Efficient Thermal Image Segmentation through Integration of Nonlinear Enhancement with Unsupervised Active Contour Model

Thermal images are exploited in many areas of pattern recognition applications. Infrared thermal image segmentation can be used for object detection by extracting regions of abnormal temperatures. However, the lack of texture and color information, low signal-to-noise ratio, and blurring effect of thermal images make segmenting infrared heat patterns a challenging task. Furthermore, many segmentation methods that are used in visible imagery may not be suitable for segmenting thermal imagery mainly due to their dissimilar intensity distributions. Thus, a new method is proposed to improve the performance of image segmentation in thermal imagery. The proposed scheme efficiently utilizes nonlinear intensity enhancement technique and Unsupervised Active Contour Models (UACM). The nonlinear intensity enhancement improves visual quality by combining dynamic range compression and contrast enhancement, while the UACM incorporates active contour evolutional function and neural networks. The algorithm is tested on segmenting different objects in thermal images and it is observed that the nonlinear enhancement has significantly improved the segmentation performance

Automatic Building Change Detection in Wide Area Surveillance

We present an automated mechanism that can detect and characterize the building changes by analyzing airborne or satellite imagery. The proposed framework can be categorized into three stages: building detection, boundary extraction and change identification. To detect the buildings, we utilize local phase and local amplitude from monogenic signal to extract building features for addressing issues of varying illumination. Then a support vector machine with Radial basis kernel is used for classification. In the boundary extraction stage, a level-set function with self-organizing map based segmentation method is used to find the building boundary and compute physical area of the building segments. In the last stage, the change of the detected building is identified by computing the area differences of the same building that captured at different times. The experiments are conducted on a set of real-life aerial imagery to show the effectiveness of the proposed method

Continuous face authentication scheme for mobile devices with tracking and liveness detection

We present a novel scheme for continuous face authentication using mobile device cameras that addresses the issue of spoof attacks and attack windows in state-of-the-art approaches. Our scheme authenticates a user based on extracted facial features. However, unlike other schemes that periodically re-authenticate a user, our scheme tracks the authenticated face and only attempts re-authentication when the authenticated face is lost. This allows our scheme to eliminate attack windows that exist in schemes authenticating periodically and immediately recognise impostor usage. We also introduce a robust liveness detection component to our scheme that can detect printed faces and face videos. We describe how the addition of liveness detection enhances the robustness of our scheme against spoof attacks, improving on state-of-the-art approaches that lack this capability. Furthermore, we create the first dataset of facial videos collected from mobile devices during different real-world activities (walking, sitting and standing) such that our results reflect realistic scenarios. Our dataset therefore allows us to give new insight into the impact of user activity on facial recognition. Our dataset also includes spoofed facial videos for liveness testing. We use our dataset alongside two benchmark datasets for our experiments. We show and discuss how our scheme improves on existing continuous face authentication approaches and efficiently enhances device security

Self-organizing approach to learn a level-set function for object segmentation in complex background environments

Boundary extraction for object region segmentation is one of the most challenging tasks in image processing and computer vision areas. The complexity of large variations in the appearance of the object and the background in a typical image causes the performance degradation of existing segmentation algorithms. One of the goals of computer vision studies is to produce algorithms to segment object regions to produce accurate object boundaries that can be utilized in feature extraction and classification. This dissertation research considers the incorporation of prior knowledge of intensity/color of objects of interest within segmentation framework to enhance the performance of object region and boundary extraction of targets in unconstrained environments. The information about intensity/color of object of interest is taken from small patches as seeds that are fed to learn a neural network. The main challenge is accounting for the projection transformation between the limited amount of prior information and the appearance of the real object of interest in the testing data. We address this problem by the use of a Self-organizing Map (SOM) which is an unsupervised learning neural network. The segmentation process is achieved by the construction of a local fitted image level-set cost function, in which, the dynamic variable is a Best Matching Unit (BMU) coming from the SOM map.The proposed method is demonstrated on the PASCAL 2011 challenging dataset, in which, images contain objects with variations of illuminations, shadows, occlusions and clutter. In addition, our method is tested on different types of imagery including thermal, hyperspectral, and medical imagery. Metrics illustrate the effectiveness and accuracy of the proposed algorithm in improving the efficiency of boundary extraction and object region detection. In order to reduce computational time, a lattice Boltzmann Method (LBM) convergence criteria is used along with the proposed self-organized active contour model for producing faster and effective segmentation. The lattice Boltzmann method is utilized to evolve the level-set function rapidly and terminate the evolution of the curve at the most optimum region. Experiments performed on our test datasets show promising results in terms of time and quality of the segmentation when compared to other state-of-the-art learning-based active contour model approaches. Our method is more than 53% faster than other state-of-the-art methods. Research is in progress to employ Time Adaptive Self- Organizing Map (TASOM) for improved segmentation and utilize the parallelization property of the LBM to achieve real-time segmentation

Self-organizing Approach to Learn a Level-set Function for Object Segmentation in Complex Background Environments

Boundary extraction for object region segmentation is one of the most challenging tasks in image processing and computer vision areas. The complexity of large variations in the appearance of the object and the background in a typical image causes the performance degradation of existing segmentation algorithms. One of the goals of computer vision studies is to produce algorithms to segment object regions to produce accurate object boundaries that can be utilized in feature extraction and classification. This dissertation research considers the incorporation of prior knowledge of intensity/color of objects of interest within segmentation framework to enhance the performance of object region and boundary extraction of targets in unconstrained environments. The information about intensity/color of object of interest is taken from small patches as seeds that are fed to learn a neural network. The main challenge is accounting for the projection transformation between the limited amount of prior information and the appearance of the real object of interest in the testing data. We address this problem by the use of a Self-organizing Map (SOM) which is an unsupervised learning neural network. The segmentation process is achieved by the construction of a local fitted image level-set cost function, in which, the dynamic variable is a Best Matching Unit (BMU) coming from the SOM map.The proposed method is demonstrated on the PASCAL 2011 challenging dataset, in which, images contain objects with variations of illuminations, shadows, occlusions and clutter. In addition, our method is tested on different types of imagery including thermal, hyperspectral, and medical imagery. Metrics illustrate the effectiveness and accuracy of the proposed algorithm in improving the efficiency of boundary extraction and object region detection. In order to reduce computational time, a lattice Boltzmann Method (LBM) convergence criteria is used along with the proposed self-organized active contour model for producing faster and effective segmentation. The lattice Boltzmann method is utilized to evolve the level-set function rapidly and terminate the evolution of the curve at the most optimum region. Experiments performed on our test datasets show promising results in terms of time and quality of the segmentation when compared to other state-of-the-art learning-based active contour model approaches. Our method is more than 53% faster than other state-of-the-art methods. Research is in progress to employ Time Adaptive Self- Organizing Map (TASOM) for improved segmentation and utilize the parallelization property of the LBM to achieve real-time segmentation

A Self-Organizing Lattice Boltzmann Active Contour (SOLBAC) Approach for Fast and Robust Object Region Segmentation

In this paper, we propose a self-organized learning based active contour model with a lattice Boltzmann convergence criteria for fast and effective segmentation preserving the precise details of the object\u27s region of interest. A dual self-organizing map approach is being used to learn the object of interest and the background independently in order to guide the active contour to extract the target region. The lattice Boltzmann method is utilized to evolve the level-set function faster and terminate the evolution of the curve at the most optimum region, which segments objects in cluttered environments. Experiments performed on a challenging dataset (PSCAL 2011) show promising results in terms of time and quality of the segmentation and that our method is more than 53% faster than other state-of-the-art learning-based active contour model approaches