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We present a robust algorithm that registers one point set to another for nonrigid case. We formulate the problem as a Gaussian mixture model (GMM) density estimation by considering one of the point sets as the GMM centroids and the other as the data points generated by GMM. We displace the centroids and make them register to the data by maximizing the likelihood. To facilitate the process, we introduce...
This paper proposes a robust minutiae based fingerprint image hashing technique. The idea is to incorporate the orientation and descriptor in the minutiae of fingerprint images using SIFT-Harris feature points. A recent shape context based perceptual hashing method has been compared against the proposed technique. Experimentally, the proposed technique has been shown to deliver better robustness against...
The interest point (IP) matching algorithms match the points either locally or spatially. We propose a local-spatial IP matching algorithm usable for articulated human body tracking. The local-based stage finds matched IP pairs of two reference and target IP lists using a local-feature-descriptors-based matching method. Then, the spatial-based stage recovers more matched pairs from the remaining unmatched...
We present an approach for object class learning using a part-based shape categorization in RGB-augmented 3D point clouds captured from cluttered indoor scenes with a Kinect-like sensor. We propose an unsupervised hierarchical learning procedure which allows to symbolically classify shape parts by different specificity levels of detailedness of their surface-structural appearance. Further, a hierarchical...
In this paper we evaluate the influence of the selection of key points and the associated features in the performance of word spotting processes. In general, features can be extracted from a number of characteristic points like corners, contours, skeletons, maxima, minima, crossings, etc. A number of descriptors exist in the literature using different interest point detectors. But the intrinsic variability...
We investigate the task of single-stroke classification into one of three classes (text, figure, or table rule lines). Individual strokes form handwriting structures such as text lines, figures, and tables in combination with peripheral strokes. To classify strokes using local contexts of neighborhood strokes, we propose a composite descriptor that represents in detail the relation between individual...
Effective information retrieval on handwritten document images has always been a challenging task, especially historical ones. In the paper, we propose a coarse-to-fine handwritten word spotting approach based on graph representation. The presented model comprises both the topological and morphological signatures of the handwriting. Skeleton-based graphs with the Shape Context labelled vertexes are...
The timely and accurate identification of plant species is a persistent challenge as pressure from human activity threatens global flora biodiversity. Most existing research on computer based plant species identification has focused on using leaf contour, signature and spectral analysis techniques alongside textural properties of the leaf lamina. However, these global feature based methods often suffer...
Spectral methods have been extensively studied for point pattern matching. In this work, we aim to render the spectral matching algorithm more robust for positional jitter and outliers. We concentrate on the issue of spectral representation for point patterns. A local structural descriptor, called the line graph spectral context, is proposed to characterize the attribute of point patterns, making...
Double Patterning Technology (DPT) conflicts express themselves as odd cycles of spacing between layout shapes. One way of resolving these is by imposing a large spacing constraint between a pair of shapes participant in an odd cycle. However, this may shrink spacing in other parts of the layout and introduce DRC violations or new DPT conflicts. In this work, we model DPT conflict resolution as a...
The work of coloring hand-drawn animation is done by manually specifying and painting each closed region in line drawings. To make this process more efficient, this research creates associations between closed regions in line drawings of adjacent frames, which need to be colored with the same color. Feature values are first computed from the shape of each closed region, and a cost of associating pairs...
This paper addresses the problem of holistic recognition of printed ligatures in Nastalique writing style of the Urdu language. The main difficulty of the recognition process lies in the large number of classes/ligatures (17,000 different possible ligatures in our Urdu text data). This large number of classes not only limits the efficiency (run-time) of the recognition algorithms, but also makes it...
In this paper, we propose a new approach for symbol description. Our method is built based on the combination of shape context of interest points descriptor and sparse representation. More specifically, we first learn a dictionary describing shape context of interest point descriptors. Then, based on information retrieval techniques, we build a vector model for each symbol based on its sparse representation...
In this paper, we propose a fast large-scale signature matching method based on locality sensitive hashing (LSH). Shape Context features are used to describe the structure of signatures. Two stages of hashing are performed to find the nearest neighbours for query signatures. In the first stage, we use M randomly generated hyper planes to separate shape context feature points into different bins, and...
This paper introduces a novel method for image classification based on both texture and depth information. The proposed method uses depth maps in order to improve on the performance of conventional texture-based classification. Depth features are extracted by capturing shapes of depth map slices. The extracted depth features are encoded in the form of sparse representation. Fusion of texture and depth...
We address the problem of estimating the alignment pose between two models using structure-specific local descriptors. Our descriptors are generated using a combination of 2D image data and 3D contextual shape data, resulting in a set of semi-local descriptors containing rich appearance and shape information for both edge and texture structures. This is achieved by defining feature space relations...
In this paper we present certain shape descriptors from the literature that enable real-time contour matching. We propose slight modifications to these descriptors and more importantly provide fast and efficient matching techniques that return distances between shapes in the order of milliseconds. We show that applying these instead of more time consuming matching algorithms matching accuracy remains...
This paper proposes a simple yet effective method to learn the hierarchical object shape model consisting of local contour fragments, which represents a category of shapes in the form of an And-Or tree. This model extends the traditional hierarchical tree structures by introducing the “switch” variables (i.e. the or-nodes) that explicitly specify production rules to capture shape variations. We thus...
Shape context has been proven to be an effective method for both local feature matching and global context description. In this paper, we propose a method to build a glocal shape context descriptor in cluttered images. By using the proposed keypoint centered multiple scale edge detection (KMSED) method, glocal shape context encodes fine-scale edges in the keypoint center region while coarse-scale...
The study of cyclic pursuit as a means to collective behavior in nature and in artificial multi-agent systems is of current interest. Here we examine the nonlinear closed loop dynamics of planar cyclic pursuit based on the constant bearing (CB) strategy. We show that there exists a family of rectilinear relative equilibria that admit nearby periodic orbits in an appropriate low dimensional space found...
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