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This paper focusses on the problem of locating object class exemplars from a large corpus of images using affinity propagation. We use attributed relational graphs to represent groups of local invariant features together with their spatial arrangement. Rather than mining exemplars from the entire graph corpus, we prefer to cluster object specific exemplars. Firstly, we obtain an object specific cluster...
Our proposed sketch-based search and retrieval system in map databases shows the promise that intuitive search can be possible even when searching in nonannotated conventional map data. We plan to perform further work in this area to allow for more intuitive and semantic queries, applications for mobile and ubiquitous computing, and integration to existing map searching applications.
This paper proposes a novel graph spectral method for image registration based on isometric projection, which utilizes purely structure information of the graph to solve a graph matching problem. We demonstrate how two graphs can be compared exactly and robustly through their isometries. To do this, firstly, a new projection method is given, by which the nodes of two graphs are projected to the normalized...
A novel method based on mixed graph structure is proposed for image representation and matching. The mixed graph structure is constructed according to the spatial relation of the regions within an image. This structure does not require redundant information to describe images. An image matching process focuses on evaluating region attributes and relationships contained in the corresponding mixed graph...
Characteristic marks on the cartridge can be viewed as a ??fingerprint?? for identification of a firearm. Sometimes, however, not all information can be obtained from just one image due to the limitations of microscope and the unsmoothed specimen surface in the cartridge case image detection. Image mosaic that refers to the combination of two or more images into a single composite image is precisely...
This paper addresses gradual transition detection which is part of video segmentation problem, and consists in identifying the boundary between consecutive shots. In this work, we propose an approach to cope with gradual transition detection in which we define and use a new dissimilarity measure based on the size of the maximum cardinality matching calculated using a bipartite graph with respect to...
In this paper the human object present in images are identified. K-means clustering algorithm is used for segmenting the images and Connected Component Analysis is used as post-processing step. Initially, templates are created from human posture in idle and various angles. The adjacency matrix is constructed from objects and it is matched with templates. A similarity measure has been proposed for...
In this paper, a global algorithm based on graph cuts theory is proposed to solve the sparse stereo matching problem. The sparse feature points are extracted by the Harris corner detector. The matching problem is transformed into a labeling problem in the sparse graph which can be solved by energy minimization. In this algorithm, the graph is constructed by sparse feature points instead of pixels,...
In this paper, we aim to address the problem of naming faces in feature-length films using video and film script. Different from the state-of-the-art methods on naming faces in the videos, most of which used a local matching between a visible face and one of the names extracted from the local video transcript, we use a global matching between names and faces as it is not easy to obtain enough local...
In this paper, we adopt constrained relaxation for distributed multi-view video coding (DMVC). The novel framework integrates the graph-based segmentation and matching to generate inter-view correlated side information without knowing the camera parameters. Moreover, graph-based representations of multi-view images are incorporated to form more distinctive feature constraints. The sparse data as a...
Density of moles is a strong predictor of malignant melanoma. Some dermatologists advocate periodic full-body scan for high-risk patients. In current practice, physicians compare images taken at different time instances to recognize changes. There is an important clinical need to follow changes in the number of moles and their appearance (size, color, texture, shape) in images from two different times...
Graph matching is an important problem in computer vision. It is used in 2D and 3D object matching and recognition. Despite its importance, there is little literature on learning the parameters that control the graph matching problem, even though learning is important for improving the matching rate, as shown by this and other work. In this paper we show for the first time how to perform parameter...
In this paper, we present a local graph matching based method for tracking cells and cell divisions. This will allow us to estimate the lineages of the cells in a 4D spatio-temporal image stack obtained using fluorescence imaging techniques. We work with plant cells, where the cells are tightly clustered in space and computing correspondences in space and time can be very challenging. The local graph...
In this paper, we present a local-driven semi-supervised learning framework to propagate the labels of the training data (with multi-label) to the unlabeled data. Instead of using each datum as a vertex of graph, we encode each extracted local feature descriptor as a vertex, and then the labels for each vertex from the training data are derived based on the context among different training data, finally...
This paper addresses the problem of establishing correspondences between two sets of visual features using higher-order constraints instead of the unary or pairwise ones used in classical methods. Concretely, the corresponding hypergraph matching problem is formulated as the maximization of a multilinear objective function over all permutations of the features. This function is defined by a tensor...
Multi-biometric systems have many advantages over the uni-biometric systems. However, multi-biometric systems lacking in many respects, such as multimodal systems not only acquire relevant and viable information for fusion, but also acquire some irrelevant and redundant information which are associated to the feature sets or with the match score sets, and this may lead to the resultant performance...
We consider the problem of tracking multiple agents moving amongst obstacles, using multiple cameras. Given an environment with obstacles, and many people moving through it, we construct a separate narrow field of view video for as many people as possible, by stitching together video segments from multiple cameras over time. We employ a novel approach to assign cameras to people as a function of time,...
In this paper, the method of image mosaics based on feature points and color transfer is presented. First, feature points are extracted and matched by sift algorithm, probabilistic screening is used to improve the accuracy of matching. Then, in lalphabeta color model, the algorithm of color transfer is improved to adjust the brightness of the images to solve the problem of different illumination....
In this work we propose a methodology for document reconstruction based on dynamic programming and a modified version of the Prim's algorithm. Firstly, we use polygonal approximation to reduce the complexity of the boundaries and extract features from them. Thereafter, these features are used to feed the LCS dynamic programming algorithm. The scores yielded by the LCS algorithm are then used into...
This paper presents a novel biometric sensor generated evidence fusion of face and palmprint images using wavelet decomposition for personnel identity verification. The approach of biometric image fusion at sensor level refers to a process that fuses multispectral images captured at different resolutions and by different biometric sensors to acquire richer and complementary information to produce...
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