Bruno Brandoli

Dalhousie University | Dal

A soja tem sido a principal commoditie agrícola brasileira, contribuindo substancialmente para a balança comercial do país. Apesar disso, doenças foliares tem prejudicado o alto rendimento da produção de soja, ocasionando a depreciação do produto final. Este artigo propõe um sistema de visão computacional para monitorar as doenças foliares da soja...

Recent studies on maritime traffic model the interplay between vessels and ports as a graph, which is often built using automatic identification system (AIS) data. However, only a few works explicitly study the evolution of such graphs and, when they do, generally consider coarse-grained time intervals. Our goal is to fill this gap by providing a c...

Magnetic resonance imaging (MRI) is a widely known medical imaging technique used to assess the heart function. Deep learning (DL) models perform several tasks in cardiac MRI (CMR) images with good efficacy, such as segmentation, estimation, and detection of diseases. Many DL models based on convolutional neural networks (CNN) were improved by dete...

In this paper we model spatio-temporal data describing the fishing activities in the Northern Adriatic Sea over four years. We build, implement and analyze a database based on the fusion of two complementary data sources: trajectories from fishing vessels (obtained from terrestrial Automatic Identification System, or AIS, data feed) and fish catch...

Magnetic resonance imaging (MRI) is a widely known medical imaging technique used to assess the heart function. Deep learning (DL) models perform several tasks in cardiac MRI (CMR) images with good efficacy, such as segmentation, estimation, and detection of diseases. Many DL models based on convolutional neural networks (CNN) were improved by dete...

Early identification of the type of skin lesion, some of them carcinogenic, is of paramount importance. Currently, the use of Convolutional Neural Networks (CNNs) is the mainline of investigation for the automated analysis of such lesions. Most of the existing works, however, were designed by transfer learning general-purpose CNN architectures, ada...

Maritime autonomous transportation has played a crucial role in the globalization of the world economy. Deep Reinforcement Learning (DRL) has been applied to automatic path planning to simulate vessel collision avoidance situations in open seas. End-to-end approaches that learn complex mappings directly from the input have poor generalization to re...

Corrosion identification and repair is a vital task in aircraft maintenance to ensure continued structural integrity. Regarding fuselage lap joints, typically, visual inspections are followed by non-destructive methodologies, which are time-consuming. The visual inspection of large areas suffers not only from subjectivity but also from the variable...

Time-series forecasting is one of the most active research topics in artificial intelligence. A still open gap in that literature is that statistical and ensemble learning approaches systematically present lower predictive performance than deep learning methods. They generally disregard the data sequence aspect entangled with multivariate data repr...

The supplemental material contains material that is not included within the paper itself.

Pesticides have been heavily used in the cultivation of major crops, contributing to the increase of crop production over the past decades. However, in many cases their appropriate use and calibration of machines rely upon dated evaluation methodologies that cannot precisely estimate how well the pesticides' are being applied to the crop. A few str...

This paper presents the results of the evaluation of five deep learning architectures for the classification of soybean pest images. The performance of Inception-v3, Resnet-50, VGG-16, VGG-19 and Xception was evaluated for different fine-tuning and transfer learning strategies over a dataset of 5,000 images captured in real field conditions. The ex...

The interest for patient trajectory prediction, a sort of computer-aided medicine, has steadily increased with the pace of artificial intelligence innovation. Notwithstanding, the design of effective systems able to predict clinical outcomes based on the history of a patient is far from trivial. Works so far are based on neural architectures with l...

Pesticide application has been heavily used in the cultivation of major crops, contributing to the increase of crop production over the past decades. However, their appropriate use and calibration of machines rely upon evaluation methodologies that can precisely estimate how well the pesticides' spraying covered the crops. A few strategies have bee...

Time-series forecasting is one of the most active research topics in predictive analysis. A still open gap in that literature is that statistical and ensemble learning approaches systematically present lower predictive performance than deep learning methods as they generally disregard the data sequence aspect entangled with multivariate data repres...

