Classifying the emotional speech content of participants in group meetings using convolutional long short-term memory network.

Emotion is a central component of verbal communication between humans. Due to advances in machine learning and the development of affective computing, automatic emotion recognition is increasingly possible and sought after. To examine the connection between emotional speech and significant group dynamics perceptions, such as leadership and contribution, a new dataset (14 group meetings, 45 participants) is collected for analyzing collaborative group work based on the lunar survival task.

A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets.

Person re-identification (re-id) is a critical problem in video analytics applications such as security and surveillance. The public release of several datasets and code for vision algorithms has facilitated rapid progress in this area over the last few years. However, directly comparing re-id algorithms reported in the literature has become difficult since a wide variety of features, experimental protocols, and evaluation metrics are employed.

Viewpoint Invariant Human Re-Identification in Camera Networks Using Pose Priors and Subject-Discriminative Features.

Human re-identification across cameras with non-overlapping fields of view is one of the most important and difficult problems in video surveillance and analysis. However, current algorithms are likely to fail in real-world scenarios for several reasons. For example, surveillance cameras are typically mounted high above the ground plane, causing serious perspective changes.

Estimating contact rates at a mass gathering by using video analysis: a proof-of-concept project.

Background: Current approaches for estimating social mixing patterns and infectious disease transmission at mass gatherings have been limited by various constraints, including low participation rates for volunteer-based research projects and challenges in quantifying spatially and temporally accurate person-to-person interactions. We developed a proof-of-concept project to assess the use of automated video analysis for estimating contact rates of attendees of the GameFest 2013 event at Rensselaer Polytechnic Institute (RPI) in Troy, New York.

Methods: Video tracking and analysis algorithms were used to estimate the number and duration of contacts for 5 attendees during a 3-minute clip from the RPI video.

Keeping a pan-tilt-zoom camera calibrated.

Pan-tilt-zoom (PTZ) cameras are pervasive in modern surveillance systems. However, we demonstrate that the (pan, tilt) coordinates reported by PTZ cameras become inaccurate after many hours of operation, endangering tracking and 3D localization algorithms that rely on the accuracy of such values. To solve this problem, we propose a complete model for a PTZ camera that explicitly reflects how focal length and lens distortion vary as a function of zoom scale.

Reduced-order constrained optimization (ROCO): clinical application to head-and-neck IMRT.

Purpose: The authors present the application of the reduced order constrained optimization (ROCO) method, previously successfully applied to the prostate and lung sites, to the head-and-neck (H&N) site, demonstrating that it can quickly and automatically generate clinically competitive IMRT plans. We provide guidelines for applying ROCO to larynx, oropharynx, and nasopharynx cases, and report the results of a live experiment that demonstrates how an expert planner can save several hours of trial-and-error interaction using the proposed approach.

Methods: The ROCO method used for H&N IMRT planning consists of three major steps.

Reduced order constrained optimization (ROCO): clinical application to lung IMRT.

Purpose: The authors use reduced-order constrained optimization (ROCO) to create clinically acceptable IMRT plans quickly and automatically for advanced lung cancer patients. Their new ROCO implementation works with the treatment planning system and full dose calculation used at Memorial Sloan-Kettering Cancer Center (MSKCC). The authors have implemented mean dose hard constraints, along with the point-dose and dose-volume constraints that the authors used for our previous work on the prostate.

Automated kymograph analysis for profiling axonal transport of secretory granules.

This paper describes an automated method to profile the velocity patterns of small organelles (BDNF granules) being transported along a selected section of axon of a cultured neuron imaged by time-lapse fluorescence microscopy. Instead of directly detecting the granules as in conventional tracking, the proposed method starts by generating a two-dimensional spatio-temporal map (kymograph) of the granule traffic along an axon segment. Temporal sharpening during the kymograph creation helps to highlight granule movements while suppressing clutter due to stationary granules.

