Magnifying micro‐movements from natural video has recently been investigated by several computer vision researchers, due to its impact in numerous applications. In this study, the authors analyse video signals and try to magnify micro‐movements/vibrations to make them visible. These micro‐movements are typically undetectable and cannot be seen by basic human vision. They utilise complex wavelets to analyse sequential frames and detect any minor change in object's spatial position. They magnify some specific complex wavelet frequency bands by a multiplication factor and reconstruct back the video signal after some manipulation and modification to make these micro‐movements seen and observable. They compare their work with recent techniques in micro‐motion magnification (Freeman et al .) and try to show the merits of each technique. These micro‐movements can later be utilised in different applications such as medical imaging, structural engineering, mechanical engineering, physical feature analysis and industrial engineering, as will be seen in their experiments.

The detection of JPEG prior compression has become an essential task in the detection of forgery in image forensics. In this paper we propose a novel DCT implementation technique that can be utilized in the detection of any hacking or tampering of JPEG/DCT compressed images. The proposed approach is based upon recent literature ideas of recompressing JPEG image blocks and detecting if this block has been compressed before or not and how many times. In this paper we proposed a DCT implementation that has a onetime signature on processed coefficients or pixels and can be used as a tool to detect if this block has been compressed before using the proposed implementation or not. Any further processing can be easily detected and identified. The proposed DCT transformation is nonorthogonal and results in a minor amount of error due to this nonorthogonality, however it maintains an excellent tradeoff between compression performance, and transform error. Illustrative examples on several processed images are presented with complexity analysis.

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.

The B-spline mathematical functions have long been utilized for signal representation, zooming and interpolation. However they have not been investigated for different image coding applications. In spite of the fact that B-splines are a semi-orthogonal basis (not full orthogonal) they still can be utilized for redundancy removal and decorrelation maximization prior to any compression scheme. This is due to their distinctive feature of preserving energy in low frequencies. In this paper we propose a novel technique for preprocessing signals/images prior to the decomposition stage in different image coders based on the B-spline decomposition. Mathematical explanation and derivation for the proposed B-spline decomposition basis is presented and analyzed. We derive our theoretic/mathematical justification, through some Eigen analysis calculations, for the enhancement in compression performance achieved with our B-spline based compression approach. We also present a straightforward approach for calculating the B-spline basis in a fast and efficient manner. Extensive simulations have been carried out on the well known SPIHT image coder with and without the proposed correlation removal methodology. Simulation results that demonstrate the effectiveness of the proposed technique are presented.

In this paper, efficient one-dimensional (1-D) fast integer transform algorithm of the DCT matrix for the H.265 standard is proposed. Based on the symmetric property of the integer transform matrix and the matrix operations, along with using the dyadic symmetry modification on the standard matrix, the efficient fast 1-D integer transform algorithm is 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 the proposed algorithms provides transformation quality improvement. With lower complexity and better transformation quality, the proposed fast algorithm is suitable to accelerate the quality-demanding video coding computations.

The natural preserve transform (NPT) has been presented as a tool for fuzzy logic watermarking. This paper describes a new image watermarking technique based on naturalness preserving transform (NPT). The proposed watermarking scheme uses NPT to encode a gray scale watermarking logo image or text, into a host image at any location. This paper presents efficient non-blind and quasi-blind watermark extraction techniques. In the quasi blind case, the extraction algorithm requires only very few information about the original image that is already conveyed by the watermarked image. Moreover, the proposed scheme does not introduce visual quality degradation into the host image. The performance and robustness of the proposed technique are tested by applying common image-processing operations such as cropping, noise degradation, and image compression. A quantitative measure is proposed to objectify performance; under this measure, the proposed technique outperforms most of the recent techniques in most cases.

Automating the process of postmortem identification of individuals using dental records is receiving increased attention. Teeth segmentation from dental radiographic films is an essential step for achieving highly automated postmortem identification. In this paper, we offer a mathematical morphology approach to the problem of teeth segmentation. We also propose a grayscale contrast stretching transformation to improve the performance of teeth segmentation. We compare and contrast our approach with other approaches proposed in the literature based on a theoretical and empirical basis. The results show that in addition to its capability of handling bitewing and periapical dental radiographic views, our approach exhibits the lowest failure rate among all approaches studied.

