1918361
Canny edge detection enhancement by scale multiplication
The technique of scale multiplication is analyzed in the framework of Canny edge detection. A scale multiplication function is defined as the product of the responses of the detection filter at two scales. Edge maps are constructed as the local maxima by thresholding the scale multiplication results. The detection and localization criteria of the scale multiplication are derived. At a small loss in the detection criterion, the localization criterion can be much improved by scale multiplication. The product of the two criteria for scale multiplication is greater than that for a single scale, which leads to better edge detection performance. Experimental results are presented.
12034638
A block-based inter-band lossless hyperspectral image compressor
We propose a hyperspectral image compressor called BH which considers its input image as being partitioned into square blocks, each lying entirely within a particular band, and compresses one such block at a time by using the following steps: first predict the block from the corresponding block in the previous band, then select a predesigned code based on the prediction errors, and finally encode the predictor coefficient and errors. Apart from giving good compression rates and being fast, BH can provide random access to spatial locations in the image. We hypothesize that BH works well because it accommodates the rapidly changing image brightness that often occurs in hyperspectral images. We also propose an intra-band compressor called LM which is worse than BH, but whose performance helps explain BH's performance.
15953437
A wavelet-based edge detection method by scale multiplication
A wavelet-based multiscale edge detection scheme is presented in this paper. By multiplying the wavelet coefficients at two adjacent scales to magnify significant structures and suppress noise, we determined edges as the local maxima directly in the scale product after an efficient thresholding, instead of first forming the edge maps at several scales and then synthesizing them together, as employed in many multiscale techniques. It is shown that the scale multiplication achieves better results than either of the two scales, especially on the localization performance. Experiments on natural images are compared with the Laplacian of Gaussian and Canny edge detection algorithms.
548723
Noise Reduction for Magnetic Resonance Images via Adaptive Multiscale Products Thresholding
Edge-preserving denoising is of great interest in medical image processing. This paper presents a wavelet-based multiscale products thresholding scheme for noise suppression of magnetic resonance images. A Canny edge detector-like dyadic wavelet transform is employed. This results in the significant features in images evolving with high magnitude across wavelet scales, while noise decays rapidly. To exploit the wavelet interscale dependencies we multiply the adjacent wavelet subbands to enhance edge structures while weakening noise. In the multiscale products, edges can be effectively distinguished from noise. Thereafter, an adaptive threshold is calculated and imposed on the products, instead of on the wavelet coefficients, to identify important features. Experiments show that the proposed scheme better suppresses noise and preserves edges than other wavelet-thresholding denoising methods.
9760560
An edge-guided image interpolation algorithm via directional filtering and data fusion
Preserving edge structures is a challenge to image interpolation algorithms that reconstruct a high-resolution image from a low-resolution counterpart. We propose a new edge-guided nonlinear interpolation technique through directional filtering and data fusion. For a pixel to be interpolated, two observation sets are defined in two orthogonal directions, and each set produces an estimate of the pixel value. These directional estimates, modeled as different noisy measurements of the missing pixel are fused by the linear minimum mean square-error estimation (LMMSE) technique into a more robust estimate, using the statistics of the two observation sets. We also present a simplified version of the LMMSE-based interpolation algorithm to reduce computational cost without sacrificing much the interpolation performance. Experiments show that the new interpolation techniques can preserve edge sharpness and reduce ringing artifacts
16021441
Hybrid inter- and intra-wavelet scale image restoration
This paper exploits both the inter- and intra-scale interdependencies that exist in wavelet coefficients to improve image restoration from noise-corrupted data. Using an over-complete wavelet expansion, we group the wavelet coefficients with the same spatial orientation at several scales. We then apply the linear minimum mean squared-error estimation to smooth noise. This scheme exploits the inter-scale correlation information of wavelet coefficients. To exploit the intra-scale dependencies, we calculate the co-variance matrix of each vector locally using a centered square-shaped window. Experiments show that the proposed hybrid scheme significantly outperforms methods exploiting only the intra- or inter-scale dependencies. The performance of noise removal also depends on wavelet filters. In our experiments a biorthogonal wavelet, which best characterizes the image inter-scale dependencies, achieves the best results.
52862068
Robust face alignment based on local texture classifiers
We propose a robust face alignment algorithm with a novel discriminative local texture model. Different from the conventional descriptive PCA local texture model in ASM, classifiers using LUT-type Haar-like features are trained from a large data set as local texture model. The strong discriminative power of the classifier greatly improves the accuracy and robustness of local searching on faces with expression variation and ambiguous contours. A Bayesian framework is configured for shape parameter optimization and the algorithm is implemented in a hierarchical structure for efficiency. Extensive experiments are reported to show its accuracy and robustness.
18028053
An Experimental Study of Increasing Diversity for Case-Based Diagnosis
Increasing diversity for case-based reasoning (CBR) is an issue that has recently drawn the attention of researchers in the CBR field. Several diversification techniques have been proposed and discussed in the literature. However, whether and to what extent those techniques can bring about benefits to end-users remains in question. In this paper, we report an experiment in applying a diversification technique to a case-based diagnosis tool in a product maintenance domain. The results of this offer some evidence in support of diversification techniques.
8272677
Parameter estimation for MRF stereo
This paper presents a novel approach for estimating parameters for MRF-based stereo algorithms. This approach is based on a new formulation of stereo as a maximum a posterior (MAP) problem, in which both a disparity map and MRF parameters are estimated from the stereo pair itself. We present an iterative algorithm for the MAP estimation that alternates between estimating the parameters while fixing the disparity map and estimating the disparity map while fixing the parameters. The estimated parameters include robust truncation thresholds, for both data and neighborhood terms, as well as a regularization weight. The regularization weight can be either a constant for the whole image, or spatially-varying, depending on local intensity gradients. In the latter case, the weights for intensity gradients are also estimated. Experiments indicate that our approach, as a wrapper for existing stereo algorithms, moves a baseline belief propagation stereo algorithm up six slots in the middlebury rankings.
9731248
A Novel Gabor-LDA Based Face Recognition Method
In this paper, a novel face recognition method based on Gabor-wavelet and linear discriminant analysis (LDA) is proposed. Given training face images, discriminant vectors are computed using LDA. The function of the discriminant vectors is two-fold. First, discriminant vectors are used as a transform matrix, and LDA features are extracted by projecting original intensity images onto discriminant vectors. Second, discriminant vectors are used to select discriminant pixels, the number of which is much less than that of a whole image. Gabor features are extracted only on these discriminant pixels. Then, applying LDA on the Gabor features, one can obtain the Gabor-LDA features. Finally, a combined classifier is formed based on these two types of LDA features. Experimental results show that the proposed method performs better than traditional approaches in terms of both efficiency and accuracy.
15318356
Variance estimation over sliding windows
Capturing characteristics of large data streams has received considerable attention. The constraints in space and time restrict the data stream processing to only one pass (or a small number of passes). Processing data streams over sliding windows make the problem more difficult and challenging. In this paper, we address the problem of maintaining ∈-approximate variance of data streams over sliding windows. To our knowledge, the best existing algorithm requires O(1/∈2 log N) space, though the lower bound for this problem is Ω(1/∈ log N). We propose the first ∈-approximation algorithm to this problem that is optimal in both space and worst case time. Our algorithm requires O(1/∈ log N) space. Furthermore, its running time is O(1) in worst case.