This paper analyzes the problems arising from the remediation of the relief effect in the transition from analog to digital of stereo photography. One of the main problems in this conversion is the portability of the awe effect that constitutes an important part of the experience when viewing a stereo pair. This image conversion process, necessary to the creation and dissemination of digital files of 19th century stereoscopic photography, is not linear. The digital stereoscopic projection cards present a number of dif ficulties for a proper consistency reproduction of the relief effect. Through the study comparison of different viewing apparatus (both digital and analogue including 3D and VR) of a specific stereo image, we will present important results achieved with a sample of 134 participants that were exposed to these devices and propose a guideline manual for the digital stereo archive. For the developing of this work it has been crucial the research done by both authors for the Stereo Visual Culture project (supported by the FCT Foundation ref. PTDC / IVCCOM /5223/2012), the stereopsis analysis made with the research center HEI-Lab (Digital Human-Environment Interaction Lab) and through the particular case study based on the VR application developed for the recreation of XIX century Carlos Relvas' studio, in exhibition at Museu Nacional de Arte Contemporânea do Chiado (Lisbon) from November 2018 until February 2019.
Plant phenotyping, or the measurement of plant traits such as stem width and plant height, is a critical step in the development and evaluation of higher yield biofuel crops. Phenotyping allows biologists to quantitatively estimate the biomass of plant varieties and therefore their potential for biofuel production. Manual phenotyping is costly, time-consuming, and errorprone, requiring a person to walk through the fields measuring individual plants with a tape measure and notebook. In this work we describe an alternative system consisting of an autonomous robot equipped with two infrared cameras that travels through fields, collecting 2.5D image data of sorghum plants. We develop novel image processing based algorithms to estimate plant height and stem width from the image data. Our proposed method has the advantage of working in situ using images of plants from only one side. This allows phenotypic data to be collected nondestructively throughout the growing cycle, providing biologists with valuable information on crop growth patterns. Our approach first estimates plant heights and stem widths from individual frames. It then uses tracking algorithms to refine these estimates across frames and avoid double counting the same plant in multiple frames. The result is a histogram of stem widths and plant heights for each plot of a particular genetically engineered sorghum variety. In-field testing and comparison with human collected ground truth data demonstrates that our system achieves 13% average absolute error for stem width estimation and 15% average absolute error for plant height estimation.