Robert L. Ewing

Feature-based wavelet shrinkage algorithm for image denoising

A selective wavelet shrinkage algorithm for digital image denoising is presented. The performance of this method is an improvement upon other methods proposed in the literature and is algorithmically simple for large computational savings. The improved performance and computational speed of the proposed wavelet shrinkage algorithm is presented and experimentally compared with established methods. The denoising method incorporated in the proposed algorithm involves a two-threshold validation process for real-time selection of wavelet coefficients. The two-threshold criteria selects wavelet coefficients based on their absolute value, spatial regularity, and regularity across multiresolution scales. The proposed algorithm takes image features into consideration in the selection process. Statistically, most images have regular features resulting in connected subband coefficients. Therefore, the resulting subbands of wavelet transformed images in large part do not contain isolated coefficients. In the proposed algorithm, coefficients are selected due to their magnitude, and only a subset of those selected coefficients which exhibit a spatially regular behavior remain for image reconstruction. Therefore, two thresholds are used in the coefficient selection process. The first threshold is used to distinguish coefficients of large magnitude and the second is used to distinguish coefficients of spatial regularity. The performance of the proposed wavelet denoising technique is an improvement upon several other established wavelet denoising techniques, as well as being computationally efficient to facilitate real-time image-processing applications.

Combined spatial and temporal domain wavelet shrinkage algorithm for video denoising

A combined spatial- and temporal-domain wavelet shrinkage algorithm for video denoising is presented in this paper. The spatial-domain denoising technique is a selective wavelet shrinkage method which uses a two-threshold criteria to exploit the geometry of the wavelet subbands of each video frame, and each frame of the image sequence is spatially denoised independently of one another. The temporal-domain denoising technique is a selective wavelet shrinkage method which estimates the level of noise corruption as well as the amount of motion in the image sequence. The amount of noise is estimated to determine how much filtering is needed in the temporal-domain, and the amount of motion is taken into consideration to determine the degree of similarity between consecutive frames. The similarity affects how much noise removal is possible using temporal-domain processing. Using motion and noise level estimates, a video denoising technique is established which is robust to various levels of noise corruption and various levels of motion.

Image Registration Using Adaptive Polar Transform

Image registration is an essential task in many image processing applications. Log-polar transform (LPT) is a well known tool for image processing for its rotation and scale invariant properties. However it suffers from nonuniform sampling which makes it not suitable for the environment when registered images are altered or occluded. Inspired by LPT, this paper presents a new algorithm that addresses the problems of the conventional LPT while maintaining the invariant properties. We introduce adaptive polar transform (APT) technique and an innovative matching mechanism that serve as image processing tool for recovering scale and rotation change of the registered image. Unlike the conventional LPT that focuses only at the fovea area of the registered image, our proposed APT gives even consideration to the entire image. Translation of the registered image is recovered with the new search scheme by using Gabor feature extraction to accelerate the localization procedure. The experimental results indicate that our proposed method outperforms conventional LPT in registering images that are altered or occluded.

Image registration using Adaptive Polar Transform

Image registration is an essential task in many image processing applications. Log-polar transform (LPT) is a well known tool for image processing for its rotation and scale invariant properties. However it suffers from nonuniform sampling which makes it not suitable for the environment when registered images are altered or occluded. Inspired by LPT, this paper presents a new algorithm that addresses the problems of the conventional LPT while maintaining the invariant properties. We introduce adaptive polar transform (APT) technique and an innovative matching mechanism that serve as image processing tool for recovering scale and rotation change of the registered image. Unlike the conventional LPT that focuses only at the fovea area of the registered image, our proposed APT gives even consideration to the entire image. Translation of the registered image is recovered with the new search scheme by using Gabor feature extraction to accelerate the localization procedure. The experimental results indicate that our proposed method outperforms conventional LPT in registering images that are altered or occluded.

2D invariant object recognition using Log-Polar transform

Object recognition is an essential task in many image processing applications. Although object appears as three-dimensional in real world, they are usually perceived as two-dimensional in digital image or video. In most cases, major problems in recognizing objects lie on the two-dimensional geometry changes in object appearances. This paper presents an innovative template matching based object recognition method that is invariant to rotation and scale changes as well as resistant to noise. The approach is achieved by combining feature based search strategy and object matching in Log-Polar domain. Translation of the object is recovered with the new Gabor feature extraction method applied in the Cartesian coordinate. The multi-resolution Log-Polar search method is invented to reduce the number of feature point for Log-Polar matching in the target image. The new similarity measure for classification and verification is also proposed. The innovative combination of these techniques yields robustness in object recognition for fast computation without any rescaling in the target image. Comparisons of the repeatability factor of the new Gabor feature extraction with other well-known techniques such as SIFT and Harris-Laplacian is presented to evaluate the two-dimensional invariant and noise resistant properties of the proposed feature extraction. Experiments with still images and noisy images are provided to verify the effectiveness of the proposed approach in practice.

