Holographic reconstruction algorithms based on wave propagation require the object’s Z-plane location. The location is determined manually by selecting an image from a set of reconstructed images over a range of Z-planes. We evaluate five autofocus metrics; the standard deviation of Laplacian and Sobel edge detectors, sum of darkest 2% of pixels, sum of the difference of adjacent reconstructed images (DAMP method), and product of the variance of two orthogonal Gabor filters. The metrics were tested on ten classes of plankton collected from field deployments of a submersible digital holographic imaging system (HOLOCAM). Our results indicate that Gabor filters provide the best focus metric performance, correctly predicting focus distance with +/- 100 um for 78% of the images (n=687). The performance of each metric is significantly dependent on the plankton class, from 46% for the round Coscinodiscus class to 100% for the Thalassionema nitzschoid class using the Gabor focus metric. Focus metric waveform analysis provides a prediction confidence to eliminate images likely to produce erroneous Z predictions. Applying focus metrics to reconstructed image segments substantially containing the object greatly improves the performance of the DAMP method. While Gabor filters are the most computationally intensive focus metric evaluated, the Gabor focus metric curves are relatively smooth and unimodal, enabling iterative search methods to reduce the number of reconstructions required to determine focus.
Holographic reconstruction algorithms based on wave propagation typically require the object’s Z-plane location. To automate reconstruction, a focus metric is required to iteratively determine the Z-plane location. The 1951 USAF resolution test chart is often used to evaluate holographic reconstruction and focus metric performance. However, plankton present a more difficult subject, as they are dense three-dimensional objects with lower contrast and greater gray-scale variance. In addition, we are using a direct inline red laser and image sensor without optics, operating below lasing threshold to avoid speckle, resulting in modest fringe production. These factors make autofocusing more difficult. In this paper we evaluate six focus methods tested on eight classes of plankton and microfiber (n = 64), measuring Z prediction accuracy and computation time. We show that focus method performance varies with class, suggesting that best performance can be achieved by selecting the focus metric based on the specimen class of interest.
Plankton is at the bottom of the food chain. Microscopic phytoplankton account for about 50% of all photosynthesis on Earth, corresponding to 50 billion tons of carbon each year, or about 125 billion tonnes of sugar[1]. Plankton is also the food for most species of fish, and therefore it represents the backbone of the aquatic environment. Thus, monitoring plankton is paramount to infer potential dangerous changes to the ecosystem. In this work we use a collection of plankton species extracted from a large dataset of images from the Woods Hole Oceanographic Institute (WHOI), to establish a basic set of morphological features for supporting the use of plankton as a biosensor. Using a perturbation detection approach, we show that it is possible to detect deviation from the average space of features for each species of plankton microorganisms, that we propose could be related to environmental threat or perturbations. Such an approach, can open the way for the development of an automatic Artificial Intelligence (AI) based system for using plankton as biosensor.
Changes in morphology and swimming dynamics of plankton by exposure to toxic chemicals are studied using a novel a new paradigm of image acquisition and computer vision system. Single cell ciliate Stentor coeruleus enclosed in a drop of water provide a means to automatically deposit many individual samples on a at surface. Chemicals of interest are automatically added to each drop while the dynamical and morphological changes are captured with an optical microscope. With computer vision techniques, we analyze the motion trajectory of each plankton sample, along with its shape information, quantifying the sub-lethal impact of chemicals on plankton health. The system enables large screening of hundreds of chemicals of environmental interest which may make their way into water habitats.
Biologists use optical microscopes to study plankton in the lab, but their size, complexity and cost makes widespread deployment of microscopes in lakes and oceans challenging. Monitoring the morphology, behavior and distribution of plankton in situ is essential as they are excellent indicators of marine environment health and provide a majority of Earth’s oxygen and carbon sequestration. Direct in-line holographic microscopy (DIHM) eliminates many of these obstacles, but image reconstruction is computationally intensive and produces monochromatic images. By using one laser and one white LED, it is possible to obtain the 3D location plankton by triangulation, limiting holographic reconstruction to only the voxels occupied by the plankton, reducing computation by several orders of magnitude. The color information from the white LED assists in the classification of plankton, as phytoplankton contains green-colored chlorophyll. The reconstructed plankton images are rendered in a 3D interactive environment, viewable from a browser, providing the user the experience of observing plankton from inside a drop of water.
The dramatic rise in identity theft, the ever pressing need to provide convenience in checkout services to attract and retain loyal customers, and the growing use of multi-function signature captures devices in the retail sector provides favorable conditions for the deployment of dynamic signature verification (DSV) in retail settings. We report on the development of a DSV system to meet the needs of the retail sector. We currently have a database of approximately 10,000 signatures collected from 600 subjects and forgers. Previous work at IBM on DSV has been merged and extended to achieve robust performance on pen position data available from commercial point of sale hardware, achieving equal error rates on skilled forgeries and authentic signatures of 1.5% to 4%.
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