Learning Background-Aware Correlation Filters for Visual Tracking

Correlation Filters (CFs) have recently demonstrated excellent performance in terms of rapidly tracking objects under challenging photometric and geometric variations. The strength of the approach comes from its ability to efficiently learn - "on the fly" - how the object is changing over time. A fundamental drawback to CFs, however, is that the background of the object is not be modelled over time which can result in suboptimal results. In this paper we propose a Background-Aware CF that can model how both the foreground and background of the object varies over time. Our approach, like conventional CFs, is extremely computationally efficient - and extensive experiments over multiple tracking benchmarks demonstrate the superior accuracy and real-time performance of our method compared to the state-of-the-art trackers including those based on a deep learning paradigm

Correlation Filters with Limited Boundaries

Correlation filters take advantage of specific properties in the Fourier domain allowing them to be estimated efficiently: O(NDlogD) in the frequency domain, versus O(D^3 + ND^2) spatially where D is signal length, and N is the number of signals. Recent extensions to correlation filters, such as MOSSE, have reignited interest of their use in the vision community due to their robustness and attractive computational properties. In this paper we demonstrate, however, that this computational efficiency comes at a cost. Specifically, we demonstrate that only 1/D proportion of shifted examples are unaffected by boundary effects which has a dramatic effect on detection/tracking performance. In this paper, we propose a novel approach to correlation filter estimation that: (i) takes advantage of inherent computational redundancies in the frequency domain, and (ii) dramatically reduces boundary effects. Impressive object tracking and detection results are presented in terms of both accuracy and computational efficiency.Comment: 8 pages, 6 figures, 2 table

Deep-LK for Efficient Adaptive Object Tracking

In this paper we present a new approach for efficient regression based object tracking which we refer to as Deep- LK. Our approach is closely related to the Generic Object Tracking Using Regression Networks (GOTURN) framework of Held et al. We make the following contributions. First, we demonstrate that there is a theoretical relationship between siamese regression networks like GOTURN and the classical Inverse-Compositional Lucas & Kanade (IC-LK) algorithm. Further, we demonstrate that unlike GOTURN IC-LK adapts its regressor to the appearance of the currently tracked frame. We argue that this missing property in GOTURN can be attributed to its poor performance on unseen objects and/or viewpoints. Second, we propose a novel framework for object tracking - which we refer to as Deep-LK - that is inspired by the IC-LK framework. Finally, we show impressive results demonstrating that Deep-LK substantially outperforms GOTURN. Additionally, we demonstrate comparable tracking performance to current state of the art deep-trackers whilst being an order of magnitude (i.e. 100 FPS) computationally efficient

MULTI-CHANNEL CORRELATION FILTERS WITH LIMITED BOUNDARIES: THEORY AND APPLICATIONS

Ph.DDOCTOR OF PHILOSOPH

Frequency of Intestinal Parasitic Infections among Individuals Referred to the Medical Center Laboratories in Nahavand City, Hamadan Province, Western Iran

Background: Intestinal parasitic infections (IPIs) cause serious public health problem in the world, especially those located in tropical and subtropical areas. This study was conducted with the aim of obtaining frequency of intestinal parasites in referred people to the Nahavand city laboratories, Hamadan province, western Iran.Materials and Methods: A comparative cross-sectional study was conducted among checkup individuals and patients referred to laboratories of Nahavand County. A total of 371 stool samples (150 from checkup individuals and 221 from patients) were selected by using systematic random sampling during summer 2014.  The stool specimens were examined macroscopically, and microscopically by using direct slide smear (saline wet mount and lugol staining), formaldehyde - diethyl ether concentration, trichrome staining and modified Ziehl-Neelsen staining techniques. The results were analyzed using SPSS version 16 and Chi-square test.Results: Ninety two patients (24.8%) were infected with single or multiple intestinal parasites. The overall prevalence of IPIs in checkup individuals and patients was 21.3% and 27.1%, respectively. The frequency of the observed intestinal parasites was: Blastocystis spp. 72 (19.4%), Entamoeba coli 7 (1/9%), Endolimax nana 7 (1/9%), Giardia lamblia 5 (1/3%), Cryptosporidium spp. 3 (0.8%), Entamoeba hartmanni 3 (0.8%), Entamoeba histolitica/E. dispar 1 (0.3%), Trichomonas hominies 1 (0.3%), Chilomastix mesnili 1 (0.3%), Iodamoeba butschlii 1 (0.3% ) and Enterobius vermicularis egg l (0.3%).Conclusion: The proportion of observed protozoan parasites 91 (24.5%) is higher than helminthes infection 1 (0.3%). The worm infections in Nahavand city was dramatically decreased over the past decades, induced increases in public health at the community level.  Blastocystis spp. was the predominant intestinal parasite in people referred to the Nahavand city laboratories.  Proportion of pathogenic IPIs among patients 4.07% (9 of 221) was higher in compare to the checkup individuals in which only one out of 150 (0.66%) Giardia lamblia was observed.

