Skip to main content
SpringerLink
Log in

Inspecting Natural and Other Variable Objects

  • Reference work entry
Machine Vision Handbook

  • 7307 Accesses

Abstract

This chapter is concerned with the design of artificial vision systems that attempt to emulate one particular human activity: inspecting and manipulating highly variable natural objects and human artifacts. During the last quarter of the twentieth century and the first decade of the twenty-first century, Machine Vision, evolved from an exotic technology, created by academics, into one that is of considerable practical and commercial value, and which now provides assistance over a wide area of manufacturing industry. Hitherto, Machine Vision has been applied extensively in industry to tasks such as inspecting close tolerance engineering artefacts, during or shortly, after, manufacture. However, Machine Vision technology can also be applied to those areas of manufacturing where natural materials and other highly variable objects are processed. Our discussion in these pages is aimed at those areas of manufacturing where wide tolerances are encountered, as well as activities, such as agriculture, horticulture, fishing, mining, etc., where similar conditions apply. However, we should not lose sight of the fact that certain so-called high-precision industries are plagued by significant degrees of uncertainty. For example, the electronics industry is renowned for working with high-precision components (integrated circuits, printed circuit boards, etc.). However, it is also concerned with highly variable entities such as solder joints, flexible cables and components with flying-lead connectors (resistors, capacitors, diodes, coils, etc.). This chapter is relevant to applications such as these, as it is to examining fruit, vegetables, animals, fish, etc.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 999.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 549.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Further Reading

Books

  • Batchelor BG, Charlier J-R (1998) Machine vision is not computer vision (Keynote paper). In: Proceedings of SPIE conference on machine vision systems for inspection and metrology VII, Boston, Nov 1998, vol 3521, pp 2–13, ISBN 0-8194-2982-1

    Google Scholar 

  • Batchelor BG, Waltz FM (2001) Intelligent machine vision: techniques, implementation and applications. Springer, London, ISBN 3-540-76224-8

    Book  Google Scholar 

  • Batchelor BG, Whelan PF (1997) Intelligent vision systems for industry. Springer, London, ISBN 3-540-19969 1

    Book  Google Scholar 

  • Crystal D (1997) The Cambridge encyclopedia of language, 2nd edn. Cambridge University press, Cambrdge. ISBN 0-521-55967-7

    Google Scholar 

  • Davies ER (2000) Image processing for the food industry. World Scientific, Sinagapore. ISBN 981-02-4022-8

    Book  MATH  Google Scholar 

  • Edwards M (2004) Detecting foreign bodies on food. Wooodhead, Cambridge. ISBN 1-85573-729-9

    Book  Google Scholar 

  • Graves M, Batchelor BG (2003) Machine vision for the inspection of natural products. Springer, London, ISBN 1-85233-525-4

    Book  Google Scholar 

  • Graves M, Smith A, Batchelor BG (1998) Approaches to foreign body detection in foods. Trends Food Sci Technol 9(1):21–27

    Article  Google Scholar 

  • Pinder C, Godfrey G (eds) (1993) Food process monitoring systems. Blackie, London, ISBN 0-7514-009-8

    Google Scholar 

  • Sun DW (2007) Computer vision technology for food quality evaluation. Academic, Burlington. ISBN 10: 0123736420

    Google Scholar 

Technical Articles

  • Aleixos N, Blasco J, Moltó E, Navarrón F (2000) Assessment of citrus fruit quality using a real-time machine vision system, ICPR00, vol I, pp 482–485

    Google Scholar 

  • Arnason H, Asmundsson M (1997) Computer vision in food handling and sorting, HPRCV97 (Chapter IV:1). Marel HF, Iceland

    Google Scholar 

  • Ávila M, Durán ML, Caro A, Antequera T, Gallardo R (2005) Thresholding methods on MRI to evaluate intramuscular fat level on Iberian Ham, IbPRIA05, vol II, p 697

    Google Scholar 

  • Ávila MM, Durán ML, Antequera T, Palacios R, Luquero M (2007) 3D reconstruction on MRI to analyse marbling and fat level in Iberian Loin, IbPRIA07, vol I, pp 145–152

    Google Scholar 

  • Backes AR, de M Sá JJ Jr, Kolb RM, Bruno OM (2009) Plant species identification using multi-scale fractal dimension applied to images of adaxial surface epidermis, CAIP09, pp 680–688

    Google Scholar 

  • Barnes M, Duckett T, Cielniak G (2009) Boosting minimalist classifiers for blemish detection in potatoes, IVCNZ09, pp 397–402

    Google Scholar 

  • Barni M, Cappellini V, Mecocci A (1997) Color-based detection of defects on chicken meat. Image Vis Comput 15(70):549–556

