Anderson Rocha - Academia.edu (original) (raw)
Papers by Anderson Rocha
This paper presents the RECOD approaches used in the MediaEval 2014 Violent Scenes Detection task... more This paper presents the RECOD approaches used in the MediaEval 2014 Violent Scenes Detection task. Our system is based on the combination of visual, audio, and text features. We also evaluate the performance of a convolutional network as a feature extractor. We combined those features using a fusion scheme. We participated in the main and the generalization tasks.
2012 19th Ieee International Conference on Image Processing, Sep 1, 2012
ABSTRACT Image categorization by means of bag of visual words has received increasing attention b... more ABSTRACT Image categorization by means of bag of visual words has received increasing attention by the image processing and vision communities in the last years. In these approaches, each image is represented by invariant points of interest which are mapped to a Hilbert Space representing a visual dictionary which aims at comprising the most discriminative features in a set of images. Notwithstanding, the main problem of such approaches is to find a compact and representative dictionary. Finding such representative dictionary automatically with no user intervention is an even more difficult task. In this paper, we propose a method to automatically find such dictionary by employing a recent developed graph-based clustering algorithm called Optimum-Path Forest, which does not make any assumption about the visual dictionary's size and is more efficient and effective than the state-of-the-art techniques used for dictionary generation.
... Points of Interest and Visual Dictionary for Retina Pathology Detection Anderson Rocha TiagoC... more ... Points of Interest and Visual Dictionary for Retina Pathology Detection Anderson Rocha TiagoCarvalho Siome Goldenstein Jacques Wainer ... for Retina Pathology Detection Anderson Rocha∗Tiago Carvalho Siome Goldenstein Jacques Wainer Abstract ...
ABSTRACT Image categorization by means of bag of visual words has received increasing attention b... more ABSTRACT Image categorization by means of bag of visual words has received increasing attention by the image processing and vision communities in the last years. In these approaches, each image is represented by invariant points of interest which are mapped to a Hilbert Space representing a visual dictionary which aims at comprising the most discriminative features in a set of images. Notwithstanding, the main problem of such approaches is to find a compact and representative dictionary. Finding such representative dictionary automatically with no user intervention is an even more difficult task. In this paper, we propose a method to automatically find such dictionary by employing a recent developed graph-based clustering algorithm called Optimum-Path Forest, which does not make any assumption about the visual dictionary's size and is more efficient and effective than the state-of-the-art techniques used for dictionary generation.
Sufficient image quality is a necessary prerequisite for reliable automatic detection systems in ... more Sufficient image quality is a necessary prerequisite for reliable automatic detection systems in several healthcare environments. Specifically for Diabetic Retinopathy (DR) detection, poor quality fundus makes more difficult the analysis of discontinuities that characterize lesions, as well as to generate evidence that can incorrectly diagnose the presence of anomalies. Several methods have been applied for classification of image quality and recently, have shown satisfactory results. However, most of the authors have focused only on the visibility of blood vessels through detection of blurring. Furthermore, these studies frequently only used fundus images from specific cameras which are not validated on datasets obtained from different retinographers. In this paper, we propose an approach to verify essential requirements of retinal image quality for DR screening: field definition and blur detection. The methods were developed and validated on two large, representative datasets collected by different cameras. The first dataset comprises 5,776 images and the second, 920 images. For field definition, the method yields a performance close to optimal with an area under the Receiver Operating Characteristic curve (ROC) of 96.0%. For blur detection, the method achieves an area under the ROC curve of 95.5%.
Biometrics systems have significantly improved person identification and authentication, playing ... more Biometrics systems have significantly improved person identification and authentication, playing an important role in personal, national, and global security. However, these systems might be deceived (or "spoofed") and, despite the recent advances in spoofing detection, current solutions often rely on domain knowledge, specific biometric reading systems, and attack types. We assume a very limited knowledge about biometric spoofing at the sensor to derive outstanding spoofing detection systems for iris, face, and fingerprint modalities based on two deep learning approaches. The first approach consists of learning suitable convolutional network architectures for each domain, while the second approach focuses on learning the weights of the network via back-propagation. We consider nine biometric spoofing benchmarks -each one containing real and fake samples of a given biometric modality and attack type -and learn deep representations for each benchmark by combining and contrasting the two learning approaches. This strategy not only provides better comprehension of how these approaches interplay, but also creates systems that exceed the best known results in eight out of the nine benchmarks. The results strongly indicate that spoofing detection systems based on convolutional networks can be robust to attacks already known and possibly adapted, with little effort, to image-based attacks that are yet to come.
Nowadays the use of tools for manipulating images and videos is increasingly common. Such tools f... more Nowadays the use of tools for manipulating images and videos is increasingly common. Such tools facilitate the task of creating manipulations and deceiving the perception of observers on the semantics of these documents. Although there are image manipulations considered innocent (e.g., correction of brightness), there are those considered malicious, such as the copy-paste and composition operations. In this paper, we discuss the main challenges present in the forensic authentication of digital documents such as images and videos as well as our most recent contributions in this context.
