14470248
MosaicShape: stochastic region grouping with shape prior
A method that combines shape-based object recognition and image segmentation is proposed for shape retrieval from images. Given a shape prior represented in a multi-scale curvature form, the proposed method identifies the target objects in images by grouping oversegmented image regions. The problem is formulated in a unified probabilistic framework, and object segmentation and recognition are accomplished simultaneously by a stochastic Markov Chain Monte Carlo (MCMC) mechanism. Within each sampling move during the simulation process, probabilistic region grouping operations are influenced by both the image information and the shape similarity constraint. The latter constraint is measured by a partial shape matching process. A generalized cluster sampling algorithm is presented in A. Barbu and S. Zhu (2003), combined with a large sampling jump and other implementation improvements, and greatly speeds up the overall stochastic process. The proposed method supports the segmentation and recognition of multiple occluded objects in images. Experimental results are provided for both synthetic and real images.
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.
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.
5927994
A New Approach to Modeling Personal Office Documents
Abstract This paper proposes a new approach to modeling documents in a personal office environment. The proposed document model (called the D _model) combines the relational and object-oriented paradigms and adopts a very natural view for describing the office documents. Documents are grouped into classes. Each class is characterized by a frame template, which describes the type for the class of documents. A frame template is instantiated by providing it with values to form a frame instance, representing a synopsis of a particular document associated with the template. Based on the nature of their contents, different frame instances can be grouped into a folder. Thus, a folder is a set of frame instances which may or may not be associated with the same template. The D _model describes documents using dual hierarchies: a document type hierarchy, depicting the structural organization of the documents, and a folder organization, representing the user's logical file structure. The document type hierarchy exploits structural commonalities between frame templates. Such a hierarchy helps to classify various documents. The folder organization mimics the user's real-world document filing system and provides the user with an intuitively clear view of his/her file structure. Such a view facilitates document retrieval and filing activities. We also present an algebra (called the D _algebra) for manipulating frame instances contained in folders. In contrast to existing algebraic languages, the D _algebra provides operators for manipulating heterogeneous sets (i.e. sets with elements of different types). The proposed document model and algebraic language have been implemented as part of TEXPROS, a personal document processing system currently running in our laboratory.
2269151
Content-based image indexing and searching using Daubechies' wavelets
Abstract. This paper describes WBIIS (Wavelet-Based Image Indexing and Searching), a new image indexing and retrieval algorithm with partial sketch image searching capability for large image databases. The algorithm characterizes the color variations over the spatial extent of the image in a manner that provides semantically meaningful image comparisons. The indexing algorithm applies a Daubechies' wavelet transform for each of the three opponent color components. The wavelet coefficients in the lowest few frequency bands, and their variances, are stored as feature vectors. To speed up retrieval, a two-step procedure is used that first does a crude selection based on the variances, and then refines the search by performing a feature vector match between the selected images and the query. For better accuracy in searching, two-level multiresolution matching may also be used. Masks are used for partial-sketch queries. This technique performs much better in capturing coherence of image, object granularity, local color/texture, and bias avoidance than traditional color layout algorithms. WBIIS is much faster and more accurate than traditional algorithms. When tested on a database of more than 10 000 general-purpose images, the best 100 matches were found in 3.3 seconds.
10726926
A scalable integrated region-based image retrieval system
We present a scalable algorithm for indexing and retrieving images based on region segmentation. The method uses statistical clustering on region features and IRM (integrated region matching), a measure developed to evaluate overall similarity between images. It incorporates properties of all the regions in the images by a region-matching scheme. The algorithm has been implemented as a part of our experimental SIMPLIcity (Semantics-sensitive Integrated Matching for Picture LIbraries) image retrieval system and tested on large-scale image databases of both general-purpose images and pathology slides. Experiments have demonstrated that this technique maintains the accuracy of the original system while reducing the matching time significantly.
21940463
A hybrid method for unconstrained handwritten numeral recognition by combining structural and neural "gas" classifiers
Abstract This paper presents a hybrid method for handwritten numeral recognition that combines two compensatory recognition algorithms by analysing their performance for several aspects. The skeleton-based structural recognition algorithm employed in this method is robust under distortion but sensitive to noise and flaws. On the other hand, the neural network classifier, which uses scaled binary images as features and the neural “gas” model for classification, is relatively immune to noise and flaws but sensitive to distortion. The different performances of the two algorithms for broken, connected or slanted numerals, and the measurement-level decision provided by the neural network are detected and combined with different strategies to develop matching rules for each recognition method. Five combination methods based on performance analysis are developed to meet different requirements. As the two algorithms have fairly compensatory properties, the proposed method improves the recognition rate and reliability by exploiting the advantages and avoiding the weaknesses of each classifier. The experimental results from a large set of data show the efficiency and robustness of the proposed method.
17726002
Average-case completeness of a word problem for groups
This paper presents a bounded word problem for groups whose random instances are hard on average. The problem is to decide, when given a finitely presented group G, words ~, y, z, and an integer k, whether (Z–lyZ)Z can be obtained from Z(x–l yz) in G using elementary Tietze transformations for at most k times. We show that the problem is average-case NP-complete when instances are chosen at random under a uniform distribution. NP-completeness of more general bounded word problems for groups is shown as well.