    Article  Google Scholar 

  • Barni M, Mussa AW, Mecocci A, Cappellini V, Durrani TS (1995) An intelligent perception system for food quality inspection using color analysis, ICIP95, vol I, pp 450–453

    Google Scholar 

  • Beare R (2001) An ultrasound imaging system for control of an automated carcass splitting machine; Ballerini L (2001) A simple method to measure homogeneity of fat distribution in meat, SCIA01(O-Th4B)

    Google Scholar 

  • Belaid A (1990) Metrology in quality control of nuts, ICPR90, vol I, pp 636–638

    Google Scholar 

  • Belhumeur PN, Chen D, Feiner S, Jacobs DW, Kress WJ, Ling HB, Lopez I, Ramamoorthi R, Sheorey S, White S, Zhang L (2008) Searching the world’s herbaria: a system for visual identification of plant species, ECCV08, vol IV, pp 116–129

    Google Scholar 

  • Benn A, Barrett-Lennard D, Hay PJ (2000) Image analysis for meat. US_Patent 6,104,827, 15 Aug 2000

    Google Scholar 

  • Berke J, Gyorffy K, Fischl G, Kárpáti L, Bakonyi J (1993) The application of digital image processing in the evaluation of agricultural experiments, CAIP93, pp 780–787

    Google Scholar 

  • Blasco J, Cubero S, Arias R, Gómez J, Juste F, Moltó E (2007) Development of a computer vision system for the automatic quality grading of mandarin segments, IbPRIA07, vol II, pp 460–466

    Google Scholar 

  • Bolle RM, Connell JH, Haas N, Mohan R, Taubin G (1996) VeggieVision: a produce recognition system, WACV96, pp 244–251

    Google Scholar 

  • Bossu J, Gee C, Truchetet F (2008) Development of a machine vision system for a real time precision sprayer. Electron Lett Comput Vision Image Anal 7(3):xx–yy

    Google Scholar 

  • Burgos-Artizzu XP, Ribeiro A, Tellaeche A, Pajares G, Fernandez-Quintanilla C (2010) Analysis of natural images processing for the extraction of agricultural elements. Image Vis Comput 28(1):138–149

    Article  Google Scholar 

  • Carrión P, Cernadas E, Gálvez JF, Rodroguez-Damián M, de Sá-Otero P (2004) Classification of honeybee pollen using a multiscale texture filtering scheme. Mach Vision Appl 15(4):186–193

    Article  Google Scholar 

  • Casanova D, De Mesquita Sá JJ Jr, Bruno OM (2009) Plant leaf identification using Gabor wavelets. Int J Imaging Syst Technol 19(3):236–243

    Article  Google Scholar 

  • Casasent D, Chen XW (2003) New training strategies for RBF neural networks for X-ray agricultural product inspection. Pattern Recognit 36(2):535–547

    Article  Google Scholar 

  • Cataltepe Z, Cetin E, Pearson T (2004) Identification of insect damaged wheat kernels using transmittance images, ICIP04, vol V, pp 2917–2920

    Google Scholar 

  • Cernadas E, Carrión P, Rodriguez PG, Muriel E, Antequera T (2005) Analyzing magnetic resonance images of Iberian pork loin to predict its sensorial characteristics. Comput Vis Image Underst 98(2):344–360

    Article  Google Scholar 

  • Cernadas E, Durán ML, Antequera T (2002) Recognizing marbling in dry-cured Iberian ham by multiscale analysis. Phys Rev Lett 23(11):1311–1321

    MATH  Google Scholar 

  • Chacon M, Manickavasagan A, Flores-Tapia D, Thomas G, Jayas DS (2007) Segmentation of wheat grains in thermal images based on pulse coupled neural networks, ICIP07, vol II, pp 273–276

    Google Scholar 

  • Chalidabhongse TH, Yimyam P, Sirisomboon P (2006) 2D/3D vision-based Mango’s feature extraction and sorting, ICARCV06, pp 1–6

    Google Scholar 

  • Chapron M, Boissard P, Assemat L (2000) A multiresolution based method for recognizing weeds in corn fields, ICPR00, vol II, pp 303–306

    Google Scholar 

  • Chapron M, Khalfi K, Boissard P, Assemat L (1997) Weed recognition by color image processing, SCIA97 (xx–yy) 9705; Hahn F, Mota R, Nobel Chile Jalapeno sorting using structured laser and neural network classifiers, CIAP97, vol II, pp 517–523

    Google Scholar 

  • Chapron M, Martin-Chefson L, Assemat L, Boissard P (199) A multiresolution weed recognition method based on multispectral image processing, SCIA99 (image analysis)

    Google Scholar 

  • Chatterjee S (2008) Anisotropic diffusion and segmentation of colored flowers, ICCVGIP08, pp 599–605

