Ensemble ellipse fitting by spatial median consensus

Thurnhofer‐Hemsi, Karl; López‐Rubio, Ezequiel; Blázquez-Parra, Elidia Beatriz; Ladrón-de-Guevara-Muñoz, M. Carmen

doi:10.1016/j.ins.2021.08.011

Cited by 3 publications

(2 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, an ensemble-based approach has been introduced that combines multiple ellipse fitting methods to improve accuracy, especially in noisy or occluded scenarios. This method employs a consensus mechanism in a specially chosen parameter space and utilizes the L1 median for robustness against defective fits [22].…”

Section: Ellipse Fitting Based Methodsmentioning

confidence: 99%

Accurate Detection of Ellipses based on Co-clustering

Wu,

Yan

2024

Preprint

View full text Add to dashboard Cite

Ellipse detection plays a vital role in computer vision, primarily due to the absence of efficient techniques to incorporate information from arcs in a global view. In this paper, we present a novel co-clustering-based approach that captures the global compatibility of arc pairs. Specifically, we compute the probability distributions of arcs and measure their similarity using Jensen-Shannon divergence to construct a compatibility matrix. Co-clustering extracts low-rank substruc-tures, each representing an ellipse. Experiments on synthetic images show a 10% improvement in accuracy and 15% in recall compared to previous methods. We also demonstrate performance on real images containing occlusions and overlapping ellipses from the Prasad dataset, the Coins dataset, the Tableware dataset and the Iris V dataset. Compared to techniques relying solely on local information , our method achieves higher accuracy in detecting small and occluded ellipses owing to its global perspective. The proposed technique provides an efficient, robust solution to ellipse detection across diverse scenarios.

show abstract

Section: Ellipse Fitting Based Methodsmentioning

confidence: 99%

Accurate Detection of Ellipses based on Co-clustering

Wu,

Yan

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The targeted point clouds were projected onto the x-z plane along the y-axis direction. The projected point clouds were fitted by the six-point circular ellipse fitting algorithm [27], and the chest girth (BC) and abdominal girth (BS) can be derived from G, where G is the circumference of the chest/abdominal fitted ellipse, and r and R are the minor and major axis lengths of the chest/abdominal fitted ellipse, respectively.…”

Section: Wither Heightmentioning

confidence: 99%

Key Region Extraction and Body Dimension Measurement of Beef Cattle Using 3D Point Clouds

et al. 2022

Agriculture

View full text Add to dashboard Cite

Body dimensions are key indicators for the beef cattle fattening and breeding process. On-animal measurement is relatively inefficient, and can induce severe stress responses among beef cattle and pose a risk for operators, thereby impacting the cattle’s growth rate and wellbeing. To address the above issues, a highly efficient and automatic method was developed to measure beef cattle’s body dimensions, including the oblique length, height, width, abdominal girth, and chest girth, based on the reconstructed three-dimensional point cloud data. The horizontal continuous slice sequence of the complete point clouds was first extracted, and the central point of the beef cattle leg region was determined from the span distribution of the point cloud clusters in the targeted slices. Subsequently, the boundary of the beef cattle leg region was identified by the “five-point clustering gradient boundary recognition algorithm” and was then calibrated, followed by the accurate segmentation of the corresponding region. The key regions for body dimension data calculation were further determined by the proposed algorithm, which forms the basis of the scientific calculation of key body dimensions. The influence of different postures of beef cattle on the measurement results was also preliminarily discussed. The results showed that the errors of calculated body dimensions, i.e., the oblique length, height, width, abdominal girth, and chest girth, were 2.3%, 2.8%, 1.6%, 2.8%, and 2.6%, respectively. In the present work, the beef cattle body dimensions could be effectively measured based on the 3D regional features of the point cloud data. The proposed algorithm shows a degree of generalization and robustness that is not affected by different postures of beef cattle. This automatic method can be effectively used to collect reliable phenotype data during the fattening of beef cattle and can be directly integrated into the breeding process.

show abstract

Robust Ellipse Fitting Based on Maximum Correntropy Criterion With Variable Center

Wang

et al. 2023

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

Ensemble ellipse fitting by spatial median consensus

Cited by 3 publications

References 44 publications

Accurate Detection of Ellipses based on Co-clustering

Accurate Detection of Ellipses based on Co-clustering

Key Region Extraction and Body Dimension Measurement of Beef Cattle Using 3D Point Clouds

Robust Ellipse Fitting Based on Maximum Correntropy Criterion With Variable Center

Contact Info

Product

Resources

About