(searched for: doi:10.13176/11.617)
Applied Sciences, Volume 10; https://doi.org/10.3390/app10124078
In spinal surgery, surgical navigation is an essential tool for safe intervention, including the placement of pedicle screws without injury to nerves and blood vessels. Commercially available systems typically rely on the tracking of a dynamic reference frame attached to the spine of the patient. However, the reference frame can be dislodged or obscured during the surgical procedure, resulting in loss of navigation. Hyperspectral imaging (HSI) captures a large number of spectral information bands across the electromagnetic spectrum, providing image information unseen by the human eye. We aim to exploit HSI to detect skin features in a novel methodology to track patient position in navigated spinal surgery. In our approach, we adopt two local feature detection methods, namely a conventional handcrafted local feature and a deep learning-based feature detection method, which are compared to estimate the feature displacement between different frames due to motion. To demonstrate the ability of the system in tracking skin features, we acquire hyperspectral images of the skin of 17 healthy volunteers. Deep-learned skin features are detected and localized with an average error of only 0.25 mm, outperforming the handcrafted local features with respect to the ground truth based on the use of optical markers.
Published: 12 June 2019
Journal of Ambient Intelligence and Humanized Computing pp 1-11; https://doi.org/10.1007/s12652-019-01342-x
An infrared camera system for real-time monitoring of invisible methane gas leakages is proposed, which can be used in production, transportation, and gas plants. Instead of using conventional lasers, a medium wavelength light-emitting diode (LED) with a center wavelength of approximately 3300 nm is used for the light source of the gas detector. We achieve compact cooling with low power consumption by using a small electronic part with a Peltier element instead of a larger cooling unit. We also propose an algorithm based on adaptive average method and adaptive histogram equalization process to extract the background efficiently. This knowledge based infrared camera system effectively detects the leakage of invisible gas. Preliminary experimental results confirm the potential and effectiveness of the developed equipment to be commercialized for use in real-time gas monitoring systems. We aim to eventually develop an image analysis system that combines image processing and knowledge engineering techniques.
IETE Journal of Research pp 1-8; https://doi.org/10.1080/03772063.2019.1619487
In this paper, a new contrast enhancement technique based on bi-dimensional empirical mode decomposition (BEMD) is proposed to enhance the target details in low contrast thermal images captured by unmanned aerial vehicle (UAV). Initially, thermal image acquired from UAV is separated into intrinsic mode functions (IMFs) and residue using BEMD. Then, local standard deviation based method is employed to the IMFs to improve the details of the image and histogram modification function is employed to the residue to enhance the contrast. The processed IMFs and residue are reconstructed to obtain the enhanced image using inverse BEMD. The performance of the proposed technique is analysed using quantitative and visual quality measures with the well-known histogram equalization, dynamic range partitioning and wavelet-based enhancement methods. Experimental results demonstrate that the proposed technique effectively enhances the contrast and details in the image with less noise and visual artefacts than other methods.
Journal of Modern Optics, Volume 66, pp 606-617; https://doi.org/10.1080/09500340.2018.1559949
In Aerial surveillance, thermal images acquired by unmanned aerial vehicle (UAV) are greatly affected due to various external interferences, which results in a low contrast image. Widely used conventional contrast enhancement methods such as histogram equalization and dynamic range partitioning techniques suffer from severe brightness changes and reduced sharpness, which in turn fail to preserve the edge details of the image. Thus for efficient target detection, it is essential to develop effective thermal infrared image contrast and edge enhancement technique. In this paper, wavelet transform (WT) and singular value decomposition (SVD)-based image enhancement technique is attempted for the target detection using thermal images captured by UAV. The discrete wavelet transform (DWT), stationary wavelet transform (SWT) and SVD are used for texture feature enhancement, edge enhancement and illumination correction, respectively. The experimental results show that the proposed technique yields higher entropy (6.7485), EMEE (2.1212), MSSIM (0.8719) and lower AMBE (21.9049) values when compared to other existing techniques.
Applied Optics, Volume 56, pp 9686-9697; https://doi.org/10.1364/ao.56.009686
Contrast enhancement plays a crucial role in infrared image pre-processing. Compared with the increasingly popular local-mapping enhancement methods, the global-mapping enhancement methods have a unique feature that reserves the thermal distribution information, which is vital in some temperature-sensitive applications. However, the main challenge of the global-mapping methods is how to enhance the contrast effectively without suffering from over-enhancement of the background and noise. To this end, we propose a novel global-mapping enhancement algorithm in this paper. First, the histogram is divided into several sub-histograms adaptively based on the heat conduction theory. By designing a metric called AHV, the background and non-background sub-histograms are distinguished, and then enhanced separately where more grayscales are allocated to non-background sub-histograms than background sub-histograms. Meanwhile, the property of the human visual system described by Weber’s law is also taken into consideration during the grayscale redistribution. The qualitative and quantitative comparisons with state-of-the-art methods on several databases demonstrate the advantages of our proposed method.
Computers & Electrical Engineering, Volume 62, pp 360-374; https://doi.org/10.1016/j.compeleceng.2017.01.010
Published: 1 August 2017
2017 Tenth International Conference on Contemporary Computing (IC3) pp 1-5; https://doi.org/10.1109/ic3.2017.8284291
Conference: 2017 Tenth International Conference on Contemporary Computing (IC3), 2017-8-10 - 2017-8-12
In this paper, we propose a power logarithm based plateau limit histogram equalization algorithm to enhance infrared images. Conventional histogram equalization methods lag significantly while enhancing infrared images whose histogram contains large spikes. To diminish the effects of large spikes log transformation is used which additionally aids in the expansion of the smaller values in the histogram. Power transformation assists in regaining the original shape of the histogram while clipping of histogram in a plateau limit is beneficial to reduce the over enhancement of the image. Plateau limit is set to the mean of modified histogram after the power-log transformation. After that, the modified histogram is equalized independently to achieve the enhanced output. Experimental results demonstrate that the proposed power-log based plateau limit histogram equalization algorithm efficiently enhances the contrast of infrared images while, quantitative measures and visual quality assessment effectively validate the superiority of the proposed algorithm with respect to the other traditional histogram equalization algorithms.
Mathematical Problems in Engineering, Volume 2016, pp 1-12; https://doi.org/10.1155/2016/9410368
For displaying high-dynamic-range images acquired by thermal camera systems, 14-bit raw infrared data should map into 8-bit gray values. This paper presents a new method for detail enhancement of infrared images to display the image with a relatively satisfied contrast and brightness, rich detail information, and no artifacts caused by the image processing. We first adopt a propagated image filter to smooth the input image and separate the image into the base layer and the detail layer. Then, we refine the base layer by using modified histogram projection for compressing. Meanwhile, the adaptive weights derived from the layer decomposition processing are used as the strict gain control for the detail layer. The final display result is obtained by recombining the two modified layers. Experimental results on both cooled and uncooled infrared data verify that the proposed method outperforms the method based on log-power histogram modification and bilateral filter-based detail enhancement in both detail enhancement and visual effect.