    Google Scholar 

  • Chen M, Dhingra K, Wu W, Yang L, Sukthankar R, Yang J (2009) PFID: Pittsburgh fast-food image dataset, ICIP09, pp 289–292

    Google Scholar 

  • Chen Z, Tao Y (2001) Food safety inspection using “from presence to classification” object-detection model. Pattern Recognit 34(12):2331–2338

    Article  MATH  Google Scholar 

  • Cho SY, Lim PT (2006) A novel virus infection clustering for flower images identification, ICPR06, vol II, pp 1038–1041

    Google Scholar 

  • Clarke J, Barman S, Remagnino P, Bailey K, Kirkup D, Mayo S, Wilkin P (2006) Venation pattern analysis of leaf images, ISVC06, vol II, pp 427–436

    Google Scholar 

  • Codrea CM, Aittokallio T, Keränen M, Tyystjärvi E, Nevalainen OS (2003) Feature learning with a genetic algorithm for fluorescence fingerprinting of plant species. Phys Rev Lett 24(15):2663–2673

    Google Scholar 

  • Davies ER, Bateman M, Mason DR, Chambers J, Ridgway C (2003) Design of efficient line segment detectors for cereal grain inspection. Phys Rev Lett 24(1–3):413–428

    Google Scholar 

  • Davies R, Heleno P, Correia BAB, Dinis J (2001) VIP3D: an application of image processing technology for quality control in the food industry, ICIP01, vol I, pp 293–296

    Google Scholar 

  • Derganc J, Likar B, Bernard R, Tomazevic D, Pernus F (2003) Real-time automated visual inspection of color tablets in pharmaceutical blisters. Real-Time Imaging 9(2):113–124

    Article  Google Scholar 

  • Díaz G, Romero E, Boyero JR, Malpica N (2009) Recognition and quantification of area damaged by Oligonychus perseae in avocado leaves, CIARP09, pp 677–684

    Google Scholar 

  • Ding K, Gunasekaran S (1998) 3-dimensional image reconstruction procedure for food microstructure evaluation. Artif Intell Rev 12(1–3):245–262

    Article  Google Scholar 

  • Dissing BS, Clemmesen LH, Loje H, Ersboll BK, Adler-Nissen J (2009) Temporal reflectance changes in vegetables, CRICV09, pp 1917–1922

    Google Scholar 

  • Dobrusin Y, Edan Y, Grinshpun J, Peiper UM, Wolf I, Hetzroni A (1993) Computer image analysis to locate targets for an agricultural robot, CAIP93, pp 775–779

    Google Scholar 

  • Edan Y (1995) Design of an autonomous agricultural robot. Appl Intell 5(1):41–50

    Article  Google Scholar 

  • Edmatsu K, Nitta Y (1981) Automated capsule inspection method. Pattern Recognit 14(1–6):365–374

    Article  Google Scholar 

  • Enomoto K, Toda M, Kuwahara Y (2009) Scallop detection from sand-seabed images for fishery investigation, CISP09, pp 1–5

    Google Scholar 

  • Erz G, Posch S (2003) A region based seed detection for root detection in minirhizotron images, DAGM03, pp 482–489

    Google Scholar 

  • Feyaerts F, Van Gool LJ (2001) Multi-spectral vision system for weed detection. Phys Rev Lett 22 (6–7):667–674

    MATH  Google Scholar 

  • Foschi PG, Liu H (2004) Active learning for detecting a spectrally variable subject in color infrared imagery. Phys Rev Lett 25(13):1509–1517

    Google Scholar 

  • Fox JS, Weldon E Jr, Ang M (1985) Machine vision techniques for finding sugarcane seedeyes, CVPR85. (Univ. of Hawaii) Hough. Feature Computation. Interesting use of pyramids and hough transform, pp 653–655

    Google Scholar 

  • France I, Duller AWG, Lamb HF, Duller GAT (1997) A comparative study of approaches to automatic pollen identification, BMVC97, pp xx–yy

    Google Scholar 

  • Fu H, Chi Z (2006) Combined thresholding and neural network approach for vein pattern extraction from leaf images. IEE P-Vis Image Signal Process 153(6):881–892

    Article  Google Scholar 

  • Galleguillos C, Babenko B, Rabinovich A, Belongie SJ (2008) Weakly supervised object localization with stable segmentations, ECCV08, vol I, pp 193–207

    Google Scholar 

  • Galleguillos C, Faymonville P, Belongie SJ (2009) BUBL: an effective region labeling tool using a hexagonal lattice, Emergent09, pp 2072–2079

    Google Scholar 

  • Galleguillos C, Rabinovich A, Belongie SJ (2008) Object categorization using co-occurrence, location and appearance, CVPR08, pp 1–8

