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J. Imaging, Volume 2, Issue 4 (December 2016)

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Open AccessArticle Non-Interferometric Tomography of Phase Objects Using Spatial Light Modulators
J. Imaging 2016, 2(4), 30; doi:10.3390/jimaging2040030
Received: 11 August 2016 / Revised: 4 October 2016 / Accepted: 11 October 2016 / Published: 19 October 2016
Cited by 1 | PDF Full-text (1942 KB) | HTML Full-text | XML Full-text
Abstract
Quantitative 3D phase retrieval techniques are based on either interferometric techniques such as holography or noninterferometric intensity-based techniques such as the transport of intensity equation (TIE). Interferometric techniques are vibration-sensitive and often use a reference beam requiring complicated optical alignment. In this work
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Quantitative 3D phase retrieval techniques are based on either interferometric techniques such as holography or noninterferometric intensity-based techniques such as the transport of intensity equation (TIE). Interferometric techniques are vibration-sensitive and often use a reference beam requiring complicated optical alignment. In this work we develop a simple, fast, and noninterferometric tomographic 3D phase retrieval technique based on the TIE which does not suffer from such drawbacks. The optical setup is a modified 4f TIE system which uses an SLM to replace the slow translation of the CCD required to record several diffraction patterns in a traditional TIE system. This novel TIE setup is suitable for dynamical events such as imaging biological processes. A rotating mechanical stage is constructed to obtain tomographic phase images of the object. The tomographic reconstruction algorithm is based on the Fourier slice theorem (backprojection algorithm) which applies to objects with a small refractive index span. Simulation and experimental results are shown as part of this work. A graphical user interface is developed to perform the TIE tomographic reconstruction algorithm and to synchronize the captured intensities by the CCD, the phase patterns displayed on the SLM, and the Arduino controlled rotating stage assembly. Full article
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Open AccessArticle 3D Clumped Cell Segmentation Using Curvature Based Seeded Watershed
J. Imaging 2016, 2(4), 31; doi:10.3390/jimaging2040031
Received: 7 September 2016 / Revised: 23 October 2016 / Accepted: 27 October 2016 / Published: 5 November 2016
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Abstract
Image segmentation is an important process that separates objects from the background and also from each other. Applied to cells, the results can be used for cell counting which is very important in medical diagnosis and treatment, and biological research that is often
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Image segmentation is an important process that separates objects from the background and also from each other. Applied to cells, the results can be used for cell counting which is very important in medical diagnosis and treatment, and biological research that is often used by scientists and medical practitioners. Segmenting 3D confocal microscopy images containing cells of different shapes and sizes is still challenging as the nuclei are closely packed. The watershed transform provides an efficient tool in segmenting such nuclei provided a reasonable set of markers can be found in the image. In the presence of low-contrast variation or excessive noise in the given image, the watershed transform leads to over-segmentation (a single object is overly split into multiple objects). The traditional watershed uses the local minima of the input image and will characteristically find multiple minima in one object unless they are specified (marker-controlled watershed). An alternative to using the local minima is by a supervised technique called seeded watershed, which supplies single seeds to replace the minima for the objects. Consequently, the accuracy of a seeded watershed algorithm relies on the accuracy of the predefined seeds. In this paper, we present a segmentation approach based on the geometric morphological properties of the ‘landscape’ using curvatures. The curvatures are computed as the eigenvalues of the Shape matrix, producing accurate seeds that also inherit the original shape of their respective cells. We compare with some popular approaches and show the advantage of the proposed method. Full article
(This article belongs to the Special Issue Image and Video Processing in Medicine)
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Open AccessArticle Mechanical Behaviour of Stainless Steels under Dynamic Loading: An Investigation with Thermal Methods
J. Imaging 2016, 2(4), 32; doi:10.3390/jimaging2040032
Received: 18 July 2016 / Revised: 29 October 2016 / Accepted: 3 November 2016 / Published: 8 November 2016
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Abstract
Stainless steels are the most exploited materials due to their high mechanical strength and versatility in producing different alloys. Although there is great interest in these materials, mechanical characterisation, in particular fatigue characterisation, requires the application of several standardised procedures involving expensive and
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Stainless steels are the most exploited materials due to their high mechanical strength and versatility in producing different alloys. Although there is great interest in these materials, mechanical characterisation, in particular fatigue characterisation, requires the application of several standardised procedures involving expensive and time-consuming experimental campaigns. As a matter of fact, the use of Standard Test Methods does not rely on a physical approach, since they are based on a statistical evaluation of the fatigue limit with a fixed probabilistic confidence. In this regard, Infra-Red thermography, the well-known, non-destructive technique, allows for the development of an approach based on evaluation of dissipative sources. In this work, an approach based on a simple analysis of a single thermographic sequence has been presented, which is capable of providing two indices of the damage processes occurring in material: the phase shift of thermoelastic signal φ and the amplitude of thermal signal at twice the loading frequency, S2. These thermal indices can provide synergetic information about the mechanical (fatigue and fracture) behaviour of austenitic AISI 316L and martensitic X4 Cr Ni Mo 16-5-1; since they are related to different thermal effects that produce damage phenomena. In particular, the use of φ and S2 allows for estimation of the fatigue limit of stainless steels at loading ratio R = 0.5 in agreement with the applied Standard methods. Within Fracture Mechanics tests, both indices demonstrate the capacity to localize the plastic zone and determine the position of the crack tip. Finally, it will be shown that the value of the thermoelastic phase signal can be correlated with the mechanical behaviour of the specific material (austenitic or martensitic). Full article
