17 April 2008 Object recognition in infrared image sequences using scale invariant feature transform
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Abstract
In this paper, we propose an automated target recognition by using scale-invariant feature transform (SIFT) in PowerPC-based infrared (IR) imaging system. An IR image can be acquired more feature values at night than in the daytime, but visual image can be acquired more feature values in the daytime. IR-based object recognition puts application into digital surveillance system because it exist some more feature values at night than in the daytime. Feature of IR image in its system appears a little feature value in the daytime. It is not comprised within an effective feature values at a visual image from an IR of the daytime. Proposed method consists of two stages. First, we must localize the interest point in position and scale of moving objects. Second, we must build a description of the interest point and recognize moving objects. Proposed method uses SIFT for an effective feature extraction in PowerPC-based IR imaging system. Proposed SIFT method consists of scale space, extrema detection, orientation assignment, key point description, and feature matching. SIFT descriptor sets up extensive range about 1.5 times than visual image when feature value of SIFT in IR image is less than visual image. Because an object in IR image is analogized by field test that it exist more expanse form than visual image. Therefore, proposed SIFT descriptor is constituted at more expanse term for a precise matching of object. Based on experimental results, the proposed method is extracted object's feature values in PowerPC-based IR imaging system, and the result is presented by experiment.
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Changhan Park, Changhan Park, Kyung-hoon Bae, Kyung-hoon Bae, Jik-Han Jung, Jik-Han Jung, } "Object recognition in infrared image sequences using scale invariant feature transform", Proc. SPIE 6968, Signal Processing, Sensor Fusion, and Target Recognition XVII, 69681P (17 April 2008); doi: 10.1117/12.777976; https://doi.org/10.1117/12.777976
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