Reflectance Transformation Imaging (RTI) is a computational photography technology that can enhance the legibility and the fine details of the sample. Colorless trace is a special kind of trace with shallow depth and its grayscale is similar to the background. It is difficult to perceive details of these traces by traditional observation and detection methods. In this paper, we propose some improved detection methods which combine RTI with the Canny algorithm to get more details of colorless trace. The threshold is one of the key factors which affect the performance of the Canny algorithm so we put forward a threshold selection method based on the gradient histogram of the image. Three detection methods that combine RTI with the improved Canny algorithm are proposed in this paper. The experimental results show that these detection methods can extract weak and colorless traces effectively, and would have great potential application in criminal investigation.
Reflectance Transformation Imaging (RTI) is a computational photography technology that allows interactive manipulation of virtual lighting within two-dimensional images to enhance object surface information. RTI is based on the reflection characteristics of the surface of the object characterized by a specific parabolic function, of which the most classic is Polynomial Texture Mapping (PTM) based on biquadratic polynomial. Therefore, RTI is ideal for diffusely reflecting objects, and metal materials with specular reflection are nearly out of consideration before. In this article, several metal samples with different surface topography were selected to explore the potential of RTI for subtle traces on metal surfaces. The results show that, with the cooperation of different image enhancement algorithms, especially Specular Enhancement and Diffuse Gain, RTI can be well applied to the detection of metal, even metal with relatively strong specular reflection. And the No-Reference Structural Sharpness (NRSS) assessment is consistent with subjective perception, confirming that RTI not only helps to explore the 2D or 3D geometric texture of the surface of metal materials, but also plays an important role in understanding and spreading the cultural phenomena implied in the content on metal surfaces. Promisingly, RTI is expected to be extended to a wider range of fields.
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