John Reidar Mathiassen

Bio: John Reidar Mathiassen is an academic researcher from SINTEF. The author has contributed to research in topics: Fish processing & Gadus. The author has an hindex of 12, co-authored 28 publications receiving 540 citations. Previous affiliations of John Reidar Mathiassen include Norwegian University of Science and Technology.

Trends in application of imaging technologies to inspection of fish and fish products

Abstract: Applications of imaging technologies are playing an important role in several major areas in research and industry, including process optimization, automated sorting and grading, and automated processing. This review looks at the trends in application of imaging technologies to inspection of fish and fish products – in particular reviewing the applications of VIS/NIR imaging, VIS/NIR imaging spectroscopy, planar and computed tomography (CT) X-ray imaging, and magnetic resonance imaging (MRI). In recent years, a large amount of research has been published on the use of imaging technologies for inspecting fish and fish products. This review article condenses a representative selection of recent research and industrial solutions in order to observe the general trends in how the imaging technologies are applied to inspection of fish and fish products. Based on these observed trends, we provide some viewpoints on the current situation and suggestions for future research directions.

Texture Similarity Measure Using Kullback-Leibler Divergence between Gamma Distributions

Abstract: We propose a texture similarity measure based on the Kullback-Leibler divergence between gamma distributions (KLGamma). We conjecture that the spatially smoothed Gabor filter magnitude responses of some classes of visually homogeneous stochastic textures are gamma distributed. Classification experiments with disjoint test and training images, show that the KLGamma measure performs better than other parametric measures. It approaches, and under some conditions exceeds, the classification performance of the best non-parametric measures based on binned marginal histograms, although it has a computational cost at least an order of magnitude less. Thus, the KLGamma measure is well suited for use in real-time image segmentation algorithms and time-critical texture classification and retrieval from large databases.

Computer Vision-Based Sorting of Atlantic Salmon (Salmo salar) Fillets According to Their Color Level

Abstract: Computer vision method was used to evaluate the color of Atlantic salmon (Salmo salar) fillets. Computer vision-based sorting of fillets according to their color was studied on 2 separate groups of salmon fillets. The images of fillets were captured using a digital camera of high resolution. Images of salmon fillets were then segmented in the regions of interest and analyzed in red, green, and blue (RGB) and CIE Lightness, redness, and yellowness (Lab) color spaces, and classified according to the Roche color card industrial standard. Comparisons of fillet color between visual evaluations were made by a panel of human inspectors, according to the Roche SalmoFan lineal standard, and the color scores generated from computer vision algorithm showed that there were no significant differences between the methods. Overall, computer vision can be used as a powerful tool to sort fillets by color in a fast and nondestructive manner. The low cost of implementing computer vision solutions creates the potential to replace manual labor in fish processing plants with automation.

Computer Vision‐Based Evaluation of Pre‐ and Postrigor Changes in Size and Shape of Atlantic Cod (Gadus morhua) and Atlantic Salmon (Salmo salar) Fillets during Rigor Mortis and Ice Storage: Effects of Perimortem Handling Stress

Abstract: The present study describes the possibilities for using computer vision-based methods for the detection and monitoring of transient 2D and 3D changes in the geometry of a given product. The rigor contractions of unstressed and stressed fillets of Atlantic salmon (Salmo salar) and Atlantic cod (Gadus morhua) were used as a model system. Gradual changes in fillet shape and size (area, length, width, and roundness) were recorded for 7 and 3 d, respectively. Also, changes in fillet area and height (cross-section profiles) were tracked using a laser beam and a 3D digital camera. Another goal was to compare rigor developments of the 2 species of farmed fish, and whether perimortem stress affected the appearance of the fillets. Some significant changes in fillet size and shape were found (length, width, area, roundness, height) between unstressed and stressed fish during the course of rigor mortis as well as after ice storage (postrigor). However, the observed irreversible stress-related changes were small and would hardly mean anything for postrigor fish processors or consumers. The cod were less stressed (as defined by muscle biochemistry) than the salmon after the 2 species had been subjected to similar stress bouts. Consequently, the difference between the rigor courses of unstressed and stressed fish was more extreme in the case of salmon. However, the maximal whole fish rigor strength was judged to be about the same for both species. Moreover, the reductions in fillet area and length, as well as the increases in width, were basically of similar magnitude for both species. In fact, the increases in fillet roundness and cross-section height were larger for the cod. We conclude that the computer vision method can be used effectively for automated monitoring of changes in 2D and 3D shape and size of fish fillets during rigor mortis and ice storage. In addition, it can be used for grading of fillets according to uniformity in size and shape, as well as measurement of fillet yield measured in thickness. The methods are accurate, rapid, nondestructive, and contact-free and can therefore be regarded as suitable for industrial purposes.

Image retrieval: Ideas, influences, and trends of the new age

Abstract: We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related fields. In this article, we survey almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and in the process discuss the spawning of related subfields. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.

Colour measurements by computer vision for food quality control – A review

Abstract: Colour is the first quality attribute of food evaluated by consumers, and is therefore an important component of food quality relevant to market acceptance. Rapid and objective measurement of food colour is required in quality control for the commercial grading of products. Computer vision is a promising technique currently investigated for food colour measurement, especially with the ability of providing a detailed characterization of colour uniformity at pixel-based level. This paper reviews the fundamentals and applications of computer vision for food colour measurement. Introduction of colour space and traditional colour measurements is also given. At last, advantages and disadvantages of computer vision for colour measurement are analyzed and its future trends are proposed.

Divergence Estimation for Multidimensional Densities Via $k$ -Nearest-Neighbor Distances

Abstract: A new universal estimator of divergence is presented for multidimensional continuous densities based on k-nearest-neighbor (k-NN) distances. Assuming independent and identically distributed (i.i.d.) samples, the new estimator is proved to be asymptotically unbiased and mean-square consistent. In experiments with high-dimensional data, the k-NN approach generally exhibits faster convergence than previous algorithms. It is also shown that the speed of convergence of the k-NN method can be further improved by an adaptive choice of k.