    Google Scholar 

  • Garcia-Consuegra J, Cisneros G, Martinez A (199) A methodology for woody crop location and discrimination in remote sensing, CIAP99, pp 810–815

    Google Scholar 

  • Ghorbel F, de Bougrenet de la Tocnaye JL (1990) Automatic control of Lamellibranch Larva growth using contour invariant feature extraction. Pattern Recognition 23(3–4):319–323

    Article  Google Scholar 

  • Gómez-Sanchis J, Camps-Valls GMoltó E, Gómez-Chova L, Aleixos N, Blasco J (2008) Segmentation of hyperspectral images for the detection of rotten mandarins, ICIAR08, pp xx–yy

    Google Scholar 

  • Gouiffes M, Fernandez-Maloigne C, Tremeau A, Collewet C (2004) Color segmentation of ink-characters: application to meat tracabeality control, ICIP04, vol I, pp 195–198

    Google Scholar 

  • Grasso GM, Recce M (1997) Scene analysis for an orange harvesting robot. Artif Intell Appl 11(3):9–15

    Google Scholar 

  • Gregori M, Lombardi L, Savini M, Scianna A (1997) Autonomous plant inspection and anomaly detection, CIAP97, vol II, pp 509–516

    Google Scholar 

  • Guo M, Ou ZY, Wei HL (2006) Inspecting ingredients of starches in starch-noodle based on image processing and pattern recognition, ICPR06, vol II, pp 877–880

    Google Scholar 

  • Han MH, Jang D, Foster J (1989) Inspection of 2-D objects using pattern matching method. Pattern Recognit 22(5):567–575

    Article  Google Scholar 

  • Harrell RC, Slaughter DC, Adsit PD (1989) A fruit-tracking system for robotic harvesting. Mach Vision Appl 2(2):69–80

    Article  Google Scholar 

  • Harron W, Dony R (2009) Predicting quality measures in beef cattle using ultrasound imaging, CIIP09, pp 96–103

    Google Scholar 

  • Heinemann PH, Pathare NP, Morrow CT (1996) An automated inspection station for machine-vision grading of potatoes. Mach Vision Appl 9(1):14–19

    Article  Google Scholar 

  • Huang Q, Jain AK, Stockman GC, Smucker AJM (1992) Automatic image analysis of plant root structures, ICPR92, vol II, pp 569–572

    Google Scholar 

  • Im C, Nishida H, Kunii TL (1998) A hierarchical method of recognizing plant species by leaf shapes, MVA98, pp xx–yy

    Google Scholar 

  • Impoco G, Licitra G (2009) An interactive level set approach to semi-automatic detection of features in food micrographs, CAIP09, pp 914–921

    Google Scholar 

  • Ishii A, Mizuta T, Todo S (1998) Detection of foreign substances mixed in a plastic bottle of medicinal solution using real-time video image processing, ICPR98, vol II, pp 1646–1650

    Google Scholar 

  • Jackman P, Sun DW, Du CJ, Allen P (2009) Prediction of beef eating qualities from colour, marbling and wavelet surface texture features using homogenous carcass treatment. Pattern Recognit 42(5):751–763

    Article  Google Scholar 

  • Jiménez AR, Ceres R, Pons JL (1999) A machine vision system using a laser radar applied to robotic fruit harvesting, CVBVS99, p 110

    Google Scholar 

  • Jiménez AR, Ceres R, Pons JL (2000) A vision system based on a laser range-finder applied to robotic fruit harvesting. Mach Vision Appl 11(6):321–329

    Article  Google Scholar 

  • Jiménez AR, Jain AK, Ceres R, Pons JL (1999b) Automatic fruit recognition: a survey and new results using range/attenuation images. Pattern Recognit 32(10):1719–1736

    Article  Google Scholar 

  • Jin WB, Gu WZ, Zhang ZF (2009) An improved method for modeling of leaf venation patterns, CISP09, pp 1–5

    Google Scholar 

  • Jones R, Frydendal I (1998) Segmentation of sugar beets using image and graph processing, ICPR98, vol II, pp 1697–1699

    Google Scholar 

  • Joutou T, Yanai K (2009) A food image recognition system with multiple kernel learning, ICIP09, pp 285–288

    Google Scholar 

  • Kaewapichai W, Kaewtrakulpong P, Prateepasen A, Khongkraphan K (2007) Fitting a pineapple model for automatic maturity grading, ICIP07, vol I, pp 257–260

    Google Scholar 

  • Kirchgessner N, Spies H, Scharr H, Schurr U (2001) Root growth analysis in physiological coordinates, CIAP01, pp 589–594

    Google Scholar 

  • Kita N, Kita Y, Yang HQ (2002) Archiving technology for plant inspection images captured by mobile active cameras “4D visible memory”, 3DPVT02, pp 208–213

