62242004
Object boundary edge selection using normal direction derivatives of a contour in a complex scene
Nguyen proposed a method [H.T., Nguyen et al., 2002] for tracking a nonparameterized object (subject) contour in a single video stream. Nguyen's approach combined outputs of two steps: creating a predicted contour and removing background edges. We propose a method to increase object tracking accuracy by improving the background edge removal process. Nguyen's background edge removal method of leaving many irrelevant edges is subject to inaccurate contour tracking. Our accurate tracking is based on reducing affects from irrelevant edges by selecting the boundary edge only. We select high-valued edge pixels of average image intensity gradients in the contour normal direction. Our experimental results show that our tracking approach is robust enough to handle a complex-textured scene.
34356748
Model-Based Human Motion Capture from Monocular Video Sequences
Generating motion and capturing motion of an articulated body for computer animation is an expensive and time-consuming task. Conventionally, animators manually generate intermediate frames between key frames, but this task is very labor-intensive. This paper presents a model-based singularity-free automatic-initialization approach to capturing human motion from widely-available, static background monocular video sequences. A 3D human body model is built and projected on a 2D projection plane to find the best fit with the foreground image silhouette. We convert the human motion capture problem into two types of parameter optimization problems: static optimization and dynamic optimization. First, we determine each model body configuration using static optimizations for every input image. Then, to obtain better description of motion, the results from all static optimizations are fed into a dynamic optimization process where the entire sequence of motion is considered for the user-specified motion. The user-specified motion is defined by the user and the final form of the motion they want. A cost function for static optimization is used to estimate the degree of overlapping between the foreground input image silhouette and a projected 3D model body silhouette. The overlapping is computed using computational geometry by converting a set of pixels from the image domain to a polygon in the real projection plane domain. A cost function for dynamic optimization is the user-specified motion based on the static optimization results as well as image fitting. Our method is used to capture various human motions: walking, pushing, kicking, and handshaking.
9979797
A truncated least squares approach to the detection of specular highlights in color images
One of the most difficult aspects of dealing with illumination effects in computer vision is accounting for specularity in the images of real objects. The specular regions in an image are often saturated - which creates problem for all image processing algorithms that use decision thresholds. Such algorithms include those for edge detection, region segmentation, etc. Detecting specularity and whenever possible compensating for it are obviously advantageous. Along these lines, this paper represents a new specularity detection and compensation method which is based on the notion of truncated least-squares approximation to the function that maps the color distribution between two images of an object under different illumination conditions. We also present a protocol for the evaluation of the current method for specularity detection. Our protocol as currently formulated uses human subjects to grade the specularity detection method.
10180647
Specularity elimination in range sensing for accurate 3D modeling of specular objects
We present a novel range sensing method that is capable of constructing accurate 3D models of specular objects. Our method utilizes a new range imaging concept called multipeak range imaging, which accounts for the effects of mutual reflections. False measurements generated by mutual reflections are then eliminated by applying a series of constraint tests based on local smoothness, global coordinate consistency and visibility consistency. We show the usefulness of our method by applying the method to three real objects with specular surfaces. The ground truth data for those three objects were also acquired in order to evaluate the elimination of false measurements and to justify the selection of the parameters in the constraint tests. Experimental results indicate that our method significantly improves upon the traditional methods for constructing reliable 3D models of specular objects with complex shapes.
9868342
Adaptive record extraction from web pages
We describe an adaptive method for extracting records from web pages. Our algorithm combines a weighted tree matching metric with clustering for obtaining data extraction patterns.We compare our method experimentally to the state-of-the-art, and show that our approach is very competitive for rigidly-structured records (such as product descriptions) and far superior for loosely-structured records (such as entrieson blogs).
28122333
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
We examine the issue of mining association rules among items in a large database of sales transactions. Mining association rules means that, given a database of sales transactions, to discover all associations among items such that the presence of some items in a transaction will imply the presence of other items in the same transaction. The mining of association rules can be mapped into the problem of discovering large itemsets where a large itemset is a group of items that appear in a sufficient number of transactions. The problem of discovering large itemsets can be solved by constructing a candidate set of itemsets first, and then, identifying, within this candidate set, these itemsets that meet the large itemset requirement. Generally, this is done iteratively for each large k-itemset in increasing order of k, where a large k-itemset is a large itemset with k items. To determine large itemsets from a huge number of candidate sets in early iterations is usually the dominating factor for the overall data mining performance. To address this issue, we develop an effective algorithm for the candidate set generation. It is a hash-based algorithm and is especially effective for the generation of a candidate set for large 2-itemsets. Explicitly, the number of candidate 2-itemsets generated by the proposed algorithm is, in orders of magnitude, smaller than that by previous methods, thus resolving the performance bottleneck. Note that the generation of smaller candidate sets enables us to effectively trim the transaction database size at a much earlier stage of the iterations, thereby reducing the computational cost for later iterations significantly. The advantage of the proposed algorithm also provides us the opportunity of reducing the amount of disk I/O required. An extensive simulation study is conducted to evaluate performance of the proposed algorithm.
9747098
A Selection Algorithm with a Practical Upper Bound on Expected Number
A selection algorithm to find the Irth smallest element of a elements is presented. The algorithm mainly consists of the partition procedure and an improved method to choose a partitioning element. The algorithm estimates the partitioning element from a small sample so that the icth element is contained in the smaller partition between two partitions resulting from partitioning. The partitioning element is determined by using estimation of the cumulative frequency distribution in the theory of non-parametric statistics. The expected number of comparisons is found to be n + min(k, n-k) + O(n2/3) through experimental tests where the sample size is approximately n2/3. The experimental results show that the performance of the algorithm is improved, compared to the two known selection algorithms, particularly when the selection index is near to the median.