Fusion of Visual and Thermal Images Using Genetic Algorithms

Demands for reliable person identification systems have increased significantly due to highly security risks in our daily life. Recently, person identification systems are built upon the biometrics techniques such as face recognition. Although face recognition systems have reached a certain level of maturity, their accomplishments in practical applications are restricted by some challenges, such as illumination variations. Current visual face recognition systems perform relatively well under controlled illumination conditions while thermal face recognition systems are more advantageous for detecting disguised faces or when there is no illumination control. A hybrid system utilizing both visual and thermal images for face recognition will be beneficial. The overall goal of this research is to develop computational methods that improve image quality by fusing visual and thermal face images. First, three novel algorithms were proposed to enhance visual face images. In those techniques, specifical nonlinear image transfer functions were developed and parameters associated with the functions were determined by image statistics, making the algorithms adaptive. Second, methods were developed for registering the enhanced visual images to their corresponding thermal images. Landmarks in the images were first detected and a subset of those landmarks were selected to compute a transformation matrix for the registration. Finally, A Genetic algorithm was proposed to fuse the registered visual and thermal images. Experimental results showed that image quality can be significantly improved using the proposed framework

Fusion of Visual and Thermal Images Using Genetic Algorithms

Biometric technologies such as fingerprint, hand geometry, face and iris recognition are widely used to identify a person's identity. The face recognition system is currently one of the most important biometric technologies, which identifies a person by comparing individually acquired face images with a set of pre-stored face templates in a database

Detection of Seagrass Scars Using Sparse Coding and Morphological Filter

We present a two-step algorithm for the detection of seafloor propeller seagrass scars in shallow water using panchromatic images. The first step is to classify image pixels into scar and non-scar categories based on a sparse coding algorithm. The first step produces an initial scar map in which false positive scar pixels may be present. In the second step, local orientation of each detected scar pixel is computed using the morphological directional profile, which is defined as outputs of a directional filter with a varying orientation parameter. The profile is then utilized to eliminate false positives and generate the final scar detection map. We applied the algorithm to a panchromatic image captured at the Deckle Beach, Florida using the WorldView2 orbiting satellite. Our results show that the proposed method can achieve \u3e90% accuracy on the detection of seagrass scars

Enhancement Technique for Aerial Images

Abstract—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. I