Fixational Instability and Natural Image Statistics: Implications for Early Visual Representations

Under natural viewing conditions small movements of the eye, head, and body prevent the maintenance of a steady direction of gaze. It is known that stimuli tend to fade when they a restabilized on the retina for several seconds. However; it is unclear whether the physiological motion of the retinal image serves a visual purpose during the brief periods of natural visual fixation. This study examines the impact of fixational instability on the statistics of the visua1 input to the retina and on the structure of neural activity in the early visual system. We show that fixational instability introduces a component in the retinal input signals that in the presence of natural images, lacks spatial correlations. This component strongly influences neural activity in a model of the LGN. It decorrelates cell responses even if the contrast sensitivity functions of simulated cells arc not perfectly tuned to counterbalance the power-law spectrum of natural images. A decorrelation of neural activity at the early stages of the visual system has been proposed to be beneficial for discarding statistical redundancies in the input signals. The results of this study suggest that fixational instability might contribute to establishing efficient representations of natural stimuli.

Combining Textual and Visual Cues for Content-based Image Retrieval on the World Wide Web

Some WWW image engines allow the user to form a query in terms of text keywords. To build the image index, keywords are extracted heuristically from HTML documents containing each image, and/or from the image URL and file headers. Unfortunately, text-based image engines have merely retro-fitted standard SQL database query methods, and it is difficult to include images cues within such a framework. On the other hand, visual statistics (e.g., color histograms) are often insufficient for helping users find desired images in a vast WWW index. By truly unifying textual and visual statistics, one would expect to get better results than either used separately. In this paper, we propose an approach that allows the combination of visual statistics with textual statistics in the vector space representation commonly used in query by image content systems. Text statistics are captured in vector form using latent semantic indexing (LSI). The LSI index for an HTML document is then associated with each of the images contained therein. Visual statistics (e.g., color, orientedness) are also computed for each image. The LSI and visual statistic vectors are then combined into a single index vector that can be used for content-based search of the resulting image database. By using an integrated approach, we are able to take advantage of possible statistical couplings between the topic of the document (latent semantic content) and the contents of images (visual statistics). This allows improved performance in conducting content-based search. This approach has been implemented in a WWW image search engine prototype.

Camera Canvas: Image Editing Software for People with Disabilities

Poster is based on the following paper: C. Kwan and M. Betke. Camera Canvas: Image editing software for people with disabilities. In Proceedings of the 14th International Conference on Human Computer Interaction (HCI International 2011), Orlando, Florida, July 2011.

Asynchronous Auction for Distributed Nonlinear Resource Allocation Problem

Resource Allocation Problems (RAPs) are concerned with the optimal allocation of resources to tasks. Problems in fields such as search theory, statistics, finance, economics, logistics, sensor & wireless networks fit this formulation. In literature, several centralized/synchronous algorithms have been proposed including recently proposed auction algorithm, RAP Auction. Here we present asynchronous implementation of RAP Auction for distributed RAPs.

Assessing the Oral Health Needs of Public Housing Residents

Objectives: “Tooth Smart Healthy Start” is a randomized clinical trial which aims to reduce the incidence of early childhood caries (ECC) in Boston public housing residents as part of the NIH funded Northeast Center for Research to Evaluate and Eliminate Dental Disparities. The purpose of this project was to assess public housing stakeholders' perception of the oral health needs of public housing residents and their interest in replicating “Tooth Smart Healthy Start” in other public housing sites across the nation. Methods: The target population was the 180 attendees of the 2010 meeting of the Health Care for Residents of Public Housing National Conference. A ten question survey which assessed conference attendees' beliefs about oral health and its importance to public housing residents was distributed. Data was analyzed using SAS 9.1. Descriptive statistics were calculated for each variable and results were stratified by participants' roles. Results: Thirty percent of conference attendees completed the survey. The participants consisted of residents, agency representatives, and housing authority personnel. When asked to rank health issues facing public housing residents, oral health was rated as most important (42%) or top three (16%) by residents. The agency representatives and housing authority personnel rated oral health among the top three (33% and 58% respectively) and top five (36% and 25% respectively). When participants ranked the three greatest resident health needs out of eight choices, oral health was the most common response. Majority of the participants expressed interest in replicating the “Tooth Smart Healthy Start” program at their sites. Conclusion: All stakeholder groups identified oral health as one of the greatest health needs of residents in public housing. Furthermore, if shown to reduce ECC, there is significant interest in implementing the program amongst key public housing stakeholders across the nation.