A Simple Digital Imaging Method for Dirt Detection on Eggshells

The objective of this research was to develop an off-line vision system to detect defective eggshells, i.e., with dirty eggshell, by employing a classification algorithm based on a few logical operations, allowing a further implementation in an on-line grading process. In particular, this work was focused to study the feasibility of identifying and differentiating dirt stains on brown eggshells caused by organic residuals, from natural stains, caused by deposits of pigments. Digital images were acquired from 384 clean and dirty brown eggshells by employing a CCD camera endowed with 15 monochromatic filters (440-940 nm). Each dirty eggshell presented only one kind of defect, i.e., blood stains, feathers and white, clear or dark faces, while clean eggshells did not present organic residuals or evidences of feather, but their external color was characterized by clear or dark natural stains. A MatLab® devoted code was developed in order to classify samples as clean or dirty. The program was constituted by three major steps: first, the research of an opportune combination of monochromatic images in order to isolate the eggshell from the background; second, the detection of the dirt stains; third, the classification of the images samples into the dirty or clean group. The proposed classification algorithm was able to correctly classify near 93% of the samples. The robustness of the proposed classification was observed applying an external validation to a second set of samples, obtaining similar percentage of correctly classified samples (92%)

Automatic Identification of Defects on Eggshell Through a Multispectral Vision System

The objective of this research was to develop an off-line artificial vision system to automatically detect defective eggshells, i.e., dirty or cracked eggshells, by employing multispectral images with the final purpose to adapt the system to an on-line grading machine. In particular, this work was focused to study the feasibility of identifying organic stains on brown eggshells (dirty eggshell), caused by blood, feathers, feces, etc., from natural stains, caused by deposits of pigments on the outer layer of clean eggshells. During the analysis a total of 384 eggs were evaluated (clean: 148, dirty: 236). Dirty samples were evaluated visually in order to classify them according to the kind of defect (blood, feathers, and white, clear or dark feces), and clean eggshells were classified on the basis of the colour of the natural stains (clear or dark). For each sample digital images were acquired by employing a Charged Coupled Device (CCD) camera endowed with 15 monochromatic filters (440-940 nm). A Matlab® function was developed in order to automate the process and analyze images, with the aim to classify samples as clean or dirty. The program was constituted by three major steps: first, the research of an opportune combination of monochromatic images in order to isolate the eggshell from the background; second, the detection of the dirt stains; third, the classification of the images samples into the dirty or clean group on the basis of geometric characteristics of the stains (area in pixel). The proposed classification algorithm was able to correctly classify near 98% of the samples with a very low processing time (0.05s). The robustness of the proposed classification was observed applying an external validation to a second set of samples (n = 178), obtaining similar percentage of correctly classified samples (97%)

Dependence of the energy resolution of a scintillating crystal on the readout integration time

The possibilty of performing high-rate calorimetry with a slow scintillating crystal is studied. In this experimental situation, to avoid pulse pile-up, it can be necessary to base the energy measurement on only a fraction of the emitted light, thus spoiling the energy resolution. This effect was experimentally studied with a BGO crystal and a photomultiplier followed by an integrator, by measuring the maximum amplitude of the signals. The experimental data show that the energy resolution is exclusively due to the statistical fluctuations of the number of photoelectrons contributing to the maximum amplitude. When such number is small its fluctuations are even smaller than those predicted by Poisson statistics. These results were confirmed by a Monte Carlo simulation which allows to estimate, in a general case, the energy resolution, given the total number of photoelectrons, the scintillation time and the integration time

BaBar Forward Endcap upgrade

The muon and neutral hadron detector (instrumented flux return or IFR) in the forward endcap of the BaBar detector at SLAC was upgraded by the installation of a new generation of resistive plate chambers (RPCs) and by increasing the absorber. The chamber replacement was made necessary by the rapid aging and efficiency loss of the original BaBar RPCs. Based on our experience with those original RPCs and 24 RPCs with thinner linseed oil treatments, improvements in the design, construction, and testing of the new generation RPCs were implemented and are described in detail. (C) 2004 Elsevier B.V. All rights reserved

NA62 Technical Design

NA62 technical design repor