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The 2-D Forward-looking Sonar (2D-FLS) is a new technology of sonar signals that increases the performance of underwater exploration of an Autonomous Underwater Vehicle (AUV). Because the AUV is a fully automatic robot that can handle various missions and does not require humans to control its operation, FLS is the best tool for underwater navigation in poor visibility conditions. In this paper, we used an FLS image sequence to estimate the height of the pipeline and create an elevation map containing information about the height in each of the pixels. In our algorithm, we employed the Markov random field to model the elevation map, since elevations of neighboring pixels are more likely to have similar values. Next, the Metropolis Algorithm (MA) is used to find the solution of the maximum a posterori equation.

Autonomous underwater vehicles (AUVs), unmanned underwater vehicles, provide lower operation costs in underwater pipeline inspection than remotely operated vehicles (ROVs), since there is no need for a human operator. However, the navigation algorithm is an important part of the successful deployment of AUVs. In this work, we propose an algorithm that extracts the direction of a pipeline based on the level set method (LSM). This direction shall be used for the automatic navigation of an AUV. The low light and turbid water prohibit the use of an optical camera. Instead, a forward-looking sonar (FLS) was selected to provide input sequence images. Due to speckle noise, the traditional image segmentation algorithm cannot be used. Thus, the LSM is applied to represent the direction of pipelines. Lastly, the post-processing which estimates the direction is used to increase the accuracy. From experiments, our algorithm can extract the pipeline direction with accuracies of as high as 94.33%.

Autonomous underwater vehicles (AUVs), unmanned underwater vehicles, are cheaper to operate than remotely operated vehicles (ROVs), since an AUV does not require a trained human operator to control. As a result, in this paper, we propose the pipeline tracking algorithm using sequences of the forward-looking sonar (FLS) images installed on a preprogram AUV. The FLS is more suitable to be used in the subsurface environment than an optical camera due to the limited visibility and floating particles in the sub-sea surface environment. In our work we first employed the speckle noise reduction to the FLS images. Then, we applied the level-set method (LSM) to extract a pipeline boundary in each frame. Lastly, the post-processing step was designed to link the unconnected segments together to from a complete pipeline. Our algorithm can perform the segmentation and tracking with more than 85% accuracy.

The Aerial Surveillance is an effective method to inspect overhead power transmission line in complicated geographical terrains. However, the practical automatic method is still needed to improve the inspection efficiency. The Canny-Edge detector and Hough transformation are used to extract the power transmission tower straight-line feature. Then, we use object-based image classification to classify the straight-line. The experiments from a set of real-world video sequence have shown the excellent performance of our approach in term of the receiver operating characteristic curve.

In this work, a fingerprint spoof detection method using an extended feature, namely Wavelet-based Local Binary Pattern (Wavelet-LBP) is introduced. Conventional wavelet-based methods calculate wavelet energy of sub-band images as the feature for discrimination while we propose to use Local Binary Pattern (LBP) operation to capture the local appearance of the sub-band images instead. The fingerprint image is firstly decomposed by two-dimensional discrete wavelet transform (2D-DWT), and then LBP is applied on the derived wavelet sub-band images. Furthermore, the extracted features are used to train Support Vector Machine (SVM) classifier to create the model for classifying the fingerprint images into genuine and spoof. Experiments that has been done on Fingerprint Liveness Detection Competition (LivDet) datasets show the improvement of the fingerprint spoof detection by using the proposed feature.

Fingerprint authentication becomes one of the solutions for security problems in smartphone. In the last few years, fingerprint-based authentication has been widely used and implemented to smartphone with a type of small area sensor called “touch sensor”. We present an approach that used phase component of image for matching called Phase-Only Correlation (POC) method that is highly robust against noise, brightness change, and image shifting. The proposed algorithm to solve partial fingerprint matching problem for small area matching against templates of a larger area problem consists of two steps; global search and local search. Global search aims to improve image acquisition, consists of several steps; rotation alignment, translational displacement alignment, and matching score calculation using BLPOC function with precise rotation. In local search step, more accurate match position from the result of the global search will be searched by shifting several pixels around the block. The authentication test using entire dataset of FVC2000 DB2 (set A) gives result of EER 1.7020%.

As a non-invasive angle closure glaucoma diagnosis procedure, Van Herick's slit-lamp Limbal Anterior Chamber Depth Estimation (LACDE) technique is preferably the current standard screening method preliminarily performed at hospitals and eye clinics. However, accuracy of depth estimation is very much dependent on the slit projection distance from the limbus (LD). This study investigated the relation and designed a parametric model to reduce the inconsistencies between results and improving the Van Herick's method accuracy. Additionally, two different types of classifiers such as Support Vector Machine (SVM) and Decision Tree with addition of LD as a predictor were trained and tested for comparison with parametric model. In the end acquired results clearly showed the effectiveness of LD consideration in LACDE, which lead to improvement in the experimental results.

A novel Template Updating system for fingerprint verification systems used in mobile applications is introduced in the paper. Based on the proposed method, the system performance is improved much more than the original one. Not only the FRR (False Reject Rate) but also the small overlap problem caused by the very narrow sensor on the mobile phone are solved. Based on the template updating system, templates are replaced with matched inputs towards a target structure which can expand the coverage of templates with large displacement and rotation. By using the test database, the system performance shows the FRR can be reduced by 79% in comparison with the one without template updating procedure. This system was adopted in practical mobile phones in the commercial market in 2009.

