3D cameras that can capture range information, in addition to color information, are increasingly prevalent in the consumer marketplace and available in many consumer mobile imaging platforms. An interesting and important application enabled by 3D cameras is photogrammetry, where the physical distance between points can be computed using captured imagery. However, for consumer photogrammetry to succeed in the marketplace, it needs to meet the accuracy and consistency expectations of users in the real world and perform well under challenging lighting conditions, varying distances of the object from the camera etc. These requirements are exceedingly difficult to meet due to the noisy nature of range data, especially when passive stereo or multi-camera systems are used for range estimation. We present a novel and robust algorithm for point-to-point 3D measurement using range camera systems in this paper. Our algorithm utilizes the intuition that users often specify end points of an object of interest for measurement and that the line connecting the two points also belong to the same object. We analyze the 3D structure of the points along this line using robust PCA and improve measurement accuracy by fitting the endpoints to this model prior to measurement computation. We also handle situations where users attempt to measure a gap such as the arms of a sofa, width of a doorway etc. which violates our assumption. Finally, we test the performance of our proposed algorithm on a dataset of over 1800 measurements collected by humans on the Dell Venue 8 tablet with Intel RealSense Snapshot technology. Our results show significant improvements in both accuracy and consistency of measurement, which is critical in making consumer photogrammetry a reality in the marketplace.
Art experience is per definition a dynamic way of processing: While perceiving the artistic object, the film, the music play, we undergo complex affective as well as cognitive experiences interactively changing the entire processing. Elaboration, understanding, aesthetic aha-insights etc. change the view on the to-be-processed entity—psychologically interpreted, the entity becomes a part of ourselves. Most methods of measuring art experience are not able to reflect on these dynamics; most of them are just object-based, e.g. correlative approaches of bringing statically assumed object-properties together with simple ratings on these "objects". Here, I will demonstrate the limitations of such approaches, accompanied by the introduction of some simple principles to be followed when art experience is the focus of research. I will then introduce some methods which can assist in unfolding the process character of art experience without interfering too much with the experience as such: For instance, by using posturography, the Continuous Evaluation Procedure (CEP) or automatic facial expression routines. When these techniques are employed with clear rationales in mind, and by deriving concrete hypotheses from a well-grounded theoretical approach, we can come much closer to the rich experience people have when encountering and elaborating art. This will assist us in our human-history-encompassing endeavor of deciphering what and how art is processed and appreciated.