Abstract:
Described herein are methods for imaging ocular devices. The methods generally involve (a) applying a scattering agent to the ocular device; and (b) imaging the ocular device using optical coherence tomography (OCT). The methods described herein provide useful structural information about the surface of the device with high sensitivity.
Abstract:
A produce data collector which includes a spectrometer which minimizes noise from scattered light and from wavelengths outside the operating range of the spectrometer. The produce data collector includes a light source for illuminating a produce item. The spectrometer obtains spectral information about the produce item in incoming reflected light from the produce item and includes a linear variable filter, a photodetector adjacent the linear variable filter, and an optical slit member above a primary surface of the linear variable filter which has a slit with a width sufficient to minimize scattering of the incoming light by interior surfaces of the linear variable filter. The produce data collector may additionally include a filter, such as an infrared filter.
Abstract:
A method for automatically measuring and quantitatively evaluating the optical quality of an ophthalmic lens, such as, for example, a contact lens. The method measures an ophthalmic lens with an optical phase measurement instrument to derive measured data. The method creates a set of objective optical quality metrics within a computational software. And, the method applies the measured data to at least one of the objective optical quality metrics to determine lens quality.
Abstract:
A produce recognition system and method which use an internal reference to calibrate a produce data collector. The produce data collector collects first data from an external reference, collects second and third data from an internal reference, and collects fourth data from a produce item. A computer determines a first calibration value from the first and second data and a second calibration value from the third data and applies the first and second calibration values to the fourth data to produce fifth data. The computer further obtains sixth data from reference produce data and compares the fifth and sixth data to identify the produce item.
Abstract:
A produce texture data collecting apparatus and method which illuminates a produce item from different directions. The apparatus includes a first light for illuminating a produce item from a first direction, a second light for illuminating the produce item from a second direction, an image capture device for capturing an image of the produce item while the produce item is being illuminated by the first and second lights.
Abstract:
An ambient light sensing apparatus and method for a produce data collector which minimize false triggering of produce data collection. The apparatus includes an image capture device which has a first receiving angle for incident light through an aperture in the produce data collector which is larger than a second receiving angle of a collector within the produce data collector which collects produce data.
Abstract:
Techniques for removing noise effects in processing a barcode signal. A photosignal produced by scanning a barcode is used to generate a digital first derivative signal. Positive and negative thresholds are generated based on the digital first derivative signal. Processing windows are defined for each region of the first derivative signal which exceeds a threshold. Each processing window is bounded by a window opening point at which the first derivative signal exceeds a threshold, and by a window closing point, which is the last crossing of the threshold by the first derivative signal before the first derivative signal makes a zero crossing. Each region is processed to locate a geometric center of the region. A logic transition is recognized for the geometric center of each region defined by a processing window.
Abstract:
Techniques for using imaging information computed from examining a scanner signal are described. When one or more objects passes within a field of view of a scanner, scan patterns emerging from one or more scanner windows and reflected from the objects back into the scanner windows produce one or more scanner signals. The scanner signals are processed to obtain beam position and beam length information to improve the accuracy of bar code decoding and to compute imaging information for objects within the field of view of the scanner. The imaging information for the objects is compared with bar code information for the objects. The expected number, size and shapes of objects indicated by the bar code information is compared with the actual number, size and shapes of objects in order to determine if valid scans occurred or if missed, double or otherwise erroneous scans occurred.
Abstract:
Techniques for using a scan pattern to estimate imaging information for an object reflecting the scan pattern are described. As an object is brought within range of a scan window, a scan beam tracing out a scan pattern comprising a plurality of scan lines causes reflection of the scan beam back into the scan window to produce a scanner signal based on reflections of the scan beam. The time at which the scanner signal indicates the presence of an object is noted and this timing information is mapped to position information identifying the position of the scan beam. The timing and position information is used to estimate imaging information about the object, including position, size, shape and motion information.
Abstract:
A method of combining spectral data with non-spectral data which uses a defined distance measure of likeness (DML) value and conditional probabilities. The method includes the steps of collecting the spectral and non-spectral data for the produce item, determining DML values between the spectral and the non-spectral data and reference produce data for a plurality of types of produce items, determining conditional probability densities for all of the types of produce items using the DML values, combining the conditional probability densities to form a combined conditional probability density, and determining probabilities for the types of produce items from the combined conditional probability density.