The global expansion of maritime activities and the development of the Automatic Identification System (AIS) have driven the advances in maritime monitoring systems in the last decade. Given the enormous volume of vessel data continuously being generated, real-time analysis of vessel behaviors is only possible because of decision support systems pr...

Unmanned Surface Vehicles technology (USVs) is an exciting topic that essentially deploys an algorithm to safely and efficiently performs a mission. Although reinforcement learning is a well-known approach to modeling such a task, instability and divergence may occur when combining off-policy and function approximation. In this work, we used deep r...

The global expansion of maritime activities and the development of the Automatic Identification System (AIS) have driven the advances in maritime monitoring systems in the last decade. Given the enormous volume of vessel data continuously being generated, real-time analysis of vessel behaviors is only possible because of decision support systems pr...

The global expansion of maritime activities and the development of the Automatic Identification System (AIS) have driven the advances in maritime monitoring systems in the last decade. Monitoring vessel behavior is fundamental to safeguard maritime operations, protecting other vessels sailing the ocean and the marine fauna and flora. Given the enor...

Unmanned Surface Vehicles technology (USVs) is an exciting topic that essentially deploys an algorithm to safely and efficiently performs a mission. Although reinforcement learning is a well-known approach to modeling such a task, instability and divergence may occur when combining off-policy and function approximation. In this work, we used deep r...

Unmanned Surface Vehicles technology (USVs) is an exciting topic that essentially deploys an algorithm to safely and efficiently performs a mission. Although reinforcement learning is a well-known approach to modeling such a task, instability and divergence may occur when combining off-policy and function approximation. In this work, we used deep...

The availability of a large amount of Automatic Identification System (AIS) data has fostered many studies on maritime vessel traffic during recent years, often representing vessels and ports relationships as graphs. Although the continuous research effort, only a few works explicitly study the evolution of such graphs and often consider coarse-gra...

Pesticide application has been heavily used in the cultivation of major crops, contributing to the increase of crop production overthe past decades. However, their appropriate use and calibration of machines rely upon evaluation methodologies that can preciselyestimate how well the pesticides’ spraying covered the crops. A few strategies have been...

The occurrence of insect pest attacks in soybean fields has worried farmers around the world. Early and automatic diagnosis of insect pests number could assess the infestation level of each plantation area to optimize the applications of pesticides in the crop and, consequently, reduce production costs and environmental impact. Recent research on i...

Soybean has been the main Brazilian agricultural commodity, contributing substantially to the country's trade balance. However, foliar diseases have hindered the high yield of soybean production, leading to depreciation of the final product. This paper proposes a computer vision system to track soybean foliar diseases in the field using images capt...

Automated medical prognosis has gained interest as artificial intelligence evolves and the potential for computer-aided medicine becomes evident. Nevertheless, it is challenging to design an effective system that, given a patient's medical history, is able to predict probable future conditions. Previous works, mostly carried out over private datase...

Plant diseases are a crucial issue in agriculture. An accurate and automatic identification of leaf diseases could help to develop an early response to reduce economic losses. Recent research in plant diseases has adopted deep neural networks. However, such research has used the models as a black-box passing the labeled images through the networks....

We propose an algebraic tool-set and related algorithms to track access problems not obviously observed in urban environments represented as street mashes. Our tool-set assumes that points of interest must be promptly accessible within the paths of a street network. Over that assumption, we introduce formalisms and computational tools to detect and...

Complex networks are nowadays employed in several applications. Modeling urban street networks is one of them, and in particular to analyze criminal aspects of a city. Several research groups have focused on such application, but until now, there is a lack of a well-defined methodology for employing complex networks in a whole crime analysis proces...

Pantanal is one of the most important biomes of the world, with a large number of wild animal species, some of them are in extinction. The automatic identification of wild animals is extremely important for the estimation of the species' population within Pantanal. However, digital processing techniques for the identification and tracking of specie...

Complex networks can be used for modeling street meshes and urban agglomerates. With such a model, many aspects of a city can be investigated to promote a better quality of life to its citizens. Along these lines, this paper proposes a set of distance-based pattern-discovery algorithmic instruments to improve urban structures modeled as complex net...

