44391380
Bi-directional gradient labeling and registration for gray-scale image segmentation
Watershed is one of the commonly used methods for image segmentation. In this paper, we introduce a new segmentation scheme based on bi-directional labeling and registration and prove that its segmentation performance is equivalent to that of conventional watershed but it is much more computational efficient The bi-directional labeling and registration scheme, which will be referred to as BIDS, involves only linear scans of image pixels. It uses one dimensional operations instead of queues while traditional segmentation algorithms are two dimensional problems. BIDS also provides unique labels for each homogeneous regions. In addition to achieving the same segmentation results as conventional watershed, BIDS is four times less computationally complex than the conventional watersheds by immersion.
1661896
Multiscale Fragile Watermarking Based on the Gaussian Mixture Model
In this paper, a new multiscale fragile watermarking scheme based on the Gaussian mixture model (GMM) is presented. First, a GMM is developed to describe the statistical characteristics of images in the wavelet domain and an expectation-maximization algorithm is employed to identify GMM model parameters. With wavelet multiscale subspaces being divided into watermarking blocks, the GMM model parameters of different watermarking blocks are adjusted to form certain relationships, which are employed for the presented new fragile watermarking scheme for authentication. An optimal watermark embedding method is developed to achieve minimum watermarking distortion. A secret embedding key is designed to securely embed the fragile watermarks so that the new method is robust to counterfeiting, even when the malicious attackers are fully aware of the watermark embedding algorithm. It is shown that the presented new method can securely embed a message bit stream, such as personal signatures or copyright logos, into a host image as fragile watermarks. Compared with conventional fragile watermark techniques, this new statistical model based method modifies only a small amount of image data such that the distortion on the host image is imperceptible. Meanwhile, with the embedded message bits spreading over the entire image area through the statistical model, the new method can detect and localize image tampering. Besides, the new multiscale implementation of fragile watermarks based on the presented method can help distinguish some normal image operations such as JPEG compression from malicious image attacks and, thus, can be used for semi-fragile watermarking
14579845
Texture image retrieval based on a Gaussian mixture model and similarity measure using a Kullback divergence
In a content-based image retrieval (CBIR) system, indexing feature vectors and the similarity measure between feature vectors are two key factors for retrieval performance. We present a new CBIR system with statistical-model based image feature extraction in the wavelet domain and a Kullback divergence based similarity measure. A two component Gaussian mixture model (GMM) in the wavelet domain is employed and the model parameters are used to form features for image indexing. A new Kullback divergence based similarity measure is then presented for image retrieval. The experimental results demonstrate that the similarity measure based on the Kullback divergence is more effective than conventional similarity measures, such as the city-block distance and the Euclidean distance. It is shown that the new CBIR system, with the combination of the GMM and the new Kullback divergence based similarity measure, outperforms most other methods in retrieval performance for texture images, while keeping a comparable level of computational complexity.
18569940
Segmentation of bright targets using wavelets and adaptive thresholding
A general systematic method for the detection and segmentation of bright targets is developed. We use the term "bright target" to mean a connected, cohesive object which has an average intensity distribution above that of the rest of the image. We develop an analytic model for the segmentation of targets, which uses a novel multiresolution analysis in concert with a Bayes classifier to identify the possible target areas. A method is developed which adaptively chooses thresholds to segment targets from background, by using a multiscale analysis of the image probability density function (PDF). A performance analysis based on a Gaussian distribution model is used to show that the obtained adaptive threshold is often close to the Bayes threshold. The method has proven robust even when the image distribution is unknown. Examples are presented to demonstrate the efficiency of the technique on a variety of targets.
