Baseline and triangulation geometry in a standard plenoptic camera

In this paper, we demonstrate light field triangulation to determine depth distances and baselines in a plenoptic camera. The advancement of micro lenses and image sensors enabled plenoptic cameras to capture a scene from different viewpoints with sufficient spatial resolution. While object distances can be inferred from disparities in a stereo viewpoint pair using triangulation, this concept remains ambiguous when applied in case of plenoptic cameras. We present a geometrical light field model allowing the triangulation to be applied to a plenoptic camera in order to predict object distances or to specify baselines as desired. It is shown that distance estimates from our novel method match those of real objects placed in front of the camera. Additional benchmark tests with an optical design software further validate the model’s accuracy with deviations of less than 0:33 % for several main lens types and focus settings. A variety of applications in the automotive and robotics field can benefit from this estimation model

GRIDDS - A Gait Recognition Image and Depth Dataset

Several approaches based on human gait have been proposed in the literature, either for medical research reasons, smart surveillance, human-machine interaction, or other purposes, whose validation highly depends on the access to common input data through available datasets, enabling a coherent performance comparison. The advent of depth sensors leveraged the emergence of novel approaches and, consequently, the usage of new datasets. In this work we present the GRIDDS - A Gait Recognition Image and Depth Dataset, a new and publicly available gait depth-based dataset that can be used mostly for person and gender recognition purposes. (c) Springer Nature Switzerland AG 2019

3D Activity Recognition using Motion History and Binary Shape Templates

This paper presents our work on activity recognition in 3D depth images. We propose a global descriptor that is accurate, compact and easy to compute as compared to the state-of-the-art for characterizing depth sequences. Activity enactment video is divided into temporally overlapping blocks. Each block (set of image frames) is used to generate Motion History Templates (MHTs) and Binary Shape Templates (BSTs) over three different views - front, side and top. The three views are obtained by projecting each video frame onto three mutually orthogonal Cartesian planes. MHTs are assembled by stacking the difference of consecutive frame projections in a weighted manner separately for each view. Histograms of oriented gradients are computed and concatenated to represent the motion content. Shape information is obtained through a similar gradient analysis over BSTs. These templates are built by overlaying all the body silhouettes in a block, separately for each view. To effectively trace shape-growth, BSTs are built additively along the blocks. Consequently, the complete ensemble of gradient features carries both 3D shape and motion information to effectively model the dynamics of an articulated body movement. Experimental results on 4 standard depth databases (MSR 3D Hand Gesture, MSR Action, Action-Pairs, and UT-Kinect) prove the efficacy as well as the generality of our compact descriptor. Further, we successfully demonstrate the robustness of our approach to (impulsive) noise and occlusion errors that commonly affect depth data

Kernelized Multiview Projection for Robust Action Recognition

Conventional action recognition algorithms adopt a single type of feature or a simple concatenation of multiple features. In this paper, we propose to better fuse and embed different feature representations for action recognition using a novel spectral coding algorithm called Kernelized Multiview Projection (KMP). Computing the kernel matrices from different features/views via time-sequential distance learning, KMP can encode different features with different weights to achieve a low-dimensional and semantically meaningful subspace where the distribution of each view is sufficiently smooth and discriminative. More crucially, KMP is linear for the reproducing kernel Hilbert space, which allows it to be competent for various practical applications. We demonstrate KMP’s performance for action recognition on five popular action datasets and the results are consistently superior to state-of-the-art techniques

Online prediction of others’ actions: the contribution of the target object, action context and movement kinematics

Previous research investigated the contributions of target objects, situational context and movement kinematics to action prediction separately. The current study addresses how these three factors combine in the prediction of observed actions. Participants observed an actor whose movements were constrained by the situational context or not, and object-directed or not. After several steps, participants had to indicate how the action would continue. Experiment 1 shows that predictions were most accurate when the action was constrained and object-directed. Experiments 2A and 2B investigated whether these predictions relied more on the presence of a target object or cues in the actor’s movement kinematics. The target object was artificially moved to another location or occluded. Results suggest a crucial role for kinematics. In sum, observers predict actions based on target objects and situational constraints, and they exploit subtle movement cues of the observed actor rather than the direct visual information about target objects and context