(This article belongs to the Special Issue The World in Infrared Imaging)
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Open AccessArticle Active Infrared Thermography for Seal Contamination Detection in Heat-Sealed Food Packaging
J. Imaging 2016, 2(4), 33; doi:10.3390/jimaging2040033
Received: 19 September 2016 / Revised: 8 November 2016 / Accepted: 10 November 2016 / Published: 16 November 2016
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Abstract
Packaging protects food products from environmental influences, assuring quality and safety throughout shelf life if properly performed. Packaging quality depends on the quality of the packaging material and of the closure or seal. A common problem possibly jeopardizing seal quality is the presence
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Packaging protects food products from environmental influences, assuring quality and safety throughout shelf life if properly performed. Packaging quality depends on the quality of the packaging material and of the closure or seal. A common problem possibly jeopardizing seal quality is the presence of seal contamination, which can cause a decreased seal strength, an increased packaging failure risk and leak formation. Therefore, early detection and removal of seal contaminated packages from the production chain is crucial. In this work, a pulsed-type active thermography method using the heated seal bars as an excitation source was studied for detecting seal contamination. Thermal image sequences of contaminated seals were recorded shortly after sealing. The detection performances of six thermal image processing methods, based on a single frame, a fit of the cooling profiles, thermal signal reconstruction, pulsed phase thermography, principal component thermography and a matched filter, were compared. High resolution digital images served as a reference to quantify seal contamination, and processed thermal images were mapped to these references. The lowest detection limit (equivalent diameter 0.60 mm) was obtained for the method based on a fit of the cooling profiles. Moreover, the detection performance of this method did not depend strongly on the time after sealing at which recording of the thermal images was started, making it a robust and generally applicable method. Full article
(This article belongs to the Special Issue The World in Infrared Imaging)
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Open AccessArticle Automated Soil Physical Parameter Assessment Using Smartphone and Digital Camera Imagery
J. Imaging 2016, 2(4), 35; doi:10.3390/jimaging2040035
Received: 29 September 2016 / Revised: 26 November 2016 / Accepted: 7 December 2016 / Published: 13 December 2016
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Abstract
Here we present work on using different types of soil profile imagery (topsoil profiles captured with a smartphone camera and full-profile images captured with a conventional digital camera) to estimate the structure, texture and drainage of the soil. The method is adapted from
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Here we present work on using different types of soil profile imagery (topsoil profiles captured with a smartphone camera and full-profile images captured with a conventional digital camera) to estimate the structure, texture and drainage of the soil. The method is adapted from earlier work on developing smartphone apps for estimating topsoil organic matter content in Scotland and uses an existing visual soil structure assessment approach. Colour and image texture information was extracted from the imagery. This information was linked, using geolocation information derived from the smartphone GPS system or from field notes, with existing collections of topography, land cover, soil and climate data for Scotland. A neural network model was developed that was capable of estimating soil structure (on a five-point scale), soil texture (sand, silt, clay), bulk density, pH and drainage category using this information. The model is sufficiently accurate to provide estimates of these parameters from soils in the field. We discuss potential improvements to the approach and plans to integrate the model into a set of smartphone apps for estimating health and fertility indicators for Scottish soils. Full article
(This article belongs to the Special Issue Image Processing in Agriculture and Forestry)
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Open AccessArticle An Excursus on Infrared Thermography Imaging
J. Imaging 2016, 2(4), 36; doi:10.3390/jimaging2040036
Received: 17 October 2016 / Revised: 5 December 2016 / Accepted: 9 December 2016 / Published: 18 December 2016
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Abstract
This work represents an overview of some of the applications of infrared thermography that have been carried out at the University of Naples Federico II over the years. The focus is on four topics: thermo-fluid-dynamics, materials inspection, cultural heritage and preventative maintenance. For
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This work represents an overview of some of the applications of infrared thermography that have been carried out at the University of Naples Federico II over the years. The focus is on four topics: thermo-fluid-dynamics, materials inspection, cultural heritage and preventative maintenance. For each topic, some results are presented as thermal, and/or phase, images with the attention being essentially devoted to the capacity of these images to communicate information. For more details on test apparatuses, procedures and data analyses, the reader is referred to the previous published work, available in the literature. Full article
(This article belongs to the Special Issue The World in Infrared Imaging)
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Open AccessArticle 3D Reconstruction of Plant/Tree Canopy Using Monocular and Binocular Vision
J. Imaging 2016, 2(4), 28; doi:10.3390/jimaging2040028
Received: 29 August 2016 / Revised: 16 September 2016 / Accepted: 19 September 2016 / Published: 29 September 2016
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Abstract
