Bio-inspired speed detection and discrimination

In the field of computer vision, a crucial task is the detection of motion (also called optical flow extraction). This operation allows analysis such as 3D reconstruction, feature tracking, time-to-collision and novelty detection among others. Most of the optical flow extraction techniques work within a finite range of speeds. Usually, the range of detection is extended towards higher speeds by combining some multiscale information in a serial architecture. This serial multi-scale approach suffers from the problem of error propagation related to the number of scales used in the algorithm. On the other hand, biological experiments show that human motion perception seems to follow a parallel multiscale scheme. In this work we present a bio-inspired parallel architecture to perform detection of motion, providing a wide range of operation and avoiding error propagation associated with the serial architecture. To test our algorithm, we perform relative error comparisons between both classical and proposed techniques, showing that the parallel architecture is able to achieve motion detection with results similar to the serial approach

Combination of Standard and Complementary Models for Audio-Visual Speech Recognition

In this work, new multi-classifier schemes for isolated word speech recognition based on the combination of standard Hidden Markov Models (HMMs) and Complementary Gaussian Mixture Models (CGMMs) are proposed. Typically, in speech recognition systems, each word or phoneme in the vocabulary is represented by a model trained with samples of each particular class. The recognition is then performed by computing which model best represents the input word/phoneme to be classified. In this paper, a novel classification strategy based on complementary class models is presented. A complementary model to a particular class j refers to a model that is trained with instances of all the considered classes, excepting the ones associated to that class j. The classification schemes proposed in this paper are evaluated over two audio-visual speech databases, considering acoustic noisy conditions. Experimental results show that improvements in the recognition rates through a wide range of signal to noise ratios (SNRs) are achieved with the proposed classification methodologies.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Prototype Robot for Computer Vision and Control Systems Applications

This paper describes a robot designed and developed by a student in the context of an Electronic Engineering degree course. This robot is composed by three wheels, two of them can be controlled inde- pendently and the third one is used for stability. The robot also includes a webcam provided with pan and tilt control. This work was focused on the implementation of a prototype useful for academic research in the areas of Computer Vision and Control Systems Dynamics. In this document, the main characteristics of this robot are described.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Combination of Standard and Complementary Models for Audio-Visual Speech Recognition

In this work, new multi-classifier schemes for isolated word speech recognition based on the combination of standard Hidden Markov Models (HMMs) and Complementary Gaussian Mixture Models (CGMMs) are proposed. Typically, in speech recognition systems, each word or phoneme in the vocabulary is represented by a model trained with samples of each particular class. The recognition is then performed by computing which model best represents the input word/phoneme to be classified. In this paper, a novel classification strategy based on complementary class models is presented. A complementary model to a particular class j refers to a model that is trained with instances of all the considered classes, excepting the ones associated to that class j. The classification schemes proposed in this paper are evaluated over two audio-visual speech databases, considering acoustic noisy conditions. Experimental results show that improvements in the recognition rates through a wide range of signal to noise ratios (SNRs) are achieved with the proposed classification methodologies.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Identification and characterization of crops through the analysis of spectral data with machine learning algorithms

This paper assesses the capability of an spectrometer used in field experiments of soybean, maize and wheat. The objective of this work is to select different wavelengths intervals of the spectral reflectance curve, within the range 632-1125 nm, as features for classification using machine learning methods. Two different classifications are presented, species selection and growth stage identification. For species classification accuracy of 92% is reached, while 99% is obtained for stage classification. In addition we propose a new index that outperforms analyzed established vegetation indices, which shows the potential advantage of using this type of devices.Sociedad Argentina de Informática e Investigación Operativ

Identification and characterization of crops through the analysis of spectral data with machine learning algorithms

This paper assesses the capability of an spectrometer used in field experiments of soybean, maize and wheat. The objective of this work is to select different wavelengths intervals of the spectral reflectance curve, within the range 632-1125 nm, as features for classification using machine learning methods. Two different classifications are presented, species selection and growth stage identification. For species classification accuracy of 92% is reached, while 99% is obtained for stage classification. In addition we propose a new index that outperforms analyzed established vegetation indices, which shows the potential advantage of using this type of devices.Sociedad Argentina de Informática e Investigación Operativ

Identification and characterization of crops through the analysis of spectral data with machine learning algorithms

This paper assesses the capability of an spectrometer used in field experiments of soybean, maize and wheat. The objective of this work is to select different wavelengths intervals of the spectral reflectance curve, within the range 632-1125 nm, as features for classification using machine learning methods. Two different classifications are presented, species selection and growth stage identification. For species classification accuracy of 92% is reached, while 99% is obtained for stage classification. In addition we propose a new index that outperforms analyzed established vegetation indices, which shows the potential advantage of using this type of devices.Sociedad Argentina de Informática e Investigación Operativ

Combination of Standard and Complementary Models for Audio-Visual Speech Recognition

In this work, new multi-classifier schemes for isolated word speech recognition based on the combination of standard Hidden Markov Models (HMMs) and Complementary Gaussian Mixture Models (CGMMs) are proposed. Typically, in speech recognition systems, each word or phoneme in the vocabulary is represented by a model trained with samples of each particular class. The recognition is then performed by computing which model best represents the input word/phoneme to be classified. In this paper, a novel classification strategy based on complementary class models is presented. A complementary model to a particular class j refers to a model that is trained with instances of all the considered classes, excepting the ones associated to that class j. The classification schemes proposed in this paper are evaluated over two audio-visual speech databases, considering acoustic noisy conditions. Experimental results show that improvements in the recognition rates through a wide range of signal to noise ratios (SNRs) are achieved with the proposed classification methodologies.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Isolated spanish digit recognition based on audio-visual features

The performance of classical speech recognition techniques based on audio features is degraded in noisy environments. The inclu-sion of visual features related to mouth movements into the recogni-tion process improves the performance of the system. This paper proposes an isolated word speech recognition system based on audio-visual features. The proposed system combines three classifiers based on au-dio, visual and audio-visual information, respectively. An audio-visual database composed by the utterances of the digits (in Spanish language) is employed to test the proposed system. The experimental results show a significant improvement on the recognition rates through a wide range of signal-to-noise ratios.IV Workshop procesamiento de señales y sistemas de tiempo real.Red de Universidades con Carreras en Informática (RedUNCI

Generic Face Animation

International audienceIn computer vision, the animation of objects has attracted a lot attention, specially the animations of 3D face models. The animation of face models requires in general to manually adapt each generic movement (open/close mouth) to each specific head geometry. In this work we propose a technique for the animation of any face model avoiding most of the manual intervention. In order to achieve this we assume that: (1) faces, despite obvious differences are quite similar and a single generic model can be used to simplify deformations and (2) using this face model, a simple interpolation technique can be used, with minimal manual intervention. Several examples are presented to verify the realism of the obtained animations