Design of steerable filters for the detection of micro-particles

Vonesch, Cédric; Stauber, Frédéric; Unser, Michaël

doi:10.1109/isbi.2013.6556629

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…The detection of impurities at micro level is highly important in biomedical fields. The identification of particles sizing 1 µm to 20 µm is essential as fungal, bacterial pathogens and human cells ranging around the size [17][18][19]. The detection of particulate matter (PM) in environment which is related to the respiratory tract is also essential in the field for constant and capillary air scrutinizing [20].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence in Particle and Impurity Detection and Removal: A Survey

Shuyuti,

Salami,

Dahari

et al. 2024

IEEE Access

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With the rapid development of artificial intelligence (AI), especially in machine learning and deep learning technologies, the particle and impurity detection and removal processes employed many industries have been improved. Particles and impurities at any size, shape and at any condition can be detected using the advanced technology in both areas. This paper presents a comprehensive overview of research papers that discuss the application of AI techniques for the detection and removal of particles and impurities. The publications featured in this review were mainly retrieved from the Web of Science (WoS) database, covering the timeframe from 2000 to 2023. This paper also covers the review on the impurity detection and removal specifically in edible bird's nest (EBN). The aim of this paper is to provide a valuable resource for the future development of AI applications in particle and impurity detection and removal technologies that have not been addressed in this study. Through the review and analysis of AI for particle and impurity detection and removal techniques in recent years, this paper includes the following parts: research trend in particle and impurity detection in general and AI in particle and impurity detection, applications of AI methods in particle and impurity detection in related industries including in EBN and AI application in particle and impurity removal. This review study will offer advantages to researchers engaged in the field of AI with regards to the detection and removal of particles and impurities.

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Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence in Particle and Impurity Detection and Removal: A Survey

Shuyuti,

Salami,

Dahari

et al. 2024

IEEE Access

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“…The resulting decomposition, termed steerable PCA, consists of principal components which are tensor products of radial functions and angular Fourier modes [5], [6], [7], [8], [9]. Beyond cryo-EM, steerable PCA has many other applications in image analysis and computer vision [10].…”

Section: Introductionmentioning

confidence: 99%

Fast Steerable Principal Component Analysis

Zhao

Shkolnisky

Singer

2016

IEEE Trans. Comput. Imaging

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Cryo-electron microscopy nowadays often requires the analysis of hundreds of thousands of 2-D images as large as a few hundred pixels in each direction. Here, we introduce an algorithm that efficiently and accurately performs principal component analysis (PCA) for a large set of 2-D images, and, for each image, the set of its uniform rotations in the plane and their reflections. For a dataset consisting of n images of size L × L pixels, the computational complexity of our algorithm is O(nL3 + L4), while existing algorithms take O(nL4). The new algorithm computes the expansion coefficients of the images in a Fourier–Bessel basis efficiently using the nonuniform fast Fourier transform. We compare the accuracy and efficiency of the new algorithm with traditional PCA and existing algorithms for steerable PCA.

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“…We present an application to real data where our continuous-domain framework is particularly well adapted because the geometry of the templates is known analytically. A preliminary version of this work was presented at ISBI 2013 [21]. In this paper, an important addition is that we provide proofs for all mathematical results.…”

mentioning

confidence: 99%

Steerable PCA for Rotation-Invariant Image Recognition

Vonesch¹,

Stauber²,

Unser³

2015

SIAM J. Imaging Sci.

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Abstract. In this paper, we propose a continuous-domain version of principal-component analysis, with the constraint that the underlying family of templates appears at arbitrary orientations. We show that the corresponding principal components are steerable. Our method can be used for designing steerable filters so that they best approximate a given collection of reference templates. We apply this framework to the detection and classification of micrometer-sized particles that are used in a microfluidic diagnostics system. This is done in two steps. First, we decompose the particles into a small number of templates and compute their steerable principal components. Then we use these principal components to automatically estimate the orientation and the class of each particle.

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Design of steerable filters for the detection of micro-particles

Cited by 4 publications

References 11 publications

Application of Artificial Intelligence in Particle and Impurity Detection and Removal: A Survey

Application of Artificial Intelligence in Particle and Impurity Detection and Removal: A Survey

Fast Steerable Principal Component Analysis

Steerable PCA for Rotation-Invariant Image Recognition

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