A comparison of monkey and human motion processing mechanisms

Single-cell recording studies have provided vision scientists with a detailed understanding of motion processing at the neuronal level in non-human primates. However, despite the development of brain imaging techniques, it is not known to what extent the response characteristics of motion-sensitive neurons in monkey brain mirror those of human motion sensitive neurons. Using a motion adaptation paradigm, the direction aftereffect, we recently provided evidence of a strong resemblance in the response functions of motion-sensitive neurons in monkey and human to moving dot patterns differing in dot density. Here we describe a series of experiments in which measurements of the direction aftereffect are used to infer the response characteristics of human motion-sensitive neurons when viewing transparent motion and moving patterns that differ in their signal-to-noise ratio (motion coherence). In the case of transparent motion stimuli, our data suggest suppressed activity of motion-sensitive neurons similar to that reported for macaque monkey. In the case of motion coherence, our results are indicative of a linear relationship between signal intensity (coherence) and neural activity; a pattern of activity which also bears a striking similarity to macaque neural activity. These findings strongly suggest that monkey and human motionsensitive neurons exhibit similar response and inhibitory characteristics.

Rao-blackwellized particle filter for human appearance and position tracking

In human motion analysis, the joint estimation of appearance, body pose and location parameters is not always tractable due to its huge computational cost. In this paper, we propose a Rao-Blackwellized Particle Filter for addressing the problem of human pose estimation and tracking. The advantage of the proposed approach is that Rao-Blackwellization allows the state variables to be splitted into two sets, being one of them analytically calculated from the posterior probability of the remaining ones. This procedure reduces the dimensionality of the Particle Filter, thus requiring fewer particles to achieve a similar tracking performance. In this manner, location and size over the image are obtained stochastically using colour and motion clues, whereas body pose is solved analytically applying learned human Point Distribution Models.

Fractal rotation isolates mechanisms for form-dependent motion in human vision

Here, we describe a motion stimulus in which the quality of rotation is fractal. This makes its motion unavailable to the translationbased motion analysis known to underlie much of our motion perception. In contrast, normal rotation can be extracted through the aggregation of the outputs of translational mechanisms. Neural adaptation of these translation-based motion mechanisms is thought to drive the motion after-effect, a phenomenon in which prolonged viewing of motion in one direction leads to a percept of motion in the opposite direction. We measured the motion after-effects induced in static and moving stimuli by fractal rotation. The after-effects found were an order of magnitude smaller than those elicited by normal rotation. Our findings suggest that the analysis of fractal rotation involves different neural processes than those for standard translational motion. Given that the percept of motion elicited by fractal rotation is a clear example of motion derived from form analysis, we propose that the extraction of fractal rotation may reflect the operation of a general mechanism for inferring motion from changes in form.

Monkey and humans exhibit similar motion-processing mechanisms

Single cell recording studies have resulted in a detailed understanding of motion-sensitive neurons in non-human primate visual cortex. However, it is not known to what extent response properties of motion-sensitive neurons in the non-human primate brain mirror response characteristics of motion-sensitive neurons in the human brain. Using a motion adaptation paradigm, the direction aftereffect, we show that changes in the activity of human motion-sensitive neurons to moving dot patterns that differ in dot density bear a strong resemblance to data from macaque monkey. We also show a division-like inhibition between neural populations tuned to opposite directions, which also mirrors neural-inhibitory behaviour in macaque. These findings strongly suggest that motion-sensitive neurons in human and non-human primates share common response and inhibitory characteristics.

Behaviours in video

With a significant increment of the number of digital cameras used for various purposes, there is a demanding call for advanced video analysis techniques that can be used to systematically interpret and understand the semantics of video contents, which have been recorded in security surveillance, intelligent transportation, health care, video retrieving and summarization. Understanding and interpreting human behaviours based on video analysis have observed competitive challenges due to non-rigid human motion, self and mutual occlusions, and changes of lighting conditions. To solve these problems, advanced image and signal processing technologies such as neural network, fuzzy logic, probabilistic estimation theory and statistical learning have been overwhelmingly investigated.

