RECODist has been awared with INOVA Prize 2017

We are very proud to announce that Gabriel Bertocco, one of our RECODists, has been awarded with the Unicamp INOVA Prize on Introduction to Innovation 2017. Supervised by Prof. Anderson Rocha and Fernanda Andaló, the work entitled “Automatic age range estimation on mobile devices” is the result of a partnership between RECOD and Motorola. The Unicamp INOVA Prize awarded to most innovative works in applied or basic sciences made by undergraduate students under scientific tutorial at Unicamp.

Another research work from RECOD, entitled “CrowdPet: Deep learning applied to the detection of dogs in the wild”, was also among the finalists in the technological area.

The related links are (in Portuguese):

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Talk: Alex Kot from NTU, Singapore

The first DéjàVu invited talked happened this week, on Nov. 27th, at IC/Unicamp. In his talk, Prof. Alex Kot, director of ROSE Lab at Nanyang Technological University (NTU), Singapore, presented the most relevant ongoing research project at the Rapid-Rich Object Search (ROSE) Lab, which is comprised of 12 faculty, 26 research staff and a total of 48 PhD students from the Schools of Electrical & Electronic Engineering (EEE) and Computer Science & Engineering (SCSE) at the NTU.

The ROSE Lab is focusing on visual object search (including object classification, recognition, & retrieval); deep learning & visual analytics (including anomaly detection, pedestrian detection, person re-identification, object tracking, action recognition); as well as multimedia forensics & biometrics (including face spoofing & liveness detection) Since 2012, the ROSE Lab has secured 24 industry partners, including Tencent (one of the largest Internet companies in Asia), NVIDIA (the world’s leading visual computing company), Accenture (one of the world’s leading management consulting companies), and OMRON (a leading Japanese industry automation company).

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1st Workshop of “The Secret of Playing Football: Brazil versus the Netherlands” project

On last Nov. 9th, 2017 the first workshop of the FAPESP research project entitled “The Secret of Playing Football: Brazil versus the Netherlands” took place at FEF/Unicamp. The presentations were given by Prof. Miguel de Arruda (FEF director), Prof. Ricardo Torres (project PI), Prof. Claudio Gobatto, Prof. Thomas Beltrame, Prof. Barreto, Prof. Felipe Moura, Prof. Paulo Santiago, Prof. Paulo Ruffino and Prof. Sergio Cunha.

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1st Advanced Computing Workshop

The 1st Advanced Computing Workshop, which is in partnership with the Army Research Office/US, took place in São Paulo on November 13th, 2017. In the video below, Prof. Anderson Rocha explains how three RECOD’s projetcs, DeepEyes, MediFor and DéjàVu, are related and how they are contributing with Digital Forensics research advances.

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RECODists Talks

During the last weeks, RECODists were performing several talks about the research projects we have been carrying on. They are:

  • Deep Learning” at EncPos 2017, IMECC Unicamp – Eduardo Valle

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DéjàVu on the news

Fapesp DéjàVu project, which will start soon, is attracting attention of Brazilian press for its high objectives and potential of impact in the society at large. They are (in Portuguese):

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Talk: Remote Sensing Image Analysis in Phenology Studies: Challenges and Research Opportunities

Next Friday, on October 20th, Prof. Ricardo Torres will talk about Remote Sensing Image Analysis in Phenology Studies: Challenges and Research Opportunities at the Workshop on Pattern Recognition for Earth Observation in conjunction with the SIBGRAPI 2017 – Conference on Graphics, Patterns and Images. Check out the workshop program.

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Graph-based Bag-of-Words for Classification

This paper, entitled Graph-based Bag-of-Words for Classification, introduces the Bag of Graphs (BoG), a Bag-of-Words model that encodes in graphs the local structures of a digital object. It presents a formal definition, introducing concepts and rules that make this model flexible and adaptable for different applications. It is defined two BoG-based methods – Bag of Singleton Graphs (BoSG) and Bag of Visual Graphs (BoVG), which create vector representations for graphs and images, respectively. The hypothesis explored in this paper is that the combination of graphs with the BoW model can create a discriminant and efficient representation based on local structures of an object, leading to fast and accurate results in classification tasks. The rationale is that the two representations are complementary and can help each other overcome their individual deficiencies.

The authors evaluate the Bag of Singleton Graphs (BoSG) for graph classification on four datasets of the IAM repository, obtaining significant results in accuracy and execution time. The method Bag of Visual Graphs (BoVG) is evaluated for image classification on Caltech and ALOI datasets, and for remote sensing image classification on images of Monte Santo and Campinas datasets. This framework opens possibilities for retrieval, classification, and clustering tasks on large datasets that use graph-based representations impractical before due to the complexity of inexact graph matching.

Fernanda B. Silva, Rafael de O. Werneck, Siome Goldenstein, Salvatore Tabbone, Ricardo da S. Torres, Graph-based Bag-of-Words for Classification, Pattern Recognition (2017), doi: 10.1016/j.patcog.2017.09.018

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Talk: Remote Sensing Image Analysis in Phenology Studies

Tomorrow, on Oct 6th 2017 at 14h IC/Unicamp auditorium, Prof. Ricardo Torres will give a talk about Remote Sensing Image Analysis in Phenology Studies. The talk is part of the Institute of Computing weekly seminars series of 2017.