The performance of an fingerprint authentication algorithm can be decreased significantly if the fingerprint image has lots of broken ridges caused by cutline, or the overlap area between the template and input is very small. For the purpose of these specific kinds of verification, a Scale Invariant Feature Transformation (SIFT) feature-based algorithm for fingerprint verification is presented. This approach is not based on traditional minutiae or ridge features. The SIFT keypoints in Gaussian scale-space and the local descriptor for each SIFT keypoint can be extracted by using this method. The verification is done by matching the descriptor, which is invariant to image scale and rotation. In this paper a proper pre-processing is carried out on the fingerprint image instead of using the original fingerprint image. This can make the algorithm adaptive to the variation of the impression condition. Furthermore, a Hough transform adapted to fingerprint verification is performed rather than only using SIFT keypoint descriptor matching. The fusion with minutiae information is also applied for efficiency and accuracy. Two specific databases are captured for experiments. Experiment results of proposed algorithm on specific databases show significant improvement compared with common minutiae-based method. Experiment results on FVC2002 Database show that Equal Error Rate (EER) and False Matching Rate (FMR) of our proposed algorithm can be decreased to about 20% of previous SIFT-based works.

As a global feature of fingerprint patterns, the Orientation Field (OF) plays an important role in fingerprint recognition systems. This paper proposes a fast binary pattern based orientation estimation with nearest-neighbor search, which can reduce the computational complexity greatly. We also propose a classified post processing with adaptive averaging strategy to increase the accuracy of the estimated OF. Experimental results confirm that the proposed method can satisfy the strict requirements of the embedded applications over the conventional approaches.

The on-line spoof fingers detection algorithm presented here adopting ridge tracing features as input to Support Vector Machine (SVM) classifier. The wavelet transforms the coefficients of ridge data along ridges into sequences. The derived information relates to geometrical distribution of pores on ridges. As a result, it is more effective to distinguish pore information between spoof and live fingers. The features of pore information on several ridges are inputted to typical SVM system to evaluate the proposed system. The results with benchmark database show the improvement on spoof finger detection.

Fingerprint authentication with small area sensor "touch sensor" becomes the most promising technology for network user authentication on mobile devices such as smart phones. In this situation, the size of touch sensors becomes so small that the conventional minutia method should be replaced with a new approach. We consider Scale Invariant Feature Transform (SIFT) approach for fingerprint authentication with a touch sensor on smart phones. In this paper, we focus on template expansion on registration in order to accept any small part of the query finger for verification.

The presence of mixed pixels is a recurring problem in extracting accurate land cover information from remote sensing images. To deal with the mixed-pixel problem, we propose to find the land cover map at the resolution higher than the observed remote sensing image. The process to obtain this higher resolution land cover map is called “super resolution land cover mapping (SRLCM).” In this work, we modeled the problem of the SRLCM as an image segmentation problem where the level set method can be applied to find the boarders between land cover classes at the sub-pixel accuracy. Our experimental results show that our proposed approach can significantly improve the classification accuracy over the Maximum likelihood classifier.

The traditional land cover mapping (LCM) algorithms assume that images are perfectly registered. In practice, this assumption may not always be valid since these images may be acquired from different sensor platforms, or at different time which may suffer small variations in platform flight paths. As a result, it is imperative to incorporate the registration error into the land cover mapping algorithm. In this paper, we propose a joint LCM and image registration algorithm under the Markov random field model. Here, the expectation-maximization algorithm is employed to search for the optimum LCM as well as the map parameters. Our result shows that the proposed MRF-Based approach can increase the accuracies of the classification maps as well as the map parameter estimation.

The improved of texture classification accuracy by using the probability weighted combination method of three texture features extraction consist of thE0020 Gray-Level Co-occurrence Matrix (GLCM), Semivariogram Function and Gaussian Markov Random Fields (GMRFs). Five different textures images are used in the experiment. The classifier that use for classify the extracted features in this research is Support Vector Machines (SVMs). The experimental result shows that the average accuracy of the combination method with probability weight up to 95.71%, which is better than the simple combination method about 2%

In this paper, we present a new method to enhance image quality at face region of head and shoulder type image sequence and to shorten processing latency to achieve synchronization between lip movement and voice (lip sync). The new method can significantly improve image quality at face region and reduce frame skip operation during high movement image coding. Improvement is done by allocating more bits budget to the face region, where the centre of perceptual interest point usually located. Total number of bits of dynamically change background region is compressed by applying temporal filter to suppress background noise. We design a new fast rate control based on non-zero coefficient evaluation to shorten compression latency. The experimental result shows the increment of face region's PSNR by around 2 dB, the decreasing of skipping operation around 60 frames during encoding of 382 frames of highly movement video sequence and the advantage of having a very small compression latency around 3 frames which can resolve the lips sync problem.

This paper presents an efficient method for the enhancement of face region image quality. The benefit of it in complex computation, preserving necessary background region information and no blocking effect make this method outperform the current existing method. Furthermore, this method provides adaptive weighting factors and a multi background region approach that allows one to control the effect of the filter as a function of available channel bit rate. An improved face region segmentation method is also addressed in this paper. Simulation results confirmed that the proposed method has significantly improved image quality of face region (up to 2.65 dB) and reduced a large number of skipped frames in comparison with ITU-T H.263+ video coding test model 10 (TMN10).