Evaluation of Binocular Eye Trackers and Algorithms for 3D Gaze Interaction in Virtual Reality Environments

Tracking user’s visual attention is a fundamental aspect in novel human-computer interaction paradigms found in Virtual Reality. For example, multimodal interfaces or dialogue-based communications with virtual and real agents greatly benefit from the analysis of the user’s visual attention as a vital source for deictic references or turn-taking signals. Current approaches to determine visual attention rely primarily on monocular eye trackers. Hence they are restricted to the interpretation of two-dimensional fixations relative to a defined area of projection. The study presented in this article compares precision, accuracy and application performance of two binocular eye tracking devices. Two algorithms are compared which derive depth information as required for visual attention-based 3D interfaces. This information is further applied to an improved VR selection task in which a binocular eye tracker and an adaptive neural network algorithm is used during the disambiguation of partly occluded objects.

Counting scars on tree stems to assess rockfall hazards: A low effort approach, but how reliable?

Rockfall is a widespread and hazardous process in mountain environments, but data on past events are only rarely available. Growth-ring series from trees impacted by rockfall were successfully used in the past to overcome the lack of archival records. Dendrogeomorphic techniques have been demonstrated to allow very accurate dating and reconstruction of spatial and temporal rockfall activity, but the approach has been cited to be labor intensive and time consuming. In this study, we present a simplified method to quantify rockfall processes on forested slopes requiring less time and efforts. The approach is based on a counting of visible scars on the stem surface of Common beech (Fagus sylvatica L.). Data are presented from a site in the Inn valley (Austria), where rocks are frequently detached from an ~ 200-m-high, south-facing limestone cliff. We compare results obtained from (i) the “classical” analysis of growth disturbances in the tree-ring series of 33 Norway spruces (Picea abies (L.) Karst.) and (ii) data obtained with a scar count on the stem surface of 50 F. sylvatica trees. A total of 277 rockfall events since A.D. 1819 could be reconstructed from tree-ring records of P. abies, whereas 1140 scars were observed on the stem surface of F. sylvatica. Absolute numbers of rockfalls (and hence return intervals) vary significantly between the approaches, and the mean number of rockfalls observed on the stem surface of F. sylvatica exceeds that of P. abies by a factor of 2.7. On the other hand, both methods yield comparable data on the spatial distribution of relative rockfall activity. Differences may be explained by a great portion of masked scars in P. abies and the conservation of signs of impacts on the stem of F. sylvatica. Besides, data indicate that several scars on the bark of F. sylvatica may stem from the same impact and thus lead to an overestimation of rockfall activity.

Recovery from monocular deprivation using binocular deprivation.

Ocular dominance (OD) plasticity is a robust paradigm for examining the functional consequences of synaptic plasticity. Previous experimental and theoretical results have shown that OD plasticity can be accounted for by known synaptic plasticity mechanisms, using the assumption that deprivation by lid suture eliminates spatial structure in the deprived channel. Here we show that in the mouse, recovery from monocular lid suture can be obtained by subsequent binocular lid suture but not by dark rearing. This poses a significant challenge to previous theoretical results. We therefore performed simulations with a natural input environment appropriate for mouse visual cortex. In contrast to previous work, we assume that lid suture causes degradation but not elimination of spatial structure, whereas dark rearing produces elimination of spatial structure. We present experimental evidence that supports this assumption, measuring responses through sutured lids in the mouse. The change in assumptions about the input environment is sufficient to account for new experimental observations, while still accounting for previous experimental results.

Segmentation and Recognition of Multi-Food Meal Images for Carbohydrate Counting

In this paper, we propose novel methodologies for the automatic segmentation and recognition of multi-food images. The proposed methods implement the first modules of a carbohydrate counting and insulin advisory system for type 1 diabetic patients. Initially the plate is segmented using pyramidal mean-shift filtering and a region growing algorithm. Then each of the resulted segments is described by both color and texture features and classified by a support vector machine into one of six different major food classes. Finally, a modified version of the Huang and Dom evaluation index was proposed, addressing the particular needs of the food segmentation problem. The experimental results prove the effectiveness of the proposed method achieving a segmentation accuracy of 88.5% and recognition rate equal to 87%

Automated counting of bacterial colony forming units on agar plates.

Manual counting of bacterial colony forming units (CFUs) on agar plates is laborious and error-prone. We therefore implemented a colony counting system with a novel segmentation algorithm to discriminate bacterial colonies from blood and other agar plates.A colony counter hardware was designed and a novel segmentation algorithm was written in MATLAB. In brief, pre-processing with Top-Hat-filtering to obtain a uniform background was followed by the segmentation step, during which the colony images were extracted from the blood agar and individual colonies were separated. A Bayes classifier was then applied to count the final number of bacterial colonies as some of the colonies could still be concatenated to form larger groups. To assess accuracy and performance of the colony counter, we tested automated colony counting of different agar plates with known CFU numbers of S. pneumoniae, P. aeruginosa and M. catarrhalis and showed excellent performance.

