Seokwon Yeom

Three-dimensional imaging and recognition of microorganism using single-exposure on-line (SEOL) digital holography

We address three-dimensional (3D) visualization and recognition of microorganisms using single-exposure on-line (SEOL) digital holography. A coherent 3D microscope-based Mach-Zehnder interferometer records a single on-line Fresnel digital hologram of microorganisms. Three-dimensional microscopic images are reconstructed numerically at different depths by an inverse Fresnel transformation. For recognition, microbiological objects are segmented by processing the background diffraction field. Gabor-based wavelets extract feature vectors with multi-oriented and multi-scaled Gabor kernels. We apply a rigid graph matching (RGM) algorithm to localize predefined shape features of biological samples. Preliminary experimental and simulation results using sphacelaria alga and tribonema aequale alga microorganisms are presented. To the best of our knowledge, this is the first report on 3D visualization and recognition of microorganisms using on-line digital holography with single-exposure.

Photon counting passive 3D image sensing for automatic target recognition

In this paper, we propose photon counting three-dimensional (3D) passive sensing and object recognition using integral imaging. The application of this approach to 3D automatic target recognition (ATR) is investigated using both linear and nonlinear matched filters. We find there is significant potential of the proposed system for 3D sensing and recognition with a low number of photons. The discrimination capability of the proposed system is quantified in terms of discrimination ratio, Fisher ratio, and receiver operating characteristic (ROC) curves. To the best of our knowledge, this is the first report on photon counting 3D passive sensing and ATR with integral imaging.

Real-time automated 3D sensing, detection, and recognition of dynamic biological micro-organic events

In this paper, we present an overview of three-dimensional (3D) optical imaging techniques for real-time automated sensing, visualization, and recognition of dynamic biological microorganisms. Real time sensing and 3D reconstruction of the dynamic biological microscopic objects can be performed by single-exposure on-line (SEOL) digital holographic microscopy. A coherent 3D microscope-based interferometer is constructed to record digital holograms of dynamic micro biological events. Complex amplitude 3D images of the biological microorganisms are computationally reconstructed at different depths by digital signal processing. Bayesian segmentation algorithms are applied to identify regions of interest for further processing. A number of pattern recognition approaches are addressed to identify and recognize the microorganisms. One uses 3D morphology of the microorganisms by analyzing 3D geometrical shapes which is composed of magnitude and phase. Segmentation, feature extraction, graph matching, feature selection, and training and decision rules are used to recognize the biological microorganisms. In a different approach, 3D technique is used that are tolerant to the varying shapes of the non-rigid biological microorganisms. After segmentation, a number of sampling patches are arbitrarily extracted from the complex amplitudes of the reconstructed 3D biological microorganism. These patches are processed using a number of cost functions and statistical inference theory for the equality of means and equality of variances between the sampling segments. Also, we discuss the possibility of employing computational integral imaging for 3D sensing, visualization, and recognition of biological microorganisms illuminated under incoherent light. Experimental results with several biological microorganisms are presented to illustrate detection, segmentation, and identification of micro biological events.

Three-dimensional identification of biological microorganism using integral imaging

In this paper, we address the identification of biological microorganisms using microscopic integral imaging (II). II senses multi-view directional information of 3D objects illuminated by incoherent light. A micro-lenslet array generates a set of elemental images by projecting a 3D scene onto a detector array. In computational reconstruction of II, 3D volumetric scenes are numerically reconstructed by means of a geometrical ray projection method. The identification of the biological samples is performed using the 3D volume of the reconstructed object. In one approach, the multivariate statistical distribution of the reference sample is measured in 3D space and compared with an unknown input sample by means of statistical discriminant functions. The multivariate empirical cumulative density of the 3D volume image is determined for classification. On the other approach, the graph matching technique is applied to 3D volumetric images with Gabor feature extraction. The reference morphology is identified in unknown input samples using 3D grids. Experimental results are presented for the identification of sphacelaria alga and tribonema aequale alga. We present experimental results for both 3D and 2D imaging. To the best of our knowledge, this is the first report on 3D identification of microorganisms using II.

Real-time outdoor concealed-object detection with passive millimeter wave imaging

Millimeter wave imaging is finding rapid adoption in security applications such as the detection of objects concealed under clothing. A passive imaging system can be realized as a stand-off type sensor that can operate in open spaces, both indoors and outdoors. In this paper, we address real-time outdoor concealed-object detection and segmentation with a radiometric imaging system operating in the W-band. The imaging system is equipped with a dielectric lens and a receiver array operating at around 94 GHz. Images are analyzed by multilevel segmentation to identify a concealed object. Each level of segmentation comprises vector quantization, expectation-maximization, and Bayesian decision making to cluster pixels on the basis of a Gaussian mixture model. In addition, we describe a faster process that adopts only vector quantization for the first level segmentation. Experiments confirm that the proposed methods provide fast and reliable detection and segmentation for a moving human subject carrying a concealed gun.

