Theory and Algorithms Commons^™

Fusion Of Visual And Thermal Images Using Genetic Algorithms, Sertan Erkanli, Jiang Li, Ender Oguslu, Shangce Gao (Ed.)

Electrical & Computer Engineering Faculty Publications

No abstract provided.

Sparse Coding For Hyperspectral Images Using Random Dictionary And Soft Thresholding, Ender Oguslu, Khan Iftekharuddin, Jiang Li, Mark Allen Neifeld (Ed.), Amit Ashok (Ed.)

Electrical & Computer Engineering Faculty Publications

Many techniques have been recently developed for classification of hyperspectral images (HSI) including support vector machines (SVMs), neural networks and graph-based methods. To achieve good performances for the classification, a good feature representation of the HSI is essential. A great deal of feature extraction algorithms have been developed such as principal component analysis (PCA) and independent component analysis (ICA). Sparse coding has recently shown state-of-the-art performances in many applications including image classification. In this paper, we present a feature extraction method for HSI data motivated by a recently developed sparse coding based image representation technique. Sparse coding consists of a …

Fast Stochastic Wiener Filter For Super-Resolution Image Restoration With Information Theoretic Visual Quality Assessment, Amr Hussein Yousef, Jiang Li, Mohammad Karim, Mark Allen Neifeld (Ed.), Amit Ashok (Ed.)

Electrical & Computer Engineering Faculty Publications

Super-resolution (SR) refers to reconstructing a single high resolution (HR) image from a set of subsampled, blurred and noisy low resolution (LR) images. The reconstructed image suffers from degradations such as blur, aliasing, photo-detector noise and registration and fusion error. Wiener filter can be used to remove artifacts and enhance the visual quality of the reconstructed images. In this paper, we introduce a new fast stochastic Wiener filter for SR reconstruction and restoration that can be implemented efficiently in the frequency domain. Our derivation depends on the continuous-discrete-continuous (CDC) model that represents most of the degradations encountered during the image-gathering …

Toward Automatic Subpixel Registration Of Unmanned Airborne Vehicle Images, Amr Hussein Yousef, Jiang Li, Mohammad Karim, Mark Allen Neifeld (Ed.), Amit Ashok (Ed.)

Electrical & Computer Engineering Faculty Publications

Many applications require to register images within subpixel accuracy like computer vision especially super-resolution (SR) where the estimated subpixel shifts are very crucial in the reconstruction and restoration of SR images. In our work we have an optical sensor that is mounted on an unmanned airborne vehicle (UAV) and captures a set of images that contain sufficient overlapped area required to reconstruct a SR image. Due to the wind, The UAV may encounter rotational effects such as yaw, pitch and roll which can distort the acquired as well as processed images with shear, tilt or perspective distortions. In this paper …

2d Face Database Diversification Based On 3d Face Modeling, Qun Wang, Jiang Li, Vijayan K. Asari, Mohammad A. Karim, Manuel Filipe Costa (Ed.)

Electrical & Computer Engineering Faculty Publications

Pose and illumination are identified as major problems in 2D face recognition (FR). It has been theoretically proven that the more diversified instances in the training phase, the more accurate and adaptable the FR system appears to be. Based on this common awareness, researchers have developed a large number of photographic face databases to meet the demand for data training purposes. In this paper, we propose a novel scheme for 2D face database diversification based on 3D face modeling and computer graphics techniques, which supplies augmented variances of pose and illumination. Based on the existing samples from identical individuals of …

Automatic Detection Of Aircraft Emergency Landing Sites, Yu-Fei Shen, Zia-Ur Rahman, Dean Krusienski, Jiang Li, Zia-Ur Rahman (Ed.), Stephen E. Reichenbach (Ed.), Mark Allen Neifeld (Ed.)

Electrical & Computer Engineering Faculty Publications

An automatic landing site detection algorithm is proposed for aircraft emergency landing. Emergency landing is an unplanned event in response to emergency situations. If, as is unfortunately usually the case, there is no airstrip or airfield that can be reached by the un-powered aircraft, a crash landing or ditching has to be carried out. Identifying a safe landing site is critical to the survival of passengers and crew. Conventionally, the pilot chooses the landing site visually by looking at the terrain through the cockpit. The success of this vital decision greatly depends on the external environmental factors that can impair …

Bcc Skin Cancer Diagnosis Based On Texture Analysis Techniques, Shao-Hui Chuang, Xiaoyan Sun, Wen-Yu Chang, Gwo-Shing Chen, Adam Huang, Jiang Li, Frederic D. Mckenzie

Electrical & Computer Engineering Faculty Publications

In this paper, we present a texture analysis based method for diagnosing the Basal Cell Carcinoma (BCC) skin cancer using optical images taken from the suspicious skin regions. We first extracted the Run Length Matrix and Haralick texture features from the images and used a feature selection algorithm to identify the most effective feature set for the diagnosis. We then utilized a Multi-Layer Perceptron (MLP) classifier to classify the images to BCC or normal cases. Experiments showed that detecting BCC cancer based on optical images is feasible. The best sensitivity and specificity we achieved on our data set were 94% …

Histogram Analysis Of Adc In Brain Tumor Patients, Debrup Banerjee, Jihong Wang, Jiang Li, Norbert J. Pelc (Ed.), Ehsan Samei (Ed.), Robert M. Nishikawa (Ed.)

