MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

Variables selection methods in near-infrared spectroscopy.

Hyperspectral imaging systems are starting to be used as a scientific tool for food quality assessment. A typical hyperspectral image is composed of a set of a relatively wide range of monochromatic images corresponding to continuous wavelengths that normally contain redundant information or may exhibit a high degree of correlation. In addition, computation of the classifiers used to deal with the data obtained from the images can become excessively complex and time-consuming for such high-dimensional datasets, and this makes it difficult to incorporate such systems into an industry that demands standard protocols or high-speed processes. Therefore, recent works have focused on the development of new systems based on this technology that are capable of analysing quality features that cannot be inspected using visible imaging. Many of those studies have also centred on finding new statistical techniques to reduce the hyperspectral images to multispectral ones, which are easier to implement in automatic, non-destructive systems. This article reviews recent works that use hyperspectral imaging for the inspection of fruit and vegetables. It explains the different technologies available to acquire the images and their use for the non-destructive inspection of the internal and external features of these products. Particular attention is paid to the works aimed at reducing the dimensionality of the images, with details of the statistical techniques most commonly used for this task.

/pdf/recent-advances-and-applications-of-hyperspectral-imaging-2e322l2se1.pdf

Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment

Food safety is a great public concern, and outbreaks of food-borne illnesses can lead to disturbance to the society. Consequently, fast and nondestructive methods are required for sensing the safety situation of produce. As an emerging technology, hyperspectral imaging has been successfully employed in food safety inspection and control. After presenting the fundamentals of hyperspectral imaging, this paper provides a comprehensive review on its application in determination of physical, chemical, and biological contamination on food products. Additionally, other studies, including detecting meat and meat bone in feedstuffs as well as organic residue on food processing equipment, are also reported due to their close relationship with food safety control. With these applications, it can be demonstrated that miscellaneous hyperspectral imaging techniques including near-infrared hyperspectral imaging, fluorescence hyperspectral imaging, and Raman hyperspectral imaging or their combinations are powerful tools for food safety surveillance. Moreover, it is envisaged that hyperspectral imaging can be considered as an alternative technique for conventional methods in realizing inspection automation, leading to the elimination of the occurrence of food safety problems at the utmost.

Application of Hyperspectral Imaging in Food Safety Inspection and Control: A Review

Applied Discriminant Analysis

Reflectance and transmittance visible and near-infrared spectroscopy were used to detect fumonisin in single corn kernels infected with Fusarium verticillioides. Kernels with >100 ppm and <10 ppm could be classed accurately as fumonisin positive or negative, respectively. Classification results were generally better for oriented kernels than for kernels that were randomly placed in the spectrometer viewing area. Generally, models based on reflectance spectra have higher correct classification than models based on transmittance spectra. Statistical analyses indicated that including near-infrared wavelengths in calibrations improved classifications, and some calibrations were improved by including visible wavelengths. Thus, the color and chemical constituents of the infected kernel contribute to classification models. These results show that this technology can be used to rapidly and nondestructively screen single corn kernels for the presence of fumonisin, and may be adaptable to on-line detection...

Reflectance and Transmittance Spectroscopy Applied to Detecting Fumonisin in Single Corn Kernels Infected with Fusarium verticillioides

Development of a calibration to predict maize seed composition using single kernel near infrared spectroscopy

http://yoksis.bilkent.edu.tr/pdf/files/10.1016-j.dsp.2005.08.002.pdf

Feasibility of impact-acoustic emissions for detection of damaged wheat kernels

Detection of contaminated hazelnuts and ground red chili pepper flakes by multispectral imaging

An acoustical sorting system was developed to separate pistachio nuts with closed shells from those with open
shells. The system includes a microphone, digital signal processing hardware, material handling equipment, and an air reject
mechanism. It was found that upon impact with a steel plate, nuts with closed shells emit sound with higher signal magnitudes
for the first 0.33 ms than do nuts with open shells. After this interval, nuts with closed shells emit sounds with lower signal
magnitudes than those with open shells. Linear discriminant analysis was used to classify nuts using three features extracted
from the microphone signal during the first 1.4 ms after impact. One of the discriminant features is the integrated absolute
value of microphone output signal during the first 0.11 ms after impact. The other two features are the number of data points
in the digitized microphone signal, between 0.6 and 1.4 ms after impact, that have a slope and signal magnitude below preset
threshold levels. The classification accuracy of this system is approximately 97%. Throughput rate is approximately 40 nuts/s.
Cost is about $7,000 to $10,000 per channel. This cost is much lower than that of color sorters used to remove other pistachio
defects while throughput is comparable. Currently, closed–shell pistachio nuts are removed by mechanical devices. These
devices have a lower classification accuracy (95%) and damage kernels in open–shell pistachios by “pricking” them with
a needle. The needle hole can give the appearance of an insect tunnel and cause rejection by the consumer. The newly
developed system does not cause such damage. Increased sorting accuracy of the acoustic sorter, coupled with low cost,
enables a payback period of less than one year.

Tom C. Pearson

Papers

Reflectance and Transmittance Spectroscopy Applied to Detecting Fumonisin in Single Corn Kernels Infected with Fusarium verticillioides

Development of a calibration to predict maize seed composition using single kernel near infrared spectroscopy

Feasibility of impact-acoustic emissions for detection of damaged wheat kernels

Detection of contaminated hazelnuts and ground red chili pepper flakes by multispectral imaging

Detection of Pistachio Nuts with Closed Shells Using Impact Acoustics