Skeleton based visual pattern recognition. Applications to tabletop interaction

211 p. :il.

Application of Entropy and Fractal Dimension Analyses to the Pattern Recognition of Contaminated Fish Responses in Aquaculture

The objective of the work was to develop a non-invasive methodology for image acquisition, processing and nonlinear trajectory analysis of the collective fish response to a stochastic event. Object detection and motion estimation were performed by an optical flow algorithm in order to detect moving fish and simultaneously eliminate background, noise and artifacts. The Entropy and the Fractal Dimension (FD) of the trajectory followed by the centroids of the groups of fish were calculated using Shannon and permutation Entropy and the Katz, Higuchi and Katz-Castiglioni's FD algorithms respectively. The methodology was tested on three case groups of European sea bass (Dicentrarchus labrax), two of which were similar (C1 control and C2 tagged fish) and very different from the third (C3, tagged fish submerged in methylmercury contaminated water). The results indicate that Shannon entropy and Katz-Castiglioni were the most sensitive algorithms and proved to be promising tools for the non-invasive identification and quantification of differences in fish responses. In conclusion, we believe that this methodology has the potential to be embedded in online/real time architecture for contaminant monitoring programs in the aquaculture industry.

Dealing with the Effects of Sensor Displacement in Wearable Activity Recognition

Most wearable activity recognition systems assume a predefined sensor deployment that remains unchanged during runtime. However, this assumption does not reflect real-life conditions. During the normal use of such systems, users may place the sensors in a position different from the predefined sensor placement. Also, sensors may move from their original location to a different one, due to a loose attachment. Activity recognition systems trained on activity patterns characteristic of a given sensor deployment may likely fail due to sensor displacements. In this work, we innovatively explore the effects of sensor displacement induced by both the intentional misplacement of sensors and self-placement by the user. The effects of sensor displacement are analyzed for standard activity recognition techniques, as well as for an alternate robust sensor fusion method proposed in a previous work. While classical recognition models show little tolerance to sensor displacement, the proposed method is proven to have notable capabilities to assimilate the changes introduced in the sensor position due to self-placement and provides considerable improvements for large misplacements.

Wide Screening of Phage-Displayed Libraries Identifies Immune Targets in Planta

Microbe-Associated Molecular Patterns and virulence effectors are recognized by plants as a first step to mount a defence response against potential pathogens. This recognition involves a large family of extracellular membrane receptors and other immune proteins located in different sub-cellular compartments. We have used phage-display technology to express and select for Arabidopsis proteins able to bind bacterial pathogens. To rapidly identify microbe-bound phage, we developed a monitoring method based on microarrays. This combined strategy allowed for a genome-wide screening of plant proteins involved in pathogen perception. Two phage libraries for high-throughput selection were constructed from cDNA of plants infected with Pseudomonas aeruginosa PA14, or from combined samples of the virulent isolate DC3000 of Pseudomonas syringae pv. tomato and its avirulent variant avrRpt2. These three pathosystems represent different degrees in the specificity of plant-microbe interactions. Libraries cover up to 26107 different plant transcripts that can be displayed as functional proteins on the surface of T7 bacteriophage. A number of these were selected in a bio-panning assay for binding to Pseudomonas cells. Among the selected clones we isolated the ethylene response factor ATERF-1, which was able to bind the three bacterial strains in competition assays. ATERF-1 was rapidly exported from the nucleus upon infiltration of either alive or heat-killed Pseudomonas. Moreover, aterf-1 mutants exhibited enhanced susceptibility to infection. These findings suggest that ATERF-1 contains a microbe-recognition domain with a role in plant defence. To identify other putative pathogen-binding proteins on a genome-wide scale, the copy number of selected-vs.-total clones was compared by hybridizing phage cDNAs with Arabidopsis microarrays. Microarray analysis revealed a set of 472 candidates with significant fold change. Within this set defence-related genes, including well-known targets of bacterial effectors, are over-represented. Other genes non-previously related to defence can be associated through this study with general or strain-specific recognition of Pseudomonas.

Towards a standard methodology to evaluate internal cluster validity indices

The evaluation and comparison of internal cluster validity indices is a critical problem in the clustering area. The methodology used in most of the evaluations assumes that the clustering algorithms work correctly. We propose an alternative methodology that does not make this often false assumption. We compared 7 internal cluster validity indices with both methodologies and concluded that the results obtained with the proposed methodology are more representative of the actual capabilities of the compared indices.

Deep learning review and its applications

Deep neural networks have recently gained popularity for improv- ing state-of-the-art machine learning algorithms in diverse areas such as speech recognition, computer vision and bioinformatics. Convolutional networks especially have shown prowess in visual recognition tasks such as object recognition and detection in which this work is focused on. Mod- ern award-winning architectures have systematically surpassed previous attempts at tackling computer vision problems and keep winning most current competitions. After a brief study of deep learning architectures and readily available frameworks and libraries, the LeNet handwriting digit recognition network study case is developed, and lastly a deep learn- ing network for playing simple videogames is reviewed.