    Google Scholar 

  • Kunii TL, Im C, Nishida H (1998) Recognizing plant species by leaf shapes: a case study of the acer family, ICPR98, vol II, pp 1171–1173

    Google Scholar 

  • Laemmer E, Deruyver A, Sowinska M (2002) Watershed and adaptive pyramid for determining the apple’s maturity state, ICIP02, vol I, pp 789–792

    Google Scholar 

  • Larsen R, Arngren M, Hansen PW, Nielsen AA (2009) Kernel based subspace projection of near infrared hyperspectral images of maize kernels, SCIA09, pp 560–569

    Google Scholar 

  • Lee CL, Chen SY (2006) Classification of leaf images. Int J Imaging Syst Technol 16(1):15–23

    Article  Google Scholar 

  • Lefebvre M, Gil S, Glassey MA, Baur C, Pun T (1992) 3D computer vision for agrotics: the potato operation, an overview, ICPR92, vol I, pp 207–210

    Google Scholar 

  • Lepistö L, Kunttu I, Autio J, Visa A (2005) Classification of natural images using supervised and unsupervised classifier combinations, CIAP05, pp 770–777

    Google Scholar 

  • Lepistö L, Kunttu I, Lähdeniemi M, Tähti T, Nurmi J (2007) Grain size measurement of crystalline products using maximum difference method, SCIA07, pp 403–410

    Google Scholar 

  • Lepistö L, Kunttu I, Visa A (2005) Color-based classification of natural rock images using classifier combinations, SCIA05, pp 901–909

    Google Scholar 

  • Li MW, Zhang W (2009) Research and implement of head milled rice detection high-speed algorithm based on FPGA, CISP09, pp 1–4

    Google Scholar 

  • Li YF, Lee MH (1996) Applying vision guidance in robotic food handling. IEEE Rob Autom Mag 3(1):4–12

    Article  Google Scholar 

  • Liu QS, Liu GH, Using tasseled cap transformation of CBERS-02 images to detect dieback or dead Robinia pseudoacacia plantation, CISP09, pp 1–5

    Google Scholar 

  • Lumme J, Karjalainen M, Kaartinen H, Kukko A, Hyyppä J, Hyyppä H, Jaakkola A, Kleemola J (2008) Terrestrial laser scanning of agricultural crops, ISPRS08 (B5: 563 ff)

    Google Scholar 

  • Ma W, Zha HB (2008) Convenient reconstruction of natural plants by images, ICPR08, pp 1–4

    Google Scholar 

  • Ma W, Zha HB, Liu J, Zhang XP, Xiang B (2008) Image-based plant modeling by knowing leaves from their apexes, ICPR08, pp 1–4

    Google Scholar 

  • Mao WH, Ji BP, Zhan JC, Zhang XC, Hu XA (2009) Apple location method for the apple harvesting robot, CISP09, pp 1–5

    Google Scholar 

  • Martínez-Usó A, Pla F, García-Sevilla P (2005) Multispectral image segmentation by energy minimization for fruit quality estimation, IbPRIA05, vol II, pp 689

    Google Scholar 

  • Mathiassen JR, Misimi E, Skavhaug A (2007) A simple computer vision method for automatic detection of melanin spots in atlantic salmon fillets, IMVIP07, pp 192–200

    Google Scholar 

  • McQueen AM, Cherry CD, Rando JF, Schler MD, Latimer DL, McMahon SA, Turkal RJ, Reddersen BR (2000) Object recognition system and method. US_Patent 6,069,696, 30 May 2000

    Google Scholar 

  • Meade R (2006) Oven conveyor alignment system apparatus and method. US_Patent 7,131,529, 7 Nov 2006; US_Patent 7,222,726, 29 May 2007

    Google Scholar 

  • Merler M, Galleguillos C, Belongie SJ (2007) Recognizing groceries in situ using in vitro training data, SLAM07, pp 1–8

    Google Scholar 

  • Miao ZJ, Gandelin MH, Yuan BZ (2006) A new image shape analysis approach and its application to flower shape analysis. Image Vis Comput 24(10):1115–1122

    Article  Google Scholar 

  • Miao ZJ (2000) Zernike moment-based image shape analysis and its application. Phys Rev Lett 21(2):169–177

    Google Scholar 

  • Mizuno S, Noda K, Ezaki N, Takizawa H, Yamamoto S (2007) Detection of Wilt by analyzing color and stereo vision data of plant, MIRAGE07, pp 400–411

    Google Scholar 

  • Moeslund TB, Aagaard M, Lerche D (2005) 3D pose estimation of cactus leaves using an active shape model, WACV05, vol I, pp 468–473

    Google Scholar 

  • Mühlich M, Truhn D, Nagel K, Walter A, Scharr H, Aach T (2008) Measuring plant root growth, DAGM08, pp xx–yy