Title: Remote Sensing Image Analysis in Phenology Studies: Challenges and Research Opportunities

Abstract: Environmental changes are becoming an important issue in the global agenda. Typical change understanding ecological studies rely on the use of huge volumes of remote sensing image, demanding the definition of effective and efficient services for appropriate storing, retrieval, and knowledge extraction. This lecture will focus on presenting ongoing research initiatives focused on the specification and implementation of appropriate systems to handle large-scale remote sensing image collections. Special attention will be given to recent research results on image processing, machine learning, and time series analysis in the context of the e-Phenology project. The e-Phenology is a multidisciplinary project that combines research efforts in Computer Science and Phenology. Its objective is to address practical and theoretical problems involved in the use of new technologies to the remote observation of phenology, aiming to detect local environmental changes.

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Two Ph.D. thesis defense: Hilario Seibel and Danille Rodrigues

The RECODists Hilario Seibel Jr. and Daniele Rodrigues have earned their Ph.D. degrees last Friday September 29th at IC/Unicamp. The details are shown below:

Ph.D. candidate: Hilario Seibel
Title: Super-resolution in low-quality videos for forensics, surveillance, and mobile applications
Supervisor: Siome Goldenstein
Co-supervisor: Anderson Rocha

Super-resolution (SR) algorithms are methods for achieving high-resolution (HR) enlargements of pixel-based images. In multi-frame super resolution, a set of low-resolution (LR) images of a scene are combined to construct an image with higher resolution. Super resolution is an inexpensive solution to overcome the limitations of image acquisition hardware systems, and can be useful in several cases in which the device cannot be upgraded or replaced, but multiple frames of the same scene can be obtained. In this work, we explore SR possibilities for natural images, in scenarios wherein we have multiple frames of a same scene. We design and develop variations of an algorithm which rely on exploring geometric properties in order to combine pixels from LR observations into an HR grid; two variations of a method that combines inpainting techniques to multi-frame super resolution; and three variations of an algorithm that uses adaptive filtering and Tikhonov regularization to solve a least-square problem.

Multi-frame super resolution is possible when there is motion and non-redundant information from LR observations. However, combining a large number of frames into a higher resolution image may not be computationally feasible by complex super-resolution techniques. The first application of the proposed methods is in consumer-grade photography with a setup in which several low-resolution images gathered by recent mobile devices can be combined to create a much higher resolution image. Such always-on low-power environment requires e active high-performance algorithms, that run fastly and with a low-memory footprint.

The second application is in Digital Forensic, with a setup in which low-quality surveil- lance cameras throughout the cities could provide important cues to identify a suspect vehicle, for example, in a crime scene. However, license-plate recognition is especially di cult under poor image resolutions. Hence, we design and develop a novel, free and open-source framework underpinned by SR and Automatic License-Plate Recognition (ALPR) techniques to identify license-plate characters in low-quality real-world traffic videos, captured by cameras not designed for the ALPR task, aiding forensic analysts in understanding an event of interest. The framework handles the necessary conditions to identify a target license plate, using a novel methodology to locate, track, align, su- per resolve, and recognize its alphanumerics. The user receives as outputs the rectitude and super-resolved license-plate, richer in details, and also the sequence of license-plates characters that have been automatically recognized in the super-resolved image.

We present quantitative and qualitative validations of the proposed algorithms and its applications. Our experiments show, for example, that SR can increase the number of correctly recognized characters posing the framework as an important step toward providing forensic experts and practitioners with a solution for the license-plate recognition problem under difficult acquisition conditions. Finally, we also suggest a minimum number of images to use as input in each application.

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Ph.D. candidate: Daniele Rodrigues
Title: Complex Network Measurements in Graph-based Spatio-Temporal Soccer Match Analysis
Supervisor: Ricardo Torres

Soccer match analysis is of paramount importance in the definition of appropriate training programs and game strategies. The increasing availability of sport-related data in the recent years, due to the use of modern tracking systems, has allowed advances in sports analytics, providing coaches with valuable information for match and teams analysis. The availability of these data, on the other hand, challenges science to develop tools capable of storing, visualizing, and analyzing this large volume of information. Soccer analyses are usually performed using matches’ statistics, events (e.g., passes and shots on goal) and players location data. Related studies have been representing the matches’ events as a single graph, where players are vertices and edges are actions performed among them during the match. The graph is then analyzed from a complex network measurement perspective. Although this approach provides interesting insights about the tactical actions occurred during the game, revealing some tactical patterns, it disregards the spatio-temporal aspects inherent to the sport, as the positioning of the players on the pitch, and the moment in time when relevant actions occur.

This thesis addresses these shortcomings by presenting a soccer game analysis framework. We propose a new approach for soccer match analysis, based on graphs, that considers the spatio-temporal characteristics, intrinsic to the dynamic of soccer. We propose to represent the match as a temporal graph, by encoding players’ location on the pitch into instant graphs, in which vertices represent players in their real location and edges are defined based on their distance in the field and the possibility of short pass exchanges. We demonstrate that this representation, named opponent-aware graph, which takes into account the presence of opponents, and the diversity entropy measurement are effective tools for determining the role of attacking players in a match and the probability of successful passes. By taking into account different measurements of complex networks in temporal graphs, this study also investigates the feasibility of using complex network measurements and machine learning algorithms to characterize the role of players in a match. The results allow to further characterize the decision-making process of players, providing interesting insights to coaches and researchers for possibly improving training strategies. This study also addresses the visualization of temporal graphs problem by introducing the Graph Visual Rhythm, a novel image-based representation to visualize changing patterns typically found in temporal graphs. This representation is based on the concept of visual rhythms, motivated by its capacity of providing a lot of contextual information about graph dynamics in a compact way. We validate the use of graph visual rhythms through the creation of a visual analytics tool to support the decision-making process based on complex-network-oriented soccer match analysis.

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