Results 251 to 260 of about 5,057,517 (289)
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1989
A number of standard facilities are available on UNIX which enable communication between user and user, and between system administrator and user. The purpose of this chapter is to describe these facilities. Only local communication commands are described here. For inter-UNIX communication features, refer to Chapter 13.
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A number of standard facilities are available on UNIX which enable communication between user and user, and between system administrator and user. The purpose of this chapter is to describe these facilities. Only local communication commands are described here. For inter-UNIX communication features, refer to Chapter 13.
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Markov-localization through color features comparison
Proceedings of the 2004 IEEE International Symposium on Intelligent Control, 2004., 2005Self-localization plays a fundamental role in all the activities of a service mobile robot, from simple point-to-point navigation to complex fetch-and-carry tasks. In particular, in presence of an environment which changes dynamically, a trade-off must be found between apparently opposite characteristics: uniqueness (i.e.
CASTELNOVI, MATTIA +2 more
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2002
We introduce a new type of local feature based on the phase and amplitude responses of complex-valued steerable filters. The design of this local feature is motivated by a desire to obtain feature vectors which are semi-invariant under common image deformations, yet distinctive enough to provide useful identity information.
Gustavo Carneiro, Allan D. Jepson
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We introduce a new type of local feature based on the phase and amplitude responses of complex-valued steerable filters. The design of this local feature is motivated by a desire to obtain feature vectors which are semi-invariant under common image deformations, yet distinctive enough to provide useful identity information.
Gustavo Carneiro, Allan D. Jepson
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Dynamic local feature analysis
Biometric Technology Today, 2007Biometric facial recognition can work in a number of ways. Patrick Lo, executive director and CIO of Titanium Technology discusses the results of a research paper by the company's Research Centre, and outlines how Local Feature Analysis (LFA) and Dynamic Local Feature Analysis (DLFA) can be applied to face recognition.
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2012
This chapter presents a Feature Local Binary Patterns (FLBP) method that encodes both local and feature information, where the feature pixels may be broadly defined by, for example, the edge pixels, the intensity peaks or valleys in an image, or new feature information derived from the local binary patterns or LBP.
Jiayu Gu, Chengjun Liu
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This chapter presents a Feature Local Binary Patterns (FLBP) method that encodes both local and feature information, where the feature pixels may be broadly defined by, for example, the edge pixels, the intensity peaks or valleys in an image, or new feature information derived from the local binary patterns or LBP.
Jiayu Gu, Chengjun Liu
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Interactive Facial Feature Localization
2012We address the problem of interactive facial feature localization from a single image. Our goal is to obtain an accurate segmentation of facial features on high-resolution images under a variety of pose, expression, and lighting conditions. Although there has been significant work in facial feature localization, we are addressing a new application area,
Vuong Le +4 more
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PATTERN RECOGNITION WITH LOCAL INVARIANT FEATURES
2005Local invariant features have shown to be very successful for recognition. They are robust to occlusion and clutter, distinctive as well as invariant to image transformations. In this chapter recent progress on local invariant features is summarized.
Schmid, Cordelia +4 more
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Feature-FL: Feature-Based Fault Localization
IEEE Transactions on Reliability, 2022Yan Lei +5 more
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2016
In this chapter we examine several concepts related to local feature descriptor design—namely local patterns, shapes, spectra, distance functions, classification, matching, and object recognition. The main focus is local feature metrics, as shown in Fig. 4.1. This discussion follows the general vision taxonomy that is presented in Chap. 5, and includes
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In this chapter we examine several concepts related to local feature descriptor design—namely local patterns, shapes, spectra, distance functions, classification, matching, and object recognition. The main focus is local feature metrics, as shown in Fig. 4.1. This discussion follows the general vision taxonomy that is presented in Chap. 5, and includes
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