Electrical & Computer Engineering Faculty Publications

At various stage of progression, most brain tumors are not homogenous. In this presentation, we retrospectively studied the distribution of ADC values inside tumor volume during the course of tumor treatment and progression for a selective group of patients who underwent an anti-VEGF trial. Complete MRI studies were obtained for this selected group of patients including pre- and multiple follow-up, post-treatment imaging studies. In each MRI imaging study, multiple scan series were obtained as a standard protocol which includes T1, T2, T1-post contrast, FLAIR and DTI derived images (ADC, FA etc.) for each visit. All scan series (T1, T2, FLAIR, …

3d Face Reconstruction From Limited Images Based On Differential Evolution, Qun Wang, Jiang Li, Vijayan K. Asari, Mohammad A. Karim, Andrew G. Tescher (Ed.)

Electrical & Computer Engineering Faculty Publications

3D face modeling has been one of the greatest challenges for researchers in computer graphics for many years. Various methods have been used to model the shape and texture of faces under varying illumination and pose conditions from a single given image. In this paper, we propose a novel method for the 3D face synthesis and reconstruction by using a simple and efficient global optimizer. A 3D-2D matching algorithm which employs the integration of the 3D morphable model (3DMM) and the differential evolution (DE) algorithm is addressed. In 3DMM, the estimation process of fitting shape and texture information into 2D …

Eeg Artifact Removal Using A Wavelet Neural Network, Hoang-Anh T. Nguyen, John Musson, Jiang Li, Frederick Mckenzie, Guangfan Zhang, Roger Xu, Carl Richey, Tom Schnell, Thomas E. Pinelli (Ed.)

Electrical & Computer Engineering Faculty Publications

In this paper we developed a wavelet neural network. (WNN) algorithm for Electroencephalogram (EEG) artifact removal without electrooculographic (EOG) recordings. The algorithm combines the universal approximation characteristics of neural network and the time/frequency property of wavelet. We compared the WNN algorithm with the ICA technique and a wavelet thresholding method, which was realized by using the Stein's unbiased risk estimate (SURE) with an adaptive gradient-based optimal threshold. Experimental results on a driving test data set show that WNN can remove EEG artifacts effectively without diminishing useful EEG information even for very noisy data.

Prediction Of Brain Tumor Progression Using Multiple Histogram Matched Mri Scans, Debrup Banerjee, Loc Tran, Jiang Li, Yuzhong Shen, Frederic Mckenzie, Jihong Wang, Ronald M. Summers (Ed.), Bram Van Ginneken (Ed.)

Electrical & Computer Engineering Faculty Publications

In a recent study [1], we investigated the feasibility of predicting brain tumor progression based on multiple MRI series and we tested our methods on seven patients' MRI images scanned at three consecutive visits A, B and C. Experimental results showed that it is feasible to predict tumor progression from visit A to visit C using a model trained by the information from visit A to visit B. However, the trained model failed when we tried to predict tumor progression from visit B to visit C, though it is clinically more important. Upon a closer look at the MRI scans …

Marine Buoy Detection Using Circular Hough Transform, Loc Tran, Justin Selfridge, Gene Hou, Jiang Li

Electrical & Computer Engineering Faculty Publications

A low cost method for buoy detection in maritime settings is presented using inexpensive digital cameras. In this method, the circular Hough transform is applied to an edge image to circular objects in the image. The center of these circles will signify the locations of each buoy. The known color information of the buoys is also used to enhance the performance by removing false detections. The algorithm is compared to an approach that locates buoys purely on color information. In order to validate the method, we test the approach synthetically and also with real images captured from a small surface …

Enhancement Technique For Aerial Images, Sertan Erkanli, Ahmet Gungor Pakfiliz, Jiang Li

Electrical & Computer Engineering Faculty Publications

Recently, we proposed an enhancement technique for uniformly and non-uniformly illuminated dark images that provides high color accuracy and better balance between the luminance and the contrast in images to improve the visual representations of digital images. In this paper we define an improved version of the proposed algorithm to enhance aerial images in order to reduce the gap between direct observation of a scene and its recorded image.

Parameter Optimization For Image Denoising Based On Block Matching And 3d Collaborative Filtering, Ramu Pedada, Emin Kugu, Jiang Li, Zhanfeng Yue, Yuzhong Shen, Josien P.W. Pluim (Ed.), Benoit M. Dawant (Ed.)

