Background Modeling Through Statistical Edge-Segment Distributions

Ramirez Rivera, Adin,Murshed, Mahbub,Jaemyun Kim,Oksam Chae IEEE 2013 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDE Vol.23 No.8

<P>Background modeling is challenging due to background dynamism. Most background modeling methods fail in the presence of intensity changes, because the model cannot handle sudden changes. A solution to this problem is to use intensity-robust features. Despite the changes of an edge's shape and position among frames, edges are less sensitive than a pixel's intensity to illumination changes. Furthermore, background models in the presence of moving objects produce ghosts in the detected output, because high quality models require ideal backgrounds. In this paper, we propose a robust statistical edge-segment-based method for background modeling of non-ideal sequences. The proposed method learns the structure of the scene using the edges' behaviors through the use of kernel-density distributions. Moreover, it uses segment features to overcome the shape and position variations of the edges. Hence, the use of segments gives us local information of the scene, and that helps us to predict the objects and background precisely. Furthermore, we accumulate segments to build edge distributions, which allow us to perform unconstrained training and to overcome the ghost effect. In addition, the proposed method uses adaptive thresholding (in the segments) to detect the moving objects. Therefore, this approach increases the accuracy over previous methods, which use fixed thresholds.</P>

Spatiotemporal Directional Number Transitional Graph for Dynamic Texture Recognition

Ramirez Rivera, Adin,Chae, Oksam IEEE 2015 IEEE transactions on pattern analysis and machine Vol.37 No.10

<P>Spatiotemporal image descriptors are gaining attention in the image research community for better representation of dynamic textures. In this paper, we introduce a dynamic-micro-texture descriptor, i.e., spatiotemporal directional number transitional graph (DNG), which describes both the spatial structure and motion of each local neighborhood by capturing the direction of natural flow in the temporal domain. We use the structure of the local neighborhood, given by its principal directions, and compute the transition of such directions between frames. Moreover, we present the statistics of the direction transitions in a transitional graph, which acts as a signature for a given spatiotemporal region in the dynamic texture. Furthermore, we create a sequence descriptor by dividing the spatiotemporal volume into several regions, computing a transitional graph for each of them, and represent the sequence as a set of graphs. Our results validate the robustness of the proposed descriptor in different scenarios for expression recognition and dynamic texture analysis.</P>

Caffeine as a source for nitrogen doped graphene, and its functionalization with silver nanowires in-situ

Ramirez-Gonzalez, Daniel,Cruz-Rivera, Jose de J.,Tiznado, Hugo,Rodriguez, Angel G.,Guillen-Escamilla, Ivan,Zamudio-Ojeda, Adalberto Techno-Press 2020 Advances in nano research Vol.9 No.1

In this work, we report the use of caffeine as an alternative source of nitrogen to successfully dope graphene (quaternary 400.6 eV and pyridinic at 398 eV according XPS), as well as the growth of silver nanowires (in-situ) in the surface of nitrogen doped graphene (NG) sheets. We used the improved graphene oxide method (IGO), chemical reduction of graphene oxide (GOx), and impregnation with caffeine as source of nitrogen for doping and subsequently, silver nanowires (NW) grow in the surface by the reduction of silver salts in the presence of NG, achieving a numerous of growth of NW in the graphene sheets. As supporting experimental evidence, the samples were analyzed using conventional characterization techniques: SEM-EDX, XRD, FT-IR, micro RAMAN, TEM, and XPS.

Local Directional Ternary Pattern for Facial Expression Recognition

Byungyong Ryu,Ramirez Rivera, Adin,Jaemyun Kim,Oksam Chae IEEE 2017 IEEE TRANSACTIONS ON IMAGE PROCESSING - Vol.26 No.12

<P>This paper presents a new face descriptor, local directional ternary pattern (LDTP), for facial expression recognition. LDTP efficiently encodes information of emotion-related features (i.e., eyes, eyebrows, upper nose, and mouth) by using the directional information and ternary pattern in order to take advantage of the robustness of edge patterns in the edge region while overcoming weaknesses of edge-based methods in smooth regions. Our proposal, unlike existing histogram-based face description methods that divide the face into several regions and sample the codes uniformly, uses a two-level grid to construct the face descriptor while sampling expression-related information at different scales. We use a coarse grid for stable codes (highly related to non-expression), and a finer one for active codes (highly related to expression). This multi-level approach enables us to do a finer grain description of facial motions while still characterizing the coarse features of the expression. Moreover, we learn the active LDTP codes from the emotion-related facial regions. We tested our method by using person-dependent and independent cross-validation schemes to evaluate the performance. We show that our approaches improve the overall accuracy of facial expression recognition on six data sets.</P>

Background Modelling Using Edge-Segment Distributions

Kim, Jaemyun,Murshed, Mahbub,Rivera, Adin Ramirez,Chae, Oksam SAGE Publications 2013 International Journal of Advanced Robotic Systems Vol.10 No.2

Solidification Kinetics of a Near Eutectic Al-Si Alloy, Unmodified and Modified with Sr

R. Aparicio,G. Barrera,G. Trapaga,M. Ramirez-Argaez,C. Gonzalez-Rivera 대한금속·재료학회 2013 METALS AND MATERIALS International Vol.19 No.4

The purpose of this work was to explore the differences in solidification kinetics between unmodified and Sr modified eutectic Al-Si alloy as revealed by Fourier Thermal Analysis (FTA) and grain-growth kinetics characterization. Thermal analysis were performed in cylindrical stainless steel cups coated with a thin layer of boron nitride, using two type-K thermocouples connected to a data acquisition system. Grain growth kinetics characterization was carried out using solid fraction evolution and grain density data. FTA results for the non modified and modified alloys suggest that there are changes in the solidification rate during eutectic nucleation followed, during growth, by similar solidification rate evolutions, suggesting that this parameter is governed principally by the heat extraction conditions. On the other hand the change of the grain growth parameters estimated for the experimental probes suggest that the presence of Sr may modify the relationship between grain growth rate and undercooling in eutectic Al-Si.

Antiamoebic Activity of Petiveria alliacea Leaves and Their Main Component, Isoarborinol

( Lizeth M. Zavala-ocampo ),( Eva Aguirre-hernandez ),( Nury Perez-hernandez ),( Gildardo Rivera ),( Laurence A. Marchat ),( Esther Ramirez-moreno ) 한국미생물생명공학회(구 한국산업미생물학회) 2017 Journal of microbiology and biotechnology Vol.27 No.8

Petiveria alliacea L. (Phytolacaceae) is a medicinal plant with a broad range of traditional therapeutic properties, including the treatment of dysentery and intestinal infections caused by protozoan parasites. However, its effects against Entamoeba histolytica have not been reported yet. We investigated the antiamoebic activity present in the leaves of P. alliacea Antiamoebic activity was evaluated in methanolic and aqueous extracts, as well as in the hexanic, methanolic, and EtOAc fractions. The P. alliacea methanolic extract showed a better antiamoebic activity than the aqueous extract with an IC₅₀ = 0.51 mg/ml. Likewise, the hexanic fraction was the most effective fraction, showing a dose-dependent activity against E. histolytica, with an IC₅₀ = 0.68 mg/ml. Hexanic subfraction 12-19 showed the highest antiamoebic activity at 0.8 mg/ml, producing 74.3% growth inhibition without any toxicity in mammal cells. A major component in subfraction 12-19 was identified as isoarborinol, which produced 51.4% E. histolytica growth inhibition at 0.05 mg/ml without affecting mammal cells. The P. alliacea leaf extract has antiamoebic activity that can be attributed to a major metabolite known as isoarborinol.