Multimodal Detection of Engagement in Groups of Children Using Rank Learning

In collaborative play, children exhibit different levels of engagement. Some children are engaged with other children while some play alone. In this study, we investigated multimodal detection of individual levels of engagement using a ranking method and non-verbal features: turn-taking and body movement. Firstly, we automatically extracted turn-taking and body movement features in naturalistic and challenging settings. Secondly, we used an ordinal annotation scheme and employed a ranking method considering the great heterogeneity and temporal dynamics of engagement that exist in interactions. We showed that levels of engagement can be characterised by relative levels between children. In particular, a ranking method, Ranking SVM, outperformed a conventional method, SVM classification. While either turn-taking or body movement features alone did not achieve promising results, combining the two features yielded significant error reduction, showing their complementary power

Identifying Prototypical Components in Behaviour Using Clustering Algorithms

Quantitative analysis of animal behaviour is a requirement to understand the task solving strategies of animals and the underlying control mechanisms. The identification of repeatedly occurring behavioural components is thereby a key element of a structured quantitative description. However, the complexity of most behaviours makes the identification of such behavioural components a challenging problem. We propose an automatic and objective approach for determining and evaluating prototypical behavioural components. Behavioural prototypes are identified using clustering algorithms and finally evaluated with respect to their ability to represent the whole behavioural data set. The prototypes allow for a meaningful segmentation of behavioural sequences. We applied our clustering approach to identify prototypical movements of the head of blowflies during cruising flight. The results confirm the previously established saccadic gaze strategy by the set of prototypes being divided into either predominantly translational or rotational movements, respectively. The prototypes reveal additional details about the saccadic and intersaccadic flight sections that could not be unravelled so far. Successful application of the proposed approach to behavioural data shows its ability to automatically identify prototypical behavioural components within a large and noisy database and to evaluate these with respect to their quality and stability. Hence, this approach might be applied to a broad range of behavioural and neural data obtained from different animals and in different contexts

Cryopreservation Effect on Proliferative and Chondrogenic Potential of Human Chondrocytes Isolated from Superficial and Deep Cartilage

[Abstract] Objectives: To compare the proliferative and chondrogenic potential of fresh and frozen chondrocytes isolated from superficial and deep articular cartilage biopsies. Materials and Methodology: The study included 12 samples of fresh and frozen healthy human knee articular cartilage. Cell proliferation was tested at 3, 6 and 9 days. Studies of mRNA quantification, protein expression and immunofluorescence for proliferation and chondrogenic markers were performed. Results: Stimulation of fresh and frozen chondrocytes from both superficial and deep cartilage with fetal bovine serum produced an increase in the proliferative capacity compared to the non-stimulated control group. In the stimulated fresh cells group, the proliferative capacity of cells from the deep biopsy was greater than that from cells from the superficial biopsy (0.046 vs 0.028, respectively, p<0.05). There was also a significant difference between the proliferative capacity of superficial zone fresh (0.028) and frozen (0.051) chondrocytes (p<0.05). CCND1 mRNA and protein expression levels, and immunopositivity for Ki67 revealed a higher proliferative capacity for fresh articular chondrocytes from deep cartilage. Regarding the chondrogenic potential, stimulated fresh cells showed higher SOX9 and Col II expression in chondrocytes from deep than from superficial zone (p<0.05, T student test). Conclusions: The highest rate of cell proliferation and chondrogenic potential of fresh chondrocytes was found in cells obtained from deep cartilage biopsies, whereas there were no statistically significant differences in proliferative and chondrogenic capacity between biopsy origins with frozen chondrocytes. These results indicate that both origin and cryopreservation affect the proliferative and chondrogenic potential of chondrocytes.Servizo Galego de Saúde; PS07/84Instituto de Salud Carlos III; CIBER BBN CB06-01-0040Ministerio Ciencia e Innovacion; PLE2009-0144Ministerio Ciencia e Innovación; PI 08/202