Emerging non-destructive imaging techniques for fruit damage detection: Image processing and analysis

2021 ◽  
Author(s):  
Naveen Kumar Mahanti ◽  
R. Pandiselvam ◽  
Anjineyulu Kothakota ◽  
Padma Ishwarya ◽  
Subir Kumar Chakraborty ◽  
...  
Keyword(s):  
Image Processing ◽  
Damage Detection ◽  
Imaging Techniques ◽  
Image Processing And Analysis ◽  
Fruit Damage ◽  
Non Destructive

scholarly journals An Ultra-Wideband Frequency System for Non-Destructive Root Imaging

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2438 ◽  
Author(s):  
Thomas Truong ◽  
Anh Dinh ◽  
Khan Wahid
Keyword(s):  
Image Processing ◽  
Root System ◽  
Plant Root ◽  
Ultra Wideband ◽  
Soil Conditions ◽  
System Architectures ◽  
Image Processing And Analysis ◽  
Processing Module ◽  
Data Acquisition Module ◽  
Non Destructive

Understanding the root system architecture of plants as they develop is critical for increasing crop yields through plant phenotyping, and ultra-wideband imaging systems have shown potential as a portable, low-cost solution to non-destructive imaging root system architectures. This paper presents the design, implementation, and analysis of an ultra-wideband imaging system for use in imaging potted plant root system architectures. The proposed system is separated into three main subsystems: a Data Acquisition module, a Data Processing module, and an Image Processing and Analysis module. The Data Acquisition module consists of simulated and experimental implementations of a non-contact synthetic aperture radar system to measure ultra-wideband signal reflections from concealed scattering objects in a pot containing soil. The Data Processing module is responsible for interpreting the measured ultra-wideband signals and producing an image using a delay-and-sum beamforming algorithm. The Image Processing and Analysis module is responsible for improving image quality and measuring root depth and average root diameter in an unsupervised manner. The Image Processing and Analysis module uses a modified top-hat transformation alongside quantization methods based on energy distributions in the image to isolate the surface of the imaged root. Altogether, the proposed subsystems are capable of imaging and measuring concealed taproot system architectures with controlled soil conditions; however, the performance of the system is highly dependent on knowledge of the soil conditions. Smaller roots in difficult imaging conditions require future work into understanding and compensating for unwanted noise. Ultimately, this paper sought to provide insight into improving imaging quality of ultra-wideband (UWB) imaging systems for plant root imaging for other works to be followed.

Extending Acoustic Microscopy for Comprehensive Failure Analysis Applications

2010 ◽  
Author(s):  
Sebastian Brand ◽  
Matthias Petzold ◽  
Peter Czurratis ◽  
Peter Hoffrogge
Keyword(s):  
Image Processing ◽  
Failure Analysis ◽  
Signal Analysis ◽  
Flip Chip ◽  
Fault Isolation ◽  
Acoustic Microscopy ◽  
Specific Information ◽  
Full Potential ◽  
Time Frequency ◽  
Non Destructive

Abstract In industrial manufacturing of microelectronic components, non-destructive failure analysis methods are required for either quality control or for providing a rapid fault isolation and defect localization prior to detailed investigations requiring target preparation. Scanning acoustic microscopy (SAM) is a powerful tool enabling the inspection of internal structures in optically opaque materials non-destructively. In addition, depth specific information can be employed for two- and three-dimensional internal imaging without the need of time consuming tomographic scan procedures. The resolution achievable by acoustic microscopy is depending on parameters of both the test equipment and the sample under investigation. However, if applying acoustic microscopy for pure intensity imaging most of its potential remains unused. The aim of the current work was the development of a comprehensive analysis toolbox for extending the application of SAM by employing its full potential. Thus, typical case examples representing different fields of application were considered ranging from high density interconnect flip-chip devices over wafer-bonded components to solder tape connectors of a photovoltaic (PV) solar panel. The progress achieved during this work can be split into three categories: Signal Analysis and Parametric Imaging (SA-PI), Signal Analysis and Defect Evaluation (SA-DE) and Image Processing and Resolution Enhancement (IP-RE). Data acquisition was performed using a commercially available scanning acoustic microscope equipped with several ultrasonic transducers covering the frequency range from 15 MHz to 175 MHz. The acoustic data recorded were subjected to sophisticated algorithms operating in time-, frequency- and spatial domain for performing signal- and image analysis. In all three of the presented applications acoustic microscopy combined with signal- and image processing algorithms proved to be a powerful tool for non-destructive inspection.