The need for higher agricultural productivity has demanded the intensive use of pesticides. However, their correct use depends on assessment methods that can accurately predict how well the pesticides' spraying covered the intended crop region. Some methods have been proposed in the literature, but their high cost and low portability harm their wid...

Complex networks can be used for modeling street meshes and urban agglomerates. With such a model, many aspects of a city can be investigated to promote a better quality of life to its citizens. Along these lines, this paper proposes a set of distance-based pattern-discovery algorithmic instruments to improve urban structures modeled as complex net...

Complex networks are commonly used to model urban street networks, which allows aiding the analysis of criminal activities in cities. Despite several works focusing on such application, there is a lack of a clear methodology focused in the analysis of crime behavior. In this sense, we propose a methodology for employing complex networks in the anal...

The need for higher agricultural productivity has demanded the intensive use of pesticides. However, their correct use depends on assessment methods that can accurately predict how well the pesticides' spraying covered the intended crop region. Some methods have been proposed in the literature, but their high cost and low portability harm their wid...

Soybean has been the main Brazilian agricultural commodity, contributing substantially to the country's trade balance. However, foliar diseases are the key factor that can undermine the soy production, usually caused by fungi, bacteria, viruses, and nematodes. This letter proposes a computer vision system to track soybean foliar diseases in the fie...

We introduce a method for shape recognition based on the angular analysis of Complex Networks. Our method models shapes as Complex Networks defining a more descriptive representation of the inner angularity of the shape’s perimeter. The result is a set of measures that better describe shapes if compared to previous approaches that use only the vert...

Texture is one of the primary visual features used to computationally describe the patterns found in nature. Existing computational methods, however, do not successfully discriminate the complexity of texture patterns. Such methods disregard the possibility of describing images by benefiting from the complex systems properties that are characterist...

Here we show that epidemic propagation models, allied with network mapping techniques and community-related measures, can aid in the characterization of criminal behavior and dispersion in a city.

Complex networks have been widely used in science and technology because of their ability to represent several systems. One of these systems is found in Biochemistry, in which the synthesis of new nanoparticles is a hot topic. However, the interpretation of experimental results in the search of new nanoparticles poses several challenges. This is du...

Complex networks are nowadays employed in several applications. Modeling urban street networks is one of them, and in particular to analyze criminal aspects of a city. Several research groups have focused on such application, but until now, there is a lack of a well-defined methodology for employing complex networks in a whole crime analysis proces...

Soybean is one of the ten greatest crops in the world, answering for billion-dollar businesses every year. This crop suffers from insect herbivory that costs millions from producers. Hence, constant monitoring of the crop foliar damage is necessary to guide the application of insecticides. However, current methods to measure foliar damage are expen...

Soybean is one of the ten greatest crops in the world, answering for billion-dollar businesses every year. This crop suffers from insect herbivory that costs millions from producers. Hence, constant monitoring of the crop foliar damage is necessary to guide the application of insecticides. However, current methods to measure foliar damage are expen...

The detection of diseases is of vital importance to increase the productivity of soybean crops. The presence of the diseases is usually conducted visually, which is time-consuming and imprecise. To overcome these issues, there is a growing demand for technologies that aim at early and automated disease detection. In this line of work, we introduce...

Resumo—In recent years, image classification has been extensively studied in computer vision to be a challenging topic. Applications of image classification include scene, object, face recognition, among others. This work aims to add spatial information in the bag-of-visual-words (BOVW) by combining the spatial pyramid technique with the modeling o...

Resumo—The texture analysis is one of the most important research areas in computer vision. Currently, complex network has emerged as an approach for representing images due to its flexibility for modeling several problems. Generally, the application of the complex network theory involves two steps: representing the structure of interest into a net...

In this paper, we propose a novel approach for dynamic texture representation based on complex networks. In the proposed approach, each pixel of the video is mapped into a node of the complex network. Initially, a regular complex network is obtained by connecting two nodes if the Euclidean distance between their related pixels is equal or less than...

