Ishwar K. Sethi

TL;DR: This work forms the correspondence problem as an optimization problem and proposes an iterative algorithm to find trajectories of points in a monocular image sequence and demonstrates the efficacy of this approach considering synthetic, laboratory, and real scenes.

TL;DR: This work describes a scheme that is able to classify audio segments into seven categories consisting of silence, single speaker speech, music, environmental noise, multiple speakers' speech, simultaneous speech and music, and speech and noise, and shows that cepstral-based features such as the Mel-frequency cep stral coefficients (MFCC) and linear prediction coefficients (LPC) provide better classification accuracy compared to temporal and spectral features.

TL;DR: This paper investigates different cross-modal association methods using the linear correlation model, and introduces a novel method for cross- modal association called Cross-modAL Factor Analysis (CFA), which shows several advantages in analysis performance and feature usage.

TL;DR: How the mapping of decision trees into a multilayer neural network structure can be exploited for the systematic design of a class of layered neural networks, called entropy nets (which have far fewer connections), is shown.

TL;DR: This paper proposes a new active learning approach, confidence-based active learning, based on identifying and annotating uncertain samples, which takes advantage of current classifiers' probability preserving and ordering properties and is robust without additional computational effort.