2D silhouette and 3D skeletal models for human detection and tracking

In this paper we propose a statistical model for detection and tracking of human silhouette and the corresponding 3D skeletal structure in gait sequences. We follow a point distribution model (PDM) approach using a Principal Component Analysis (PCA). The problem of non-lineal PCA is partially resolved by applying a different PDM depending of pose estimation; frontal, lateral and diagonal, estimated by Fisher's linear discriminant. Additionally, the fitting is carried out by selecting the closest allowable shape from the training set by means of a nearest neighbor classifier. To improve the performance of the model we develop a human gait analysis to take into account temporal dynamic to track the human body. The incorporation of temporal constraints on the model increase reliability and robustness.

Exploiting spatio-temporal constraints for robust 2D pose tracking

We present a Spatio-temporal 2D Models Framework (STMF) for 2D-Pose tracking. Space and time are discretized and a mixture of probabilistic "local models" is learnt associating 2D Shapes and 2D Stick Figures. Those spatio-temporal models generalize well for a particular viewpoint and state of the tracked action but some spatio-temporal discontinuities can appear along a sequence, as a direct consequence of the discretization. To overcome the problem, we propose to apply a Rao-Blackwellized Particle Filter (RBPF) in the 2D-Pose eigenspace, thus interpolating unseen data between view-based clusters. The fitness to the images of the predicted 2D-Poses is evaluated combining our STMF with spatio-temporal constraints. A robust, fast and smooth human motion tracker is obtained by tracking only the few most important dimensions of the state space and by refining deterministically with our STMF.

CCN2/CTGF increases expression of miR-302 microRNAs, which target the TGF beta type II receptor with implications for nephropathic cell phenotypes

Signalling interplay between transforming growth factor-beta (TGF beta) and CCN2 [also called connective tissue growth factor (CTGF)] plays a crucial role in the progression of diabetic nephropathy and has been implicated in cellular differentiation. To investigate the potential role of microRNAs (miRNAs) in the mediation of this signalling network, we performed miRNA screening in mesangial cells treated with recombinant human CCN2. Analysis revealed a cohort of 22 miRNAs differentially expressed by twofold or more, including members of the miR-302 family. Target analysis of miRNA to 3'-untranslated regions (3'-UTRs) identified TGF beta receptor II (T beta RII) as a potential miR-302 target. In mesangial cells, decreased T beta RII expression was confirmed in response to CCN2 together with increased expression of miR-302d. T beta RII was confirmed as an miR-302 target, and inhibition of miR-302d was sufficient to attenuate the effect of CCN2 on T beta RII. Data from the European Renal cDNA Biopsy Bank revealed decreased T beta RII in diabetic patients, suggesting pathophysiological significance. In a mouse model of fibrosis (UUO), miR-302d was increased, with decreased T beta RII expression and aberrant signalling, suggesting relevance in chronic fibrosis. miR-302d decreased TGF beta-induced epithelial mesenchymal transition (EMT) in renal HKC8 epithelial cells and attenuated TGF beta-induced mesangial production of fibronectin and thrombospondin. In summary, we demonstrate a new mode of regulation of TGF beta by CCN2, and conclude that the miR-302 family has a role in regulating growth factor signalling pathways, with implications for nephropathic cell fate transitions.

Kernelized structural classification for 3D dogs body parts detection

Despite pattern recognition methods for human behavioral analysis has flourished in the last decade, animal behavioral analysis has been almost neglected. Those few approaches are mostly focused on preserving livestock economic value while attention on the welfare of companion animals, like dogs, is now emerging as a social need. In this work, following the analogy with human behavior recognition, we propose a system for recognizing body parts of dogs kept in pens. We decide to adopt both 2D and 3D features in order to obtain a rich description of the dog model. Images are acquired using the Microsoft Kinect to capture the depth map images of the dog. Upon depth maps a Structural Support Vector Machine (SSVM) is employed to identify the body parts using both 3D features and 2D images. The proposal relies on a kernelized discriminative structural classificator specifically tailored for dogs independently from the size and breed. The classification is performed in an online fashion using the LaRank optimization technique to obtaining real time performances. Promising results have emerged during the experimental evaluation carried out at a dog shelter, managed by IZSAM, in Teramo, Italy.