An observation-constrained generative approach for probabilistic classification of image regions
In this paper, we propose a probabilistic region classification scheme for natural scene images. In conventional generative methods, a generative model is learnt for each class using all the available training data belonging to that class. However, if an input image has been generated from only a subset of the model support, use of the full model to assign generative probabilities can produce serious artifacts in the probability assignments. This problem arises mainly when the different classes have multimodal distributions with considerable overlap in the feature space. We propose an approach to constrain the class generative probability of a set of newly observed data by exploiting the distribution of the new data itself and using linear weighted mixing. A Kullback–Leibler Divergence-based fast model selection procedure is also proposed for learning mixture models in a low dimensional feature space. The preliminary results on the natural scene images support the effectiveness of the proposed approach.

title: Man-made structure detection in natural images using a causal multiscale random field
abstract: This paper presents a generative model based approach to man-made structure detection in 2D (two-dimensional) natural images. The proposed approach uses a causal multiscale random field suggested by Bouman and Shapiro (1994) as a prior model on the class labels on the image sites. However, instead of assuming the conditional independence of the observed data, we propose to capture the local dependencies in the data using a multiscale feature vector. The distribution of the multiscale feature vectors is modeled as mixture of Gaussians. A set of robust multi-scale features is presented that captures the general statistical properties of man-made structures at multiple scales without relying on explicit edge detection. The proposed approach was validated on real-world images from the Corel data set, and a performance comparison with other techniques is presented.

title: Discriminative random fields: a discriminative framework for contextual interaction in classification
abstract: In this work we present discriminative random fields (DRFs), a discriminative framework for the classification of image regions by incorporating neighborhood interactions in the labels as well as the observed data. The discriminative random fields offer several advantages over the conventional Markov random field (MRF) framework. First, the DRFs allow to relax the strong assumption of conditional independence of the observed data generally used in the MRF framework for tractability. This assumption is too restrictive for a large number of applications in vision. Second, the DRFs derive their classification power by exploiting the probabilistic discriminative models instead of the generative models used in the MRF framework. Finally, all the parameters in the DRF model are estimated simultaneously from the training data unlike the MRF framework where likelihood parameters are usually learned separately from the field parameters. We illustrate the advantages of the DRFs over the MRF framework in an application of man-made structure detection in natural images taken from the Corel database.

title: ExScal: elements of an extreme scale wireless sensor network
abstract: Project ExScal (for extreme scale) fielded a 1000+ node wireless sensor network and a 200+ node peer-to-peer ad hoc network of 802.11 devices in a 13km by 300m remote area in Florida, USA during December 2004. In comparison with previous deployments, the ExScal application is relatively complex and its networks are the largest ones of either type fielded to date. In this paper, we overview the key requirements of ExScal, the corresponding design of the hardware/software platform and application, and some results of our experiments.

title: Exploiting spatial locality in data caches using spatial footprints
abstract: Modern cache designs exploit spatial locality by fetching large blocks of data called cache lines on a cache miss. Subsequent references to words within the same cache line result in cache hits. Although this approach benefits from spatial locality, less than half of the data brought into the cache gets used before eviction. The unused portion of the cache line negatively impacts performance by wasting bandwidth and polluting the cache by replacing potentially useful data that would otherwise remain in the cache.This paper describes an alternative approach to exploit spatial locality available in data caches. On a cache miss, our mechanism, called Spatial Footprint Predictor (SFP), predicts which portions of a cache block will get used before getting evicted. The high accuracy of the predictor allows us to exploit spatial locality exhibited in larger blocks of data yielding better miss ratios without significantly impacting the memory access latencies. Our evaluation of this mechanism shows that the miss rate of the cache is improved, on average, by 18% in addition to a significant reduction in the bandwidth requirement.

title: A Smorgasbord of Features for Statistical Machine Translation
abstract: We describe a methodology for rapid experimentation in statistical machine translation which we use to add a large number of features to a baseline system exploiting features from a wide range of levels of syntactic representation. Feature values were combined in a log-linear model to select the highest scoring candidate translation from an n-best list. Feature weights were optimized directly against the BLEU evaluation metric on held-out data. We present results for a small selection of features at each level of syntactic representation.

title: A fast piecewise deformable method for multi-modality image registration
abstract: Medical image fusion is becoming increasingly popular for enhancing diagnostic accuracy by intelligently 'fusing' information obtained from two different images. These images may be obtained from the same modality at different time instances or from multiple modalities recording complementary information. Due to the nature of the human body and also due to patient motion and breathing, there is a need for deformable registration algorithms in medical imaging. Typical nonparametric (deformable) registration algorithms such as the fluid-based, demons and curvature-based techniques are computationally intensive and have been demonstrated for mono-modality registrations only. We propose a fast and deformable algorithm using a 2-tiered strategy wherein a global MI-based affine registration is followed by a local piecewise refinement. We have tested this method on CT and PET images and validated the same using clinical experts

title: A Weighted Finite State Transducer Implementation of the Alignment Template Model for Statistical Machine Translation
abstract: We present a derivation of the alignment template model for statistical machine translation and an implementation of the model using weighted finite state transducers. The approach we describe allows us to implement each constituent distribution of the model as a weighted finite state transducer or acceptor. We show that bitext word alignment and translation under the model can be performed with standard FSM operations involving these transducers. One of the benefits of using this framework is that it obviates the need to develop specialized search procedures, even for the generation of lattices or N-Best lists of bitext word alignments and translation hypotheses. We evaluate the implementation of the model on the French-to-English Hansards task and report alignment and translation performance.