A setup based on the differential interference contrast imaging technique is implemented by replacing the linearly polarized beams from the conventional technique with spatially inhomogeneous polarized beams. This setup works in a reflective mode for material discrimination and for surface measurement, exploiting the information delivered by an analysis of the polarization state. A genetic algorithm that considers Malus’s law adjusts the collected data from a flat reference, and the resulting model is applied to the testing objects. Our study shows that, under the same setup, spatially inhomogeneous polarized beams offer a better height and composite material discrimination, in comparison to the use of linearly polarized beams. We used, as testing objects, reflective composite materials and highly reflective surface structures that have lateral dimensions up to 8 mm and depth variations from 50 μm to 3 mm. These surfaces can be related to applications in the semiconductor and metallic materials industry.
In this work, we present the implementation of an experimental setup for controlling the phase gradient of arbitrary light beams, using a spatial light modulator. Simple arbitrary shapes are initially proposed based on their parametric equations, and the desired beam shape, as well as its phase behavior are interpreted through an algorithm. The analysis of the electric field distribution and its manipulation through the topological charge and the normal direction respect every position of an arbitrary shape, allow to encode in a Spatial Light Modulator the behavior of the phase of a light beam. The far field intensity profiles are captured, studied and compared to those designed. The phase of a set of generated beams is tested using a linear polarizer as an analyzer and by an optical trapping setup.
We present the implementation of a feature extraction approach for the automated screening of Salmonella sp., a task visually carried out by a microbiologist, where the resulting color characteristics of the culture media plate indicate the presence of this strain. The screening of Salmonella sp. is based on the inoculation and incubation of a sample on an agar plate, allowing the isolation of this strain, if present. This process uses three media: Xylose lysine deoxycholate, Salmonella Shigella, and Brilliant Green agar plates, which exhibit specific color characteristics over the colonies and over the surrounding medium for a presumed positive interpretation. Under a controlled illumination environment, images of plates are captured and the characteristics found over each agar are processed separately. Each agar is analyzed using statistical descriptors for texture, to determine the presence of colonies, followed by the extraction of color features. A comparison among the color features seen over the three media, according to the FDA Bacteriological Analytical Manual, determines the presence of Salmonella sp. on a given sample. The implemented process proves that the task addressed can be accomplished under an image processing approach, leading to the future validation and automation of additional screening processes.
We present the methodology for the surface measurement of small testing objects with maximum depth variation of 3mm, using a polarimetric approach. The experimental setup is based on the function of a Differential Interference Contrast microscope, which works as a shearing interferometer. As it is expected, when it comes to an application for non-microscopic samples, certain modifications should be considered on the development of the measurement system. This work focuses on such details that lead to the profiling of a testing object of known dimensions. An algorithm that computes height distribution based on the polarimetric data is implemented and the resulting surface profile is analyzed. Finally, our conclusions about the requirements for an improvement of the presented measurement setup are listed.
Correlation filters for object recognition represent an attractive alternative to feature based methods. These filters are
usually synthesized as a combination of several training templates. These templates are commonly chosen in an ad-hoc
manner by the designer, therefore, there is no guarantee that the best set of templates is chosen. In this work, we propose
a new approach for the design of composite correlation filters using a multi-objective evolutionary algorithm in
conjunction with a variable length coding technique. Given a vast search space of feasible templates, the algorithm finds
a subset that allows the construction of a filter with an optimized performance in terms of several performance metrics.
The resultant filter is capable of recognizing geometrically distorted versions of a target in high cluttering and noisy
conditions. Computer simulation results obtained with the proposed approach are presented and discussed in terms of
several performance metrics. These results are also compared to those obtained with existing correlation filters.