5541899
Automatic identification of digital video based on shot-level sequence matching
To locate a video clip in large collections is very important for retrieval applications, especially for digital rights management. In this paper, we present a novel technique for automatic identification of digital video. This new algorithm is based on dynamic programming that fully uses the temporal dimension to measure the similarity between two video sequences. A normalized chromaticity histogram is used as a feature which is illumination-invariant. Dynamic programming is applied on shot-level to find the optimal nonlinear mapping between video sequences. Two new normalized distance measures are presented for video sequence matching. One measure is based on the normalization of the optimal path found by dynamic programming. The other measure combines both the visual features and the temporal information. Experimental results show that the shot-level approach is robust to frame rate conversion, color correction, and compressions. The proposed distance measures are suitable for variable-length comparisons.
5695951
Video Event Detection using ICA Mixture Hidden Markov Models
In this paper, a framework that combines feature extraction, model learning, and likelihood computation, is presented for video event detection. First, the independent component analysis (ICA) is applied to the raw feature space to extract the spatial features. Then, a framework based on ICA mixture hidden Markov models (ICAMHMM) is used to exploit the spatial and temporal characteristics of the training data. After the model is learnt, the likelihood for a given video sequence is computed and then used to classify the video into a semantic event. Golf video sequences are used for simulations. The results show that the proposed method can effectively detect semantic video events.
8280671
Information-Based Classification by Aggregating Emerging Patterns
Emerging patterns (EPs) are knowledge patterns capturing contrasts between data classes. In this paper, we propose an information-based approach for classification by aggregating emerging patterns. The constraint-based EP mining algorithm enables the system to learn from large-volume and high-dimensional data; the new approach for selecting representative EPs and efficient algorithm for finding the EPs renders the system high predictive accuracy and short classification time. Experiments on many benchmark datasets show that the resulting classifiers have good overall predictive accuracy, and are often also superior to other state-of-the-art classification systems such as C4.5, CBA and LB.
1561680
Design of full band IIR digital differentiators
This paper presents an efficient method for designing full band IIR digital differentiators in the complex Chebyshev sense. The proposed method is based on the formulation of a generalized eigenvalue problem by using the Remez multiple exchange algorithm. Therefore, a set of filter coefficients can be easily obtained by solving the eigenvalue problem to find the absolute minimum eigenvalue, and then the complex Chebyshev approximation is attained through a few iterations starting from a given initial guess. The proposed algorithm is computationally efficient because it not only retains the speed inherent in the Remez exchange algorithm, but also simplifies the interpolation step. One design example is presented and compared with the conventional methods. It can be seen that the design results obtained by using the proposed method are better than that in the conventional methods.
10484270
Obligatory activator-polymerase addition order at promoters
The kinetics of open complex formation were measured by migration retardation assay and DNase I footprinting at the activator-dependent promoters ara P1, lac P1 and gal P1. In each case, the rate of open complex formation was significantly faster if the activator, AraC for ara and CAP for lac and gal, had been added before RNA polymerase. The results indicate that complexes of transcriptional activators, RNA polymerase and promoter can exist in two states, one which can form open complexes rapidly and one which cannot.
3561000
Extended whole program paths
We describe the design, generation and compression of the extended whole program path (eWPP) representation that not only captures the control flow history of a program execution but also its data dependence history. This representation is motivated by the observation that typically a significant fraction of data dependence history can be recovered from the control flow trace. To capture the remainder of the data dependence history we introduce disambiguation checks in the program whose control flow signatures capture the results of the checks. The resulting extended control flow trace enables the recovery of otherwise unrecoverable data dependences. The code for the checks is designed to minimize the increase in the program execution time and the extended control flow trace size when compared to directly collecting control flow and dependence traces. Our experiments show that compressed eWPPs are only 4% of the size of combined compressed control flow and dependence traces and their collection requires 20% more runtime overhead than overhead required for directly collecting the control flow and dependence traces.
8572893
Access-Mode Predictions for Low-Power Cache Design
An access-mode prediction technique based on cache hit and miss speculation for cache design-achieves minimal energy consumption. Using this method, cache accesses can be adaptively switched between the way-prediction and the phased accessing modes.