Three-dimensional (3D) reconstruction of a tree canopy is an important step in order to measure canopy geometry, such as height, width, volume, and leaf cover area. In this research, binocular stereo vision was used to recover the 3D information of the canopy. Multiple
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Three-dimensional (3D) reconstruction of a tree canopy is an important step in order to measure canopy geometry, such as height, width, volume, and leaf cover area. In this research, binocular stereo vision was used to recover the 3D information of the canopy. Multiple images were taken from different views around the target. The Structure-from-motion (SfM) method was employed to recover the camera calibration matrix for each image, and the corresponding 3D coordinates of the feature points were calculated and used to recover the camera calibration matrix. Through this method, a sparse projective reconstruction of the target was realized. Subsequently, a ball pivoting algorithm was used to do surface modeling to realize dense reconstruction. Finally, this dense reconstruction was transformed to metric reconstruction through ground truth points which were obtained from camera calibration of binocular stereo cameras. Four experiments were completed, one for a known geometric box, and the other three were: a croton plant with big leaves and salient features, a jalapeno pepper plant with median leaves, and a lemon tree with small leaves. A whole-view reconstruction of each target was realized. The comparison of the reconstructed box’s size with the real box’s size shows that the 3D reconstruction is in metric reconstruction. Full article
(This article belongs to the Special Issue Image Processing in Agriculture and Forestry)
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Open AccessArticle Visual Analytics of Complex Genomics Data to Guide Effective Treatment Decisions
J. Imaging 2016, 2(4), 29; doi:10.3390/jimaging2040029
Received: 21 May 2016 / Revised: 18 September 2016 / Accepted: 23 September 2016 / Published: 30 September 2016
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Abstract
In cancer biology, genomics represents a big data problem that needs accurate visual data processing and analytics. The human genome is very complex with thousands of genes that contain the information about the individual patients and the biological mechanisms of their disease. Therefore,
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In cancer biology, genomics represents a big data problem that needs accurate visual data processing and analytics. The human genome is very complex with thousands of genes that contain the information about the individual patients and the biological mechanisms of their disease. Therefore, when building a framework for personalised treatment, the complexity of the genome must be captured in meaningful and actionable ways. This paper presents a novel visual analytics framework that enables effective analysis of large and complex genomics data. By providing interactive visualisations from the overview of the entire patient cohort to the detail view of individual genes, our work potentially guides effective treatment decisions for childhood cancer patients. The framework consists of multiple components enabling the complete analytics supporting personalised medicines, including similarity space construction, automated analysis, visualisation, gene-to-gene comparison and user-centric interaction and exploration based on feature selection. In addition to the traditional way to visualise data, we utilise the Unity3D platform for developing a smooth and interactive visual presentation of the information. This aims to provide better rendering, image quality, ergonomics and user experience to non-specialists or young users who are familiar with 3D gaming environments and interfaces. We illustrate the effectiveness of our approach through case studies with datasets from childhood cancers, B-cell Acute Lymphoblastic Leukaemia (ALL) and Rhabdomyosarcoma (RMS) patients, on how to guide the effective treatment decision in the cohort. Full article
(This article belongs to the Special Issue Big Visual Data Processing and Analytics)
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Other

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Open AccessTechnical Note Machine-Vision Systems Selection for Agricultural Vehicles: A Guide
J. Imaging 2016, 2(4), 34; doi:10.3390/jimaging2040034
Received: 12 September 2016 / Revised: 14 November 2016 / Accepted: 15 November 2016 / Published: 22 November 2016
Cited by 3 | PDF Full-text (12072 KB) | HTML Full-text | XML Full-text
Abstract
Machine vision systems are becoming increasingly common onboard agricultural vehicles (autonomous and non-autonomous) for different tasks. This paper provides guidelines for selecting machine-vision systems for optimum performance, considering the adverse conditions on these outdoor environments with high variability on the illumination, irregular terrain
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Machine vision systems are becoming increasingly common onboard agricultural vehicles (autonomous and non-autonomous) for different tasks. This paper provides guidelines for selecting machine-vision systems for optimum performance, considering the adverse conditions on these outdoor environments with high variability on the illumination, irregular terrain conditions or different plant growth states, among others. In this regard, three main topics have been conveniently addressed for the best selection: (a) spectral bands (visible and infrared); (b) imaging sensors and optical systems (including intrinsic parameters) and (c) geometric visual system arrangement (considering extrinsic parameters and stereovision systems). A general overview, with detailed description and technical support, is provided for each topic with illustrative examples focused on specific applications in agriculture, although they could be applied in different contexts other than agricultural. A case study is provided as a result of research in the RHEA (Robot Fleets for Highly Effective Agriculture and Forestry Management) project for effective weed control in maize fields (wide-rows crops), funded by the European Union, where the machine vision system onboard the autonomous vehicles was the most important part of the full perception system, where machine vision was the most relevant. Details and results about crop row detection, weed patches identification, autonomous vehicle guidance and obstacle detection are provided together with a review of methods and approaches on these topics. Full article
(This article belongs to the Special Issue Image Processing in Agriculture and Forestry)
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