Segmenting the prostate and rectum in CT imagery using anatomical constraints.

The automatic segmentation of the prostate and rectum from 3D computed tomography (CT) images is still a challenging problem, and is critical for image-guided therapy applications. We present a new, automatic segmentation algorithm based on deformable organ models built from previously segmented training data. The major contributions of this work are a new segmentation cost function based on a Bayesian framework that incorporates anatomical constraints from surrounding bones and a new appearance model that learns a nonparametric distribution of the intensity histograms inside and outside organ contours.

Reduced-order constrained optimization in IMRT planning.

This paper presents a new algorithm for constrained intensity-modulated radiotherapy (IMRT) planning, made tractable by a dimensionality reduction using a set of plans obtained by fast, unconstrained optimizations. The main result is to reduce planning time by an order of magnitude, producing viable five field prostate IMRT plans in about 5 min. Broadly, the algorithm has three steps.

Reslicing axially sampled 3D shapes using elliptic Fourier descriptors.

We propose a new method that interpolates between parallel slices from a 3D shape for the purposes of reslicing and putting into correspondence organ shapes acquired from volumetric medical imagery. By interpolating the coefficients of elliptic Fourier descriptors for a set of parallel contours, a new set of slices can be directly generated at desired axial locations. Neither an explicit correspondence between points on adjacent contours nor a 3D interpolating surface needs to be obtained.

Reduced-order parameter optimization for simplifying prostate IMRT planning.

Intensity-modulated radiotherapy (IMRT) has become an effective tool for cancer treatment with radiation. However, even expert radiation planners still need to spend a substantial amount of time manually adjusting IMRT optimization parameters such as dose limits and costlet weights in order to obtain a clinically acceptable plan. In this paper, we describe two main advances that simplify the parameter adjustment process for five-field prostate IMRT planning.

Automated semantic analysis of changes in image sequences of neurons in culture.

Quantitative studies of dynamic behaviors of live neurons are currently limited by the slowness, subjectivity, and tedium of manual analysis of changes in time-lapse image sequences. Challenges to automation include the complexity of the changes of interest, the presence of obfuscating and uninteresting changes due to illumination variations and other imaging artifacts, and the sheer volume of recorded data. This paper describes a highly automated approach that not only detects the interesting changes selectively, but also generates quantitative analyses at multiple levels of detail.

Learning the relationship between patient geometry and beam intensity in breast intensity-modulated radiotherapy.

Intensity modulated radiotherapy (IMRT) has become an effective tool for cancer treatment with radiation. However, even expert radiation planners still need to spend a substantial amount of time adjusting IMRT optimization parameters in order to get a clinically acceptable plan. We demonstrate that the relationship between patient geometry and radiation intensity distributions can be automatically inferred using a variety of machine learning techniques in the case of two-field breast IMRT.

Automated cell lineage construction: a rapid method to analyze clonal development established with murine neural progenitor cells.

Understanding cell lineage relationships is fundamental to understanding development, and can shed light on disease etiology and progression. We present a method for automated tracking of lineages of proliferative, migrating cells from a sequence of images. The method is applicable to image sequences gathered either in vitro or in vivo.

Image change detection algorithms: a systematic survey.

Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. This paper presents a systematic survey of the common processing steps and core decision rules in modern change detection algorithms, including significance and hypothesis testing, predictive models, the shading model, and background modeling. We also discuss important preprocessing methods, approaches to enforcing the consistency of the change mask, and principles for evaluating and comparing the performance of change detection algorithms.

Model-based segmentation of medical imagery by matching distributions.

The segmentation of deformable objects from three-dimensional (3-D) images is an important and challenging problem, especially in the context of medical imagery. We present a new segmentation algorithm based on matching probability distributions of photometric variables that incorporates learned shape and appearance models for the objects of interest. The main innovation over similar approaches is that there is no need to compute a pixelwise correspondence between the model and the image.