CNN BASED COLOR CONSTANCY ALGORITHM LEVENTE T ¨ OR ¨ OK AND ´ AKOS ZAR ´ ANDY Analogic and Neural Computing Laboratory, Computer and Automation Research Institute Hungarian Academy of Sciences, Budapest, Kende u. 13-17, 1111, Hungary E-mail:torok@sztaki.hu, zarandy@sztaki.hu Color Constancy (CC) is a perceptional phenomena in which living species, capable of color vision, perceive objects’ color apart from the spectral distribution of light applied to illuminate the objects. The algorithm that can recover objects’ original color and display them as if they were illuminated by spectrally even (white) light is called CC algorithm. In contrast to other solutions our approach offers on-line possibilities in applications as its operation needs consists of mainly local interactions that is well suited to the architecture of Cellular Neural/Non-linear Networks’ (CNN). In recent paper, we have offered a brief survey of common CC approaches, introduced the principles of our CC algorithm, compared ACE4K on-chip results versus simulation, examined the robustness of our algorithm and outlined a newly developed setup for reliable color image recording. 1 Introduction Color Constancy (CC) is of great significance in satisfying the light adaptation re- quirements of visual systems. The ability of trichromatic and bichromatic creatures to recognize objects’ color even if they were illuminated by highly chromic light is often referred as the Color Constancy. Here we are aiming to implement an algo- rithm that can retrieve the color of scenes in such circumstances as well. Although the phenomena was first reported almost 70 years ago, the race for finding the best CC algorithm is still not over, as is indicated by a recent NASA patent a and other still pending patents. The main driving force behind our research is the recognition of the ability to achieve the proposed process on-line by means of a single Cellular Neural/Non-linear Network (CNN) chip, equipped with logarithmic, tricolor sen- sors. The real-time capability makes our solution unique, which is rooted in the locality of the proposed algorithm that fits well to the massively parallel architec- ture of CNN Universal Machine (CNN-UM) 11 . In the next sections, a short review of the formal mathematical description on the problem, already known solutions (Sec. 2), comparision of ACE4K solution versus PC based simulations (Sec. 4) and robustness examination of our model will be given. The developed image displaying technique (Sec. 3), on-chip results and robustness examination are new results. The Retinex model is widely referred in connection with CC that emerges as a subject time-to-time. As a consequence of an extension to the aforementioned model it has became feasible on CNN-UM 5 . The current research effort aimed to adopt the described algorithm to ACE4K 13 which is an analog 64x64 CMOS VLSI chip implementation of CNN-UM. a US patent #5,991,456 registered in November 1999. http://dragon.larc.nasa.gov/retinex/ cnnCC: submitted to World Scientific on May 16, 2002 1