Electronic nose inhibition in a spiking neural network for noise cancellation

An olfaction detection spiking neural network that detects binary odor patterns is analyzed and implemented. This paper presents a new method for inhibiting spiking neural networks by modulating a detection threshold. Interference noise from active odors is measured by a single inhibitory neuron. The inhibition neuron changes the detection threshold to create tolerance for a system with multiple odors present. A digital implementation of the inhibition is simulated. Comparative results prove that threshold modulation reduces false-positive detection error in high noise scenarios where fifteen odors are active simultaneously.

Investigations toward multistatic passive radar imaging

Potential for imaging in passive multistatic radar systems is investigated primarily in terms of illuminator type and coherency. A typical set of transmitters in the UHF and VHF bands based upon the local illuminators in the Columbus, Ohio region is presented to constitute a realistic passive imaging environment. A passive radar signal model is then developed and demonstrates one possible processing implementation for imaging across multiple distributed illuminators. From this, the spatial frequency representation of an airborne target traversing a common flight path is presented. This k-space formulation is assessed as a tool for predicting imaging performance, along with potential limitations of the approach for accurately modeling realistic imaging scenarios of multistatic passive radar systems. Finally, simulation results show the -3 dB point target response to be <;1 m for FM transmitters and <;0.2 m for DTV, for the realistic Columbus-based imaging environment.

Image fusion of the Terahertz-visual NAECON Grand Challenge data

Terahertz (THz) sensing has been developed over the past three decades for concealed weapons detection, medical imaging, and non-destructive evaluation; however methods for THz image exploitation have not been well reported. We test a multiscale image fusion algorithm for the 2011 IEEE National Aerospace and Electronics Conf. (NAECON) Grand Challenge which consists of Terahertz (THz) and visual images. The study consists of image characterization (signals distribution), image processing (data fusion), and image analysis (edge detection). We found that THz image characterization did not necessarily follow a distinct Gaussian distribution, THz imagery fusion with visual data supported target detection, and that image analysis enhanced target assessment. For the initial experiment, we assess the target segmentation through edge detection, image fusion results, and image fusion quality assessment. The preliminary image exploitation and fusion results can further develop THz collection over clothing-obscured concealed weapons imaging, parameter optimization, and targeting evaluation.

Stereo Visual Tracking Within Structured Environments for Measuring Vehicle Speed

We present a novel visual tracking method for measuring the speed of a moving vehicle within a structured environment using stationary stereo cameras. In the proposed method, visual stereo tracking and motion estimation in 3-D are integrated within the framework of particle filtering. The visual tracking processes in the two views are coupled with each other since they are dependent upon the same 3-D motion and correlated in the observations. Considering that the vehicles motion is physically constrained by the environment, we further utilize the path constraint reconstructed from stereo views to reduce the uncertainty about the vehicles motion and improve the accuracy for both tracking and speed measuring. The proposed method overcomes the challenges arising from the limitation of depth accuracy in a long-range stereo, and the experiments on the synthetic and real-world sequences have demonstrated its effectiveness and accuracy in both the tracking performance and the speed measurement.

Scaling function waveform for effective side-lobe suppression in radar signal

Scaling function of generic wavelet is proposed to be the radar waveform of a radar signal. Its shows significant advantage on conventional linear frequency modulated (LFM) or chirp radar waveform in side-lobe compression. To increase the bandwidth of the radar waveform, we further propose a new radar waveform named chirp-Z, which is composed of wavelet packets. The generation of the chirp-Z signal is introduced, and the signal is compared with the chirp radar pulse with the same duration and same bandwidth chirp. The result demonstrates that chirp-Z signal has much better side-lobe suppression than the conventional chirp signal. Furthermore, due to the composition of wavelet packets in the chirp-Z signal, subband adaptation in both amplitude and phase adjustment becomes flexible in responding to the variation of environments. The latter enables powerful cognitive radar.