Recent advances in image processing using image texture features for food quality assessment

The use of computer vision technology has been highly successful in food classification problems in the past and it has continued this success in recent times. There does however exist a number of opportunities to progress computer vision technology further, these opportunities are critically examined based on cost and feasibility. A range of hardware options are considered along with a range of software options. The economic cost of implementing new hardware continues to prove a major impediment. Thus future efforts need to be focused on maximising the potential benefits of the existing hardware framework and instead concentrate on developing improved software. Of the improved software available the aspect that offers the greatest promise is more efficient analysis of food surface texture attributes which will lead to more powerful understanding of the relationships between quality factors and experimentally measured food quality.

[1]  Ú. Gonzales-Barrón,et al.  Discrimination of crumb grain visual appearance of organic and non-organic bread loaves by image texture analysis , 2008 .

[2]  Da-Wen Sun,et al.  Pizza quality evaluation using computer vision: Part 1. Pizza base and sauce spread , 2003 .

[3]  P. Allen,et al.  Comparison of various wavelet texture features to predict beef palatability. , 2009, Meat science.

[4]  Da-Wen Sun,et al.  Recent developments and applications of image features for food quality evaluation and inspection – a review , 2006 .

[5]  B. K. Yadav,et al.  Dimensional changes in milled rice (Oryza sativa L.) kernel during cooking in relation to its physicochemical properties by image analysis , 2007 .

[6]  Cheng-Jin Du,et al.  Estimating the surface area and volume of ellipsoidal ham using computer vision , 2006 .

[7]  W. S. Lee,et al.  Identification of citrus disease using color texture features and discriminant analysis , 2006 .

[8]  F. Mendoza,et al.  Determination of senescent spotting in banana (Musa cavendish) using fractal texture Fourier image , 2008 .

[9]  Da-Wen Sun,et al.  Improving quality inspection of food products by computer vision: a review , 2004 .

[10]  Da-Wen Sun,et al.  Recent advances in the use of computer vision technology in the quality assessment of fresh meats , 2011 .

[11]  Da-Wen Sun,et al.  Learning techniques used in computer vision for food quality evaluation: a review , 2006 .

[12]  P. Allen,et al.  Identification of important image features for pork and turkey ham classification using colour and wavelet texture features and genetic selection. , 2010, Meat science.

[13]  T. Nishimura,et al.  Observation of the distribution of Zn protoporphyrin IX (ZPP) in Parma ham by using purple LED and image analysis. , 2006, Meat science.

[14]  Cheng-Jin Du,et al.  Prediction of beef eating quality from colour, marbling and wavelet texture features. , 2008, Meat science.

[15]  Andrés Caro,et al.  Monitoring the ripening process of Iberian ham by computer vision on magnetic resonance imaging. , 2007, Meat science.

[16]  Margarita Ruiz-Altisent,et al.  Olive classification according to external damage using image analysis. , 2008 .

[17]  Trygve Randen,et al.  Filtering for Texture Classification: A Comparative Study , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  S. Devahastin,et al.  Application of wavelet transform coupled with artificial neural network for predicting physicochemical properties of osmotically dehydrated pumpkin , 2009 .

[19]  Franco Pedreschi,et al.  Color of Salmon Fillets By Computer Vision and Sensory Panel , 2010 .

[20]  Digvir S. Jayas,et al.  Wavelet Analysis of Signals in Agriculture and Food Quality Inspection , 2010 .

[21]  Da-Wen Sun,et al.  Recent applications of image texture for evaluation of food qualities—a review , 2006 .

[22]  Patrick Jackman,et al.  Correlation of consumer assessment of longissimus dorsi beef palatability with image colour, marbling and surface texture features. , 2010, Meat science.

[23]  Yuchou Chang,et al.  Robust color space conversion and color distribution analysis techniques for date maturity evaluation , 2008 .

[24]  J. Tan,et al.  Classification of tough and tender beef by image texture analysis. , 2001, Meat science.

[25]  Da-Wen Sun,et al.  Recent developments in the applications of image processing techniques for food quality evaluation , 2004 .

[26]  Victor Alchanatis,et al.  Real-time underwater sorting of edible fish species , 2007 .

