26189
Detecting Instances of Shape Classes That Exhibit Variable Structure
This paper proposes a method for detecting shapes of variable structure in images with clutter. The term “variable structure” means that some shape parts can be repeated an arbitrary number of times, some parts can be optional, and some parts can have several alternative appearances. The particular variation of the shape structure that occurs in a given image is not known a priori. Existing computer vision methods, including deformable model methods, were not designed to detect shapes of variable structure; they may only be used to detect shapes that can be decomposed into a fixed, a priori known, number of parts. The proposed method can handle both variations in shape structure and variations in the appearance of individual shape parts. A new class of shape models is introduced, called Hidden State Shape Models, that can naturally represent shapes of variable structure. A detection algorithm is described that finds instances of such shapes in images with large amounts of clutter by finding globally optimal correspondences between image features and shape models. Experiments with real images demonstrate that our method can localize plant branches that consist of an a priori unknown number of leaves and can detect hands more accurately than a hand detector based on the chamfer distance.
2016479
Shape-Based Curve Growing Model and Adaptive Regularization for Pulmonary Fissure Segmentation in CT
This paper presents a shape-based curve-growing algorithm for object recognition in the field of medical imaging. The proposed curve growing process, modeled by a Bayesian network, is influenced by both image data and prior knowledge of the shape of the curve. A maximum a posteriori (MAP) solution is derived using an energy-minimizing mechanism. It is implemented in an adaptive regularization framework that balances the influence of image data and shape prior in estimating the curve, and reflects the causal dependencies in the Bayesian network. The method effectively alleviates over-smoothing, an effect that can occur with other regularization methods. Moreover, the proposed framework also addresses initialization and local minima problems. Robustness and performance of the proposed method are demonstrated by segmentation of pulmonary fissures in computed tomography (CT) images.
30981403
An Optimization Problem in Virtual Endoscopy
This paper studies a graph optimization problem occurring in virtual endoscopy, which concerns finding the central path of a colon model created from helical computed tomography (CT) image data. The central path is an essential aid for navigating through complex anatomy such as colon. Recently, Ge et al. (1998) devised an efficient method for finding the central path of a colon. The method first generates colon data from a helical CT data volume by image segmentation. It then generates a 3D skeleton of the colon. In the ideal situation, namely, if the skeleton does not contain branches, the skeleton will be the desired central path. However, almost always the skeleton contains extra branches caused by holes in the colon model, which are artifacts produced during image segmentation. To remove false branches, Ge et al. (1998) formulated a graph optimization problem for obtaining the central path. This paper presents a refined formulation and justifies that the solution of the refined optimization problem represents the accurate central path of a colon. We then provide a fast algorithm for solving the problem.
13574031
Mining genes in DNA using GeneScout
In this paper we present a new system, called GeneScout, for predicting gene structures in vertebrate genomic DNA. The system contains specially designed hidden Markov models (HMMs) for detecting functional sites including protein-translation start sites, mRNA splicing junction donor and acceptor sites, etc. Our main hypothesis is that, given a vertebrate genomic DNA sequence S, it is always possible to construct a directed acyclic graph G such that the path for the actual coding region of S is in the set of all paths on G. Thus, the gene detection problem is reduced to that of analyzing the paths in the graph G. A dynamic programming algorithm is used to find the optimal path in G. The proposed system is trained using an expectation-maximization (EM) algorithm and its performance on vertebrate gene prediction is evaluated using the 10-way cross-validation method. Experimental results show the good performance of the proposed system and its complementarity to a widely used gene detection system.
1111081
Adaptive User Profile Model and Collaborative Filtering for Personalized News
In recent years, personalized news recommendation has received increasing attention in IR community. The core problem of personalized recommendation is to model and track users’ interests and their changes. To address this problem, both content-based filtering (CBF) and collaborative filtering (CF) have been explored. User interests involve interests on fixed categories and dynamic events, yet in current CBF approaches, there is a lack of ability to model user’s interests at the event level. In this paper, we propose a novel approach to user profile modeling. In this model, user’s interests are modeled by a multi-layer tree with a dynamically changeable structure, the top layers of which are used to model user interests on fixed categories, and the bottom layers are for dynamic events. Thus, this model can track the user’s reading behaviors on both fixed categories and dynamic events, and consequently capture the interest changes. A modified CF algorithm based on the hierarchically structured profile model is also proposed. Experimental results indicate the advantages of our approach.
6624380
Structural Features of Toxic Chemicals for Specific Toxicity
We have studied the structural features of toxic chemicals from the RTECS database associated with specific toxicity. The frameworks, functional groups, and structure patterns of molecules are taken into account. Potential active frameworks, groups, and structure patterns for specific toxicity are gained by computational chemistry approaches. These structural features of toxic chemicals will be helpful in understanding activities of toxic chemicals and useful in predicting the toxicity of chemicals, especially in the early stage of drug design.
11677932
Fast Similarity Search in Three-Dimensional Structure Databases
Given a database D of three-dimensional (3D) molecular structures and a target molecule Q, the similarity search problem is to find the molecules O in D that match Q after allowing for an arbitrary number of whole-structure rotations and translations as well as a certain number of edit operations. The edit operations include relabeling an atom, deleting an atom, and inserting an atom. This search operation arises in many biochemical applications. In this paper we study the similarity search problem and a class of related queries. We present a computer vision based technique, called geometric hashing, for processing these queries. Experimental results on a database of 3D molecular structures obtained from the National Cancer Institute indicate the good performance of the presented technique.