Abstract

Texture mapping algorithms use mesh parameterization methods to find an optimal map for the vertices of a 3D model in texture space. These techniques vary in the properties they try to optimize such as stretch and skewness of the texture when mapped onto the surface. While most of them do well in terms of quality, they tend to be computationally intensive for large mesh models, which limits their use in interactive applications. We propose a greedy alternative that is significantly faster than current algorithms and achieves comparable quality. We use a priority queue to store polygons and use tangential vectors to guide the texture over the surface. Our algorithm is simple to implement and can texture over a million polygons per second on a typical desktop.The algorithm does not impose any constraints on the meshtopology and we do not require the model to be cut into patches before texturing. Stretch and distortion measures are stable across models and are comparable to current algorithms. We also propose a method to generate self tileable textures for use in conjunction with our texture mapping algorithm. We present qualitative and quantitative results in comparison with several other texture mapping algorithms.The efficiency and robustness of our algorithm makes it use-ful in interactive modeling applications and texture mapping large mesh models such as heritage monuments.

Abstract

Algorithms for detection of repeating patterns in images of-ten assume that the repetition is regular and highly similar across the instances. Approximate repetitions are also of interest in many domains such as hand carved sculptures, wall decorations, groups of natural objects, etc. Detection of such repetitive structures can help in applications such as image retrieval, image in painting, 3D reconstruction, etc. In this work, we look at a specific class of approximate repetitions:those in images of hand carved relief structures. We present a robust hierarchical method for detecting such repetitions.Given a single image with reliefs, our algorithm finds dense matches of local features across the image at various scales.The matching features are then grouped based on their ge-ometric configuration to find repeating elements. We also propose a method to group the repeating elements to segment the repetitive patterns in an image. In relief images,foreground and background have nearly the same texture,and matching of a single feature would not provide reliable evidence of repetition. Our grouping algorithm integrates evidences of repetition to reliably find repeating patterns.Input image is processed on a scale-space pyramid to effectively detect all possible repetitions at different scales. Our method has been tested on images with large varieties of complex repetitive patterns and the qualitative results show the robustness of our approach.

Abstract

This paper addresses the issue of moving object tracking from video. Two steps of processing are PCA reconstruction for classification the area and Optical flow-based tracking of feature points. Optimal flow-based tracking algorithm predicts and restores the feature of area in the real time object tracking. The proposed system is computationally more efficient for learning of dynamic object and object tracking to shape information. PCA based method compare to other methods is powerful algorithm in object detection and the Optical flowbased tracking algorithm could be used for decreasing the false point tracking and remove the error of the motion tracking in real time system tracking. The proposed algorithm tracks a set of feature points; during the tracking, feature is restored inside the predicted region. One important contribution of this work is to provide a restoration process for missing feature points, which occurs at almost every frame under realistic, noisy environment.

Abstract

This paper proposes a hash-based indexing method to speed up fingerprint identification in large databases. For each minutia, its local neighborhood information is com-puted with features defined based on the geometric arrangements of its neighboring minutiae points. The features used are provably invariant to translation, rotation, scale and shear. These features are used to create an affine invariant local descriptor, called an arrangement vector, for each minutia. To account for missing and spurious minutiae, we consider subsets of the neighboring minutiae and hashes of these structures are used in the indexing process. The primary goal of the work is to explore the effectiveness of affine invariant features for representing local minutiae structures. Experiments on FVC 2002 databases show that representation is quite effective even though the technique performs slightly below the state-of-the-art methods. One could use the representation in combination with other techniques to improve the overall performance.

Abstract

Biometric identification often involves explicit comparison of a probe against each template stored in a database.This process becomes extremely time-consuming as the size of the database increases. Filtering approaches use a lightweight comparison to select a smaller set of candidate templates from the database for explicit comparison. However,most existing filtering schemes use specific features that are hand-crafted for the biometric trait at each stage of the filtering. In this work, we explore the effectiveness of weak features in a cascade for filtering fingerprint databases. We start with a set of potential indexing features computed from minutiae triplets and minutiae quadruplets. Each stage of filtering consists of projecting the probe onto a specific line and the removal of database samples outside a window around the probe. The critical problem in this process is the selection of lines for projection at each stage of the filtering. We show that by using a set of random lines and the proposed fitness function, one can achieve better results that optimization methods such as PCA or LDA. Experimental results show that using an ensemble of projections we can reduce the penetration to26%at a hit rate of99%. As each stage of the cascade is extremely fast, and filtering is progressive along the cascade, one can terminate the cascade at any point to achieve the desired performance. One can also combine this method with other indexing methods to improve the overall accuracy and speed. We present de-tailed experimental results on various aspects of the process on the FVC 2002 dataset.

