3,400 research outputs found
Hypergraph Modelling for Geometric Model Fitting
In this paper, we propose a novel hypergraph based method (called HF) to fit
and segment multi-structural data. The proposed HF formulates the geometric
model fitting problem as a hypergraph partition problem based on a novel
hypergraph model. In the hypergraph model, vertices represent data points and
hyperedges denote model hypotheses. The hypergraph, with large and
"data-determined" degrees of hyperedges, can express the complex relationships
between model hypotheses and data points. In addition, we develop a robust
hypergraph partition algorithm to detect sub-hypergraphs for model fitting. HF
can effectively and efficiently estimate the number of, and the parameters of,
model instances in multi-structural data heavily corrupted with outliers
simultaneously. Experimental results show the advantages of the proposed method
over previous methods on both synthetic data and real images.Comment: Pattern Recognition, 201
A mask-based approach for the geometric calibration of thermal-infrared cameras
Accurate and efficient thermal-infrared (IR) camera calibration is important for advancing computer vision research within the thermal modality. This paper presents an approach for geometrically calibrating individual and multiple cameras in both the thermal and visible modalities. The proposed technique can be used to correct for lens distortion and to simultaneously reference both visible and thermal-IR cameras to a single coordinate frame. The most popular existing approach for the geometric calibration of thermal cameras uses a printed chessboard heated by a flood lamp and is comparatively inaccurate and difficult to execute. Additionally, software toolkits provided for calibration either are unsuitable for this task or require substantial manual intervention. A new geometric mask with high thermal contrast and not requiring a flood lamp is presented as an alternative calibration pattern. Calibration points on the pattern are then accurately located using a clustering-based algorithm which utilizes the maximally stable extremal region detector. This algorithm is integrated into an automatic end-to-end system for calibrating single or multiple cameras. The evaluation shows that using the proposed mask achieves a mean reprojection error up to 78% lower than that using a heated chessboard. The effectiveness of the approach is further demonstrated by using it to calibrate two multiple-camera multiple-modality setups. Source code and binaries for the developed software are provided on the project Web site
Lifelong Spectral Clustering
In the past decades, spectral clustering (SC) has become one of the most
effective clustering algorithms. However, most previous studies focus on
spectral clustering tasks with a fixed task set, which cannot incorporate with
a new spectral clustering task without accessing to previously learned tasks.
In this paper, we aim to explore the problem of spectral clustering in a
lifelong machine learning framework, i.e., Lifelong Spectral Clustering (L2SC).
Its goal is to efficiently learn a model for a new spectral clustering task by
selectively transferring previously accumulated experience from knowledge
library. Specifically, the knowledge library of L2SC contains two components:
1) orthogonal basis library: capturing latent cluster centers among the
clusters in each pair of tasks; 2) feature embedding library: embedding the
feature manifold information shared among multiple related tasks. As a new
spectral clustering task arrives, L2SC firstly transfers knowledge from both
basis library and feature library to obtain encoding matrix, and further
redefines the library base over time to maximize performance across all the
clustering tasks. Meanwhile, a general online update formulation is derived to
alternatively update the basis library and feature library. Finally, the
empirical experiments on several real-world benchmark datasets demonstrate that
our L2SC model can effectively improve the clustering performance when
comparing with other state-of-the-art spectral clustering algorithms.Comment: 9 pages,7 figure
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