TiViPE related publications

Publications related to TiViPE.

  1. T. Lourens and E. I. Barakova. User-Friendly Robot Environment for Creation of Social Scenarios [2.56 MB pdf]. In J. M. Ferrandez, J. R. Alvarez, F. de la Paz, and F. J. Toledo, editors, IWINAC 2011, number 6686 in Lecture Notes in Computer Science, pages 212-221, La Palma, Spain, May-June 2011. Springer-verlag.

    Abstract

    This paper proposes a user-friendly framework for designing robot behaviors by users with minimal understanding of programming. It is a step towards an end-user platform which is meant to be used by domain specialists for creating social scenarios, i.e. scenarios in which not high precision of movement is needed but frequent redesign of the robot behavior is a necessity. We show by a hand shaking experiment how convincing it is to construct robot behavior in this framework.

  2. T. Lourens. TiViPE -Tino’s Visual Programming Environment [115 KB pdf]. In The 28th Annual International Computer Software & Applications Conference, IEEE COMPSAC 2004, pages 10-15, 2004.

    Abstract

    TiViPE [4] is a component based visual programming environment (VPE) that enables users to build programs by construction of a network of components interactively. A single module (component), represented by a graphical icon, is a computational unit. Multiple icons can be connected to each other to yield a directed graph (a network) that represent a program. TiViPE is, in appearance similar to programs such as AVS [9], Vee [3, 1], OpenDX [6], Khoros [12], LabVIEW [7], NeatVision [10], and ViPEr [8], but presents some fundamental differences. TiViPE integrates documentation with an existing routine call (that has been programmed in C++, C, Fortran, or Java), and automatically generates C++ code that is compiled to stand-alone program. This program is able to execute the specified routine, provide a graphical icon, or give html-formatted documentation about the routine. Hence, within TiViPE there is no textual programming for the user. TiViPE strongly re-uses code, which is inherent to visual programming, and automatic code regeneration by compounding a network of modules to a single module, which leads to faster programming. TiViPE supports networking and parallel processing in a natural way, and allows the user to modify an activated network. TiViPE also aims at rapid prototyping which demands user friendliness, programming by existing modules for basic users, and focuses on the documentation of a module. TiViPE has been used in the field of computer vision, robotics, and computational neuroscience.

  3. T. Lourens and E. I. Barakova. My Sparring Partner is a Humanoid Robot -A parallel framework for improving social skills by imitation [1646 KB pdf]. In J. R. Alvarez, editor, IWINAC 2009, number 5602 in Lecture Notes in Computer Science, pages 344-352, Santiago de Compostella, Spain, June 2009. Springer-verlag.

    Abstract

    This paper presents a framework for parallel tracking of human hands and faces in real time, and is a partial solution to a larger project on human-robot interaction which aims at training autistic children using a humanoid robot in a realistic non-restricted environment. In addition to the framework, the results of tracking different hand waving patterns are shown. These patterns provide an easy to understand profile of hand waving, and can serve as the input for a classification algorithm.

  4. T. Lourens and E. I. Barakova. Orientation Contrast Sensitive Cells in Primate V1 -a computational model [607 KB pdf]. Natural Computing, 6(3):241–252, September 2007.

    Abstract

    Many cells in the primary visual cortex respond differently when a stimulus is placed outside their classical receptive field (CRF) compared to the stimulus within the CRF alone, permitting integration of information at early levels in the visual processing stream that may play a key role in intermediate-level visual tasks, such a perceptual pop-out (Knierim and van Essen, 1992; Nothdurft et al., 1999), contextual modulation (Levitt and Lund, 1997; Das and Gilbert, 1999; Dragoi and Sur, 2000), and junction detection (Sillito et al., 1995; Das and Gilbert, 1999; Jones et al., 2002). In this paper we construct a computational model in programming environment TiViPE (Lourens, 2004) of orientation contrast type of cells and demonstrate that the model closely resembles the functional behavior of the neuronal responses of non orientation (within the CRF) sensitive 4CB cells (Jones et al., 2002), and give an explanation of the indirect information flow in V1 that explains the behavior of orientation contrast sensitivity. The computational model of orientation contrast cells demonstrates excitatory responses at edges near junctions that might facilitate junction detection, but the model does not reveal perceptual pop-out.

  5. T. Lourens and E. I. Barakova. Simulation of Orientation Contrast Sensitive Cell Behavior in TiViPE [455 KB pdf]. In J. Mira and J. R. Alvarez, editors, IWINAC 2005, number 3561 in Lecture Notes in Computer Science, pages 57–66, Las Palmas de Gran Canaria, Spain, June 2005. Springer-verlag.

    Abstract

    Many cells in the primary visual cortex respond differently when a stimulus is placed outside their classical receptive field (CRF) compared to the stimulus within the CRF alone, permitting integration of information at early levels in the visual processing stream that may play a key role in intermediate-level visual tasks, such a perceptual popout [11], contextual modulation [7, 3, 4], and junction detection [13, 3, 5]. In this paper we construct a computational model in programming environment TiViPE [9] of orientation contrast type of cells and demonstrate that the model closely resembles the functional behavior of the neuronal responses of non orientation (within the CRF) sensitive 4CB cells [5], and give an explanation of the indirect information flow in V1 that explains the behavior of orientation contrast sensitivity.

  6. T. Lourens and E. I. Barakova. TiViPE Simulation of a Cortical Crossing Cell Model. [1786 KB pdf]. In J. Cabastany, A. Prieto, and D. F. Sandoval, editors, IWANN 2005, number 3512 in Lecture Notes in Computer Science, pages 122-129, Barcelona, Spain, June 2005. Springer-verlag.

    Abstract

    Many cells in cat and monkey visual cortex (area V1 and area 17) respond to gratings and bar patterns of different orientation between center and surround [18]. It has been shown that these cells respond on average 3.3 times stronger to a crossing pattern than to a single bar [16]. In this paper a computational model for a group of neurons that respond solely to crossing patterns is proposed, and has been implemented in visual programming environment TiViPE [10]. Simulations show that the operator responds very accurately to crossing patterns that have an angular difference between 2 bars of 40 degrees or more, the operator responds appropriately to bar widths that are bound by 50 to 200 percent of the preferred bar width and is insensitive to non-uniform illumination conditions, which appear to be consistent with the experimental results.

  7. T. Lourens, E. I. Barakova, H. G. Okuno, and H. Tsujino. A computational model of monkey cortical grating cells [478 KB pdf]. Biological Cybernetics, 92(1):61–70, January 2005. DOI: 10.1007/s00422-004-0522-2.

    Abstract

    Grating cells were discovered in the V1 and V2 areas of the monkey visual cortex by von der Heydt et al. (1992). These cells responded vigorously to grating patterns of appropriate orientation and periodicity. Computational models inspired by these findings were used as texture operator (Kruizinga and Petkov 1995, 1999; Petkov and Kruizinga 1997) and for the emergence and self-organization of grating cells (Brunner et al. 1998; Bauer et al. 1999). The aim of this paper is to create a grating cell operator G that demonstrates similar responses to monkey grating cells by applying operator G to the same stimuli as in the experiments carried out by von der Heydt et al. (1992). Operator G will be tested on images that contain periodic patterns as suggested by De Valois and De Valois (1988). In order to learn more about the role of grating cells in natural vision, operator G is applied to 338 real-world images of textures obtained from three different databases. The results suggest that grating cells respond strongly to regular alternating periodic patterns of a certain orientation. Such patterns are common in images of human-made structures, like buildings, fabrics, and tiles, and to regular natural periodic patterns, which are relatively rare in nature.

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