Robust non-blind color video watermarking using QR decomposition and entropy analysis

Issues such as content identification, document and image security, audience measurement, ownership and copyright among others can be settled by the use of digital watermarking. Many recent video watermarking methods show drops in visual quality of the sequences. The present work addresses the aforementioned issue by introducing a robust and imperceptible non-blind color video frame watermarking algorithm. The method divides frames into moving and non-moving parts. The non-moving part of each color channel is processed separately using a block-based watermarking scheme. Blocks with an entropy lower than the average entropy of all blocks are subject to a further process for embedding the watermark image. Finally a watermarked frame is generated by adding moving parts to it. Several signal processing attacks are applied to each watermarked frame in order to perform experiments and are compared with some recent algorithms. Experimental results show that the proposed scheme is imperceptible and robust against common signal processing attacks

CentralNet: a Multilayer Approach for Multimodal Fusion

This paper proposes a novel multimodal fusion approach, aiming to produce best possible decisions by integrating information coming from multiple media. While most of the past multimodal approaches either work by projecting the features of different modalities into the same space, or by coordinating the representations of each modality through the use of constraints, our approach borrows from both visions. More specifically, assuming each modality can be processed by a separated deep convolutional network, allowing to take decisions independently from each modality, we introduce a central network linking the modality specific networks. This central network not only provides a common feature embedding but also regularizes the modality specific networks through the use of multi-task learning. The proposed approach is validated on 4 different computer vision tasks on which it consistently improves the accuracy of existing multimodal fusion approaches

Empowerment through journalism: social change through youth media production in northeast Brazil [abstract]

Abstract only availableJournalism is a process in which people can begin to understand their realities and can be used as a powerful force in democratic societies for or against change. Specifically, youth journalism engages students in identifying themes that elicit social and emotional involvement and a high level of motivation to participate. This thesis intends to explore the question of how journalism can be used as a tool of empowerment in building the capacity of youth to become aware of their own realities and communicate these realities to others through a newspaper. I also explore how the production is linked to social justice by analyzing how it allows the youth of Daruê Malungo, a Center for Arts and Education, in Recife, Brazil to examine visible and invisible systems shaping their interactions and identities. My methodology for this research included teaching a journalism class using Paulo Freire's theory in the Pedagogy of the Oppressed and the development of a newspaper made by the students. I argue that the newspaper by the students at Daruê Malungo allowed them to navigate experiences of difference in terms of race, class, privilege, and oppression. Their production was linked to social justice because it was cry, “”um lamento” as the students decided to name their newspaper, for social action in terms of the racial prejudice that still surrounds them, the violence and drug problems in their community, the lack of education they receive, the pollution and abuse of the environment, and an explanation of how they express themselves through their culture. This journalism production created a space for youth development and empowerment, in which students said they weren't afraid to be silent anymore: they were given the opportunity to tell their community, their country and the world what was important to them and why they wanted change.School for International Trainin

Exploiting feature representations through similarity learning, post-ranking and ranking aggregation for person re-identification

Person re-identification has received special attention by the human analysis community in the last few years. To address the challenges in this field, many researchers have proposed different strategies, which basically exploit either cross-view invariant features or cross-view robust metrics. In this work, we propose to exploit a post-ranking approach and combine different feature representations through ranking aggregation. Spatial information, which potentially benefits the person matching, is represented using a 2D body model, from which color and texture information are extracted and combined. We also consider background/foreground information, automatically extracted via Deep Decompositional Network, and the usage of Convolutional Neural Network (CNN) features. To describe the matching between images we use the polynomial feature map, also taking into account local and global information. The Discriminant Context Information Analysis based post-ranking approach is used to improve initial ranking lists. Finally, the Stuart ranking aggregation method is employed to combine complementary ranking lists obtained from different feature representations. Experimental results demonstrated that we improve the state-of-the-art on VIPeR and PRID450s datasets, achieving 67.21% and 75.64% on top-1 rank recognition rate, respectively, as well as obtaining competitive results on CUHK01 dataset.Comment: Preprint submitted to Image and Vision Computin

CELL DEATH AND VIABILITY IN MARINE PHYTOPLANKTON

CITONATAplicación de ensayos in vitro para la detección precoz de ficotoxinas en muestras de poblaciones fitoplanctónicas multiespecífica

Duplications in nomenclature

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62822/1/389539a0.pd

Overcoming Calibration Problems in Pattern Labeling with Pairwise Ratings: Application to Personality Traits

We address the problem of calibration of workers whose task is to label patterns with continuous variables, which arises for instance in labeling images of videos of humans with continuous traits. Worker bias is particularly difficult to evaluate and correct when many workers contribute just a few labels, a situation arising typically when labeling is crowd-sourced. In the scenario of labeling short videos of people facing a camera with personality traits, we evaluate the feasibility of the pairwise ranking method to alleviate bias problems. Workers are exposed to pairs of videos at a time and must order by preference. The variable levels are reconstructed by fitting a Bradley-Terry-Luce model with maximum likelihood. This method may at first sight, seem prohibitively expensive because for N videos, p=N(N−1)/2 pairs must be potentially processed by workers rather that N videos. However, by performing extensive simulations, we determine an empirical law for the scaling of the number of pairs needed as a function of the number of videos in order to achieve a given accuracy of score reconstruction and show that the pairwise method is affordable. We apply the method to the labeling of a large scale dataset of 10,000 videos used in the ChaLearn Apparent Personality Trait challenge

Seniors’ ability to decode differently aged facial emotional expressions

The present investigation aims at assessing elders' ability to decode facial emotional expressions conveyed by differently aged people in order to confirm (or disconfirm) the appropriateness of the 'own age bias' theory, as well as investigate effects of different ages and different emotional categories. The study, involves 44 healthy elders (23 females), aged 65+ (mean age=75.09; SD=±7.9) which were requested to label 76 pictures depicting elders, middle-aged and young women and men displaying the six facial emotional expressions of disgust, anger, fear, sadness, happiness and neutrality. Results show a complex pattern of influences that calls for more deep investigations on the features to be accounted by providing socially and emotionally believable interfaces of effective and efficient algorithms to detect and decode their users' emotional facial expressions

Convolutional Neural Network Super Resolution for Face Recognition in Surveillance Monitoring

Due to the importance of security in society, monitoring activities and recognizing specific people through surveillance video cameras play an important role. One of the main issues in such activity arises from the fact that cameras do not meet the resolution requirement for many face recognition algorithms. In order to solve this issue, in this paper we are proposing a new system which super resolves the image using deep learning convolutional network followed by the Hidden Markov Model and Singular Value Decomposition based face recognition. The proposed system has been tested on many well-known face databases such as FERET, HeadPose, and Essex University databases as well as our recently introduced iCV Face Recognition database (iCV-F). The experimental results show that the recognition rate is improving considerably after apply the super resolution