Texture plays an important role in computer vision tasks. Several methods of texture analysis are available. However, these methods are not capable of extracting rich detail in images. This paper presents a novel approach to image texture classification based on the artificial crawler model. Here, we propose a new rule of movement that moves artifi...

Texture is an important visual attribute used to describe images. There are many methods available for texture analysis. However, they do not capture the details richness of the image surface. In this paper, we propose a new method to describe textures using the artificial crawler model. This model assumes that each agent can interact with the envi...

In this paper, we propose a novel approach for texture analysis based on artificial crawler model. Our method assumes that each agent can interact with the environment and each other. The evolution process converges to an equilibrium state according to the set of rules. For each textured image, the feature vector is composed by signatures of the li...

Texture is an important visual attribute used to plant leaf identification. Although there are many methods of texture analysis, some of them specifically for interpreting leaf images is still a challenging task because of the huge pattern variation found in nature. In this paper, we investigate the leaf texture modeling based on the partial differ...

In this paper, we propose a novel approach for texture analysis based on artificial crawler model. Our method assumes that each agent can interact with the environment and each other. The evolution process converges to an equilibrium state according to the set of rules. For each textured image, the feature vector is composed by signatures of the li...

Texture analysis is an important field of investigation that has received a great deal of interest from computer vision community. In this paper, we propose a novel approach for texture modeling based on partial differential equation (PDE). Each image $f$ is decomposed into a family of derived sub-images. $f$ is split into the $u$ component, obtain...

This paper presents a new method for dynamic texture recognition based on spatiotemporal Gabor filters. Dynamic textures have emerged as a new field of investigation that extends the concept of self-similarity of texture image to the spatiotemporal domain. To model a dynamic texture, we convolve the sequence of images to a bank of spatiotemporal Ga...

Texture is an important visual attribute used to discriminate images. Although statistical features have been successful, texture descriptors do not capture the richness of details present in the images. In this paper we propose a novel approach for texture analysis based on partial differential equations (PDE) of Perona and Malik. Basically, an in...

This paper presents a comparison between two image segmentation approaches based on background subtrac-tion and supervised learning. Real images from two im-portant issues, which have been studied by several com-puter vision research groups, were used in our experiments: namely, sign language interpretation and mouse behavior classification. Accord...

Providing realistic, high-resolution and high fidelity representation of motions ia essential in the cloth simulation problem. In order to make high resolution simulations tractable, several algorithms have been developed that manage cloth-object interactions efficiently through specialized data structures such as AABB trees. However, implementatio...

Shape representation provides fundamental features for many applications in computer vision and it is known to be important cues for human vision. This paper presents an experimental study on recognition of mice behavior. We investigate the performance of the four shape recognition methods, namely Chain-Code, Curvature, Fourier descriptors and Zern...

Many image-related applications rely on the fact that the dataset under investigation is correctly represented by features. However, defining a set of features that properly represents a dataset is still a challenging and, in most cases, an exhausting task. Most of the available techniques, especially when a large number of features is considered,...

Classification is an important task for computer-aided diagnosis systems (CADs). However, many classifiers may not perform well, presenting poor generalization and high computational cost, especially when dealing with highdimensional datasets. Thus, feature selection can greatly mitigate these problems. In this paper, we propose two filter-based fe...

This paper proposes a novel way to combine different observation models in a particle filter framework. This, so called, auto-adjustable observation model, enhance the particle filter accuracy when the tracked objects overlap without infringing a great runtime penalty to the whole tracking system. The approach has been tested under two important re...

The application of hidden models of Markov (HMM) has been fre- quently used in behaviors recognition system. In this paper the HMM is applied for recognition of three mice behaviors: self-cleaning, horizontal and vertical exploration. There were carried out experiments with the variations of some parameters of the model, where a percentage of corre...

This paper presents an application of the hidden Markov models (HMMs) to the recognition of snakes behaviors, an important and hard problem that, as far as the authors know, has not been tackled before, by the computer vision community. Experiments were conducted using different HMM configurations, including modifications on the number of internal...