Robust ellipse fitting based on Lagrange programming neural network and locally competitive algorithm

Shi, Zhang-Lei; Wang, Hao; Leung, Chi-Sing; So, Hing Cheung; Liang, Junli; Tsang, Kim Fung; Constantinides, A.G.

doi:10.1016/j.neucom.2020.02.100

Cited by 12 publications

(3 citation statements)

References 45 publications

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“…The LPNN technique [42] can be used in many applications, such as locating a target in a radar system [9] and 1 -norm-based sparse recovery [8], and ellipse fitting [43]. It was developed for solving a general non-linear constrained optimization problem:…”

Section: Lpnnmentioning

confidence: 99%

A Lagrange Programming Neural Network Approach with an ℓ0-Norm Sparsity Measurement for Sparse Recovery and Its Circuit Realization

et al. 2022

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Many analog neural network approaches for sparse recovery were based on using ℓ1-norm as the surrogate of ℓ0-norm. This paper proposes an analog neural network model, namely the Lagrange programming neural network with ℓp objective and quadratic constraint (LPNN-LPQC), with an ℓ0-norm sparsity measurement for solving the constrained basis pursuit denoise (CBPDN) problem. As the ℓ0-norm is non-differentiable, we first use a differentiable ℓp-norm-like function to approximate the ℓ0-norm. However, this ℓp-norm-like function does not have an explicit expression and, thus, we use the locally competitive algorithm (LCA) concept to handle the nonexistence of the explicit expression. With the LCA approach, the dynamics are defined by the internal state vector. In the proposed model, the thresholding elements are not conventional analog elements in analog optimization. This paper also proposes a circuit realization for the thresholding elements. In the theoretical side, we prove that the equilibrium points of our proposed method satisfy Karush Kuhn Tucker (KKT) conditions of the approximated CBPDN problem, and that the equilibrium points of our proposed method are asymptotically stable. We perform a large scale simulation on various algorithms and analog models. Simulation results show that the proposed algorithm is better than or comparable to several state-of-art numerical algorithms, and that it is better than state-of-art analog neural models.

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Section: Lpnnmentioning

confidence: 99%

A Lagrange Programming Neural Network Approach with an ℓ0-Norm Sparsity Measurement for Sparse Recovery and Its Circuit Realization

et al. 2022

Self Cite

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“…This entails different types of field data, as illustrated in Table II, where data mining techniques are applied for the analysis of real-time monitoring and acquisition of prognostics as well as diagnosis data for measuring operational performance linked to usage profile. Data are continuously collected for performance monitoring according to a set of precursors for an individual type of device [22]. In contrast, intermittent fault detection that addresses issues related to faults that often occur randomly is assessed with Mahalanobis distance (MD) technique for univariate data reduction [23].…”

Section: Field Datamentioning

confidence: 99%

Prognostics-Based Reliability Optimization for Consumer Healthcare Devices

Fong

Hong

2022

IEEE Access

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This paper discusses the technical details for adopting prognostics and health management (PHM) implementation in consumer healthcare wearable devices. Consumer healthcare appliances differ from medical devices in that they do not undergo the Food and Drug Administration (FDA) approval process. The main objective of deriving a reliability assessment and optimization scheme using prognostics and health management methodology is to utilize condition-based monitoring for preventive maintenance and minimize warranty repairs. The methodologies include physics-of-failure based self-cognizant prognostics for operational reliability assessment and resource management approach that takes into consideration the usage profile of individual devices.

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“…Although the performance has been improved to some extent, the inherit disadvantage of ℓ 2 norm-based methods is that the square term will enlarge the influence of outliers and lead to a rough result. To overcome this shortcoming, the ℓ 1 [12] and ℓ 0 norms [13] are cited to address this issue; theoretically, it turns to be less sensitive to outliers for algorithms based on ℓ p -norm (0 < p < 2), and the selection of iterative initial value is still pending yet. Given the advantage of ℓ 1 -norm and direct algebraic distance minimizing strategy, a natural way is to replace the ℓ 2 item with ℓ 1 in Fitzgibbon's model thus comes out our ℓ 1 model, and we will explore its efficacy in the following sections.…”

Section: Introductionmentioning

confidence: 99%

Direct Least Absolute Deviation Fitting of Ellipses

Zhou

Zhong

et al. 2020

Mathematical Problems in Engineering

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Scattered data from edge detection usually involve undesired noise which seriously affects the accuracy of ellipse fitting. In order to alleviate this kind of degradation, a method of direct least absolute deviation ellipse fitting by minimizing the ℓ1 algebraic distance is presented. Unlike the conventional ℓ2 estimators which tend to produce a satisfied performance on ideal and Gaussian noise data, while do a poor job for non-Gaussian outliers, the proposed method shows very competitive results for non-Gaussian noise. In addition, an efficient numerical algorithm based on the split Bregman iteration is developed to solve the resulting ℓ1 optimization problem, according to which the computational burden is significantly reduced. Furthermore, two classes of ℓ2 solutions are introduced as the initial guess, and the selection of algorithm parameters is studied in detail; thus, it does not suffer from the convergence issues due to poor initialization which is a common drawback existing in iterative-based approaches. Numerical experiments reveal that the proposed method is superior to its ℓ2 counterpart and outperforms some of the state-of-the-art algorithms for both Gaussian and non-Gaussian artifacts.

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Robust ellipse fitting based on Lagrange programming neural network and locally competitive algorithm

Cited by 12 publications

References 45 publications

A Lagrange Programming Neural Network Approach with an ℓ0-Norm Sparsity Measurement for Sparse Recovery and Its Circuit Realization

A Lagrange Programming Neural Network Approach with an ℓ0-Norm Sparsity Measurement for Sparse Recovery and Its Circuit Realization

Prognostics-Based Reliability Optimization for Consumer Healthcare Devices

Direct Least Absolute Deviation Fitting of Ellipses

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