We present a novel method to construct low-dimensional linear discriminative subspaces in this paper. Our method is
simple and the calculation cost is little. The new subspace we construct is low dimensional while retaining discriminative
information of original feature space. This means that we can make full use of discriminative information both in
original ranks space and original null space by constructing a low-dimensional subspace and its discriminative matrix.
The performance achieved by our method shows its great potential in resolving image classification problems.
In this paper, a strategy of reconstructing high resolution facial image based on that of low resolution is put
forward. Rather than only relying on low resolution input image, we construct a face representation dictionary
based on training high resolution facial images to compensate for the information difference between low and
high resolution images. This restoration is realized through enrolling a low resolution facial image dictionary
which is acquired through directly downsampling the learned high resolution dictionary. After the representation
coefficient vector of a low resolution input image on low resolution dictionary is obtained through ℓ1-optimization
algorithm, this coefficient can be transplanted into high resolution dictionary directly to restore the high resolution
image corresponding to input face. This approach was validated on the Extended Yale database.
Obtaining high-resolution images from low-resolution ones has been an important topic in computer vision field. This is
a very hard problem since low-resolution images will always lose some information when down sampled from high-resolution
ones. In this article, we proposed a novel image super-resolution method based on the sparse assumption.
Compared to many existing example-based image super-resolution methods, our method is based on single original low-resolution
image, i.e. our method does not need any training examples. Compared to other interpolation based approach,
like nearest neighbor, bilinear or bicubic, our method takes advantage of the inner properties of high-resolution images,
thus obtains a better result. The main approach for our method is based on the recently developed theory called sparse
representation and compress sensing. Many experiments show our method can lead to competitive or even superior
results in quality to images produced by other super-resolution methods, while our method need much fewer additional
information.
Fingerprint matching is the most important part in the field of fingerprint recognition. In this paper, a novel fingerprint
matching algorithm based on the probabilistic graphical model and 3-tree model is proposed. First, minutiae matching
problems are considered as a special point-set matching. Fingerprint minutiae are viewed as random variables. Each
minutia pairs have some probability to be matched. Second, an algorithm is proposed to generate the graphical model
and choose "signal points", which dynamically have corresponding points in other point set. We choose three base
minutiae pairs as signal pairs. Third, the model is converted into a Junction Tree. A 3-tree model is built and the
potentials of other minutiae pairs are calculated through Junction Tree (J.T.) algorithm. Then we translate the matching
problem into the best matching problem of a weighted bipartite graph. Finally, the number of common matching pairs
can be got through maximum flow algorithm. The similarity of two fingerprints is evaluated using the number of
common matching pairs and the maximal posteriori probability. In order to deal with part-matching problems, we use the
smallest convex hull which contains all the matched minutiae. Experiments evaluated on FVC 2004 show both
effectiveness and efficiency of our methods.
The segmentation of fingerprint images plays an important role in fingerprint recognition. A new algorithm based on
Local Fourier Transform (LFT) for the fingerprint segmentation is proposed in this paper. Firstly, we perform the Local
Fourier Transform on image to get eight independent Local Fourier coefficients per pixel. Then, block features are
extracted by calculating the 2nd, 4th, 6th order moments of the local Fourier coefficients of every pixel in the block. After
that, a Fisher linear discriminant classifier is trained for the classification per block. Finally, mathematical morphology
and region boundary smoothing is applied as postprocessing to obtain compact clusters and to reduce the number of
classification errors. The experimental results on the databases of FVC2004 demonstrate the robustness and the
efficiency of the proposed method.
In this paper, we present a novel hybrid image coding scheme for real-time applications of computer screen video transmission. Based on the Mixed Raster Content (MRC) multilayer imaging model, the background picture is compressed with lossy JPEG, and the foreground layer consisting of text and graphics is compressed with a block-based lossless coding algorithm, which integrates shape-based coding, palette-based coding, palette reuse, and LZW algorithm. The key technique is to extract text and graphics from background pictures accurately and with low complexity. Shape primitives, such as lines, rectangles, and isolated pixels with prominent colors, are found to be significant clues for textual and graphical contents. The shape-based coding in our lossless algorithm provides intelligence to extract the computer-generated text and graphics elegantly and easily. Experimental results demonstrate the efficiency and low complexity of our proposed hybrid image coding scheme.
Image segmentation is a typical problem of image analysis. The aim is to partitioning a grayscale image to disjoint regions of coherent illuminance or homogenous texture. There are many segmentation methods of region-based, contour-based or region-contour-joint approaches to deal with different type images. Here we treat color coherent region as a special texture, and we used a new texture descriptor based on local Fourier coefficients histogram adaptive for this extended texture representation. Then a texture-based image segmentation algorithm is proposed. By utilizing the texture features of a region and the K-mean cluster algorithm we obtain a coarse segmentation of an image. Then by refining the region boundary iteratively a final segmentation can be resulted. Because our texture feature is also suitable for gray-coherence region, this algorithm can protect the gray-coherence from over-segmentation. For the same time, we reprove the boundary refinement by replaced with three steps: horizontal refinement, vertical refinement and boundary integrality checking. We also proposed the pre-processing and post-processing method for this algorithm. The segmentation performance is demonstrated on several synthesis texture images and aerial images.
An active origin-based affine matching method was proposed in this paper. The basic idea is, the origin which has two adjustable parameters on the affine coordinate frame and the affine coordinate frame are both active. So the affine transformation between the model affine coordinate frame and the image affine coordinate frame has two adjustable parameters which will be applied to affine matching. Theoretical analysis and experiments demonstrated the proposed method being better than the conventional method.
In this paper, we study the invariant subspace under the fractional linear tranform in the dual space, and give the general solution of trihedrons which are corresponding to a line drawing.
In this paper, we present a new approach for quadric curves based stereo vision. We use the decomposability of the quadric form to establish the correspondence of two quadric curves which are the projections of a planar conic and to globally reconstruct the conic in space. An experimental result is given.
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