Utility-based summarization of home videos

The aim of this work is to devise an effective method for static summarization of home video sequences. Based on the premise that the user watching a summary is interested in people related (how many, who, emotional state) or activity related aspects, we formulate a novel approach to video summarization that works to specifically expose relevant video frames that make the content spotting tasks possible. Unlike existing approaches, which work on low-level features which often produce the summary not appealing to the viewer due to the semantic gap between low-level features and high-level concepts, our approach is driven by various utility functions (identity count, identity recognition, emotion recognition, activity recognition, sense of space) that use the results of face detection, face clustering, shot clustering and within cluster frame alignment. The summarization problem is then treated as the problem of extracting the set of key frames that have the maximum combined utility.

Image source detection : a case study on Facebook images taken by iPhones

Forged and tempered digital images become increasingly common on Facebook to aid computer frauds. The situation is worsened as many users can use a phone to take a photo and upload it to Facebook within two clicks, which highlights the need of image forensics for the cyber fraud cases. In this paper, we show the existence of the Facebook image filter which automatically changes the Facebook photos and consequently challenges the validity of forensic results. We aim to enable forensic investigators to relate a seized camera and a Facebook image. Specifically, we utilize intrinsic sensor pattern noise produced by a camera's lens to derive forensically useful information as Photo Response Non-Uniformity (PRNU) patterns. We propose to compare the PRNU patterns of a Facebook image and the flat field images produced by the candidate cameras. And we conclude this method to be effective by successfully identifying the correct iPhone from a list of four for a given Face book image.

Seabird and marine mammal management options in the face of climate change

Climate change is already impacting Australia’s oceans. Responses by marine life to both climate variability and change have been documented for low trophic levels, however, responses for Australia’s iconic higher trophic level marine taxa are poorly understood, including for many conservation-dependent seabirds and marine mammals. We report initial results from a national study evaluating impacts an adaptation options. Individual time series and combined analyses show consistent responses to historical climate signals, however, improved monitoring protocols are needed to maximize detection of any climate-related demographic signals. Despite difference in sampling , the development of regional multi-species-indices of environmental change provides robust climate indicators over large regions.

On Person Authentication by Fusing Visual and Thermal Face Biometrics

Recognition algorithms that use data obtained by imaging faces in the thermal spectrum are promising in achieving invariance to extreme illumination changes that are often present in practice. In this paper we analyze the performance of a recently proposed face recognition algorithm that combines visual and thermal modalities by decision level fusion. We examine (i) the effects of the proposed data preprocessing in each domain, (ii) the contribution to improved recognition of different types of features, (iii) the importance of prescription glasses detection, in the context of both 1-to-N and 1-to-1 matching (recognition vs. verification performance). Finally, we discuss the significance of our results and, in particular, identify a number of limitations of the current state-of-the-art and propose promising directions for future research.