Electrical & Computer Engineering Faculty Publications

Clinical MRI images are generally corrupted by random noise during acquisition with blurred subtle structure features. Many denoising methods have been proposed to remove noise from corrupted images at the expense of distorted structure features. Therefore, there is always compromise between removing noise and preserving structure information for denoising methods. For a specific denoising method, it is crucial to tune it so that the best tradeoff can be obtained. In this paper, we define several cost functions to assess the quality of noise removal and that of structure information preserved in the denoised image. Strength Pareto Evolutionary Algorithm 2 (SPEA2) …

Vegetation Identification Based On Satellite Imagery, Vamsi K.R. Mantena, Ramu Pedada, Srinivas Jakkula, Yuzhong Shen, Jiang Li, Hamid R. Arabnia (Ed.)

Electrical & Computer Engineering Faculty Publications

Automatic vegetation identification plays an important role in many applications including remote sensing and high performance flight simulations. This paper presents a method to automatically identify vegetation based upon satellite imagery. First, we utilize the ISODATA algorithm to cluster pixels in the images where the number of clusters is determined by the algorithm. We then apply morphological operations to the clustered images to smooth the boundaries between clusters and to fill holes inside clusters. After that, we compute six features for each cluster. These six features then go through a feature selection algorithm and three of them are determined to …

Validating Pareto Optimal Operation Parameters Of Polyp Detection Algorithms For Ct Colonography, Jiang Li, Adam Huang, Nicholas Petrick, Jianhua Yao, Ronald M. Summers, Maryellen L. Giger (Ed.), Nico Karssemeijer (Ed.)

Electrical & Computer Engineering Faculty Publications

We evaluated a Pareto front-based multi-objective evolutionary algorithm for optimizing our CT colonography (CTC) computer-aided detection (CAD) system. The system identifies colonic polyps based on curvature and volumetric based features, where a set of thresholds for these features was optimized by the evolutionary algorithm. We utilized a two-fold cross-validation (CV) method to test if the optimized thresholds can be generalized to new data sets. We performed the CV method on 133 patients; each patient had a prone and a supine scan. There were 103 colonoscopically confirmed polyps resulting in 188 positive detections in CTC reading from either the prone or …

Using Pareto Fronts To Evaluate Polyp Detection Algorithms For Ct Colonography, Adam Huang, Jiang Li, Ronald M. Summers, Nicholas Petrick, Amy K. Hara

Electrical & Computer Engineering Faculty Publications

We evaluate and improve an existing curvature-based region growing algorithm for colonic polyp detection for our CT colonography (CTC) computer-aided detection (CAD) system by using Pareto fronts. The performance of a polyp detection algorithm involves two conflicting objectives, minimizing both false negative (FN) and false positive (FP) detection rates. This problem does not produce a single optimal solution but a set of solutions known as a Pareto front. Any solution in a Pareto front can only outperform other solutions in one of the two competing objectives. Using evolutionary algorithms to find the Pareto fronts for multi-objective optimization problems has been …

Exploration Of Computational Methods For Classification Of Movement Intention During Human Voluntary Movement From Single Trial Eeg, Ou Bai, Peter Lin, Sherry Vorbach, Jiang Li, Steve Furlani, Mark Hallett

Electrical & Computer Engineering Faculty Publications

Objective: To explore effective combinations of computational methods for the prediction of movement intention preceding the production of self-paced right and left hand movements from single trial scalp electroencephalogram (EEG).

Methods: Twelve naïve subjects performed self-paced movements consisting of three key strokes with either hand. EEG was recorded from 128 channels. The exploration was performed offline on single trial EEG data. We proposed that a successful computational procedure for classification would consist of spatial filtering, temporal filtering, feature selection, and pattern classification. A systematic investigation was performed with combinations of spatial filtering using principal component analysis (PCA), independent component analysis …

Hybrid Committee Classifier For A Computerized Colonic Polyp Detection System, Jiang Li, Jianhua Yao, Nicholas Petrick, Ronald M. Summers, Amy K. Hara, Joseph M. Reinhardt (Ed.), Josien P.W. Pluim (Ed.)

Electrical & Computer Engineering Faculty Publications

We present a hybrid committee classifier for computer-aided detection (CAD) of colonic polyps in CT colonography (CTC). The classifier involved an ensemble of support vector machines (SVM) and neural networks (NN) for classification, a progressive search algorithm for selecting a set of features used by the SVMs and a floating search algorithm for selecting features used by the NNs. A total of 102 quantitative features were calculated for each polyp candidate found by a prototype CAD system. 3 features were selected for each of 7 SVM classifiers which were then combined to form a committee of SVMs classifier. Similarly, features …