Three-dimensional color object visualization and recognition using multi-wavelength computational holography

In this paper, we address 3D object visualization and recognition with multi-wavelength digital holography. Color features of 3D objects are obtained by the multiple-wavelengths. Perfect superimposition technique generates reconstructed images of the same size. Statistical pattern recognition techniques: principal component analysis and mixture discriminant analysis analyze multi-spectral information in the reconstructed images. Class-conditional probability density functions are estimated during the training process. Maximum likelihood decision rule categorizes unlabeled images into one of trained-classes. It is shown that a small number of training images is sufficient for the color object classification.

Three-dimensional distortion-tolerant object recognition using photon-counting integral imaging

This paper addresses three-dimensional distortion-tolerant object recognition using photon-counting integral imaging (II). A photon-counting linear discriminant analysis (LDA) is proposed for classification photonlimited images. In the photon-counting LDA, classical irradiance images are used to train the classifier. The unknown objects used to test the classifier are labeled by the number of photons detected. The optimal solution of the Fishers LDA for photon-limited images is found to be different from the case when irradiance values are used. This difference results in one of the merits of a photon-counting LDA, namely that the high dimensionality of the image can be handled without preprocessing. Thus, the singularity problem of the Fishers LDA encountered in the use of irradiance images can be avoided. By using photon-counting II, we build a compact distortiontolerant recognition system that makes use of the multiple-perspective imaging of II to enhance the recognition performance. Experimental and simulation results are presented to classify out-of-plane rotated objects. The performance is analyzed in terms of mean-squared distance (MSD) between the irradiance images. It is shown that a low level of photons is sufficient in the proposed technique.

Real-Time 3-D Sensing, Visualization and Recognition of Dynamic Biological Microorganisms

We introduce optical imaging techniques for three-dimensional (3-D)visualization and identification of microorganisms. Three-dimensional sensing and reconstruction is performed by single-exposure on-line (SEOL)digital holography. A coherent microscope-based Mach-Zehnder interferometer records Fresnel digital holograms of microorganisms. Complex amplitude holographic images are computationally reconstructed at different depths by an inverse Fresnel transformation. For pattern recognition/identification, two approaches are addressed. One is 3-D morphology-based recognition and the other is shape-tolerant 3-D recognition. In the first approach, a series of image recognition techniques is used to analyze 3-D geometrical shapes of microorganisms, which is composed of magnitude and phase distributions. Segmentation, feature extraction, graph matching, feature selection, training, and decision rules are presented. For the second approach, a number of sampling segments are arbitrarily extracted from the reconstructed 3-D biological microorganism. These samples are processed using a number of cost functions and statistical inference theory for the equality of means and equality of variances between the sampling segments. Experimental results with sphacelaria alga, tribonema aequale alga, and polysiphonia alga are presented.

Photon-counting passive 3D image sensing for reconstruction and recognition of partially occluded objects

In this paper, we discuss the reconstruction and the recognition of partially occluded objects using photon counting integral imaging (II). Irradiance scenes are numerically reconstructed for the reference target in three-dimensional (3D) space. Photon counting scenes are estimated for unknown input objects using maximum likelihood estimation (MLE) of Poisson parameter. We propose nonlinear matched filtering in 3D space to recognize partially occluded targets. The recognition performance is substantially improved from the nonlinear matched filtering of elemental images without 3D reconstruction. The discrimination capability is analyzed in terms of Fisher ratio (FR) and receiver operating characteristic (ROC) curves.

DISTANCE ESTIMATION OF CONCEALED OBJECTS WITH STEREOSCOPIC PASSIVE MILLIMETER-WAVE IMAGING

Millimeter waves can be used to detect concealed objects because they can penetrate clothing. Therefore, millimeter wave imaging draws increasing attention in security applications for the detection of objects under clothing. In such applications, it is critical to estimate the distances from objects concealed in open spaces. In this paper, we develop a segmentation-based stereo-matching method based on passive millimeter wave imaging to estimate the longitudinal distance from a concealed object. In this method, the concealed object area is segmented and extracted by a k-means algorithm with splitting initialization, which provides an iterative solution for unsupervised learning. The distance from a concealed object is estimated on the basis of discrepancy between corresponding centers of the segmented objects in the image pair. The conventional stereo-matching equation is modifled according to the scanning properties of the passive millimeter wave imaging system. We experimentally demonstrate that the proposed method can accurately estimate distances from concealed objects.