    Google Scholar 

  • Nam YY, Hwang EJ, Kim DY (2008) A similarity-based leaf image retrieval scheme: joining shape and venation features. Comput Vis Image Underst 110(2):245–259

    Article  Google Scholar 

  • Ni H, Gunasekaran S (1998) A computer vision method for determining length of cheese shreds. Artif Intell Rev 12(1–3):27–37

    Article  Google Scholar 

  • Nilsback ME, Zisserman A (2008) Automated flower classification over a large number of classes, ICCVGIP08, pp 722–729, IEEE DOI Link

    Google Scholar 

  • Nilsback ME, Zisserman A (2010) Delving deeper into the whorl of flower segmentation. Image Vis Comput 28(6):1049–1062, Elsevier DOI Link

    Article  Google Scholar 

  • Pajares G, Tellaeche A, Burgosartizzu XP, Ribeiro A (2007) Design of a computer vision system for a differential spraying operation in precision agriculture using hebbian learning. IET Comput Vision 1(3–4):93–99

    Article  MathSciNet  Google Scholar 

  • Pandit RB, Tang J, Liu F, Mikhaylenko G (2007) A computer vision method to locate cold spots in foods in microwave sterilization processes. Pattern Recognit 40(12):3667–3676

    Article  MATH  Google Scholar 

  • Park JK, Hwang EJ, Nam YY (2008) Utilizing venation features for efficient leaf image retrieval. J Syst Softw 81(1):71–82

    Article  Google Scholar 

  • Patel VC, McClendon RW, Goodrum JW (1998) Color computer vision and artificial neural networks for the detection of defects in poultry eggs. Artif Intell Rev 12(1–3):163–176

    Article  MATH  Google Scholar 

  • Patel VC, McClendon RW, Goodrum JW (1994) Crack detection in eggs using computer vision and neural networks. Artif Intell Appl 8(2):21–31

    Google Scholar 

  • Patel VC, McClendon RW, Goodrum JW (1996) Detection of cracks in eggs using color computer vision and artificial neural networks. Artif Intell Appl 10(3):19–28

    Google Scholar 

  • Peleg K, Cohen O, Ziv M, Kimmel E (1993) Machine identification of buds in images of plant shoots. Mach Vision Appl 6(4):224–232

    Article  Google Scholar 

  • Plebe A, Grasso G (2001) Localization of spherical fruits for robotic harvesting. Mach Vision Appl 13(2):70–79

    Article  Google Scholar 

  • Polder G, van der Heijden GWAM, Young IT (2003) Tomato sorting using independent component analysis on spectral images. Real-Time Imaging 9(4):253–259

    Article  Google Scholar 

  • Portman N, Grenander U, Vrscay ER (2009) Direct estimation of biological growth properties from image data using the “GRID” model, ICIAR09, pp 832–843

    Google Scholar 

  • Qi LY, Yang QH, Bao GJ, Xun Y, Zhang L (2009) A dynamic threshold segmentation algorithm for cucumber identification in greenhouse, CISP09, pp 1–4

    Google Scholar 

  • Qingyu W, Nan J, Heng L, Bin H (2009) A system for dynamically monitoring and warning algae blooms in Taihu Lake based on remote sensing, CISP09, pp 1–5

    Google Scholar 

  • Rabinovich A, Vedaldi A, Galleguillos C, Wiewiora E, Belongie SJ, Objects in context, ICCV07, pp 1–8

    Google Scholar 

  • Recce M, Plebe A, Taylor J, Tropiano G (1998) Video grading of oranges in real time. Artif Intell Rev 12(1–3):117–136

    Article  MATH  Google Scholar 

  • Rodenacker K, Gais P, Juetting U, Hense BA (2001) (Semi-) automatic recognition of microorganisms in water, ICIP01, vol III, pp 30–33

    Google Scholar 

  • Rodriguez-Damian M, Cernadas E, Formella A, Fernandez-Delgado M, DeSa-Otero P (2006) Automatic detection and classification of grains of pollen based on shape and texture. IEEE Trans Syst Man Cybern, C: Appl Rev 36(4):531–542

    Article  Google Scholar 

  • Rodroguez-Damian M, Cernadas E, de Sa-Otero P, Formella A (2004) Pollen classification using brightness-based and shape-based descriptors, ICPR04, vol II, pp 212–215

    Google Scholar 

  • Ronneberger O, Burkhardt H, Schultz E (2002) General-purpose object recognition in 3D volume data sets using gray-scale invariants: classification of airborne pollen-grains recorded with a confocal laser scanning microscope, ICPR02, vol II, pp 290–295