[27]  José Blasco,et al.  Original paper: Automatic sorting of satsuma ( Citrus unshiu ) segments using computer vision and morphological features , 2009 .

[28]  Patrick Jackman,et al.  Comparison of the predictive power of beef surface wavelet texture features at high and low magnification. , 2009, Meat science.

[29]  Meegalla R. Chandraratne,et al.  Classification of lamb carcass using machine vision: Comparison of statistical and neural network analyses , 2007 .

[30]  Da‐Wen Sun,et al.  Automatic measurement of pores and porosity in pork ham and their correlations with processing time, water content and texture. , 2006, Meat science.

[31]  Da-Wen Sun,et al.  Retrospective Shading Correction of Confocal Laser Scanning Microscopy Beef Images for Three-Dimensional Visualization , 2009 .

[32]  J. Aguilera,et al.  Computer Vision and Stereoscopy for Estimating Firmness in the Salmon (Salmon salar) Fillets , 2010 .

[33]  Sandro M. Goñi,et al.  Geometry modelling of food materials from magnetic resonance imaging , 2008 .

[34]  Da-Wen Sun,et al.  Application of Computer Vision Systems for Objective Assessment of Food Qualities , 2011 .

[35]  F. J. García-Ramos,et al.  Non-destructive technologies for fruit and vegetable size determination - a review , 2009 .

[36]  Da-Wen Sun,et al.  Prediction of beef eating qualities from colour, marbling and wavelet surface texture features using homogenous carcass treatment , 2009, Pattern Recognit..

[37]  P. Allen,et al.  Prediction of beef palatability from colour, marbling and surface texture features of longissimus dorsi , 2010 .

[38]  Matthew Anderson,et al.  Proposal for a Standard Default Color Space for the Internet - sRGB , 1996, CIC.

[39]  Da-Wen Sun,et al.  Inspection and grading of agricultural and food products by computer vision systems—a review , 2002 .

[40]  José Blasco,et al.  Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm , 2007 .

[41]  Richard A. Russell,et al.  Segmentation of fluorescence microscopy images for quantitative analysis of cell nuclear architecture. , 2009, Biophysical journal.

[42]  Elias M. Stein,et al.  Fourier Analysis: An Introduction , 2003 .

[43]  Da-Wen Sun,et al.  Inspecting pizza topping percentage and distribution by a computer vision method , 2000 .

[44]  Franco Pedreschi,et al.  Description of the kinetic enzymatic browning in banana (Musa cavendish) slices using non-uniform color information from digital images , 2009 .

[45]  Victor Alchanatis,et al.  Classification of guppies’ (Poecilia reticulata) gender by computer vision , 2008 .

[46]  Da-Wen Sun,et al.  Automatic segmentation of beef longissimus dorsi muscle and marbling by an adaptable algorithm. , 2009, Meat science.

[47]  José Blasco,et al.  Development of a machine for the automatic sorting of pomegranate (Punica granatum) arils based on computer vision , 2009 .

[48]  C Borggaard,et al.  In-line image analysis in the slaughter industry, illustrated by Beef Carcass Classification. , 1996, Meat science.

[49]  H. Heymann,et al.  Image texture features as indicators of beef tenderness. , 1999, Meat science.

[50]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[51]  Meegalla R. Chandraratne,et al.  Prediction of lamb tenderness using image surface texture features , 2006 .

[52]  José Miguel Aguilera,et al.  Quantification of enzymatic browning kinetics in pear slices using non-homogenous L∗ color information from digital images , 2009 .

[53]  Gerald Kaiser,et al.  A Friendly Guide to Wavelets , 1994 .

[54]  José Miguel Aguilera,et al.  Description of food surfaces and microstructural changes using fractal image texture analysis , 2002 .

[55]  Jorge Chanona-Pérez,et al.  Changes on Dough Rheological Characteristics and Bread Quality as a Result of the Addition of Germinated and Non-Germinated Soybean Flour , 2008 .

[56]  Meegalla R. Chandraratne,et al.  Prediction of lamb carcass grades using features extracted from lamb chop images , 2006 .