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High-resolution satellite images usually contain a significant amount of shadow cast by elevated objects. Image information is affected by such shadows. Shadows obscure image information partially and/or totally, which leads to interpretation errors. This article aims to develop a complete shadow detection approach using all available bands of a multispectral image. Therefore, the near-infrared band is employed in addition to visible bands while generating a C1C2C3 colour model to achieve a modified C3 index. A comparative study is then applied to the original C1C2C3, hue, saturation, and value colour model and the modified model, to validate the developed index. The results show the superiority of the proposed approach in shadow detection, with overall accuracy around 94%.

High-resolution satellite images usually contain a significant amount of shadow cast by elevated objects. Image information is affected by such shadows. Shadows obscure image information partially and/or totally, which leads to interpretation errors. This article aims to develop a complete shadow detection approach using all available bands of a multispectral image. Therefore, the near-infrared band is employed in addition to visible bands while generating a C1C2C3 colour model to achieve a modified C3 index. A comparative study is then applied to the original C1C2C3, hue, saturation, and value colour model and the modified model, to validate the developed index. The results show the superiority of the proposed approach in shadow detection, with overall accuracy around 94%.

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In this paper, efficient one-dimensional (1-D) fast integer transform algorithms of the DCT matrix for the H.265 standard are proposed. Based on the symmetric property of the integer transform matrix and the matrix operations, which denote the row/column permutations and the matrix decompositions, along with using the dyadic symmetry modification on the standard matrix, the efficient fast 1-D integer transform algorithms are developed. Therefore, the computational complexities of the proposed fast integer transform are smaller than those of the direct method. In addition to computational complexity reduction one of the proposed algorithms provides transformation quality improvement, while the other provides more computational complexity reduction while maintaining almost the same transformation quality.

This paper addresses the problem of developing an automated system for postmortem identification using dental records. The Automated Dental Identification System (ADIS) can be used by law enforcement agencies to locate missing persons using databases of dental x-rays. Currently, this search and identification process is carried out manually, which makes it very time consuming and unreliable. In this paper, we propose an architecture for ADIS, we define the functionality of its components, and we briefly describe some of the techniques used in realizing these components.

In this paper, we describe and analyze the performance of two iris-encoding techniques. The first technique is based on Principle Component Analysis (PCA) encoding method while the second technique is a combination of Principal Component Analysis with Independent Component Analysis (ICA) following it. Both techniques are applied globally. PCA and ICA are two well known methods used to process a variety of data. Though PCA has been used as a preprocessing step that reduces dimensions for obtaining ICA components for iris, it has never been analyzed in depth as an individual encoding method. In practice PCA and ICA are known as methods that extract global and fine features, respectively. It is shown here that when PCA and ICA methods are used to encode iris images, one of the critical steps required to achieve a good performance is compensation for rotation effect. We further study the effect of varying the image resolution level on the performance of the two encoding methods. The major motivation for this study is the cases in practice where images of the same or different irises taken at different distances have to be compared. The performance of encoding techniques is analyzed using the CASIA dataset. The original images are non-ideal and thus require a sequence of preprocessing steps prior to application of encoding methods. We plot a series of Receiver Operating Characteristics (ROCs) to demonstrate various effects on the performance of the iris-based recognition system implementing PCA and ICA encoding techniques.

We describe and analyze the performance of a non-ideal iris recognition system. The system is designed to process non-ideal iris images in two steps: (i) estimation of the gaze direction and (ii) processing and encoding of the rotated iris image. We use two objective functions to estimate the gaze direction: Hamming distance and Daugman’s integro-differential operator and determine an estimated angle by picking the value that optimizes the selected objective function. After the angle is estimated, the off-angle iris image undergoes geometric transformations involving the estimated angle and is further processed as if it were a frontal view image. The encoding technique developed in this work is based on application of the global Independent Component Analysis (ICA) to masked iris images. We use two datasets: CASIA dataset and a special dataset of offangle iris images collected at WVU to verify the performance of the encoding technique and angle estimator, respectively. A series of Receiver Operating Characteristics (ROCs) demonstrates various effects on the performance of the non-ideal iris based recognition system implementing the global ICA encoding.

In this paper, we measure the effect of the lighting direction in facial images on the performance of 2 well-known face recognition algorithms, an appearance based method and a facial feature based method. We collect hundreds/thousands of facial images of subjects with a fixed pose and under different lighting conditions through a unique facial acquisition laboratory designed specifically for this purpose. Then we present a methodology for automatically detecting the lighting direction of different face images based on statistics derived from the image. We also detect if there is any glare regions in some lighting directions. Finally we determine the most reliable lighting direction that will lead to a good quality/high performance facial image from both techniques based on our experiments with the acquired data.

A new signal compression scheme is proposed. It is based on all pass extraction from the received signal's transfer function. The all pass parameters are closely related to a linear prediction polynomial LP of the same order, of the received data. Results have shown that, this new algorithm yields far smaller signal's reconstruction errors when compared with other known methods, for the same compression ratio CR. This algorithm is used in image compression and coding, as follows: First, the image is segmented into blocks and the 2-D DCT of each block, is computed. Next, each 2-D DCT matrix is zigzag scanned to yield a 1-D vector, which is subsequently compressed using the proposed scheme. The image is reconstructed in a reverse manner, using the compressed vectors. The image's compressed parameters is further compressed using schemes like EZW or SPHIT coders. Simulation results have revealed that the proposed compression scheme competes very well with JPEG compression schemes, especially when the images have many details.