    Google Scholar 

  • Saitoh T, Aoki K, Kaneko T (2004) Automatic recognition of blooming flowers, ICPR04, vol I, pp 27–30

    Google Scholar 

  • Saitoh T, Kaneko T (2000) Automatic recognition of wild flowers, ICPR00, vol II, pp 507–510

    Google Scholar 

  • Samal A, Peterson B, Holliday DJ (1994) Recognizing plants using stochastic L-systems, ICIP94, vol I, pp 183–187

    Google Scholar 

  • Sánchez AJ, Marchant JA (2000) Fusing 3D information for crop/weeds classification, ICPR00, vol IV, pp 295–298

    Google Scholar 

  • Sanchiz JM, Pla F, Marchant JA (1998) An approach to the vision tasks involved in an autonomous crop protection vehicle. Eng Appl Artif Intell 11(2):175–187

    Article  Google Scholar 

  • Sanchiz JM, Pla F, Marchant JA, Brivot R (1995) Plant tracking-based motion analysis in a crop field, CAIP95, pp 302–309

    Google Scholar 

  • Schatzki TF, Witt SC, Wilkins DE, Lenker DH (1981a) Characterization of growing lettuce from density contours: I. Head selection. Pattern Recognit 13(5):333–340

    Article  Google Scholar 

  • Schatzki TF, Witt SC, Wilkins DE, Lenker DH (1981b) Characterization of growing lettuce from density contours: II. Statistics. Pattern Recognit 13(5):341–346

    Article  Google Scholar 

  • Šeatovic D (2008) A segmentation approach in novel real time 3D plant recognition system, CVS08, pp xx–yy

    Google Scholar 

  • Shi CJ, Ji GR (2009) Recognition method of weed seeds based on computer vision, CISP09, pp 1–4

    Google Scholar 

  • Shiranita K, Hayashi K, Otsubo A, Miyajima T, Takiyama R (2000) Grading meat quality by image processing. Pattern Recognit 33(1):97–104

    Article  Google Scholar 

  • Shiranita K, Miyajima T, Takiyama R (1998) Determination of meat quality by texture analysis. Phys Rev Lett 19(14):1319–1324

    MATH  Google Scholar 

  • Somers B, Delalieux S, Verstraeten WW, Verbesselt J, Lhermitte S, Coppin P (2009) Magnitude- and shape-related feature integration in hyperspectral mixture analysis to monitor weeds in citrus orchards. IEEE Trans Geosci Remote Sens 47(11):3630–3642

    Article  Google Scholar 

  • Song Y, Wilson RG, Edmondson R, Parsons N (2007) Surface modelling of plants from stereo images, 3DIM07, pp 312–319

    Google Scholar 

  • Sun CM, Berman M, Coward D, Osborne B (2007) Thickness measurement and crease detection of wheat grains using stereo vision. Phys Rev Lett 28(12):1501–1508

    Google Scholar 

  • Sun DW (ed) (2008) Computer vision technology for food quality evaluation. Elsevier, San Diego, ISBN: 978-0-12-373642-0

    Google Scholar 

  • Sun X, Gong HJ, Zhang F, Chen KJ (2009) A digital image method for measuring and analyzing color characteristics of various color scores of beef, CISP09, pp 1–6

    Google Scholar 

  • Tadeo F, Matia D, Laya D, Santos F, Alvarez T, Gonzalez S (2001) Detection of phases in sugar crystallization using wavelets, ICIP01, vol III, pp 178–181

    Google Scholar 

  • Tang XD, Liu MH, Zhao H, Tao W (2009) Leaf extraction from complicated background, CISP09, pp 1–5

    Google Scholar 

  • Tao Y (1996) Spherical transform of fruit images for online defect extraction of mass objects. Opt Eng 35(2):344–350

    Article  Google Scholar 

  • Taouil K, Chtourou Z, Kamoun L (2008) Machine vision based quality monitoring in olive oil conditioning, IPTA08, pp 1–4

    Google Scholar 

  • Taylor-Hell JF, Baranoski GVG, Rokne JG (2005) State of the art in the realistic modeling of plant venation systems. Int J Image Graph 5(3):663–678

    Article  Google Scholar 

  • Tellaeche A, Burgos-Artizzu XP, Pajares G, Ribeiro A (2008) A vision-based method for weeds identification through the Bayesian decision theory. Pattern Recognit 41(2):521–530

    Article  MATH  Google Scholar 

  • Teng CH, Kuo YT, Chen YS (2009) Leaf segmentation, its 3D position estimation and leaf classification from a few images with very close viewpoints, ICIAR09, pp 937–946

    Google Scholar 

  • Toraichi K, Kwan PWH, Katagishi K, Sugiyama T, Wada K, Mitsumoto M, Nakai H, Yoshikawa F (2002) On a fluency image coding system for beef marbling evaluation. Phys Rev Lett 23(11):1277–1291