Today's multimedia applications demand sophisticated compression and classification techniques in order to store, transmit, and retrieve audio-visual information efficiently. Over the last decade, perceptually based image compression methods have been gaining importance. These methods take into account the abilities (and the limitations) of human visual perception (HVP) when performing compression. The upcoming MPEG 7 standard also addresses the need for succinct classification and indexing of visual content for efficient retrieval. However, there has been no research that has attempted to exploit the characteristics of the human visual system to perform both compression and classification jointly. One area of HVP that has unexplored potential for joint compression and classification is spatial frequency perception. Spatial frequency content that is perceived by humans can be characterized in terms of three parameters, which are: 1) magnitude; 2) phase; and 3) orientation. While the magnitude of spatial frequency content has been exploited in several existing image compression techniques, the novel contribution of this paper is its focus on the use of phase coherence for joint compression and classification in the wavelet domain. Specifically, this paper describes a human visual system-based method for measuring the degree to which an image contains coherent (perceptible) phase information, and then exploits that information to provide joint compression and classification. Simulation results that demonstrate the efficiency of this method are presented.

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 off-angle 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.

Over the last decade perceptually based image compression has gained significant importance. This is because it relies on Human Visual Perception (HVP) in measuring the reconstruction quality in the compression process, as humans are the end users for images. Visual data that is perceived by humans can be characterized in terms of three parameters. Magnitude, Phase and Orientation of the spatial frequency content. While existing perceptually based image compression techniques exploits the first parameter, the novel contribution of this paper is its focus on the use of phase data for perceptually based texture compression. In this paper a HVS based texture characterization approach is applied to measure the perceived (by humans) phase coherence in the image. Then images are more compressed after removing the unperceived phase redundancy. Finally subjective tests are performed to measure the reconstruction quality of the proposed compression approach. The proposed compression algorithm has been applied in the JPEG2000 framework. Simulation results that demonstrate the efficiency of the proposed approach are presented.

Wavelets are used in most of the recent image compression standards, because they provide scalability, multi-resolution, and energy concentration. Most of these wavelet image coders employ bi-orthogonal filters. This provides the advantages of selective frequency localization and Perfect Reconstruction (PR), compared to classical orthogonal filters. Bi-orthogonal filters also obviate the need for any phase compensation in the pyramidal filter structure. Lifting has emerged as a powerful method for implementing bi-orthogonal wavelet filters. It exploits the similarity of the filter coefficients between the low pass and high pass filters to provide a higher speed of execution, compared to conventional convolution-based coders. In this paper, we propose a novel lifting-based system that achieves better compression than existing wavelet kernels. The proposed lifting-based system uses bi-orthogonal filters in its architecture, and can operate in the JPEG2000 framework. In the proposed system, the coefficients of either the low-pass or high-pass filter can be modified, without changing the coefficients of the other filter. This is done in a manner that enhances compression while preserving the perfect reconstruction (PR) condition, and increasing the regularity of the modified pair of filters. Extensive simulations have been performed in the JPEG2000 domain to compare the efficiency of our kernels to the lifting wavelet kernels provided by the JPEG2000 standard

The recent MPEG 4 and JPEG 2000 standards address the need for content based coding and manipulation of visual media. The upcoming MPEG 7 standard proposes content descriptors, which succinctly describe the visual content for the purpose of efficient retrieval. This implies that there is an impending need for efficient and effective joint compression and indexing approaches. Several compressed domain indexing techniques have been presented in the recent literature. These are based on the extraction of features from the compression parameters to derive the indices. However, there is little work in the domain of exploring the use of these features to serve the purposes of both compression and indexing. In this paper, we propose a new technique for joint compression and indexing in the wavelet domain. We note that wavelet based compression is used in JPEG 2000 and (for texture coding) in MPEG 4. In the proposed technique, the wavelet decomposed image is first preprocessed to extract features which are then used for compressing the image as well as for deriving the indices. Extensive simulations have been performed in the JPEG 2000 compressed domain to demonstrate the efficiency of the proposed technique.