Abstract

3D textures are often described by parametric functions for each pixel, that models the variation in its appearance with respect to varying lighting direction. However, parametric models such as Polynomial Texture Maps (PTMs) tend to smoothen the changes in appearance. We propose a technique to effectively model natural material surfaces and their interactions with changing light conditions. We show that the direct and global components of the image have different nature, and when modeled separately, leads to a more accurate and compact model of the 3D surface texture. For a given lighting position, both components are computed separately and combined to render a new image. This method models sharp shadows and specularities, while preserving the structural relief and surface color. Thus rendered image have enhanced photorealism as compared to images rendered by existing single pixel models such as PTMs.

Abstract

We propose a novel approach to enhance the finger­print image and extract features such as directional fields, minutiae and singular points reliably using a Hierarchical Markov Random Field Model. Unlike traditional finger­print enhancement techniques, we use previously learned prior patterns from a set of clean fingerprints to restore a noisy one. We are able to recover the ridge and valley structure from degraded and noisy fingerprint images by formulating it as a hierarchical interconnected MRF that processes the information at multiple resolutions. The top layer incorporates the compatibility between an observed degraded fingerprint patch and prior training patterns in addition to ridge continuity across neighboring patches. A second layer accounts for spatial smoothness of the orienta­tion field and its discontinuity at the singularities. Further layers could be used for incorporating higher level priors such as the class of the fingerprint. The strength of the pro­posed approach lies in its flexibility to model possible vari­ations in fingerprint images as patches and from its abil­ity to incorporate contextual information at various resolu­tions. Experimental results (both quantitative and qualita­tive) clearly demonstrate the effectiveness of this approach.

Abstract

This paper deals with extraction of fingerprint features directly from gray scale images by the method of ridge trac­ing. While doing so, we make substantial use of contextual information gathered during the tracing process. Narrow bandpass based filtering methods for fingerprint image en­hancement are extremely robust as noisy regions do not af­fect the result of cleaner ones. However, these method often generate artifacts whenever the underlying image does not fit the filter model, which may be due to the presence of noise and singularities. The proposed method allows us to use the contextual information to better handle such noisy regions. Moreover, the various parameters used in the al­gorithm have been made adaptive in order to circumvent human supervision. The experimental results from our al­gorithm have been compared with those from Gabor based filtering and feature extraction, as well as with the origi­nal ridge tracing work from Maio and Maltoni [11]. The results clearly indicate that the proposed approach makes ridge tracing more robust to noise and makes the extracted features more reliable.

Abstract

In order to incorporate various writing styles orfonts in a character recognizer, it is critical that a large amount of labeled data is available, which is difficult to obtain. In this work, we present a semi-supervised SVM based framework that can incorporate the unlabeled data for improvement of recognition performance. Existing semi-supervised learning methods for SVMs work well only for two-class problems. We propose a method to extend this to large-class problems by incorporating a participation termin to the optimization process. The proposed system uses a Decision Directed Acyclic Graphs (DDAG) of SVM classifiers,which have proven to be very effective for such recognition problems. We present experimental results on three different digits dataset with varying complexity, as well as additional multi-class datasets from the UCI repository for comparison with existing approaches. In addition we show that approximate annotations at the word or sentence level can be used for evaluation as well as active learning to further improve there cognition results.

Abstract

Biometric identification often involves explicit com-parison of a probe template against each template stored in a database. This approach becomes extremely time-consuming as the size of the database increases. Filtering approaches use alight-weight comparison to reduce the database to smaller set of candidates for explicit comparison. However, most existing filtering schemes use specific features that are hand-crafted for the biometric trait at each stage of the filtering. In this work,we show that a cascade of simple linear projections on random lines can achieve significant levels of filtering. Each stage offiltering consists of projecting the probe onto a specific line and removal of database samples outside a window around the probe.The approach provides a way of automatic generation of filters and avoids the need of developing specific features for different biometric traits. The method also provides us with a variety of parameters such as the projection lines, the number and order of projections, and the window sizes to customize the filtering process to a specific application. Experimental results show that using an ensemble of projections reduce the search space by 60%without increasing the false negative identification rate.