A novel approach for scale and rotation adaptive estimation based on time series alignment

Delong, Zhao; Du, Fengming

doi:10.1007/s00371-018-1598-3

Cited by 4 publications

(3 citation statements)

References 33 publications

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“…In D5, a lightweight contour prediction network is constructed based on [95]. HOG, Canny, Sobel C3 [98] with multiple templates C4 SURF [99] with parallel constraints…”

Section: Deployment Workmentioning

confidence: 99%

A Common Knowledge-Driven Generic Vision Inspection Framework for Adaptation to Multiple Scenarios, Tasks, and Objects

Zhao,

Kong,

et al. 2024

Sensors

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The industrial manufacturing model is undergoing a transformation from a product-centric model to a customer-centric one. Driven by customized requirements, the complexity of products and the requirements for quality have increased, which pose a challenge to the applicability of traditional machine vision technology. Extensive research demonstrates the effectiveness of AI-based learning and image processing on specific objects or tasks, but few publications focus on the composite task of the integrated product, the traceability and improvability of methods, as well as the extraction and communication of knowledge between different scenarios or tasks. To address this problem, this paper proposes a common, knowledge-driven, generic vision inspection framework, targeted for standardizing product inspection into a process of information decoupling and adaptive metrics. Task-related object perception is planned into a multi-granularity and multi-pattern progressive alignment based on industry knowledge and structured tasks. Inspection is abstracted as a reconfigurable process of multi-sub-pattern space combination mapping and difference metric under appropriate high-level strategies and experiences. Finally, strategies for knowledge improvement and accumulation based on historical data are presented. The experiment demonstrates the process of generating a detection pipeline for complex products and continuously improving it through failure tracing and knowledge improvement. Compared to the (, 69.802 mm) and 0.883 obtained by state-of-the-art deep learning methods, the generated pipeline achieves a pose estimation ranging from (, 153.584 mm) to (, 52.308 mm) and a detection rate ranging from 0.462 to 0.927. Through verification of other imaging methods and industrial tasks, we prove that the key to adaptability lies in the mining of inherent commonalities of knowledge, multi-dimensional accumulation, and reapplication.

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“…In D5, a lightweight contour prediction network is constructed based on [95]. HOG, Canny, Sobel C3 [98] with multiple templates C4 SURF [99] with parallel constraints…”

Section: Deployment Workmentioning

confidence: 99%

A Common Knowledge-Driven Generic Vision Inspection Framework for Adaptation to Multiple Scenarios, Tasks, and Objects

Zhao,

Kong,

et al. 2024

Sensors

Self Cite

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“…Klassen et al [25] employed the distance and area integral invariants to deal with the noise sensitivity problem of differential invariants. Zhao and Du [26] found the extreme points of each ring region in the resulting image of ring projection transformation. The feature sequence containing the pixel gray values and shape structure information is extracted by analyzing the distribution of extreme points.…”

Section: Related Workmentioning

confidence: 99%

“…Different starting points may lead to very different similarity results, especially in local sequence matching. Zhao and Du [26] adopted the cyclic shift (CS) method to obtain the maximum similarity by continuously shifting the sequence. Although the cyclic shift method can acquire the maximum similarity, the computation is expensive.…”

Section: Contour Starting Point Selectionmentioning

confidence: 99%

Shape Matching Based on Multi-Scale Invariant Features

Zhang

Xin-guo

2019

IEEE Access

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Shape matching has been extensively used in various fields. The local feature-based or global feature-based algorithms can hardly describe the shape comprehensively due to their inherent defects. Combining the local and global feature to describe the shape has become a trend. In this paper, an improved discrete curve evolution algorithm is proposed which combines the discrete curve evolution with the uniform sampling and achieves a better description of the shape contour. Three simple and intuitive multi-scale features which represent both the global and local features of shapes are designed from aspects of the spatial relationship of contour points, structural information of contour sequence, and shape geometry feature. A cyclic Smith-Waterman algorithm is introduced to solve local contour matching and starting point selection. Experimental results demonstrate that our proposed features are translation, rotation, and scaling invariant, and have good robustness to deformation. Retrieval accuracies of Kimia99, Kimai216, and MPEG-7 indicate that our method can bring out a better performance. INDEX TERMS Shape matching, improved discrete curve evolution, multi-scale invariant features, cyclic Smith-Waterman algorithm. I. INTRODUCTION

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Adaptive cropping and deskewing of scanned documents based on high accuracy estimation of skew angle and cropping value

2020

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A novel approach for scale and rotation adaptive estimation based on time series alignment

Cited by 4 publications

References 33 publications

A Common Knowledge-Driven Generic Vision Inspection Framework for Adaptation to Multiple Scenarios, Tasks, and Objects

A Common Knowledge-Driven Generic Vision Inspection Framework for Adaptation to Multiple Scenarios, Tasks, and Objects

Shape Matching Based on Multi-Scale Invariant Features

Adaptive cropping and deskewing of scanned documents based on high accuracy estimation of skew angle and cropping value

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