Functional Asplund Metrics for Pattern Matching, Robust to Variable Lighting Conditions
DOI:
https://doi.org/10.5566/ias.2292Keywords:
Asplund metrics, Double-sided probing, Logarithmic Image Processing, Map of Asplund distances, Mathematical Morphology, Pattern matching, Robustness to lighting variationAbstract
In this paper, we propose a complete framework to process images captured under uncontrolled lighting and especially under low lighting. By taking advantage of the Logarithmic Image Processing (LIP) context, we study two novel functional metrics: i) the LIP-multiplicative Asplund metric which is robust to object absorption variations and ii) the LIP-additive Asplund metric which is robust to variations of source intensity or camera exposure-time. We introduce robust to noise versions of these metrics. We demonstrate that the maps of their corresponding distances between an image and a reference template are linked to Mathematical Morphology. This facilitates their implementation. We assess them in various situations with different lightings and movement. Results show that those maps of distances are robust to lighting variations. Importantly, they are efficient to detect patterns in low-contrast images with a template acquired under a different lighting.
References
Ancuti CO, Ancuti C, De Vleeschouwer C, Bekaert P (2018). Color balance and fusion for underwater image enhancement. IEEE T Image Process 27:379–93.
Asplund E (1960). Comparison Between Plane Symmetric Convex Bodies and Parallelograms. Math Scand 8:171–80.
Banon G, Faria S (1997). Morphological approach for template matching. In: Proc X Brazilian Symp on Comput Graphics and Image Process.
Banon GJF, Barrera J (1993). Decomposition of mappings between complete lattices by mathematical morphology, part I: General lattices. Signal Process 30:299 – 327.
Barat C, Ducottet C, Jourlin M (2003). Pattern matching using morphological probing. In: IEEE Image Proc, vol. 1.
Barat C, Ducottet C, Jourlin M (2010). Virtual doublesided image probing: A unifying framework for non-linear grayscale pattern matching. Pattern Recogn 43:3433–47.
Bouaynaya N, Schonfeld D (2008). Theoretical foundations of spatially-variant mathematical morphology part II: Gray-level images. IEEE T Pattern Anal 30:837–50.
Bourbaki N (2007). Intégration: Chapitres 1 à 4. Bourbaki, Nicolas. Springer Berlin Heidelberg.
Brailean J, Sullivan B, Chen C, Giger M (1991). Evaluating the EM algorithm for image processing using a human visual fidelity criterion. In: Int Conf Acoust Spee.
Butterfly (2010). Image from the YFCC100M dataset. http://www.flickr.com/photos/ 45563311@N04/4350683057/. Licence CC BY-NC-SA 2.0.
Cantoni V, Cinque L, Guerra C, Levialdi S, Lombardi L (1998). 2-D object recognition by multiscale tree matching. Pattern Recogn 31:1443 – 1454.
Carre M, Jourlin M (2014). LIP operators: Simulating exposure variations to perform algorithms independent of lighting conditions. In: Int Conf on Multimedia Computing and Syst (ICMCS).
Chen W, Er MJ, Wu S (2006). Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain. IEEE T Syst Man Cy B 36:458–66.
Deng G (2011). A generalized unsharp masking algorithm. IEEE T Image Process 20:1249–61.
Deshayes V, Guilbert P, Jourlin M (2015). How simulating exposure time variations in the LIP model. Application: moving objects acquisition. In: Acta Stereol., Proc. 14th ICSIA.
Deza MM, Deza E (2016). Encyclopedia of Distances. Springer Berlin Heidelberg.
Dougherty ER (1991). Application of the Hausdorff metric in gray-scale mathematical morphology via truncated umbrae. J Vis Commun Image R 2:177–87.
Faraji MR, Qi X (2016). Face recognition under varying illuminations using logarithmic fractal dimension-based complete eight local directional patterns. Neurocomputing 199:16–30.
Foresti GL, Micheloni C, Snidaro L, Remagnino P, Ellis T (2005). Active video-based surveillance system: the low-level image and video processing techniques needed for implementation. IEEE Signal Proc Mag 22:25–37.
Geiger A, Lauer M, Wojek C, Stiller C, Urtasun R (2014). 3D traffic scene understanding from movable platforms. IEEE T Pattern Anal 36:1012– 25.
