021-02-01T09:17:18Z)

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.