Counting is a spatial process: evidence from eye movements

Spatial-numerical associations (small numbers-left/lower space and large numbers-right/upper space) are regularly found in simple number categorization tasks. These associations were taken as evidence for a spatially oriented mental number line. However, the role of spatial-numerical associations during more complex number processing, such as counting or mental arithmetic is less clear. Here, we investigated whether counting is associated with a movement along the mental number line. Participants counted aloud upward or downward in steps of 3 for 45 s while looking at a blank screen. Gaze position during upward counting shifted rightward and upward, while the pattern for downward counting was less clear. Our results, therefore, confirm the hypothesis of a movement along the mental number line for addition. We conclude that space is not only used to represent number magnitudes but also to actively operate on numbers in more complex tasks such as counting, and that the eyes reflect this spatial mental operation.

Methods for preparing and counting biochemical varves

Four different preparation and counting methods for biochemical varves were compared in order to assess counting errors and to standardize these techniques. The properties of two embedding methods, namely the shock-freeze, freeze-dry and the water-acetone-epoxy-exchange method, are discussed. Varve counts were carried out on fresh sediment and on sediment thin-sections, on the latter by manual and by automated counting using image-analysis software. Counting on fresh sediment and using image-analysis generally underestimated the number of varves, especially in sections with inconspicuous varves. A comparison between multiple varve counts carried out by a single analyst and different analysts showed no significant differences in the mean varve counts.

Measurement of isotopic uranium in water for compliance monitoring by liquid scintillation counting with alpha/beta discrimination

A simple and inexpensive method is described for analysis of uranium (U) activity and mass in water by liquid scintillation counting using $\alpha$/$\beta$ discrimination. This method appears to offer a solution to the need for an inexpensive protocol for monitoring U activity and mass simultaneously and an alternative to the potential inaccuracy involved when depending on the mass-to-activity conversion factor or activity screen.^ U is extracted virtually quantitatively into 20 ml extractive scintillator from a 1-$\ell$ aliquot of water acidified to less than pH 2. After phase separation, the sample is counted for a 20-minute screening count with a minimum detection level of 0.27 pCi $\ell\sp{-1}$. $\alpha$-particle emissions from the extracted U are counted with close to 100% efficiency with a Beckman LS6000 LL liquid scintillation counter equipped with pulse-shape discrimination electronics. Samples with activities higher than 10 pCi $\ell\sp-1$ are recounted for 500-1000 minutes for isotopic analysis. Isotopic analysis uses events that are automatically stored in spectral files and transferred to a computer during assay. The data can be transferred to a commercially available spreadsheet and retrieved for examination or data manipulation. Values for three readily observable spectral features can be rapidly identified by data examination and substituted into a simple formula to obtain $\sp{234}$U/$\sp{238}$U ratio for most samples. U mass is calculated by substituting the isotopic ratio value into a simple equation.^ The utility of this method for the proposed compliance monitoring of U in public drinking water supplies was field tested with a survey of drinking water from Texas supplies that had previously been known to contain elevated levels of gross $\alpha$ activity. U concentrations in 32 samples from 27 drinking water supplies ranged from 0.26 to 65.5 pCi $\ell\sp{-1}$, with seven samples exceeding the proposed Maximum Contaminant Level of 20 $\mu$g $\ell\sp{-1}$. Four exceeded the proposed activity screening level of 30 pCi $\ell\sp{-1}$. Isotopic ratios ranged from 0.87 to 41.8, while one sample contained $\sp{234}$U activity of 34.6 pCi $\ell\sp{-1}$ in the complete absence of its parent, $\sp{238}$U. U mass in the samples with elevated activity ranged from 0.0 to 103 $\mu$g $\ell\sp{-1}$. A limited test of screening surface and groundwaters for contamination by U from waste sites and natural processes was also successful. ^

Abundance and biomass of mesozooplankton in the Levantine Basin during the Bilim 2 cruise in April 2008