Learning Robust Model Predictive Control for Voltage Control of Islanded Microgrid

This paper proposes a novel control design for voltage tracking of an islanded AC microgrid in the presence of {nonlinear} loads and parametric uncertainties at the primary level of control. The proposed method is based on the Tube-Based Robust Model Predictive Control (RMPC), an online optimization-based method which can handle the constraints and uncertainties as well. The challenge with this method is the conservativeness imposed by designing the tube based on the worst-case scenario of the uncertainties. This weakness is amended in this paper by employing a combination of a learning-based Gaussian Process (GP) regression and RMPC. The advantage of using GP is that both the mean and variance of the loads are predicted at each iteration based on the real data, and the resulted values of mean and the bound of confidence are utilized to design the tube in RMPC. The theoretical results are also provided to prove the recursive feasibility and stability of the proposed learning based RMPC. Finally, the simulation results are carried out on both single and multiple DG (Distributed Generation) units

Frequency of Intestinal Parasites among Zoo Animal by Morphometric Criteria and First Report of the Bivitellobilharzia nairi from Elephant (Elephasmaximus maximus) in Iran

Background: Intestinal parasitic infections are major causative agents of wildlife health complications among different parts of the world. This study aimed to investigate the gastro-intestinal parasites in feces of the zoo animals based on parasitological and morphometric criteria. Methods: One hundred fresh fecal samples were collected from 35 species of animal lived in Eram park zoo, Tehran, Central Iran during Oct 2015 to Jun 2015. All collected samples were examined by microscopic observation following direct wet mount preparation (normal saline and Lugol's iodine), formalin-ether concentration, and permanent staining. The morphometric aspects of the recovered eggs were surveyed with the aid of Camera Lucida (×400). Results: 65.7% (23/35) of zoo animal species were infected with intestinal parasites. The superfamily Trichostrongyloidea (6/16) and Strongylus sp. (16/4) were the most prevalent helminthic infections, while Blastocystis sp. (6/14), Entamoeba cyst (3/14) and Eimeria sp. (3/14) were the common protozoan parasites. For the first time, Bivitellobilharzia nairi egg was identified an elephant at Iran. Intestinal parasitic infections were apparently circulating among animals of the Eram park zoo. Conclusion: Identified parasitic infections can consider as a threatening source to visitors and workers' health that have contact with animals or their feces. Therefore, the effectual preventive strategies should be addressed to determine the risk factors, mechanisms of cross-transmission of parasite, the importance of applying the hygienic practices and well adjusting deworming programs for the animals, zoo workers and visitors

Dense Feature Aggregation and Pruning for RGBT Tracking

How to perform effective information fusion of different modalities is a core factor in boosting the performance of RGBT tracking. This paper presents a novel deep fusion algorithm based on the representations from an end-to-end trained convolutional neural network. To deploy the complementarity of features of all layers, we propose a recursive strategy to densely aggregate these features that yield robust representations of target objects in each modality. In different modalities, we propose to prune the densely aggregated features of all modalities in a collaborative way. In a specific, we employ the operations of global average pooling and weighted random selection to perform channel scoring and selection, which could remove redundant and noisy features to achieve more robust feature representation. Experimental results on two RGBT tracking benchmark datasets suggest that our tracker achieves clear state-of-the-art against other RGB and RGBT tracking methods.Comment: arXiv admin note: text overlap with arXiv:1811.0985

Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment

COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19’s spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long/Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Artificial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications