Feature detection algorithms in computed images

The problem of sensing a medium by several sensors and retrieving interesting features is a very general one. The basic framework of the problem is generally the same for applications from MRI, tomography, Radar SAR imaging to subsurface imaging, even though the data acquisition processes, sensing geometries and sensed properties are different. In this thesis we introduced a new perspective to the problem of remote sensing and information retrieval by studying the problem of subsurface imaging using GPR and seismic sensors. We have shown that if the sensed medium is sparse in some domain then it can be imaged using many fewer measurements than required by the standard methods. This leads to much lower data acquisition times and better images representing the medium. We have used the ideas from Compressive Sensing, which show that a small number of random measurements about a signal is sufficient to completely characterize it, if the signal is sparse or compressible in some domain. Although we have applied our ideas to the subsurface imaging problem, our results are general and can be extended to other remote sensing applications. A second objective in remote sensing is information retrieval which involves searching for important features in the computed image of the medium. In this thesis we focus on detecting buried structures like pipes, and tunnels in computed GPR or seismic images. The problem of finding these structures in high clutter and noise conditions, and finding them faster than the standard shape detecting methods like the Hough transform is analyzed. One of the most important contributions of this thesis is, where the sensing and the information retrieval stages are unified in a single framework using compressive sensing. Instead of taking lots of standard measurements to compute the image of the medium and search the necessary information in the computed image, a much smaller number of measurements as random projections are taken. The data acquisition and information retrieval stages are unified by using a data model dictionary that connects the information to the sensor data.Ph.D.Committee Chair: McClellan, James H.; Committee Member: Romberg, Justin K.; Committee Member: Scott, Waymond R. Jr.; Committee Member: Vela, Patricio A.; Committee Member: Vidakovic, Bran

Information theoretic approach for assessing image fidelity in photon-counting arrays

The method of photon-counting integral imaging has been introduced recently for three-dimensional object sensing, visualization, recognition and classification of scenes under photon-starved conditions. This paper presents an information-theoretic model for the photon-counting imaging (PCI) method, thereby providing a rigorous foundation for the merits of PCI in terms of image fidelity. This, in turn, can facilitate our understanding of the demonstrated success of photon-counting integral imaging in compressive imaging and classification. The mutual information between the source and photon-counted images is derived in a Markov random field setting and normalized by the source-image’s entropy, yielding a fidelity metric that is between zero and unity, which respectively corresponds to complete loss of information and full preservation of information. Calculations suggest that the PCI fidelity metric increases with spatial correlation in source image, from which we infer that the PCI method is particularly effective for source images with high spatial correlation; the metric also increases with the reduction in photon-number uncertainty. As an application to the theory, an image-classification problem is considered showing a congruous relationship between the fidelity metric and classifier’s performance

Separation-Based Joint Decoding in Compressive Sensing

We introduce a joint decoding method for compressive sensing that can simultaneously exploit sparsity of individual components of a composite signal. Our method can significantly reduce the total number of variables decoded jointly by separating variables of large magnitudes in one domain and using only these variables to represent the domain. Furthermore, we enhance the separation accuracy by using joint decoding across multiple domains iteratively. This separation-based approach improves the decoding time and quality of the recovered signal. We demonstrate these benefits analytically and by presenting empirical results.Engineering and Applied Science

Sporadic absorption tomography using a conical shell X-ray beam

We demonstrate tomography by measuring a sporadic sequence of ring shaped projections collected during a translational scan. We show that projections using 10% sampling may be used to construct optical sections with peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) of the order of 40 dB and 0.9, respectively. This relatively small degradation in image fidelity was achieved for a 90% potential reduction in X-ray dose coupled with a reduction in scan time. Our approach is scalable in both X-ray energy and inspection volume. A driver for our method is to complement previously reported conical shell beam techniques concerning the measurement of diffracted flux for structural analysis. This work is of great relevance to time critical analytical scanning applications in security screening, process control and diagnostic imaging