Deducing ink-transmittance spectra from reflectance and transmittance measurements of prints

By M. Hébert, R. Hersch. Electronic imaging, 2007

561 citations recorded by the source corpus.

Subfields: Archeology, Atomic and Molecular Physics, and Optics, Computational Mechanics

View Paper: https://www.semanticscholar.org/paper/effa943e421bd91f4c3614db99e7108b0e20e139

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Archeology

1. Jan Peters
2. Stefan Schaal
3. Yukihiko Funaki
4. René van den Brink
5. Boram Park
6. Youngsub Chun

In Atomic and Molecular Physics, and Optics

1. Jan Peters
2. Aaron F. Bobick
3. Stefan Schaal
4. Aaron Hertzmann
5. Yukihiko Funaki
6. René van den Brink
7. Boram Park
8. Youngsub Chun

In Computational Mechanics

1. Jan Peters
2. Stefan Schaal
3. Aaron Hertzmann
4. Yukihiko Funaki
5. René van den Brink
6. Boram Park
7. Youngsub Chun
8. Sylvain Calinon

Citations in the corpus

• Imitation and Reinforcement Learning (2010, background): “Please refer to [9] for a review of imitation learning methods.”
• Learning Shared Latent Structure for Image Synthesis and Robotic Imitation (2005, background): “Another domain where finding common structure is crucial is imitation learning, also called “learning by watching” [11, 12, 6].”
• Consistency, population solidarity, and egalitarian solutions for TU-games (2016, methodology): “6The equal division solution satisfies several well-known consistency axioms that are also satisfied by the Shapley value such as those of Sobolev (1973) and Hart and Mas-Colell (1988, 1989), which are not satisfied by the CIS-value.”
• Axiomatizations of a Class of Equal Surplus Sharing Solutions for TU-Games (2009, background): “The usual standardness for two-player games states that in a two player game every player earns its own worth plus half of what remains of the worth of the two-player (‘grand’) coalition (see, e.g. Hart and Mas-Colell, 1988, 1989).”
• On Human Action (2011, background): “In robotics, generative implies an explicit modeling of each of the categories of action: the notion of generative models often embraces representations that not only support the recognition of an action but also permit the robot to synthesize such an action itself [6, 11, 43, 44].”
• Machine learning of motor skills for robotics (2007, background): “Building upon previous work (Peters et al., 2003a; Schaal, Peters, Nakanishi, & Ijspeert, 2003; Schaal et al., 2004), and based upon our review of related approaches, we outline steps towards a general framework for motor skill learning in this section.”