Validation of a new image analysis procedure for quantifying filamentous bacteria in activated sludge

2014 ◽  
Vol 70 (6) ◽  
pp. 955-963 ◽  
Author(s):  
Ewa Liwarska-Bizukojc ◽  
Marcin Bizukojc ◽  
Olga Andrzejczak
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Activated Sludge ◽  
Analysis Procedure ◽  
Automated Image Analysis ◽  
Filamentous Bacteria ◽  
Image Processing And Analysis ◽  
Manual Counting ◽  
Analysis Application ◽  
Activated Sludge Systems

Quantification of filamentous bacteria in activated sludge systems can be made by manual counting under a microscope or by the application of various automated image analysis procedures. The latter has been significantly developed in the last two decades. In this work a new method based upon automated image analysis techniques was elaborated and presented. It consisted of three stages: (a) Neisser staining, (b) grabbing of microscopic images, and (c) digital image processing and analysis. This automated image analysis procedure possessed the features of novelty. It simultaneously delivered data about aggregates and filaments in an individual calculation routine, which is seldom met in the procedures described in the literature so far. What is more important, the macroprogram performing image processing and calculation of morphological parameters was written in the same software which was used for grabbing of images. Previously published procedures required using two different types of software, one for image grabbing and another one for image processing and analysis. Application of this new procedure for the quantification of filamentous bacteria in the full-scale as well as laboratory activated sludge systems proved that it was simple, fast and delivered reliable results.

Microdeformations in Sands by Digital Image Processing and Analysis

1996 ◽  
Vol 1548 (1) ◽  
pp. 31-37 ◽  
Author(s):  
Scott A. Raschke ◽  
Roman D. Hryciw ◽  
Gregory W. Donohoe
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Small Scale ◽  
Two Dimensional ◽  
Localized Deformation ◽  
Displacement Fields ◽  
Particulate Media ◽  
Image Processing And Analysis ◽  
Video Images

Laboratory experiments are typically performed on particulate media to study stress-deformation behavior and to verify or calibrate computer models from controlled or measured boundary stresses and displacements. However, such data do not permit the formation of shear bands, displacement fields within flowing granular media, and other small-scale localized deformation phenomena to be identified. Described are two semiautomated computer vision techniques for accurately determining the two-dimensional displacement field in granular soils from video images obtained through a transparent planar viewing window. The techniques described are applicable for studying the behavior of particulate media under plane strain and certain axisymmetric test conditions. Digital image processing and analysis routines are used in two different computer programs, Tracker and Tracer, Tracker uses a graphical user interface that allows individual particles to be selected and tracked through a sequence of digital video images. A contrast edge detection algorithm delineates the two-dimensional projected boundaries of particles. The location of the centroid of each particle selected for tracking is determined from the boundary to quantify the trajectory of each particle. Tracer maps the trace or trajectory of specially dyed fluorescent particles in a sequence of video frames. A thresholding technique segments individual particle trajectories. Together, Tracker and Tracer provide a set of tools for identifying small-scale displacement fields in particulate assemblies deforming under either quasi-static or rapid loading (such as gravity flow).

scholarly journals Image Processing and Analysis in Biomechanics

2010 ◽  
Vol 2010 (1) ◽  
Author(s):  
João Manuel R. S. Tavares ◽  
R. M. Natal Jorge
Keyword(s):  
Image Processing ◽  
Image Processing And Analysis

scholarly journals Versatile Framework for Medical Image Processing and Analysis with Application to Automatic Bone Age Assessment

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Chen Zhao ◽  
Jungang Han ◽  
Yang Jia ◽  
Lianghui Fan ◽  
Fan Gou
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Medical Image ◽  
Deep Neural Networks ◽  
Medical Image Processing ◽  
Bone Age ◽  
Age Assessment ◽  
Image Processing And Analysis ◽  
Learning Technique ◽  
Bone Age Assessment

Deep learning technique has made a tremendous impact on medical image processing and analysis. Typically, the procedure of medical image processing and analysis via deep learning technique includes image segmentation, image enhancement, and classification or regression. A challenge for supervised deep learning frequently mentioned is the lack of annotated training data. In this paper, we aim to address the problems of training transferred deep neural networks with limited amount of annotated data. We proposed a versatile framework for medical image processing and analysis via deep active learning technique. The framework includes (1) applying deep active learning approach to segment specific regions of interest (RoIs) from raw medical image by using annotated data as few as possible; (2) generative adversarial Network is employed to enhance contrast, sharpness, and brightness of segmented RoIs; (3) Paced Transfer Learning (PTL) strategy which means fine-tuning layers in deep neural networks from top to bottom step by step to perform medical image classification or regression tasks. In addition, in order to understand the necessity of deep-learning-based medical image processing tasks and provide clues for clinical usage, class active map (CAM) is employed in our framework to visualize the feature maps. To illustrate the effectiveness of the proposed framework, we apply our framework to the bone age assessment (BAA) task using RSNA dataset and achieve the state-of-the-art performance. Experimental results indicate that the proposed framework can be effectively applied to medical image analysis task.

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