A critical step in defect detection for semiconductor process is to align a test image against a reference. This includes both spatial alignment and grayscale alignment. For the latter, a direct least square approach is not very applicable because the presence of defects would skew the parameters. Instead, we use a linear programming formulation which has the advantage of having a fast algorith...

In this paper we present an experimental toolbox for automatic tree-to-tree alignment based on local classification and alignment inference. The aligner implements a recurrent architecture for structural prediction using history features and a sequential classification procedure. The discriminative base classifier uses a log-linear model which enables simple integration of various features extr...

This paper describes a structural method for object alignment by pose clustering. The idea underlying pose clustering is to decompose the objects under consideration into k-tuples of primitive parts. By bringing pairs of k-tuples into correspondence, sets of alignment parameters are estimated. The global alignment corresponds to the set of parameters with maximum votes. The work reported here o...

Object alignment is one of the basic operations in drawing programs. Current solutions provide mainly three ways for carrying out this operation: either by issuing an alignment command, or by using direct positioning with the help of gravity active points, or by making use of constraints. The first technique has limited functionality, and the other two may be mysterious for a novice. We describ...

This thesis is devoted to two NP-complete combinatorial optimization problems arising in computational biology, the well-studied multiple sequence alignment problem and the new formulated interval constraint coloring problem. It shows that advanced mathematical programming techniques are capable of solving large scale real-world instances from biology to optimality. Furthermore, it reveals alte...

Structural divergence presents a challenge to the use of syntax in statistical machine translation. We address this problem with a new algorithm for alignment of loosely matched non-isomorphic dependency trees. The algorithm selectively relaxes the constraints of the two tree structures while keeping computational complexity polynomial in the length of the sentences. Experimentation with a larg...

We present a general framework to incorporate prior knowledge such as heuristics or linguistic features in statistical generative word alignment models. Prior knowledge plays a role of probabilistic soft constraints between bilingual word pairs that shall be used to guide word alignment model training. We investigate knowledge that can be derived automatically from entropy principle and bilingu...

Multiple sequence alignment of distantly related viral proteins remains a challenge to all currently available alignment methods. The hidden Markov model approach offers a new, flexible method for the generation of multiple sequence alignments. The results of studies attempting to infer appropriate parameter constraints for the generation of de novo HMMs for globin, kinase, aspartic acid protea...

Most of the current voice conversion systems model the joint density of source and target features using a Gaussian mixture model. An inherent property of this approach is that the source and target features have to be properly aligned for the training. It is intuitively clear that the accuracy of the alignment has some effect on the conversion quality but this issue has not been thoroughly stu...

In this paper, we propose a novel manifold alignment method by learning the underlying common manifold with supervision of corresponding data pairs from different observation sets. Different from the previous algorithms of semi-supervised manifold alignment, our method learns the explicit corresponding projections from each original observation space to the common embedding space everywhere. Be...