    MATH  Google Scholar 

  • Vazquez-Fernandez E, Dacal-Nieto A, Martin F, Formella A, Torres-Guijarro S, Gonzalez-Jorge H (2009) A computer vision system for visual grape grading in wine cellars, CVS09, pp 335–344

    Google Scholar 

  • Vioix JB, Douzals JP, Truchetet F, Assémat L, Guillemin JP (2002) Spatial and spectral methods for weed detection and localization. Eurasip J Appl Signal Process 7:679–685

    Google Scholar 

  • Wang HM (2009) Impact of magnetic field on Mung Bean Ultraweak Luminescence, CISP09, pp 1–3

    Google Scholar 

  • Wang Q, Ronneberger O, Burkhardt H (2007) 3D invariants with high robustness to local deformations for automated pollen recognition, DAGM07, pp 425–435

    Google Scholar 

  • Watchareeruetai U, Takeuchi Y, Matsumoto T, Kudo H, Ohnishi N (2006) Computer vision based methods for detecting weeds in lawns. Mach Vision Appl 17(5):287–296

    Article  Google Scholar 

  • Wijethunga P, Samarasinghe S, Kulasiri D, Woodhead I (2009) Towards a generalized colour image segmentation for kiwifruit detection, IVCNZ09, pp 62–66

    Google Scholar 

  • Wiwart M, Koczowska I, Borusiewicz A (2001) Estimation of Fusarium head blight of triticale using digital image analysis of grain, CAIP01(563 ff)

    Google Scholar 

  • Xie NH, Li X, Zhang XQ, Hu WM, Wang JZ (2008) Boosted cannabis image recognition, ICPR08, pp 1–4

    Google Scholar 

  • Xun Y, Yang QH, Bao G, Gao F, Li W (2009) Recognition of broken corn seeds based on contour curvature, CISP09, pp 1–5

    Google Scholar 

  • Ye L, Cao LZ, Ogunbona P, Li WQ (2005) Description of evolutional changes in image time sequences using MPEG-7 visual descriptors, VLBV05, pp 189–197

    Google Scholar 

  • Yoshikawa F, Toraichi K, Wada K, Ostu N, Nakai H, Mitsumoto M, Katagishi K (2000) On a grading system for beef marbling. Phys Rev Lett 21(12):1037–1050

    MATH  Google Scholar 

  • Zeng G, Birchfield ST, Wells CE (2006) Detecting and measuring fine roots in minirhizotron images using matched filtering and local entropy thresholding. Mach Vision Appl 17(4):265–278

    Article  Google Scholar 

  • Zeng G, Birchfield ST, Wells CE (2010) Rapid automated detection of roots in minirhizotron images. Mach Vision Appl 21(3):xx–yy

    Article  Google Scholar 

  • Zhang YH, Zhang YS, He ZF, Tang XY (2007) Automatic inspection of tobacco leaves based on MRF image model, ISVC07, vol II, pp 662–670

    Google Scholar 

  • Zhang ZY, Wang FX, de B Harrington P (2009) Two-dimensional mid- and near-infrared correlation spectroscopy for rhubarb Identification, CISP09, pp 1–6

    Google Scholar 

  • Zhou LY, Chalana V, Kim Y (1998) PC-based machine vision system for real-time computer-aided potato inspection. Int J Imaging Syst Technol 9(6):423–433

    Article  Google Scholar 

  • Zhu WX, Lu CF, Li XC, Kong LW (2009) Dead birds detection in modern chicken farm based on SVM, CISP09, pp 1–5

    Google Scholar 

  • Zou J, Nagy G (2004) Evaluation of model-based interactive flower recognition, ICPR04, vol II, pp 311–314

    Google Scholar 

  • Zou XB, Zhao J, Li YX (2007) Apple color grading based on organization feature parameters. Phys Rev Lett 28(15):2046–2053

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Informatics, Cardiff University, Queen’s Buildings, 5 The Parade, Cardiff, CF24 3AA, Wales, UK

    Dr. Bruce G. Batchelor (Professor Emeritus)

Authors
  1. Dr. Bruce G. Batchelor
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bruce G. Batchelor .

Editor information

Editors and Affiliations

  1. School of Computer Science and Informatics, Cardiff University, Cardiff, Wales, UK

    Bruce G. Batchelor (Professor Emeritus) (Professor Emeritus)

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag London Ltd.

About this entry

Cite this entry

Batchelor, B.G. (2012). Inspecting Natural and Other Variable Objects. In: Batchelor, B.G. (eds) Machine Vision Handbook. Springer, London. https://doi.org/10.1007/978-1-84996-169-1_2

Download citation

Publish with us

Policies and ethics

Search

Navigation