Topic
Literature survey
About: Literature survey is a research topic. Over the lifetime, 15372 publications have been published within this topic receiving 459196 citations.
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TL;DR: This study provides an overview of Gabor filters in image processing, a short literature survey of the most significant results, and establishes invariance properties and restrictions to the use of Gbps filters in feature extraction.
Abstract: For almost three decades the use of features based on Gabor filters has been promoted for their useful properties in image processing. The most important properties are related to invariance to illumination, rotation, scale, and translation. These properties are based on the fact that they are all parameters of Gabor filters themselves. This is especially useful in feature extraction, where Gabor filters have succeeded in many applications, from texture analysis to iris and face recognition. This study provides an overview of Gabor filters in image processing, a short literature survey of the most significant results, and establishes invariance properties and restrictions to the use of Gabor filters in feature extraction. Results are demonstrated by application examples.
334 citations
TL;DR: In this paper, various criteria and parameters have been proposed in the literature for predicting mixed-mode crack growth directions and rates, and the physical basis and limitations for each criterion are briefly reviewed, and corresponding experimental supports are discussed.
332 citations
TL;DR: The decay constants used in geo- and cosmochronology usually are assigned uncertainties of ca. 1% but there are very much larger unaccounted discrepancies between decay constants reported by different "counting groups" as well as differences between results derived from counting experiments and from the comparison of ages obtained on the same samples by utilizing different radioactive clocks.
329 citations
TL;DR: The purpose of this paper is to describe industrial aspects of combined fleet composition and routing in maritime and road-based transportation, and to present the current status of research in the form of a comprehensive literature review.
327 citations
TL;DR: A categorisation of the ME literature based on the implicit problem space partitioning using a tacit competitive process between the experts is presented, and the first group is called the mixture of implicitly localised experts (MILE), and the second is called mixture of explicitly localised Experts (MELE), as it uses pre-specified clusters.
Abstract: Mixture of experts (ME) is one of the most popular and interesting combining methods, which has great potential to improve performance in machine learning. ME is established based on the divide-and-conquer principle in which the problem space is divided between a few neural network experts, supervised by a gating network. In earlier works on ME, different strategies were developed to divide the problem space between the experts. To survey and analyse these methods more clearly, we present a categorisation of the ME literature based on this difference. Various ME implementations were classified into two groups, according to the partitioning strategies used and both how and when the gating network is involved in the partitioning and combining procedures. In the first group, The conventional ME and the extensions of this method stochastically partition the problem space into a number of subspaces using a special employed error function, and experts become specialised in each subspace. In the second group, the problem space is explicitly partitioned by the clustering method before the experts' training process starts, and each expert is then assigned to one of these sub-spaces. Based on the implicit problem space partitioning using a tacit competitive process between the experts, we call the first group the mixture of implicitly localised experts (MILE), and the second group is called mixture of explicitly localised experts (MELE), as it uses pre-specified clusters. The properties of both groups are investigated in comparison with each other. Investigation of MILE versus MELE, discussing the advantages and disadvantages of each group, showed that the two approaches have complementary features. Moreover, the features of the ME method are compared with other popular combining methods, including boosting and negative correlation learning methods. As the investigated methods have complementary strengths and limitations, previous researches that attempted to combine their features in integrated approaches are reviewed and, moreover, some suggestions are proposed for future research directions.
325 citations