Fusion of Global-Local Features for Image Quality Inspection of Shipping Label

• URL: http://arxiv.org/abs/2008.11440v1
• Date: Wed, 26 Aug 2020 08:25:34 GMT
• Title: Fusion of Global-Local Features for Image Quality Inspection of Shipping Label
• Authors: Sungho Suh, Paul Lukowicz and Yong Oh Lee
• Abstract summary: We propose an input image quality verification method combining global and local features. The experimental results regarding real captured and generated images show that the proposed method achieves better performance than other methods.
• Score: 6.458496335718508
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The demands of automated shipping address recognition and verification have increased to handle a large number of packages and to save costs associated with misdelivery. A previous study proposed a deep learning system where the shipping address is recognized and verified based on a camera image capturing the shipping address and barcode area. Because the system performance depends on the input image quality, inspection of input image quality is necessary for image preprocessing. In this paper, we propose an input image quality verification method combining global and local features. Object detection and scale-invariant feature transform in different feature spaces are developed to extract global and local features from several independent convolutional neural networks. The conditions of shipping label images are classified by fully connected fusion layers with concatenated global and local features. The experimental results regarding real captured and generated images show that the proposed method achieves better performance than other methods. These results are expected to improve the shipping address recognition and verification system by applying different image preprocessing steps based on the classified conditions.

Related papers

• Breaking the Frame: Image Retrieval by Visual Overlap Prediction [53.17564423756082]
  We propose a novel visual place recognition approach, VOP, that efficiently addresses occlusions and complex scenes. The proposed method enables the identification of visible image sections without requiring expensive feature detection and matching.
  arXiv  Detail & Related papers  (2024-06-23T20:00:20Z)
• 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)
• GLFF: Global and Local Feature Fusion for AI-synthesized Image Detection [29.118321046339656]
  We propose a framework to learn rich and discriminative representations by combining multi-scale global features from the whole image with refined local features from informative patches for AI synthesized image detection. GLFF fuses information from two branches: the global branch to extract multi-scale semantic features and the local branch to select informative patches for detailed local artifacts extraction.
  arXiv  Detail & Related papers  (2022-11-16T02:03:20Z)
• Joint Learning of Deep Texture and High-Frequency Features for Computer-Generated Image Detection [24.098604827919203]
  We propose a joint learning strategy with deep texture and high-frequency features for CG image detection. A semantic segmentation map is generated to guide the affine transformation operation. The combination of the original image and the high-frequency components of the original and rendered images are fed into a multi-branch neural network equipped with attention mechanisms.
  arXiv  Detail & Related papers  (2022-09-07T17:30:40Z)
• SImProv: Scalable Image Provenance Framework for Robust Content Attribution [80.25476792081403]
  We present SImProv, a framework to match a query image back to a trusted database of originals. SimProv consists of three stages: a scalable search stage for retrieving top-k most similar images; a re-ranking and near-duplicated detection stage for identifying the original among the candidates. We demonstrate effective retrieval and manipulation detection over a dataset of 100 million images.
  arXiv  Detail & Related papers  (2022-06-28T18:42:36Z)
• ObjectFormer for Image Manipulation Detection and Localization [118.89882740099137]
  We propose ObjectFormer to detect and localize image manipulations. We extract high-frequency features of the images and combine them with RGB features as multimodal patch embeddings. We conduct extensive experiments on various datasets and the results verify the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-03-28T12:27:34Z)
• Compound Frechet Inception Distance for Quality Assessment of GAN Created Images [7.628527132779575]
  One notable application of GANs is developing fake human faces, also known as "deep fakes" Measuring the quality of the generated images is inherently subjective but attempts to objectify quality using standardized metrics have been made. We propose to improve the robustness of the evaluation process by integrating lower-level features to cover a wider array of visual defects.
  arXiv  Detail & Related papers  (2021-06-16T06:53:27Z)
• LocalTrans: A Multiscale Local Transformer Network for Cross-Resolution Homography Estimation [52.63874513999119]
  Cross-resolution image alignment is a key problem in multiscale giga photography. Existing deep homography methods neglecting the explicit formulation of correspondences between them, which leads to degraded accuracy in cross-resolution challenges. We propose a local transformer network embedded within a multiscale structure to explicitly learn correspondences between the multimodal inputs.
  arXiv  Detail & Related papers  (2021-06-08T02:51:45Z)
• Fully-Automated Packaging Structure Recognition in Logistics Environments [60.56493342808093]
  We propose a method for complete automation of packaging structure recognition. Our algorithm is based on deep learning models, more precisely convolutional neural networks for instance segmentation in images. We show that the solution is capable of correctly recognizing the packaging structure in approximately 85% of our test cases, and even more (91%) when focusing on most common package types.
  arXiv  Detail & Related papers  (2020-08-11T10:57:23Z)

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.