Object tracking in wide area motion imagery is a complex problem that consists of object detection and target tracking over time. This challenge can be solved by human analysts who naturally have the ability to keep track of an object in a scene. A computer vision solution for object tracking has the potential to be a much faster and efficient solution. However, a computer vision solution faces certain challenges that do not affect a human analyst. To overcome these challenges, a tracking process is proposed that is inspired by the known advantages of a human analyst. First, the focus of a human analyst is emulated by doing processing only the local object search area. Second, it is proposed that an intensity enhancement process should be done on the local area to allow features to be detected in poor lighting conditions. This simulates the ability of the human eye to discern objects in complex lighting conditions. Third, it is proposed that the spatial resolution of the local search area is increased to extract better features and provide more accurate feature matching. A quantitative evaluation is performed to show tracking improvement using the proposed method. The three databases, each grayscale sequences that were obtained from aircrafts, used for these evaluations include the Columbus Large Image Format database, the Large Area Image Recorder database, and the Sussex database.
We present a hyperspectral image enhancement technique that utilizes spectral angle information
to improve the local contrast of shadow regions and increases spatial resolution of the output
color image determined by the enhancement process. The proposed visibility improvement
technique is presented in a two-stage approach. The first stage of the algorithm improves the
contrast within the image, thus enhancing the textural details of the scene. To minimize the
effects of illumination variations on the visibility of objects in the scene, the spectral angle
mapper (SAM) is employed, which allows the local pixel information to be insensitive to
changes in illumination. A color restoration process is used to provide an enhanced color image
from computed spectral angle between the reference spectrum and unknown spectra. This step
enables us to colorize the output image along with the enhanced shadow regions. In the second
stage, the spatial resolution of the contrast enhanced image is increased by using single image
super resolution technique on the enhanced image. The super resolution technique employs a
nonlinear interpolation based on multi-level local Fourier phase features. The combination of the
enhancement, color restoration, and super resolution approaches provide better visibility of
objects in the shadow regions. The effectiveness of the proposed technique is verified using realworld
hyperspectral data.
Security and surveillance videos, due to usage in open environments, are likely subjected to low resolution,
underexposed, and overexposed conditions that reduce the amount of useful details available in the collected images.
We propose an approach to improve the image quality of low resolution images captured in extreme lighting conditions
to obtain useful details for various security applications. This technique is composed of a combination of a nonlinear
intensity enhancement process and a single image super resolution process that will provide higher resolution and better
visibility. The nonlinear intensity enhancement process consists of dynamic range compression, contrast enhancement,
and color restoration processes. The dynamic range compression is performed by a locally tuned inverse sine nonlinear
function to provide various nonlinear curves based on neighborhood information. A contrast enhancement technique is
used to obtain sufficient contrast and a nonlinear color restoration process is used to restore color from the enhanced
intensity image. The single image super resolution process is performed in the phase space, and consists of defining
neighborhood characteristics of each pixel to estimate the interpolated pixels in the high resolution image. The
combination of these approaches shows promising experimental results that indicate an improvement in visibility and an
increase in usable details. In addition, the process is demonstrated to improve tracking applications. A quantitative
evaluation is performed to show an increase in image features from Harris corner detection and improved statistics of
visual representation. A quantitative evaluation is also performed on Kalman tracking results.
A new image enhancement technique based on a self-tunable transformation function to improve the visual quality of images captured with low dynamic range devices in extreme lighting conditions is presented. This technique consists of four processes: histogram adjustment, dynamic range compression, contrast enhancement, and nonlinear color restoration. Histogram adjustment on each spectral band is performed to minimize the effect of illumination. Dynamic range compression is accomplished by a newly designed inverse sine nonlinear function that provides various nonlinear curvatures with an image dependent parameter. A nonlinear curve generated by this parameter is used to modify the intensity of each pixel in the luminance image. A nonlinear color restoration process based on the chromatic information and luminance of the original image is employed. The effectiveness of this technique is evaluated on various natural images and aerial images, and compared with other state-of the art techniques. A quantitative evaluation is performed by estimating the number of Harris corners and speeded up robust features on wide area motion imagery data. The application of the proposed algorithm on face detection is also demonstrated. The evaluation results demonstrate that the proposed method holds significant benefits for surveillance and security applications and also as a preprocessing technique for object detection and tracking applications.
In this paper, a novel inverse sine nonlinear transformation based image enhancement technique is proposed to improve
the visual quality of images captured in extreme lighting conditions. This method is adaptive, local and simple. The
proposed technique consists of four main stages namely histogram adjustment, dynamic range compression, contrast
enhancement and nonlinear color restoration. Histogram adjustment on each spectral band is performed to belittle the
effect of illumination. Dynamic range compression is accomplished by an inverse sine nonlinear function with a locally
tunable image dependent parameter based on the local statistics of each pixel’s neighborhood regions of the luminance
image. A nonlinear color restoration process based on the chromatic information and luminance of the original image is
employed. A statistical quantitative evaluation is performed with the state of the art techniques to analyze and compare
the performance of the proposed technique. The proposed technique is also tested on face detection in complex lighting
conditions. The results of this technique on images captured in hazy/foggy weather environment are also presented. The
evaluation results confirm that the proposed method can be applied to surveillance, security applications in complex
lighting environments.
An adaptive technique for image enhancement based on a specifically designed nonlinear function is presented in this
paper. The enhancement technique constitutes three main processes-adaptive intensity enhancement, contrast
adjustment, and color restoration. A sine function with an image dependent parameter is used to tune the intensity of
each pixel in the luminance image. This process provides dynamic range compression by boosting the luminance of
darker pixels while reducing the intensity of brighter pixels and maintaining local contrast. The normalized reflectance
image is added to the enhanced image to preserve the details. The quality of the enhanced image is improved by applying
a local contrast enhancement followed by a contrast stretch process. A basic linear color restoration process based on the
chromatic information of the original image is employed to convert the enhanced intensity image back to a color image.
The performance of the algorithm is compared with other state of the art enhancement techniques and evaluated using a
statistical image quality evaluation method.
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