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In this paper, we present four image descriptors for HEp-2 cell staining patterns classification, including LBP, Gabor, DCT, and a global appearance statistical descriptor. A multiclass boosting SVM algorithm is proposed to integrate these descriptors together: (1) within each boosting round, four multiclass posterior probability SVMs are trained corresponding to four descriptors, and then combined...
Shock filters and related tools, like coherence-enhancing filters, are popular methods for denoising and creating artistic effects. They iteratively apply morphological operators with a constant structuring element. We propose in this article to improve the original shock filtering scheme using smoothed local histograms. Our method exhibits better performance and control of the erosion and dilation...
In this paper we present a novel method for automatic text-line parameter selection for stereo image pairs. The parameters are selected such that correspondence between the same content in a stereo pair is maximized. Automatic parameter selection has been carried out by establishing robust text-line correspondence which is also a contribution of the presented work. The proposed method is applied to...
The performance (in terms of accuracy and speed) of present day document analysis systems, both handwritten and printed, depends on the preprocessing stage. In the existing literature we observe accuracy against speed trade-off. That is, superior binarization accuracy is arrived at the cost of increased processing time. The present paper proposes an improved binarization approach, which after a Pre...
A system is presented for extracting key metrics from fonts used in historical documents. The system identifies important landmarks on a page, such as margins, paragraphs, and lines, and applies frequency analysis techniques to identify relevant sizes. The system was validated by comparing its measurements to the measurements of a human expert on randomly selected samples, and differed on average...
Dimensionality reduction has been regarded as a key step for high-dimensional data processing and analysis. Max-min distance analysis (MMDA) for dimension reduction is proposed to solve the class separation problem and the minimum pairwise distance between class centers is maximized in the low-dimensional subspace. However, the proposed algorithm ignores the distribution of class centers. Despite...
In contrast with Isomap, which learns the low-dimension embedding, and solves problem under the classic Multi-dimension Scaling (MDS) framework, we propose a dimensionality reduction technique, called Orthogonal Isometric Projection (OIP), in this paper. We consider an explicit orthogonal linear projection by capturing the geodesic distance, which is able to handle new data straightforward, and leads...
Many emerging application areas in video and image processing require real-time or faster visual concept detection. Examples include indexing of online user-generated video content and 24/7 archiving of TV broadcasts. The current state-of-the-art in concept detection uses bag-of-visual-words features with computationally heavy kernel-based classifiers. We argue that this approach is not feasible for...
Sparse coding is a widespread framework in signal and image processing. For instance, it has been employed in image/video classification to decompose visual feature vectors, such as local gradient descriptors into a linear combination of few elements of an over-complete basis, which is called dictionary. In order to learn such sparse representations, greedy algorithms like Orthogonal Matching Pursuit...
In this paper, we propose a new form of regularization that is able to utilize the label information of a data set for learning kernels. We first present the definition of extended ideal kernel for both labeled and unlabeled data of multiple classes. Based on this extended ideal kernel, we propose an ideal regularization which is a linear function of the kernel matrix to be learned. The ideal regularization...
Streaming data are any data that are sequentially presented to a system such that future data cannot be accessed. By their nature, streaming data are often large data sets and can quickly outgrow the working memory for a typical computer. Clustering is one of the primary tasks used in the pattern recognition and data mining communities and kernel k-means is a well-studied and popular algorithm. However,...
Fingerprint liveness detection consists in verifying if an input fingerprint image, acquired by a fingerprint verification system, belongs to a genuine user or is an artificial replica. Although several hardware- and software-based approaches have been proposed so far, this issue still remains unsolved due to the very high difficulty in finding effective features for detecting the fingerprint liveness...
An automatic text recognizer needs, in first place, to localize the text in the image the more accurately possible. For this purpose, we present in this paper a robust method for text detection. It is composed of three main stages: a segmentation stage to find character candidates, a connected component analysis based on fast-to-compute but robust features to accept characters and discard non-text...
In this paper we present a novel method for robust stereo matching on document image pairs. The matching itself is performed using an affine-invariant similarity measurement to compensate for perspective distortions, where affine invariance is achieved by normalization using second-order statistics, to finally allow a simple pixel-wise comparison. To handle the inherent high self-similarity of the...
Intuitive and easily interpretable performance measures, repeatability and matching performance, for local feature detectors and descriptors were introduced by Mikolajczyk et al. [10, 9]. They, however, measured performance in a wide baseline setting that does not correspond to the visual object categorisation problem which is a popular application of the detectors and descriptors. The limitation...
The conventional EM algorithms may suffer from the following two problems. First, it may converge to a local maximum. Second, the algorithm may suffer from singularity. A novel Enhanced EM algorithm (EEM) using a realization of maximum-entropy uniform distribution as initial condition is proposed. A global optimal solution can be obtained. In addition, a positive perturbation scheme is adopted to...
As an extension of AVC, SVC provides the ability to adapt to heterogeneous environments. However, transcoding between SVC and AVC becomes necessary due to the existence of legacy AVC-based systems. This paper proposes a low-complexity SVC-to-AVC CGS transcoder in the pixel domain, which achieves approximately the same coding efficiency as the full re-encoding method. The output AVC bitstream modes...
This paper proposes a motion compensation method using sparse representation for video coding technique. A new generation video compression technology known as the HEVC (High Efficiency Video Coding) suggests a novel motion compensation called local intensity compensation. Local intensity compensation represents a current block by a linear combination of some reference blocks. In this study, the weight...
Search optimization algorithms have the challenge of balancing between exploration of the search space (e.g., map locations, image pixels) and exploitation of learned information (e.g., prior knowledge, regions of high fitness). To address this challenge, we present a very basic framework which we call Zombie Survival Optimization (ZSO), a novel swarm intelligence approach modeled after the foraging...
In this paper a new variation of Support Vector Machines (SVM) is introduced. The proposed method is called Subclass Support Vector Machine (SSVM) and makes use of principles from Discriminant Analysis field using subclasses. The major difference over SVM is that it takes into account the existence of subclasses in the classes and tries to minimize the distribution of the samples within each subclass...
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