Signature features with the visibility transformation

The signature in rough path theory provides a graduated summary of a path through an examination of the effects of its increments. Inspired by recent developments of signature features in the context of machine learning, we explore a transformation that is able to embed the effect of the absolute position of the data stream into signature features. This unified feature is particularly effective for its simplifying role in allowing the signature feature set to accommodate nonlinear functions of absolute and relative values

Skeleton-Based Gesture Recognition Using Several Fully Connected Layers with Path Signature Features and Temporal Transformer Module

The skeleton based gesture recognition is gaining more popularity due to its wide possible applications. The key issues are how to extract discriminative features and how to design the classification model. In this paper, we first leverage a robust feature descriptor, path signature (PS), and propose three PS features to explicitly represent the spatial and temporal motion characteristics, i.e., spatial PS (S PS), temporal PS (T PS) and temporal spatial PS (T S PS). Considering the significance of fine hand movements in the gesture, we propose an ”attention on hand” (AOH) principle to define joint pairs for the S PS and select single joint for the T PS. In addition, the dyadic method is employed to extract the T PS and T S PS features that encode global and local temporal dynamics in the motion. Secondly, without the recurrent strategy, the classification model still faces challenges on temporal variation among different sequences. We propose a new temporal transformer module (TTM) that can match the sequence key frames by learning the temporal shifting parameter for each input. This is a learning-based module that can be included into standard neural network architecture. Finally, we design a multi-stream fully connected layer based network to treat spatial and temporal features separately and fused them together for the final result. We have tested our method on three benchmark gesture datasets, i.e., ChaLearn 2016, ChaLearn 2013 and MSRC-12. Experimental results demonstrate that we achieve the state-of-the-art performance on skeleton-based gesture recognition with high computational efficiency

Learning stochastic differential equations using RNN with log signature features

This paper contributes to the challenge of learning a function on streamed multimodal data through evaluation. The core of the result of our paper is the combination of two quite different approaches to this problem. One comes from the mathematically principled technology of signatures and log-signatures as representations for streamed data, while the other draws on the techniques of recurrent neural networks (RNN). The ability of the former to manage high sample rate streams and the latter to manage large scale nonlinear interactions allows hybrid algorithms that are easy to code, quicker to train, and of lower complexity for a given accuracy. We illustrate the approach by approximating the unknown functional as a controlled differential equation. Linear functionals on solutions of controlled differential equations are the natural universal class of functions on data streams. Following this approach, we propose a hybrid Logsig-RNN algorithm that learns functionals on streamed data. By testing on various datasets, i.e. synthetic data, NTU RGB+D 120 skeletal action data, and Chalearn2013 gesture data, our algorithm achieves the outstanding accuracy with superior efficiency and robustness

Modelo para la clasificación del aguacate Hass en sus estados comerciales y de exportación, mediante el uso de redes neuronales convolucionales

El mercado del aguacate hass en Colombia ha tenido un crecimiento en los últimos años debido a su alta comercialización internacional, por este motivo se han gestado procesos de producción del aguacate hass en todo el territorio nacional con una pronunciada influencia de inversionistas extranjeros. En el caso particular del departamento de Risaralda se ha generado mucho interés por introducirse en el mercado del aguacate Hass en especial en pequeños y medianos productores de la agroindustria, ellos están optando por la producción masiva de este fruto, sin embargo, luego de un estudio de campo, se pudo determinar que para poder ser exportado, el aguacate debe cumplir con ciertas condiciones fisiológicas y físicas, este proceso de clasificación se realiza de manera manual en la mayoría de los casos..

Modelo para la clasificación del aguacate Hass en sus estados comerciales y de exportación, mediante el uso de redes neuronales convolucionales

El mercado del aguacate hass en Colombia ha tenido un crecimiento en los últimos años debido a su alta comercialización internacional, por este motivo se han gestado procesos de producción del aguacate hass en todo el territorio nacional con una pronunciada influencia de inversionistas extranjeros. En el caso particular del departamento de Risaralda se ha generado mucho interés por introducirse en el mercado del aguacate Hass en especial en pequeños y medianos productores de la agroindustria, ellos están optando por la producción masiva de este fruto, sin embargo, luego de un estudio de campo, se pudo determinar que para poder ser exportado, el aguacate debe cumplir con ciertas condiciones fisiológicas y físicas, este proceso de clasificación se realiza de manera manual en la mayoría de los casos..