Abstract
This paper presents a novel color texture-based method for object detection in images. To demonstrate our technique, a vehicle license plate (LP) localization system is developed. A support vector machine (SVM) is used to analyze the color textural properties of LPs. No external feature extraction module is used, rather the color values of the raw pixels that make up the color textural pattern are fed directly to the SVM, which works well even in high-dimensional spaces. Next, LP regions are identified by applying a continuously adaptive meanshift algorithm (CAMShift) to the results of the color texture analysis. The combination of CAMShift and SVMs produces not only robust and but also efficient LP detection as time-consuming color texture analyses for less relevant pixels are restricted, leaving only a small part of the input image to be analyzed.
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Kim, K.I., Jung, K., Kim, J.H. (2002). Color Texture-Based Object Detection: An Application to License Plate Localization. In: Lee, SW., Verri, A. (eds) Pattern Recognition with Support Vector Machines. SVM 2002. Lecture Notes in Computer Science, vol 2388. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45665-1_23
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DOI: https://doi.org/10.1007/3-540-45665-1_23
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