123309619
From fragments to salient closed boundaries: an in-depth study
This paper conducts an in-depth study on a classical perceptual-organization problem: finding salient closed boundaries from a set of boundary fragments detected in a noisy image. In this problem, a saliency boundary is formed by identifying and connecting a subset of fragments according to the simple Gestalt laws of closure, continuity, and proximity. Our specific interest is focused on the methods that aim to achieve boundary closure, an important global property of perceptual salient boundaries. In this paper, we analyze and compare three such methods that are developed in recent years: (a) Elder and Zucker's method based on the shortest-path algorithm, (b) Williams and Thornber's method combining the spectral-analysis and the strongly-connected-component algorithms, and (c) Wang, Kubota, and Siskind's method based on ratio-contour algorithm. Both theoretic analysis and experimental study show that, with a unified setting of fragment saliency, Wang, Kubota and Siskind's method more appropriately constrains the search space for the closed boundaries, and usually produces better performance than or at least comparable performance as the other two methods. Particularly, Wang, Kubota, and Siksind's method can always guarantee the boundary closure and simplicity, which may not be always hold in the other two methods. We construct and collect a variety of synthesized and real images for this comparison.
14786568
Global Detection of Salient Convex Boundaries
As an important geometric property of many structures or structural components, convexity plays an important role in computer vision and image understanding. In this paper, we describe a general approach that can force various edge-grouping algorithms to detect only convex structures from a set of boundary fragments. The basic idea is to remove some fragments and fragment connections so that, on the remaining ones, a prototype edge-grouping algorithm that detects closed boundaries without the convexity constraint can only produce convex closed boundaries. We show that this approach takes polynomial time and preserves the grouping optimality by not excluding any valid convex boundary from the search space. Choosing the recently developed ratio-contour algorithm as the prototype grouping algorithm, we develop a new convex-grouping algorithm, which can detect convex salient boundaries with good continuity and proximity in a globally optimal fashion. To facilitate the application of this convex-grouping algorithm, we develop a new fragment-connection method based on four-point Bezier curves. We demonstrate the performance of this convex-grouping algorithm by conducting experiments on both synthetic and real images. In addition, we provide a comparison with some prior edge-grouping algorithms. Finally, we show that the proposed convex-grouping algorithm can be further extended to detect convex open boundaries, derive region-based image hierarchies, and incorporate some simple human-computer interactions.
790783
Landmark-based shape deformation with topology-preserving constraints
This paper presents a novel approach for landmark-based shape deformation, in which fitting error and shape difference are formulated into a support vector machine (SVM) regression problem. To well describe nonrigid shape deformation, this paper measures the shape difference using a thin-plate spline model. The proposed approach is capable of preserving the topology of the template shape in the deformation. This property is achieved by inserting a set of additional points and imposing a set of linear equality and/or inequality constraints. The underlying optimization problem is solved using a quadratic programming algorithm. The proposed method has been tested using practical data in the context of shape-based image segmentation. Some relevant practical issues, such as missing detected landmarks and selection of the regularization parameter are also briefly discussed.
9005420
Tracking of object with SVM regression
This paper presents a novel feature-matching based approach for rigid object tracking. The proposed method models the tracking problem as discovering the affine transforms of object images between frames according to the extracted feature correspondences. False feature matches (outliers) are automatically detected and removed with a new SVM regression technique, where outliers are iteratively identified as support vectors with the gradually decreased insensitive margin /spl epsi/. This method, in addition to object tracking, can also be used for general feature-based epipolar constraint estimation, in which it can quickly detect outliers even if they make up, in theory, over 50% of the whole data. We have applied the proposed method to track real objects under cluttering backgrounds with very encouraging results.
10317661
Nonrigid Shape Correspondence Using Landmark Sliding, Insertion and Deletion
The growing usage of statistical shape analysis in medical imaging calls for effective methods for highly accurate shape correspondence. This paper presents a novel landmark-based method to correspond a set of 2D shape instances in a nonrigid fashion. Different from prior methods, the proposed method combines three important factors in measuring the shape-correspondence error: landmark-correspondence error, shape-representation error, and shape-representation compactness. In this method, these three important factors are explicitly handled by the landmark sliding, insertion, and deletion operations, respectively. The proposed method is tested on several sets of structural shape instances extracted from medical images. We also conduct an empirical study to compare the developed method to the popular Minimum Description Length method.