Grünbaum B (1963). Measures of symmetry for convex sets. In: P Symp Pure Math, vol. 7. Providence, R.I.: Amer. Math. Soc.
Hautière N, Labayrade R, Aubert D (2006). Realtime disparity contrast combination for onboard estimation of the visibility distance. IEEE T Intell Transp 7:201–12.
Heijmans H (1994). Morphological image operators. No. vol. 25 in Adv Imag Elect Phys: Supplement. Academic Press.
Heijmans H, Ronse C (1990). The algebraic basis of mathematical morphology I. Dilations and erosions. Comput Vision Graphics and Image Process 50:245 – 295.
Hussain Shah J, Sharif M, Raza M, Murtaza M, Ur-Rehman S (2015). Robust face recognition technique under varying illumination. J Appl Res Technol 13:97–105.
Huttenlocher DP, Klanderman GA, Rucklidge WJ (1993). Comparing images using the Hausdorff distance. IEEE T Pattern Anal 15:850–63.
Jourlin M (2016). Logarithmic Image Processing: Theory and Applications, vol. 195 of Adv Imag Elect Phys. Elsevier Science.
Jourlin M, Carré M, Breugnot J, Bouabdellah M (2012). Chapter 7 - Logarithmic image processing: Additive contrast, multiplicative contrast, and associated metrics. In: Adv Imag Elect Phys, vol. 171. Elsevier, 357 – 406.
Jourlin M, Couka E, Abdallah B, Corvo J, Breugnot J (2014). Asplund’s metric defined in the Logarithmic Image Processing (LIP) framework: A new way to perform double-sided image probing for non-linear grayscale pattern matching. Pattern Recogn 47:2908 – 2924.
Jourlin M, Pinoli J (1988). A model for logarithmic image-processing. J Microsc Oxford 149:21–35.
Jourlin M, Pinoli J (2001). Logarithmic image processing: The mathematical and physical framework for the representation and processing of transmitted images. In: Adv Imag Elect Phys, vol. 115. Elsevier, 129 – 196.
Jourlin M, Pinoli JC (1995). Image dynamic range enhancement and stabilization in the context of the logarithmic image processing model. Signal Process 41:225 – 237.
Khosravi M, Schafer R (1996). Template matching based on a grayscale hit-or-miss transform. IEEE T Image Process 5:1060–6.
Lai ZR, Dai DQ, Ren CX, Huang KK (2014). Multilayer surface albedo for face recognition with reference images in bad lighting conditions. IEEE T Image Process 23:4709–23.
Li J, Lu BL (2009). An adaptive image Euclidean distance. Pattern Recogn 42:349 – 357.
Lowe DG (2004). Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60:91–110.
Matheron G (1967). Eléments pour une théorie des milieux poreux. Masson, Paris.
Mayet F, Pinoli JC, Jourlin M (1996). Physical justifications and applications of the LIP model for the processing of transmitted light images. Trait Signal 13:251 – 262.
Messelodi S, Modena CM, Segata N, Zanin M (2005). A Kalman filter based background updating algorithm robust to sharp illumination changes. In: Lect Notes Comput Sc, vol. 3617. Berlin, Heidelberg: Springer.
Meylan L, Susstrunk S (2006). High dynamic range image rendering with a retinex-based adaptive filter. IEEE T Image Process 15:2820–30.
Najman L, Talbot H (2013). Mathematical Morphology: From Theory to Applications. Wiley-Blackwell, 1st ed.
Navarro L, Courbebaisse G, Jourlin M (2014). Chapter two - Logarithmic wavelets. In: Adv Imag Elect Phys, vol. 183. Elsevier, 41 – 98.
Navarro L, Deng G, Courbebaisse G (2013). The symmetric logarithmic image processing model. Digital Signal Process 23:1337 – 1343.
Noyel G (2011). Method of monitoring the appearance of the surface of a tire. https://patentscope.wipo.int/search/en/WO2011131410. Patent, International PCT no WO2011131410 (A1). Also published as: US9002093 (B2), FR2959046 (B1), JP5779232 (B2), EP2561479 (A1), CN102844791 (B), BR112012025402 (A2).
