Multi-scale pixel-based image fusion using multivariate empirical mode decomposition.

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences

Data-driven Signal Decomposition Approaches: A Comparative Analysis

Signal decomposition (SD) approaches aim to decompose non-stationary signals into their constituent amplitude- and frequency-modulated components. This represents an important preprocessing step in many practical signal processing pipelines, providing useful knowledge and insight into the data and relevant underlying system(s) while also facilitating tasks such as noise or artefact removal and feature extraction. The popular SD methods are mostly data-driven, striving to obtain inherent well-behaved signal components without making many prior assumptions on input data. Among those methods include empirical mode decomposition (EMD) and variants, variational mode decomposition (VMD) and variants, synchrosqueezed transform (SST) and variants and sliding singular spectrum analysis (SSA). With the increasing popularity and utility of these methods in wide-ranging application, it is imperative to gain a better understanding and insight into the operation of these algorithms, evaluate their accuracy with and without noise in input data and gauge their sensitivity against algorithmic parameter changes. In this work, we achieve those tasks through extensive experiments involving carefully designed synthetic and real-life signals. Based on our experimental observations, we comment on the pros and cons of the considered SD algorithms as well as highlighting the best practices, in terms of parameter selection, for the their successful operation. The SD algorithms for both single- and multi-channel (multivariate) data fall within the scope of our work. For multivariate signals, we evaluate the performance of the popular algorithms in terms of fulfilling the mode-alignment property, especially in the presence of noise.Comment: Resubmission with changes in the reference lis

Multi-Scale Pixel-Based Image Fusion Using Multivariate Empirical Mode Decomposition

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences

High Performance Video Stream Analytics System for Object Detection and Classification

Due to the recent advances in cameras, cell phones and camcorders, particularly the resolution at which they can record an image/video, large amounts of data are generated daily. This video data is often so large that manually inspecting it for object detection and classification can be time consuming and error prone, thereby it requires automated analysis to extract useful information and meta-data. The automated analysis from video streams also comes with numerous challenges such as blur content and variation in illumination conditions and poses. We investigate an automated video analytics system in this thesis which takes into account the characteristics from both shallow and deep learning domains. We propose fusion of features from spatial frequency domain to perform highly accurate blur and illumination invariant object classification using deep learning networks. We also propose the tuning of hyper-parameters associated with the deep learning network through a mathematical model. The mathematical model used to support hyper-parameter tuning improved the performance of the proposed system during training. The outcomes of various hyper-parameters on system's performance are compared. The parameters that contribute towards the most optimal performance are selected for the video object classification. The proposed video analytics system has been demonstrated to process a large number of video streams and the underlying infrastructure is able to scale based on the number and size of the video stream(s) being processed. The extensive experimentation on publicly available image and video datasets reveal that the proposed system is significantly more accurate and scalable and can be used as a general purpose video analytics system.N/

Análisis de la variabilidad intraespecífica en Trypanosoma cruzi: susceptibilidad a la combinación de benznidazol y clomipramina durante la infección aguda experimental.

Tesis (Grado Doctor en Ciencias Biológicas)--Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Lugar de Trabajo: Centro de Estudios e Investigación de la Enfermedad de Chagas y Leishmaniasis. Cátedra de Física Biomédica, Facultad de Ciencias Médicas. Universidad Nacional de Córdoba. Instituto de Investigaciones en Ciencias de la Salud - INICSA -CONICET - Universidad Nacional de Córdoba. 2016 . 117 h. + CD. tabls.; grafs.; figuras. Contiene Referencia Bibliográfica y Publicaciones Derivadas de la Tesis. Abstract en español e inglés.La especie Trypanosoma cruzi, causante de la enfermedad de Chagas, está compuesta por diversas subpoblaciones que exhiben un alto grado de polimorfismo cuando son analizadas por métodos bioquímicos y moleculares, presentando distintas características biológicas y comportamiento frente a la acción de fármacos. Estas subpoblaciones, han sido agrupadas en seis unidades discretas de tipificación (UDT) o linajes denominados TcI - VI. La variabilidad genética del parásito en un mismo huésped podría presentar un tropismo diferencial a los distintos órganos del mismo. En el presente trabajo se determinaron los linajes presentes en tres aislamientos de T. cruzi (dos obtenidos de individuos con infección con T. cruzi congénito, Casibla y Lucky; y uno a partir de un Triatoma infestans de Santiago del Estero, SGO-Z12). Como así también se evaluó, el efecto de la combinación de fármacos (benznidazol [Bz] y clomipramina [Clo]) in vitro e in vivo sobre los linajes de T. cruzi pertenecientes a los 3 aislamientos. Se realizaron diferentes métodos de tipificación previamente descriptos, utilizando la reacción en cadena de la polimerasa (PCR), PCR en tiempo real y enzimas de restricción. Para determinar el efecto combinado de las drogas in vitro se incubaron tripomastigotes de T. cruzi pertenecientes a los 3 aislamientos, administrando las drogas en forma individual y asociadas. Para el estudio in vivo se infectaron 108 ratones separados en tres grupos con cada aislamiento y se los dividió en distintos esquemas de tratamiento que incluyeron la combinación de Bz+Clo. La eficacia de la fármacoterapia se evaluó a través de la sobrevida de los ratones hasta los 35 días post infección, cuantificación de parásitos en sangre y tipificación de los linajes presentes en sangre, músculo cardíaco y esquelético. Se determinó que los tres aislamientos naturales de T. cruzi están compuestos por una mezcla de linajes TcII y TcVI. La distribución de los linajes en los tejidos infectados no fue homogénea, encontrándose variaciones entre los animales analizados. La combinación de Bz+Clo in vitro, presentó un efecto sinérgico sobre los tres aislamientos de T. cruzi. El tratamiento in vivo con Bz+Clo disminuyó la carga parasitaria en relación al grupo infectado no tratado (p<0,05), presentando menos efectos adversos (alteraciones histológicas en hígado y riñón) que aquellos tratados con Bz. Con respecto al efecto del tratamiento sobre la distribución de los linajes de T. cruzi, se observó diferente sensibilidad de los mismos, ya que algunos no fueron detectados. En base a los resultados obtenidos, se reafirma la complejidad en el abordaje del tratamiento de la enfermedad de Chagas. Nuestros resultados demuestran que para valorar la efectividad de la terapéutica se debería tener en cuenta la variabilidad que posee el T. cruzi y sus diferentes susceptibilidades frente al tratamiento. Por otro lado, la combinación de fármacos con diferentes mecanismos de acción y acción sinérgica, podría provocar menos efectos adversos en el huésped al disminuir las dosis y a su vez aumentar su efectividad.Fil: Strauss, Mariana. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Escuela de Biología, Argentina