4791224
Image Segmentation with Minimum Mean Cut
We introduce a new graph-theoretic approach to image segmentation based on minimizing a novel class of ‘mean cut’ cost functions. Minimizing these cost functions corre sponds to finding a cut with minimum mean edge weight in a connected planar graph. This approach has several advantages over prior approaches to image segmentation. First, it allows cuts with both open and closed boundaries. Second, it guarantees that the partitions are connected. Third , the cost function does not introduce an explicit bias, such as a preference for large-area foregrounds, smooth or short boundaries, or similar-weight partitions. This lack of bia s allows it to produce segmentations that are better aligned with image edges, even in the presence of long thin regions. Finally, the global minimum of this cost function is largely insensitive to the precise choice of edge-weight function. In particular, we show that the global minimum is invariant under a linear transformation of the edge weights and thus insensitive to image contrast. Building on algorithms by Ahuja, Magnanti, and Orlin (1993), we present a polynomial-time algorithm for finding a global minimum of the mean-cut cost function and illustrate the results of applying that algorithm to several synthetic and real images.
45928612
Transmission Range Designation Broadcasting Methods for Wireless Ad Hoc Networks
The broadcast operation in ad hoc wireless networks is essential, but expensive in terms of power consumption. In DP (dominant pruning) algorithms, the number of forward nodes is treated as a criterion to measure the consumed power. However, this principle is not suitable anymore if the power-adaptive characteristic is supported and each node can detect the strength of a received packet and tune its transmission power level. Thus, in this paper, we propose a modified version of the DP algorithm with the objective being to minimize the total power consumption of forward nodes. Comparing with original DP algorithms, the sender in our algorithm not only chooses its forward nodes, but also designates the transmission ranges of its forward nodes. As a result, the designated transmission ranges not only maintain the same coverage as DP algorithms, but also reduce the total power consumption. We propose a new forward nodes selection process where the weight of a node is dependent on the incremental transmission cost rather than the effective degree. Although the resultant number of forward nodes in the proposed approach is often greater than DP algorithms, the power consumption shown in simulations is less than DP algorithms.
14423228
A New Median Graph Algorithm
Median graph is an important new concept introduced to represent a set of graphs by a representative graph. Computing the median graph is an NP-Complete problem. In this paper, we propose an approximate algorithm for computing the median graph. Our algorithm performs in two steps. It first carries out a node reduction process using a clustering method to extract a subset of most representative node labels. It then searches for the median graph candidates from the reduced subset of node labels according to a deterministic strategy to explore the candidate space. Comparison with the genetic search based algorithm will be reported. This algorithm can be used to build a graph clustering algorithm.
15396849
Convex grouping combining boundary and region information
Convexity is an important geometric property of many natural and man-made structures. Prior research has shown that it is imperative to many perceptual-organization and image-understanding tasks. This paper presents a new grouping method for detecting convex structures from noisy images in a globally optimal fashion. Particularly, this method combines both region and boundary information: the detected structural boundary is closed and well aligned with detected edges while the enclosed region has good intensity homogeneity. We introduce a ratio-form cost function for measuring the structural desirability, which avoids a possible bias to detect small structures. A new fragment-pruning algorithm is developed to achieve the structural convexity. The proposed method can also be extended to detect open boundaries, which correspond to the structures that are partially cropped by the image perimeter and incorporate a human-computer interaction for detecting a convex boundary around a specified point. We test the proposed method on a set of real images and compare it with the Jacobs'convex-grouping method
11278363
A Distributed Artificial Network Solving Complex and Multiple Causal Associations
Causal reasoning (known also as abduction) is a hard task that cognitive agents perform reliably and quickly. A particular class of causal reasoning that raises several difficulties is the cancellation class. Cancellation occurs when a set of causes (hypotheses) cancel each other's explanation with respect to a given effect (observation). For example, a cloudy sky may suggest a rainy weather; whereas a shiny sky may suggest the absence of rain. In the current paper, we extend a recent neural model to handle cancellation interactions. We conduct a sensitivity analysis of this proposal on ad hoc problems put at extreme cases. Finally, we test the model on a large database and propose objective criteria to quantitatively evaluate its performance. Simulation results are very satisfactory and should encourage research.
42912843
NChiql: The Chinese Natural Language Interface to Databases
Numerous natural language interface to databases (NLIDBs) developed in the mid-eighties demonstrated impressive characteristic in certain application areas, but NLIDBs did not gained the expected rapid and wide commercial acceptance. We argue that there are two good reasons explain why: limited portability and poor usability. This paper describes the design and implementation of NChiql, a Chinese natural language interface to databases. In order to bright the essence of these problems, we provide an abstract model (AM) in NChiql. We try to give a solution for these problems based on the model in our system. In this paper, we depict a novel method based on database semantics (SCM) to handle the Chinese natural language query, which greatly promotes the system's usability. The experiments show that NChiql has good usability and high correctness.