Hyperspectral imaging applied to end-of-life (EOL) concrete recycling

The recovery of materials from DW is an important target of the recycling industry and it is important to know which materials are presents in order to set up efficient sorting and/or quality control actions. The implementation of an automatic recognition system of recovered products from End-Of-Life (EOL) concrete materials can be an useful way to maximize DW conversion into secondary raw materials. In this paper a new approach, based on HyperSpectral Imaging (HSI) sensors, is investigated in order to develop suitable and low cost strategies finalized to the preliminary detection and characterization of materials constituting Demolition Waste (DW) flow stream. The described HSI quality control approach is based on the utilization of a device working in the near infrared range (1000-1700 nm). Acquired hyperspectral images were analyzed. Different chemometric methods were applied. Results showed that it is possible to recognize DW materials and to distinguish the recycled aggregates from the investigated contaminants (brick, gypsum, plastic, wood and foam)

Hyperspectral Imaging and Their Applications in the Nondestructive Quality Assessment of Fruits and Vegetables

Over the past decade, hyperspectral imaging has been rapidly developing and widely used as an emerging scientific tool in nondestructive fruit and vegetable quality assessment. Hyperspectral imaging technique integrates both the imaging and spectroscopic techniques into one system, and it can acquire a set of monochromatic images at almost continuous hundreds of thousands of wavelengths. Many researches based on spatial image and/or spectral image processing and analysis have been published proposing the use of hyperspectral imaging technique in the field of quality assessment of fruits and vegetables. This chapter presents a detailed overview of the introduction, latest developments and applications of hyperspectral imaging in the nondestructive assessment of fruits and vegetables. Additionally, the principal components, basic theories, and corresponding processing and analytical methods are also reported in this chapter

Predicting liquid loss of frozen and thawed cod from hyperspectral imaging

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Convolutional Deblurring for Natural Imaging

In this paper, we propose a novel design of image deblurring in the form of one-shot convolution filtering that can directly convolve with naturally blurred images for restoration. The problem of optical blurring is a common disadvantage to many imaging applications that suffer from optical imperfections. Despite numerous deconvolution methods that blindly estimate blurring in either inclusive or exclusive forms, they are practically challenging due to high computational cost and low image reconstruction quality. Both conditions of high accuracy and high speed are prerequisites for high-throughput imaging platforms in digital archiving. In such platforms, deblurring is required after image acquisition before being stored, previewed, or processed for high-level interpretation. Therefore, on-the-fly correction of such images is important to avoid possible time delays, mitigate computational expenses, and increase image perception quality. We bridge this gap by synthesizing a deconvolution kernel as a linear combination of Finite Impulse Response (FIR) even-derivative filters that can be directly convolved with blurry input images to boost the frequency fall-off of the Point Spread Function (PSF) associated with the optical blur. We employ a Gaussian low-pass filter to decouple the image denoising problem for image edge deblurring. Furthermore, we propose a blind approach to estimate the PSF statistics for two Gaussian and Laplacian models that are common in many imaging pipelines. Thorough experiments are designed to test and validate the efficiency of the proposed method using 2054 naturally blurred images across six imaging applications and seven state-of-the-art deconvolution methods.Comment: 15 pages, for publication in IEEE Transaction Image Processin

A Neural Network Method for Classification of Sunlit and Shaded Components of Wheat Canopies in the Field Using High-Resolution Hyperspectral Imagery

(1) Background: Information rich hyperspectral sensing, together with robust image analysis, is providing new research pathways in plant phenotyping. This combination facilitates the acquisition of spectral signatures of individual plant organs as well as providing detailed information about the physiological status of plants. Despite the advances in hyperspectral technology in field-based plant phenotyping, little is known about the characteristic spectral signatures of shaded and sunlit components in wheat canopies. Non-imaging hyperspectral sensors cannot provide spatial information; thus, they are not able to distinguish the spectral reflectance differences between canopy components. On the other hand, the rapid development of high-resolution imaging spectroscopy sensors opens new opportunities to investigate the reflectance spectra of individual plant organs which lead to the understanding of canopy biophysical and chemical characteristics. (2) Method: This study reports the development of a computer vision pipeline to analyze ground-acquired imaging spectrometry with high spatial and spectral resolutions for plant phenotyping. The work focuses on the critical steps in the image analysis pipeline from pre-processing to the classification of hyperspectral images. In this paper, two convolutional neural networks (CNN) are employed to automatically map wheat canopy components in shaded and sunlit regions and to determine their specific spectral signatures. The first method uses pixel vectors of the full spectral features as inputs to the CNN model and the second method integrates the dimension reduction technique known as linear discriminate analysis (LDA) along with the CNN to increase the feature discrimination and improves computational efficiency. (3) Results: The proposed technique alleviates the limitations and lack of separability inherent in existing pre-defined hyperspectral classification methods. It optimizes the use of hyperspectral imaging and ensures that the data provide information about the spectral characteristics of the targeted plant organs, rather than the background. We demonstrated that high-resolution hyperspectral imagery along with the proposed CNN model can be powerful tools for characterizing sunlit and shaded components of wheat canopies in the field. The presented method will provide significant advances in the determination and relevance of spectral properties of shaded and sunlit canopy components under natural light conditions