Visible reconstruction by a circular holographic display from digital holograms recorded under infrared illumination

Cataloged from PDF version of article.A circular holographic display that consists of phase-only spatial light modulators is used to reconstruct images in visible light from digital holograms recorded under infrared (10.6 μm) illumination. The reconstruction yields a holographic digital video display of a three-dimensional ghostlike image of an object floating in space where observers can move and rotate around it. © 2012 Optical Society of Americ

Optical reconstruction of digital holograms recorded at 10.6 μm: Route for 3D imaging at long infrared wavelengths

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Advances in multispectral and hyperspectral imaging for archaeology and art conservation

Multispectral imaging has been applied to the field of art conservation and art history since the early 1990s. It is attractive as a noninvasive imaging technique because it is fast and hence capable of imaging large areas of an object giving both spatial and spectral information. This paper gives an overview of the different instrumental designs, image processing techniques and various applications of multispectral and hyperspectral imaging to art conservation, art history and archaeology. Recent advances in the development of remote and versatile multispectral and hyperspectral imaging as well as techniques in pigment identification will be presented. Future prospects including combination of spectral imaging with other noninvasive imaging and analytical techniques will be discussed

Change in level of productivity in the treatment of schizophrenia with olanzapine or other antipsychotics

<p>Abstract</p> <p>Background</p> <p>When treating schizophrenia, improving patients' productivity level is a major goal considering schizophrenia is a leading cause of functional disability. Productivity level has been identified as the most preferred treatment outcome by patients with schizophrenia. However, little has been done to systematically investigate productivity levels in schizophrenia. We set out to better understand the change in productivity level among chronically ill patients with schizophrenia treated with olanzapine compared with other antipsychotic medications. We also assessed the links between productivity level and other clinical outcomes.</p> <p>Methods</p> <p>This post hoc analysis used data from 6 randomized, double-blind clinical trials of patients with schizophrenia or schizoaffective disorder, with each trial being of approximately 6 months duration. Change in productivity level was compared between olanzapine-treated patients (HGBG, n = 172; HGHJ, n = 277; HGJB, n = 171; HGLB, n = 281; HGGN, n = 159; HGDH, n = 131) and patients treated with other antipsychotic medications (separately vs. haloperidol [HGGN, n = 97; HGDH, n = 132], risperidone [HGBG, n = 167; HGGN, n = 158], quetiapine [HGJB, n = 175], ziprasidone [HGHJ, n = 271] and aripiprazole [HGLB, n = 285]). Productivity was defined as functional activities/work including working for pay, studying, housekeeping and volunteer work. Productivity level in the prior 3 months was assessed on a 5-point scale ranging from no useful functioning to functional activity/work 75% to 100% of the time.</p> <p>Results</p> <p>Chronically ill patients treated with olanzapine (OLZ) experienced significantly greater improvement in productivity when compared to patients treated with risperidone (RISP) (OLZ = 0.22 ± 1.19, RISP = -0.03 ± 1.17, p = 0.033) or ziprasidone (ZIP) (OLZ = 0.50 ± 1.38, ZIP = 0.25 ± 1.27, p = 0.026), but did not significantly differ from the quetiapine, aripiprazole or haloperidol treatment groups. Among first episode patients, OLZ therapy was associated with greater improvements in productivity levels compared to haloperidol (HAL), during the acute phase (OLZ = -0.31 ± 1.59, HAL = -0.69 ± 1.56, p = 0.011) and over the long-term (OLZ = 0.10 ± 1.50, HAL = -0.32 ± 1.91, p = 0.008). Significantly more chronically ill and first episode patients treated with olanzapine showed moderately high (>50%-75% of the time) and high levels of productivity (>75%-100% of the time) at endpoint, when compared to risperidone or haloperidol-treated patients (p < .05), respectively. Higher productivity level was associated with significantly higher study completion rates and better scores on the positive, negative, disorganized thoughts, hostility and depression subscales of the Positive and Negative Symptom Scale (PANSS).</p> <p>Conclusions</p> <p>Some antipsychotic medications significantly differed in beneficial impact on productivity level in the long-term treatment of patients with schizophrenia. Findings further highlight the link between clinical and functional outcomes, showing significant associations between higher productivity, lower symptom severity and better persistence on therapy.</p> <p>Trial Registration</p> <p>clinicaltrials.gov identifier <a href="http://www.clinicaltrials.gov/ct2/show/NCT00088049">NCT00088049</a>; <a href="http://www.clinicaltrials.gov/ct2/show/NCT00036088">NCT00036088</a></p

Device and method for motion estimation

The motion estimation unit (100) comprises a block-matcher (102) for calculating a start motion vector (110) by minimizing a predetermined cost function as a matching criterion for the block (116) of pixels with a further block of pixels (122) of a further image (120). The motion estimation unit (100) further comprises an optical flow analyzer (104) for calculating an update motion vector (111) based on the start motion vector (110) and which is designed to minimize a sum of errors associated with a set of optical flow equations corresponding to respective pixels of the block (116) of pixels. Finally the selector (106) of the motion estimation unit (100) selects the motion vector (126) by comparing the start motion vector (110) with the update motion vector (111)

Device and method for motion estimation

The motion estimation unit (100) comprises a block-matcher (102) for calculating a start motion vector (110) by minimizing a predetermined cost function as a matching criterion for the block (116) of pixels with a further block of pixels (122) of a further image (120). The motion estimation unit (100) further comprises an optical flow analyzer (104) for calculating an update motion vector (111) based on the start motion vector (110) and which is designed to minimize a sum of errors associated with a set of optical flow equations corresponding to respective pixels of the block (116) of pixels. Finally the selector (106) of the motion estimation unit (100) selects the motion vector (126) by comparing the start motion vector (110) with the update motion vector (111)

Unit for and method of motion estimation and image processing apparatus provided with such motion estimation unit

The motion estimation unit (100) comprises a block-matcher (102) for calculating a start motion vector (110) by minimizing a predetermined cost function as a matching criterion for the block (116) of pixels with a further block ofpixels (122) of a further image (120). The motion estimation unit (100) further comprises an optical flow analyzer (104) for calculating an update motion vector (111) based on the start motion vector (110) and which is designed tofind the most appropriate set of optical flow equations corresponding to respective pixels of the block (116) of pixels. This is achieved by analyzing gradient vectors of optical flow equations for pixels of the block (116) of pixels. Finally the selector 106 of the motion estimation unit (100) selects the motion vector (126) by comparing thestart motion vector (110) with the update motion vector (111)

Segmentation unit for and method of determining a second segment and image processing apparatus

A method of determining segments in a series of images based on previous segmentation results and on motion estimation. A second segment (108) of a second image (106) is determined based on a first segment (102) of a first image (100), with the first segment (102) and the second segment (108) corresponding to one object (104). The method comprises a calculation step to compare values of pixels of the first image (100) and the second image (106) in order to calculate a motion model which defines the transformation of the first segment (102) into the second segment (108). An example of such a motion model allows only translation. Translation can be described with one motion vector (110)