Feature extraction for human action recognition based on saliency map

Human Action Recognition (HAR) plays an important role in computer vision for the interaction between human and environments which has been widely used in many applications. The focus of the research in recent years is the reliability of the feature extraction to achieve high performance with the usage of saliency map. However, this task is challenging where problems are faced during human action detection when most of videos are taken with cluttered background scenery and increasing the difficulties to detect or recognize the human action accurately due to merging effects and different level of interest. In this project, the main objective is to design a model that utilizes feature extraction with optical flow method and edge detector. Besides, the accuracy of the saliency map generation is needed to improve with the feature extracted to recognize various human actions. For feature extraction, motion and edge features are proposed as two spatial-temporal cues that using edge detector and Motion Boundary Histogram (MBH) descriptor respectively. Both of them are able to describe the pixels with gradients and other vector components. In addition, the features extracted are implemented into saliency computation using Spectral Residual (SR) method to represent the Fourier transform of vectors to log spectrum and eliminating excessive noises with filtering and data compressing. Computation of the saliency map after obtaining the remaining salient regions are combined to form a final saliency map. Simulation result and data analysis is done with benchmark datasets of human actions using Matlab implementation. The expectation for proposed methodology is to achieve the state-of-art result in recognizing the human actions

Color constancy by combining low-mid-high level image cues

In general, computational methods to estimate the color of the light source are based on single, low-level image cues such as pixel values and edges. Only a few methods are proposed exploiting multiple cues for color constancy by incorporating pixel values, edge information and higher-order image statistics. However, expanding color constancy beyond these low-level image statistics (pixels, edges and n-jets) to include high-level cues and integrate all these cues together into a unified framework has not been explored. In this paper, the color of the light source is estimated using (low-level) image statistics, (intermediate-level) regions, and (high-level) scene characteristics. A Bayesian framework is proposed combining the different cues in a principled way. Our experiments show that the proposed algorithm outperforms the original Bayesian method. The mean error is reduced by 33.3% with respect to the original Bayesian method and the median error is reduced by 37.1% on the re-processed version of the Gehler color constancy dataset. Our method outperforms most of the state-of-the-art color constancy algorithms in mean angular error and obtains the highest accuracy in terms of median angular error

Desarrollo de una metodología para identificación de características fisicoquímicas de productos agrícolas a partir de su correlación con técnicas de visión de máquina

La ausencia de prácticas automatizadas en la cosecha y poscosecha de los productos agrícolas, tiene como consecuencias un incremento en las pérdidas y una disminución en la competitividad de los mismos. La estimación de características fisicoquímicas en los productos mediante imágenes digitales puede permitir mejorar procesos tales como: selección y clasificación tanto para productores como consumidores. La metodología desarrollada con este fin permite correlacionar aspectos visibles en las imágenes tales como: color, textura, tamaño y forma con parámetros fisicoquímicos medidos. Se proponen cuatro fases fundamentales: i. identificación y medición de las características, ii. procesamiento de imágenes y extracción de características, iii. estimación de las características fisicoquímicas y, finalmente, iv. validación de la correlación. Esto, permite generar sistemas de visión de máquina automáticos para estimar a futuro, dichas propiedades fisicoquímicas; mediante una técnica no destructiva y rápida. Los resultados obtenidos al aplicar la metodología para estimar algunas de las principales características fisicoquímicas en diferentes productos, son superiores al 80% en términos del coeficiente de correlación, con una disminución significativa del porcentaje de error respecto a la desviación estándar de la muestra.Abstract. The absence of automation technologies for harvest and postharvest practices on agricultural products, has as a consequence an increase in losses and a decline in the competitiveness of the same. The estimation of physicochemical characteristics in agricultural products using digital images can allow improving processes such as selection and classification for both: producers and consumers. The methodology developed for this purpose enables correlate aspects in visible images such as color, texture, size and shape with measured physicochemical parameters. Four stages was proposed: i. identification and measurement of the characteristics, ii. image processing and feature extraction, iii. physicochemical characteristics estimation and iv. validation of the correlation. By following these steps is possible to construct machine vision systems for future automatic estimations of these physicochemical properties; with a nondestructive and rapid technique. The results obtained by applying of these methodology to estimate some of the main physicochemical characteristics in different products, are over 80% in terms of the correlation coefficient with a significant decrease of the rate error relative to the standard deviation of the sample set.Maestrí