Restauració d'imatges amb soroll

Si hi ha soroll ens costa entendre el que sentim. De la mateixa manera, una imatge pot tenir soroll i això ens pot fer molt difícil reconèixer allò que estem veient. Per sort, existeixen tècniques per restaurar imatges amb soroll de manera que es tornin a veure perfectament bé. En aquest article s'explica una d'aquestes tècniques, ideada pels autors.Si hay ruido nos cuesta entender lo que escuchamos. Del mismo modo, una imagen puede tener ruido y esto nos puede dificultar el reconocimiento de lo que estamos viendo. Por suerte, existen técnicas para restaurar imágenes con ruido de manera que se vuelvan a ver perfectamente bien. En este artículo se explica una de estas técnicas, ideada por los autores.If there is noise, we can't understand what we hear. In the same way, a noisy image can make it very difficult for us to recognise what we are seeing Fortunately techniques exist to restore noisy images so that they can be seen perfectly clearly. In this article a new restoration technique is presented by its authors

3D human motion sequences synchronization using dense matching algorithm

Annual Symposium of the German Association for Pattern Recognition (DAGM), 2006, Berlin (Germany)This work solves the problem of synchronizing pre-recorded human motion sequences, which show different speeds and accelerations, by using a novel dense matching algorithm. The approach is based on the dynamic programming principle that allows finding an optimal solution very fast. Additionally, an optimal sequence is automatically selected from the input data set to be a time scale pattern for all other sequences. The synchronized motion sequences are used to learn a model of human motion for action recognition and full-body tracking purposes.This work was supported by the project 'Integration of robust perception, learning, and navigation systems in mobile robotics' (J-0929).Peer Reviewe

Moving cast shadows detection methods for video surveillance applications

Moving cast shadows are a major concern in today’s performance from broad range of many vision-based surveillance applications because they highly difficult the object classification task. Several shadow detection methods have been reported in the literature during the last years. They are mainly divided into two domains. One usually works with static images, whereas the second one uses image sequences, namely video content. In spite of the fact that both cases can be analogously analyzed, there is a difference in the application field. The first case, shadow detection methods can be exploited in order to obtain additional geometric and semantic cues about shape and position of its casting object (’shape from shadows’) as well as the localization of the light source. While in the second one, the main purpose is usually change detection, scene matching or surveillance (usually in a background subtraction context). Shadows can in fact modify in a negative way the shape and color of the target object and therefore affect the performance of scene analysis and interpretation in many applications. This chapter wills mainly reviews shadow detection methods as well as their taxonomies related with the second case, thus aiming at those shadows which are associated with moving objects (moving shadows).Peer Reviewe

Automatic learning of 3D pose variability in walking performances for gait analysis

This paper proposes an action specific model which automatically learns the variability of 3D human postures observed in a set of training sequences. First, a Dynamic Programing synchronization algorithm is presented in order to establish a mapping between postures from different walking cycles, so the whole training set can be synchronized to a common time pattern. Then, the model is trained using the public CMU motion capture dataset for the walking action, and a mean walking performance is automatically learnt. Additionally statistics about the observed variability of the postures and motion direction are also computed at each time step. As a result, in this work we have extended a similar action model successfully used for tracking, by providing facilities for gait analysis and gait recognition applications.Peer ReviewedPreprin

Variable Rate Deep Image Compression with Modulated Autoencoder

Variable rate is a requirement for flexible and adaptable image and video compression. However, deep image compression methods are optimized for a single fixed rate-distortion tradeoff. While this can be addressed by training multiple models for different tradeoffs, the memory requirements increase proportionally to the number of models. Scaling the bottleneck representation of a shared autoencoder can provide variable rate compression with a single shared autoencoder. However, the R-D performance using this simple mechanism degrades in low bitrates, and also shrinks the effective range of bit rates. Addressing these limitations, we formulate the problem of variable rate-distortion optimization for deep image compression, and propose modulated autoencoders (MAEs), where the representations of a shared autoencoder are adapted to the specific rate-distortion tradeoff via a modulation network. Jointly training this modulated autoencoder and modulation network provides an effective way to navigate the R-D operational curve. Our experiments show that the proposed method can achieve almost the same R-D performance of independent models with significantly fewer parameters.Comment: Published as a journal paper in IEEE Signal Processing Letter

Restauració d'imatges amb soroll

Si hi ha soroll ens costa entendre el que sentim. De la mateixa manera, una imatge pot tenir soroll i això ens pot fer molt difícil reconèixer allò que estem veient. Per sort, existeixen tècniques per restaurar imatges amb soroll de manera que es tornin a veure perfectament bé. En aquest article s'explica una d'aquestes tècniques, ideada pels autors.Si hay ruido nos cuesta entender lo que escuchamos. Del mismo modo, una imagen puede tener ruido y esto nos puede dificultar el reconocimiento de lo que estamos viendo. Por suerte, existen técnicas para restaurar imágenes con ruido de manera que se vuelvan a ver perfectamente bien. En este artículo se explica una de estas técnicas, ideada por los autores.If there is noise, we can't understand what we hear. In the same way, a noisy image can make it very difficult for us to recognise what we are seeing Fortunately techniques exist to restore noisy images so that they can be seen perfectly clearly. In this article a new restoration technique is presented by its authors