A new information-theoretic approach is presented for finding the registration of volumetric medical images of differing modalities. Registration is achieved by adjustment of the relative pose until the mutual information between images is maximized. In our derivation of the registration procedure

of highway traffic, pollution, wildfires, building security, water quality, and even people’s heart rates. A major benefit of these systems is that they perform in-network processing to reduce large streams of raw data into useful aggregated information. Protecting it all is critical. Here, we outline

Current approaches to object category recognition require datasets of training images to be manually prepared, with varying degrees of supervision. We present an approach that can learn an object category from just its name, by uti-lizing the raw output of image search engines available

This paper introduces a hierarchical Markov model that can learn and infer a user’s daily movements through the community. The model uses multiple levels of abstraction in order to bridge the gap between raw GPS sensor measurements and high level information such as a user’s mode of transportation

This paper presents an implemented computational model of word acquisition which learns directly from raw multimodal sensory input. Set in an information theoretic framework, the model acquires a lexicon by finding and statistically modeling consistent cross-modal structure. The model has been

, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. In this paper, we provide a comprehensive review of the development of research in learning from imbalanced data. Our focus is to provide a critical review of the nature

Two new techniques for the determination of building models from laser altimetry data are presented. Both techniques work on the original laser scanner data points without the requirement of an interpolation to a regular grid. Available ground plan information may be used, but is not required

to the tremendous variations that result from camera motion, background clutter, changes in object appearance, and scale, etc. The main challenge is how to extract reliable and informative features from the unconstrained videos. We extract both motion and static features from the videos. Since the raw features

We present a trainable model for identifying sentence boundaries in raw text. Given a corpus annotated with sentence boundaries, our model learns to classify each occurrence of., ?, and ! as either a valid or invalid sentence boundary. The training procedure requires no hand-crafted rules

. This is customarily done by use of an interactive computer program. The program (which is usually called a sequence editor) must, at a minimum, display the aligned sequences of the assembled reads and allow the user to access underlying raw data (e.g., the fluorescence trace profiles from automated sequencers