Lee K, Kim M, Lee O, Kim K
3D Information Processing Laboratory, Department of Electronics and Information Engineering, Korea University, Seoul, Korea.
Department of Medical IT Engineering, Soonchunhyang University, Asan, Korea.
Skin Res Technol. 2017 Aug;23(3):407-415. doi: 10.1111/srt.12351. Epub 2017 Jan 12.
BACKGROUND/PURPOSE: A problem in skin rendering with haptic feedback is the reconstruction of accurate 3D skin surfaces from stereo skin images to be used for touch interactions. This problem also encompasses the issue of how to accurately remove haptic spatial noise caused by the construction of disparity maps from stereo skin images, while minimizing the loss of the original skin roughness for cloning real tough textures without errors. Since the haptic device is very sensitive to high frequencies, even small amounts of noise can cause serious system errors including mechanical oscillations and unexpected exerting forces. Therefore, there is a need to develop a noise removal algorithm that preserves haptic roughness.
A new algorithm for a roughness preserving filter (RPF) that adaptively removes spatial noise, is proposed. The algorithm uses the disparity control parameter (λ) and noise control parameter (k), obtained from singular value decomposition of a disparity map. The parameter k determines the amount of noise to be removed, and the optimum value of k is automatically chosen based on a threshold of gradient angles of roughness (R ).
The RPF algorithm was implemented and verified with three real skin images. Evaluation criteria include preserved roughness quality and removed noise. Mean squared error (MSE), peak signal to noise ratio (PSNR), and objective roughness measures R and R were used for evaluation, and the results were compared against a median filter. The results show that the proposed RPF algorithm is a promising technology for removing noise and retaining maximized roughness, which guarantees stable haptic rendering for skin roughness.
The proposed RPF is a promising technology because it allows for any stereo image to be filtered without the risk of losing the original roughness. In addition, the algorithm runs automatically for any given stereo skin image with relation to the disparity parameter λ, and the roughness parameters R or R are given priority. Although this method has been optimized by graph-cut disparity map building, it can be extended to other disparity map building methods because the parameter k is determined by actual roughness R data that can be obtained by simple measurement.
背景/目的：在具有触觉反馈的皮肤渲染中，一个问题是如何从立体皮肤图像重建精确的三维皮肤表面以用于触摸交互。这个问题还包括如何准确去除由立体皮肤图像构建视差图时产生的触觉空间噪声，同时在克隆真实粗糙纹理时将原始皮肤粗糙度的损失降至最低且不产生误差。由于触觉设备对高频非常敏感，即使少量噪声也可能导致严重的系统错误，包括机械振荡和意外的作用力。因此，需要开发一种能保留触觉粗糙度的去噪算法。
提出了一种用于自适应去除空间噪声的粗糙度保留滤波器（RPF）新算法。该算法使用从视差图的奇异值分解中获得的视差控制参数（λ）和噪声控制参数（k）。参数k决定要去除的噪声量，并且基于粗糙度（R）的梯度角阈值自动选择k的最佳值。
用三张真实皮肤图像实现并验证了RPF算法。评估标准包括保留的粗糙度质量和去除的噪声。使用均方误差（MSE）、峰值信噪比（PSNR）以及客观粗糙度度量R和R进行评估，并将结果与中值滤波器进行比较。结果表明所提出的RPF算法是一种很有前景的去除噪声并保留最大粗糙度的技术，这保证了皮肤粗糙度的稳定触觉渲染。
所提出的RPF是一种很有前景的技术，因为它允许对任何立体图像进行滤波而不会有丢失原始粗糙度的风险。此外，该算法针对任何给定的与视差参数λ相关的立体皮肤图像自动运行，并且优先考虑粗糙度参数R或R。尽管此方法已通过图割视差图构建进行了优化，但它可以扩展到其他视差图构建方法，因为参数k由可通过简单测量获得的实际粗糙度R数据确定。
