45767095
A Color Image Segmentation Algorithm by Using Region and Edge Information
A novel segmentation algorithm for natural color image is proposed. Fibonacci Lattice-based Sampling is used to get the symbols of image so as to make each pixel’s label containing color information rather than only as a class marker. Next, Region map is formed based on Fibonacci Lattice symbols to depict homogeneous regions. On the other hand, by applying fuzzy homogeneity algorithm on the image, we filter it to acquire Edge map. To strengthen the ability of discrimination, both the weighted maps are combined to form Region-Edge map. Based on above processes, growing-merging method is used to segment the image. Finally, experiments show very promising results.
28794181
JSEG Based Color Separation of Tongue Image in Traditional Chinese Medicine
The process of color separation of tongue image in traditional Chinese medicine (TCM) is decomposed into two steps: region partition and color classification. In the first step, a partition method based on JSEG is proposed to obtain homogenous regions in the tongue. In the second step, a priori template and some standard color patches are designed to assist the classification according to a priori knowledge from the TCM experts, where the nearest neighbour classifier is applied for color classification. The experimental results show that the proposed approach is greatly promising to computerized tongue diagnosis.
32026454
Color-texture image segmentation by integrating directional operators into JSEG method
We presented a new color-texture image segmentation method that combined directional operators and the JSEG (J measure based SEGmentation) algorithm. In particular, two major contributions were set forth in this paper. (1) The two measures defined in JSEG and HSEG (H measure based SEGmentation) were discussed respectively and then we found the over-segmentation problem of JSEG could be attributed partly to the absence of color discontinuity in the J measure. Moreover, we have also proven in essence the H measure is isotropic operators for edge detection in images. (2) A new measure was proposed by integrating directional operators into the J measure, on which our segmentation method was based. The new segmentation method took account of both textural homogeneity and color discontinuity in local regions. Performance improvement due to the proposed modification was demonstrated on a variety of real color images.
7748348
Shape fitting with outliers
Given a set <i>H</i> of <i>n</i> hyperplanes in <i>R<sup>d</sup></i>, we present an algorithm that <i>e</i>-approximates the extent between the top and bottom <i>k</i> levels of the arrangement of <i>H</i> in time <i>O(n + (k/e)<sup>c</sup>)</i>, where <i>c</i> is a constant depending on <i>d</i>. The algorithm relies on computing a subset of <i>H</i> of size.<i>O(k/e<sup>d-1</sup>)</i>, in near linear time, such that the <i>k</i>-level of the arrangement of the subset approximates that of the original arrangement. Using this algorithm, we propose efficient approximation algorithms for shape fitting with outliers for various shapes. This is the first algorithms to handle outliers efficiently for the shape fitting problems considered.
17258193
Spatiotemporal video segmentation based on graphical models
This paper proposes a probabilistic framework for spatiotemporal segmentation of video sequences. Motion information, boundary information from intensity segmentation, and spatial connectivity of segmentation are unified in the video segmentation process by means of graphical models. A Bayesian network is presented to model interactions among the motion vector field, the intensity segmentation field, and the video segmentation field. The notion of the Markov random field is used to encourage the formation of continuous regions. Given consecutive frames, the conditional joint probability density of the three fields is maximized in an iterative way. To effectively utilize boundary information from the intensity segmentation, distance transformation is employed in local objective functions. Experimental results show that the method is robust and generates spatiotemporally coherent segmentation results. Moreover, the proposed video segmentation approach can be viewed as the compromise of previous motion based approaches and region merging approaches.
3063945
Statistical-based approach to word segmentation
This paper presents a text word extraction algorithm that takes a set of bounding boxes of glyphs and their associated text lines of a given document and partitions the glyphs into a set of text words, using only the geometric information of the input glyphs. The algorithm is probability based. An iterative, relaxation-like method is used to find the partitioning solution that maximizes the joint probability. To evaluate the performance of our test word extraction algorithm, we used a 3-fold validation method and developed a quantitative performance measure. The algorithm was evaluated on the UW-III database of some 1600 scanned document image pages. An area-overlap measure was used to find the correspondence between the detected entities and the ground-truth. For a total of 827, 433 ground truth words, the algorithm identified and segmented 800, 149 words correctly, an accuracy of 97.43%.
16439255
Privacy Aware Market Basket Data Set Generation: A Feasible Approach for Inverse Frequent Set Mining
Association rule mining has received a lot of attention in the data mining community and several algorithms were proposed to improve the performance of association rule or frequent itemset mining. The IBM Almaden synthetic data generator has been commonly used for performance evaluation. One recent work shows that the data generated is not good enough for benchmarking as it has very different characteristics from real-world data sets. Hence there is a great need to use real-world data sets as benchmarks. However, organizations hesitate to provide their data due to privacy concerns. Recent work on privacy preserving association rule mining addresses this issue by modifying real data sets to hide sensitive or private rules. However, modifying individual values in real data may impact on other, non-sensitive rules. In this paper, we propose a feasible solution to the NPcomplete problem of inverse frequent set mining. Since solving this problem by linear programming techniques is very computationally prohibitive, we apply graph-theoretical results to divide the original itemsets into components that preserve maximum likelihood estimation. We then use iterative proportional fitting method to each component. The technique is experimentally evaluated with two real data sets and one synthetic data set. The results show that our approach is effective and efficient for reconstructing market basket data set from a given set of frequent itemsets while preserving sensitive information.
45073197
On cognitive properties of human factors in engineering
Human traits and needs are the fundamental force underlying almost all phenomena in human task performances, engineering organizations, and societies. This paper explores the cognitive foundations of human traits and cognitive properties of human factors in engineering. The fundamental traits of human beings are identified, and the hierarchical model of basic human needs is formally describes. The characteristics of human factors and their influences in engineering organizations and socialization are explored. Based on the models of basic human traits, needs, and their influences, driving forces behind the human factors in engineering and society are revealed. A formal model of human errors in task performing is derived, and case studies of the error model in software engineering are presented.