Noyel G (2019a). A link between the multiplicative and additive functional Asplund’s metrics. In: Lect Notes Comp Sc, vol. 11564. Cham: Springer Int Publishing.
Noyel G (2019b). Logarithmic Mathematical Morphology: A new framework adaptive to illumination changes. In: Lect Notes Comp Sc, vol. 11401. Cham: Springer Int Publishing.
Noyel G, Angulo J, Jeulin D, Balvay D, Cuenod CA (2014). Multivariate mathematical morphology for DCE-MRI image analysis in angiogenesis studies. Image Anal Stereol 34:1–25.
Noyel G, Jeulin D, Parra-Denis E, Bilodeau M (2013). Method of checking the appearance of the surface of a tyre. https://patentscope.wipo. int/search/en/WO2013045593. Patent, International PCT no WO2013045593 (A1). Also published as US9189841 (B2), FR2980735 (B1), EP2761587 (A1), CN103843034 (A).
Noyel G, Jourlin M (2015). Asplund’s metric defined in the logarithmic image processing (LIP) framework for colour and multivariate images. In: IEEE Image Proc.
Noyel G, Jourlin M (2017a). Double-sided probing by map of Asplund’s distances using Logarithmic Image Processing in the framework of Mathematical Morphology. In: Lect Notes Comput Sc, vol. 10225. Cham: Springer Int Publishing.
Noyel G, Jourlin M (2017b). A simple expression for the map of Asplund’s distances with the multiplicative Logarithmic Image Processing (LIP) law. Personal communication, Eur Congr Stereology and Image Anal, Kaiserslautern, Germany.
Noyel G, Jourlin M (2017c). Spatio-colour Asplund’s metric and Logarithmic Image Processing for Colour images (LIPC). In: Lect Notes Comp Sc, vol. 10125. Cham: Springer.
Noyel G, Thomas R, Bhakta G, Crowder A, Owens D, Boyle P (2017). Superimposition of eye fundus images for longitudinal analysis from large public health databases. Biomed Phys Eng Express 3:045015.
Odone F, Trucco E, Verri A (2001). General purpose matching of grey level arbitrary images. In: Lect Notes Comput Sc, vol. 2059. Berlin, Heidelberg: Springer.
Peng YT, Cosman PC (2017). Underwater image restoration based on image blurriness and light absorption. IEEE T Image Process 26:1579–94.
Pizer SM, Amburn EP, Austin JD, Cromartie R, Geselowitz A, Greer T, ter Haar Romeny B, Zimmerman JB, Zuiderveld K (1987). Adaptive histogram equalization and its variations. Comput Vis Graph Image Process 39:355 – 368.
Salti S, Petrelli A, Tombari F, Fioraio N, Stefano LD (2015). Traffic sign detection via interest region extraction. Pattern Recogn 48:1039 – 1049.
Savvides M, Kumar BV (2003). Illumination normalization using logarithm transforms for face authentication. In: Lect Notes Comp Sc, vol. 2688. Springer Berlin Heidelberg.
Serra J (1982). Image Analysis and Mathematical Morphology, vol. 1. New York: Academic Press.
Serra J, ed. (1988). Image analysis and Mathematical Morphology: Theoretical advances, vol. 2. Academic Press.
Shan S, Gao W, Cao B, Zhao D (2003). Illumination normalization for robust face recognition against varying lighting conditions. In: IEEE Int SOI Conf.
Soille P (2004). Morphological Image Analysis: Principles and Applications. Berlin, Heidelberg: Springer, 2nd ed.
Thomee B, Shamma DA, Friedland G, Elizalde B, Ni K, Poland D, Borth D, Li LJ (2016). YFCC100M: The new data in multimedia research. Commun ACM 59:64–73.
van de Gronde JJ, Roerdink JBTM (2014). Group-invariant colour morphology based on frames. IEEE T Image Process 23:1276–88.
Wang L, Zhang Y, Feng J (2005). On the Euclidean distance of images. IEEE T Pattern Anal 27:1334–9.
Xie X, Lam KM (2005). Face recognition under varying illumination based on a 2D face shape model. Pattern Recogn 38:221 – 230.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 Image Analysis & Stereology
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
