15918446
Gesture Recognition Using Quadratic Curves
This paper presents a novel method for human gesture recognition based on quadratic curves. Firstly, face and hands in the images are extracted by skin color and their central points are kept tracked by a modified Greedy Exchange algorithm. Then in each trajectory, the central points are fitted into a quadratic curve and 6 invariants from this quadratic curve are computed. Following these computations, a gesture feature vector composed of 6n such invariants is constructed, where n is the number of the trajectories in this gesture. Lastly, the gesture models are learnt from the feature vectors of gesture samples and an input gesture is recognized by comparing its feature vector with those of gesture models. In this gesture recognition method, the computational cost is low because the gesture duration does not need to be considered and only simple curvilinear integral and matrix computation are involved. Experiments on hip-hop dance show that our method can achieve a recognition rate as high as 97.65% on a database of 16 different gestures, each performed by 8 different people for 8 different times.
14594559
Detecting Critical Configuration of Six Points
When space points and camera optical center lie on a twisted cubic, no matter how many corresponding pairs there are from space points to their image points, camera projection matrix cannot be uniquely determined, in other words, the configuration of camera and space points in this case is critical for camera parameter estimation. In practice, it is important to detect this critical configuration before the estimated camera parameters are used. In this work, a new method is introduced to detect this critical configuration, which is based on an effective criterion function constructed from an invariant relationship between six space points and their corresponding image points. The advantage of this method is that no explicit computation on camera projection matrix or optical center is needed. Simulations show it is quite robust and stable against noise. Experiments on real data show the criterion function can be faithfully trusted for camera parameter estimation.
3091056
Controllable and Progressive Edge Clustering for Large Networks
Node-link diagrams are widely used in information visualization to show relationships among data. However, when the size of data becomes very large, node-link diagrams will become cluttered and visually confusing for users. In this paper, we propose a novel controllable edge clustering method based on Delaunay triangulation to reduce visual clutter for node-link diagrams. Our method uses curves instead of straight lines to represent links and these curves can be grouped together according to their relative positions and directions. We further introduce progressive edge clustering to achieve continuous level-of-details for large networks.
36134180
Linear Algorithm and Hexagonal Search Based Two-Pass Algorithm for Motion Estimation
This paper presents a novel two-pass algorithm constituted by Linear Hashtable Motion Estimation Algorithm (LHMEA) and Hexagonal Search (HEXBS) for block base motion compensation. On the basis of research from previous algorithms, especially an on-the-edge motion estimation algorithm called hexagonal search (HEXBS), we propose the LHMEA and the Two-Pass Algorithm (TPA). We introduced hashtable into video compression. In this paper we employ LHMEA for the first-pass search in all the Macroblocks (MB) in the picture. Motion Vectors (MV) are then generated from the first-pass and are used as predictors for second-pass HEXBS motion estimation, which only searches a small number of MBs. The evaluation of the algorithm considers the three important metrics being time, compression rate and PSNR. The performance of the algorithm is evaluated by using standard video sequences and the results are compared to current algorithms. Experimental results show that the proposed algorithm can offer the same compression rate as the Full Search. LHMEA with TPA has significant improvement on HEXBS and shows a direction for improving other fast motion estimation algorithms, for example Diamond Search.
16588936
Identifying important concepts from medical documents
Automated medical concept recognition is important for medical informatics such as medical document retrieval and text mining research. In this paper, we present a software tool called keyphrase identification program (KIP) for identifying topical concepts from medical documents. KIP combines two functions: noun phrase extraction and keyphrase identification. The former automatically extracts noun phrases from medical literature as keyphrase candidates. The latter assigns weights to extracted noun phrases for a medical document based on how important they are to that document and how domain specific they are in the medical domain. The experimental results show that our noun phrase extractor is effective in identifying noun phrases from medical documents, so is the keyphrase extractor in identifying important medical conceptual terms. They both performed better than the systems they were compared to.
28207994
Using Clustering Approaches to Open-Domain Question Answering
This paper presents two novel clustering approaches and their application to open-domain question answering. The One-Sentence-Multi-Topicclustering approach is first presented, which clusters sentences to improve the language model for retrieving sentences. Second, regarding each cluster in the results for One-Sentence-Multi-Topicclustering as aligned sentences, we present a pattern-similarity-based clustering approach that automatically learns syntactic answer patterns to answer selection through verticaland horizontal clustering. Our experiments on Chinese question answering demonstrates that One-Sentence-Multi-Topicclustering is much better than K-Means and is comparable to PLSI when used in sentence clustering of question answering. Similarly, the pattern-similarity-based clustering also proved to be efficient in learning syntactic answer patterns, the absolute improvement in syntactic pattern-based answer extraction over retrieval-based answer extraction is about 9%.
15215532
Estimating Answer Sizes for XML Queries
Estimating the sizes of query results, and intermediate results, is crucial to many aspects of query processing. In particular, it is necessary for effective query optimization. Even at the user level, predictions of the total result size can be valuable in "next-step" decisions, such as query refinement. This paper proposes a technique to obtain query result size estimates effectively in an XML database.Queries in XML frequently specify structural patterns, requiring specific relationships between selected elements. Whereas traditional techniques can estimate the number of nodes (XML elements) that will satisfy a node-specific predicate in the query pattern, such estimates cannot easily be combined to provide estimates for the entire query pattern, since element occurrences are expected to have high correlation.We propose a solution based on a novel histogram encoding of element occurrence position. With such position histograms, we are able to obtain estimates of sizes for complex pattern queries, as well as for simpler intermediate patterns that may be evaluated in alternative query plans, by means of a position histogram join (pH-join) algorithm that we introduce. We extend our technique to exploit schema information regarding allowable structure (the no-overlap property) through the use of a coverage histogram.We present an extensive experimental evaluation using several XML data sets, both real and synthetic, with a variety of queries. Our results demonstrate that accurate and robust estimates can be achieved, with limited space, and at a miniscule computational cost. These techniques have been implemented in the context of the TIMBER native XML database [22] at the University of Michigan.