The Sesame dataset contains mesozooplankton data collected during April 2008 in the Levantine Basin (between 33.20 and 36.50 N latitude and between 30.99 and 31.008 E longitude). Mesozooplankton samples were collected by using a WP-2 closing net with 200 µm mesh size during day hours (07:00-18:00). Samples were taken from 0-50, 50-100, 100-200 m layers at 5 stations in Levantine Basin The dataset includes samples analyzed for mesozooplankton species composition, abundance and total mesozooplankton biomass. Sampling volume was estimated by multiplying the mouth area with the wire length. Sampling biomass was measured by weighing filters and then determined by sampling volume. The samples were sieved sequentially through meshes of 500 and 200 micron to separate the mesozooplankton into size fractions. The entire sample (1/2) or an aliquot of the taxon-specific mesozooplankton abundance and the total abundance of the mesozooplankton were was analyzed under the binocular microscope. Minimum 500 individuals of mesozooplankton were identified and numerated at higher taxonomic level. Taxonomic identification was done at the METU- Institute of Marine Sciences by Alexandra Gubanova,Tuba Terbiyik using the relevant taxonomic literatures. Mesozooplankton abundance and biomass were estimated by Zahit Uysal and Yesim Ak.

Abundance of mesozooplankton in the Levantine Basin during the Bilim 2 cruise in October 2008

The Sesame dataset contains mesozooplankton data collected during October 2008 in the Levantine Basin (between 33.20 and 36.50 N latitude and between 30.99 and 31.008 E longitude). Mesozooplankton samples were collected by using a WP-2 closing net with 200 µm mesh size during day hours (07:00-18:00). Samples were taken from 0-50, 50-100, 100-200 m layer at 5 stations in Levantine Basin The dataset includes samples analyzed for mesozooplankton species composition, abundance and total mesozooplankton biomass. The entire sample (1/2) or an aliquot was analyzed under the binocular microscope. Minimum 500 individuals of mesozooplankton were identified and numerated at higher taxonomic level. Taxonomic identification was done at the METU- Institute of Marine Sciences by Alexandra Gubanova,Tuba Terbiyik using the relevant taxonomic literatures. Mesozooplankton abundance and biomass were estimated by Zahit Uysal and Yesim Ak Örek. Specification via marine planktonic copepods database (http://copepodes.obs-banyuls.fr/en/).

Abundance of mesozooplankton in the Cilician Basin during the Bilim 2 cruise in March 2008

The Sesame dataset contains mesozooplankton data collected during March 2008 in the Cilician Basin (between between 35.40'- 36.79 N latitude and 33.19- 36.07 E ). Mesozooplankton samples were collected by using a WP-2 closing net with 200 micron mesh size during day hours (07:00-18:00). Samples were taken in the 0-50, 50-100, 100-200 m layer at 6 stations in the Cilician Basin. The dataset includes samples analyzed for mesozooplankton species composition, abundance and total biomass (Dry weight(mg/m**3)). Taxon-specific mesozooplankton abundance: 1/2 sample or an aliquot was analyzed under the binocular microscope. Copepod species were identified and enumerated; the other mesozooplankters were identified and enumerated at higher taxonomic level (commonly named as mesozooplankton groups). Taxonomic identification was done at the METU-Institute of Marine Sciences by Tuba Terbiyik using the relevant taxonomic literatures. Mesozooplankton total abundance: 1/2 sample or an aliquot was analyzed under the binocular microscope. Copepod species were identified and enumerated; the other mesozooplankters were identified and enumerated at higher taxonomic level (commonly named as mesozooplankton groups). Taxonomic identification was done at the METU-Institute of Marine Sciences using the relevant taxonomic literatures

Abundance of mesozooplankton in the Marmara Sea collected during the Bilim 2 cruise in April 2008

The Sesame dataset contains mesozooplankton data collected during April 2008 in the Marmara Sea (between 40°15' - 34°00N latitude and 19°00 - 23°10'E longitude). Sampling was always performed in day hours (07:00-18:00 local time). Samples were taken at 6 stations in the Marmara Sea. Mesozooplankton samples were collected by using a WP-2 closing net with 200 µm mesh size. Sample was immediately fixed and preserved in a formaldehyde-seawater solution (4% final concentration) to be successively analyzed in the laboratory for species composition, abundance and total biomass. The algal organisms materials were then seperated from the mesozooplankton subsample at the dissecting microscope in the laboratory because of the contamination of the net samples with large-sized algae and mucilaginous organic matters. Afterwards, each samples were filtered on GF/C (pre combusted and weighed) for biomass measurements for dry weight. The dataset includes samples analyzed for mesozooplankton species composition, abundance and total mesozooplankton biomass. Sampling volume was estimated by multiplying the mouth area with the wire length. Sampling biomass was measured by weighing filters and then determined according to sampling volume. 1/2 sample or an aliquot was analyzed under the binocular microscope. Copepod species were identified and enumerated; the other mesozooplankters were identified and enumerated at higher taxonomic level (commonly named as mesozooplankton groups). Taxonomic identification was done at the METU-Institute of Marine Sciences by Tuba Terbiyik using the relevant taxonomic literatures.