About: Illumination problem is a research topic. Over the lifetime, 93 publications have been published within this topic receiving 5859 citations.
Papers published on a yearly basis
TL;DR: Experiments reveal that the proposed framework outperforms the state-of-the-art methods and demonstrates promising abilities against severe illumination variation.
Abstract: The uncontrollable illumination problem is a great challenge for face recognition. In this paper, we propose a novel face recognition framework, the improved principal component regression classification (IPCRC) algorithm, which could overcome the problem of multicollinearity in linear regression. The IPCRC approach first performs principal component analysis (PCA) process to project the face images onto the face space. The first n principal components are intentionally dropped to boost the robustness against illumination changes. Then, the linear regression classification (LRC) is executed on the projected data and the identity is determined by the minimum reconstruction error. Experiments carried out on Yale B and FERET facial databases reveal that the proposed framework outperforms the state-of-the-art methods and demonstrates promising abilities against severe illumination variation.
TL;DR: In this article, an adaptive ellipse approach (AEA) was proposed to identify the mild-rust-color spots properly using an Adaptive Ellipse (AE) to overcome the non-uniform illumination problem for image segmentation.
Abstract: Image processing has been used for assessment of infrastructure surface coating conditions for years. In North America, civil engineers have utilized image recognition for steel bridge coating inspection since late 1990s. However, there is still no robust method to overcome the non-uniform illumination problem for infrastructure surface coating defect recognition to date. Therefore, this paper aims to develop a new approach to tackle the non-uniform illumination problem for rust image recognition. This paper starts with an investigation of 14 color spaces in order to find out the best color configuration for non-uniformly illuminated rust image segmentation. Then, the identified best color configuration a⁎b⁎, which has a moderate ability to filter light, is utilized to develop the proposed adaptive ellipse approach (AEA). In AEA, a rust image is partitioned into three parts: background, rust, and mild-rust-color spots. The main idea is to identify the mild-rust-color spots properly using an adaptive ellipse. Illumination adjustment is also adopted in this approach to overcome the non-uniform illumination problem. Finally, the performance of the AEA-based a⁎b⁎ configuration is compared to the K-Means method, one of the most popular and effective image recognition approaches, to show the effectiveness of the proposed AEA approach.
••19 Feb 2016
TL;DR: In this paper, the authors survey the recent advances in the area of illumination conjecture in discrete geometry, computational geometry, and geometric analysis, and describe two new approaches to make progress on the illumination problem.
Abstract: At a first glance, the problem of illuminating the boundary of a convex body by external light sources and the problem of covering a convex body by its smaller positive homothetic copies appear to be quite different. They are in fact two sides of the same coin and give rise to one of the important longstanding open problems in discrete geometry, namely, the Illumination Conjecture. In this paper, we survey the activity in the areas of discrete geometry, computational geometry and geometric analysis motivated by this conjecture. Special care is taken to include the recent advances that are not covered by the existing surveys. We also include some of our recent results related to these problems and describe two new approaches – one conventional and the other computer-assisted – to make progress on the illumination problem. Some open problems and conjectures are also presented.
TL;DR: It is observed from the survey of methods that illumination invariant representation based methods are better in terms of the number of training images required, the simplicity, computational complexity and robustness.
Abstract: Face recognition under varying illumination is one of the challenging problems in real-time applications. Numerous methods have been developed by the research community to handle the problem. Existing surveys of methods are either too old or do not cover performance analysis of illumination invariant methods. This paper is more extensive than previous surveys and covers recently developed methods. The paper focuses on passive methods which solve the illumination problem by investigating the visible light images in which the face appearance has been altered by varying illumination. The methods are classified into four broad categories, namely (1) subspace-based statistical methods (2) illumination invariant representation methods, (3) model based methods, (4) other illumination handling methods. The other illumination handling category includes the methods which do not fall under first three categories. Performance analysis and discussion of methods and an evaluation of results is presented to determine the suitability and applicability of the method(s) for specific applications. It is observed from the survey of methods that illumination invariant representation based methods are better in terms of the number of training images required, the simplicity, computational complexity and robustness.
TL;DR: A novel gradient based descriptor, namely Logarithm Gradient Histogram (LGH), which takes the illumination direction, magnitude and the spectral wavelength together into consideration, so that it can handle both homogeneous and heterogeneous lightings.
Abstract: Illumination problem is still a bottleneck of robust face recognition system, which demands extracting illumination invariant features. In this field, existing works only consider the variations caused by lighting direction or magnitude (denoted as homogeneous lighting), but the effect of spectral wavelength is always ignored and thus existing illumination invariant descriptors have its limitation on processing face images under different spectral wavelengths (denoted as heterogeneous lighting). We propose a novel gradient based descriptor, namely Logarithm Gradient Histogram (LGH), which takes the illumination direction, magnitude and the spectral wavelength together into consideration, so that it can handle both homogeneous and heterogeneous lightings. Our proposal contributes in three-folds: (1) we incorporate LMSN-LoG filter to eliminate the lighting effect of each image and extract two illumination invariant components, namely logarithm gradient orientation (LGO) and logarithm gradient magnitude (LGM); (2) we propose an effective post-processing strategy to make our model tolerant to noise and generate a histogram representation to integrate both LGO and LGM; (3) we present solid theoretical analysis on the illumination invariant properties of our proposed descriptors. Extensive experimental results on CMU-PIE, Extended YaleB, FRGC and HFB databases are reported to verify the effectiveness of our proposed model. HighlightsTwo illumination invariant components, namely logarithm gradient orientation (LGO) and logarithm gradient magnitude (LGM), are extracted.An effective post-processing strategy is proposed to integrate both LGO and LGM, generating the logarithm gradient histogram (LGH).Solid theoretical analysis on the illumination invariant properties of the proposed descriptors is presented.Competitive results are reported, both in homogeneous and heterogeneous lighting conditions.
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