Proceedings Ieee International Conference on Computer Vision Ieee International Conference on Computer Vision, Oct 1, 2007
Revista De Informatica Teorica E Aplicada, May 18, 2015
This paper presents the RECOD approaches used in the MediaEval 2014 Violent Scenes Detection task... more This paper presents the RECOD approaches used in the MediaEval 2014 Violent Scenes Detection task. Our system is based on the combination of visual, audio, and text features. We also evaluate the performance of a convolutional network as a feature extractor. We combined those features using a fusion scheme. We participated in the main and the generalization tasks.
2012 19th Ieee International Conference on Image Processing, Sep 1, 2012
ABSTRACT Image categorization by means of bag of visual words has received increasing attention b... more ABSTRACT Image categorization by means of bag of visual words has received increasing attention by the image processing and vision communities in the last years. In these approaches, each image is represented by invariant points of interest which are mapped to a Hilbert Space representing a visual dictionary which aims at comprising the most discriminative features in a set of images. Notwithstanding, the main problem of such approaches is to find a compact and representative dictionary. Finding such representative dictionary automatically with no user intervention is an even more difficult task. In this paper, we propose a method to automatically find such dictionary by employing a recent developed graph-based clustering algorithm called Optimum-Path Forest, which does not make any assumption about the visual dictionary's size and is more efficient and effective than the state-of-the-art techniques used for dictionary generation.
... Points of Interest and Visual Dictionary for Retina Pathology Detection Anderson Rocha TiagoC... more ... Points of Interest and Visual Dictionary for Retina Pathology Detection Anderson Rocha TiagoCarvalho Siome Goldenstein Jacques Wainer ... for Retina Pathology Detection Anderson Rocha∗Tiago Carvalho Siome Goldenstein Jacques Wainer Abstract ...
ABSTRACT Image categorization by means of bag of visual words has received increasing attention b... more ABSTRACT Image categorization by means of bag of visual words has received increasing attention by the image processing and vision communities in the last years. In these approaches, each image is represented by invariant points of interest which are mapped to a Hilbert Space representing a visual dictionary which aims at comprising the most discriminative features in a set of images. Notwithstanding, the main problem of such approaches is to find a compact and representative dictionary. Finding such representative dictionary automatically with no user intervention is an even more difficult task. In this paper, we propose a method to automatically find such dictionary by employing a recent developed graph-based clustering algorithm called Optimum-Path Forest, which does not make any assumption about the visual dictionary's size and is more efficient and effective than the state-of-the-art techniques used for dictionary generation.
Sufficient image quality is a necessary prerequisite for reliable automatic detection systems in ... more Sufficient image quality is a necessary prerequisite for reliable automatic detection systems in several healthcare environments. Specifically for Diabetic Retinopathy (DR) detection, poor quality fundus makes more difficult the analysis of discontinuities that characterize lesions, as well as to generate evidence that can incorrectly diagnose the presence of anomalies. Several methods have been applied for classification of image quality and recently, have shown satisfactory results. However, most of the authors have focused only on the visibility of blood vessels through detection of blurring. Furthermore, these studies frequently only used fundus images from specific cameras which are not validated on datasets obtained from different retinographers. In this paper, we propose an approach to verify essential requirements of retinal image quality for DR screening: field definition and blur detection. The methods were developed and validated on two large, representative datasets collected by different cameras. The first dataset comprises 5,776 images and the second, 920 images. For field definition, the method yields a performance close to optimal with an area under the Receiver Operating Characteristic curve (ROC) of 96.0%. For blur detection, the method achieves an area under the ROC curve of 95.5%.
Biometrics systems have significantly improved person identification and authentication, playing ... more Biometrics systems have significantly improved person identification and authentication, playing an important role in personal, national, and global security. However, these systems might be deceived (or "spoofed") and, despite the recent advances in spoofing detection, current solutions often rely on domain knowledge, specific biometric reading systems, and attack types. We assume a very limited knowledge about biometric spoofing at the sensor to derive outstanding spoofing detection systems for iris, face, and fingerprint modalities based on two deep learning approaches. The first approach consists of learning suitable convolutional network architectures for each domain, while the second approach focuses on learning the weights of the network via back-propagation. We consider nine biometric spoofing benchmarks -each one containing real and fake samples of a given biometric modality and attack type -and learn deep representations for each benchmark by combining and contrasting the two learning approaches. This strategy not only provides better comprehension of how these approaches interplay, but also creates systems that exceed the best known results in eight out of the nine benchmarks. The results strongly indicate that spoofing detection systems based on convolutional networks can be robust to attacks already known and possibly adapted, with little effort, to image-based attacks that are yet to come.
Nowadays the use of tools for manipulating images and videos is increasingly common. Such tools f... more Nowadays the use of tools for manipulating images and videos is increasingly common. Such tools facilitate the task of creating manipulations and deceiving the perception of observers on the semantics of these documents. Although there are image manipulations considered innocent (e.g., correction of brightness), there are those considered malicious, such as the copy-paste and composition operations. In this paper, we discuss the main challenges present in the forensic authentication of digital documents such as images and videos as well as our most recent contributions in this context.
Proceedings Ieee International Conference on Computer Vision Ieee International Conference on Computer Vision, Oct 1, 2007
Revista De Informatica Teorica E Aplicada, May 18, 2015