Using Convolutional Neural Network to Detect and Count Individuals on Eucalyptus Plantation

Deep Learning constitutes a modern approach for image processing with considerable potential and promising results. As Deep Learning has been successfully applied to various application domains, it has also recently employedin Precision Agriculture. Taking this into account, this work proposes the use of machine learning techniques, more specifically Convolutional Neural Networks (CNN), to detect and count individuals in eucalyptus plantation images, acquired from Unmanned Aerial Vehicles (UAV). The obtained results were provided by a Faster R-CNN Resnet101, with validation procedure performed against manual human annotation. Experimental results demonstrated a overall precision of 95.77% and the affordability of the approach for forestry inventories

Segmentação de pele e reconhecimento de gestos para interação humano-computador

This paper presents the development of a new approach to skin segmentation and hand gesture recognition in order to compose applications for Human Computer Interaction requiring real-time computing. Tests performed indicate the possibility of using the approach with low-cost equipment

Monitoramento Automatizado de Ambientes

This paper presents the preliminary results of an automated computer vision system for the enviromnent monitoring context. For this purpose, we used the OpenSURF method to extract local features of predefined patterns from static images, and stored into a database. The proposed approach was applied over video sequences captured sequentially from three cameras, and therefore the patterns identified are converted into objects of interest in a monitored enviromnent

Identificação de áreas quadriláteras através da detecçãao de bordas e o reconhecimento de linhas

To automate the beginning of a cutting process of a textile machine,digital image processing techniques were adopted. The solution is divided intotwo steps, first, the region of the cutting area is detected, then, we use theircoordinates on the image for perspective correction, then inside the cutting area,the detection up to 4 markers is fulfilled, where each passes through a typedefinition process, these types are the following: A; B; C and D. The marker’slocation is sent to the machine giving initiation of the cutting process

Classificação de coloração Imuno-histoquímica em Imagens combinando Cor e Textura como Descritor de Características

The objective of this work is to deï¬ne a Feature Vector (FV), for staining recognition in immunohistochemistry image analysis. The FV is composed by color information and statistic measures, obtained after applying a ï¬ltering method known as Local Binary Pattern. A group of experiments was deï¬ned to evaluate the FV. After the experiments it is expected that the deï¬ned FV will increase the color recognition Precision in comparison with the FV that only considers color information

Detecção e rastreamento de pessoas em imagens de vídeo empregando GPU

Two challenging tasks for building systems in Computer Vision (CV) area are people detection and tracking. Several fields of application employ these tasks for solving many problems. This paper describes the proposal of a new methodology for people detection and tracking in high-performance scenarios employing the GPU (Graphics Processing Unit). Experiments conducted with this approach demonstrate processing rates up to 80 frames per second

Desenvolvimento de um Scanner 3D Sem Contato de Baixo Custo Usando Marcadores Artificiais

Este trabalho propõe um scanner 3D de baixo custo que utiliza uma webcam, um laser e marcadores artificiais. Dado o elevado preço da maioria dos aparelhos capazes de digitalizar objetos físicos, surgiu a motivação para propor versões de baixo custo destas ferramentas. Os conceitos e as tecnologias usadas nessa ferramenta são mostradas, e por fim um protótipo é apresentado

Portable lensless wide-field microscopy imaging platform based on digital inline holography and multi-frame pixel super-resolution

In this paper, an irregular displacement-based lensless wide-field microscopy imaging platform is presented by combining digital in-line holography and computational pixel super-resolution using multi-frame processing. The samples are illuminated by a nearly coherent illumination system, where the hologram shadows are projected into a complementary metal-oxide semiconductor-based imaging sensor. To increase the resolution, a multi-frame pixel resolution approach is employed to produce a single holographic image from multiple frame observations of the scene, with small planar displacements. Displacements are resolved by a hybrid approach: (i) alignment of the LR images by a fast feature-based registration method, and (ii) fine adjustment of the sub-pixel information using a continuous optimization approach designed to find the global optimum solution. Numerical method for phase-retrieval is applied to decode the signal and reconstruct the morphological details of the analyzed sample. The presented approach was evaluated with various biological samples including sperm and platelets, whose dimensions are in the order of a few microns. The obtained results demonstrate a spatial resolution of 1.55 µm on a field-of-view of ≈30 mm2

Controle de Veéculo a partir da Visão Computacional

Detectar um caminho navegável e se manter nesse caminho é um dos principais objetivos de um veículo autônomo. Este trabalho tem a intenção de mostrar a possibilidade e eficácia de se utilizar técnicas de visão computacional e processamento digital de imagem para controlar um veículo utilizando somente visão passiva, ou seja, considerando apenas a imagem capturada por uma câmera fixada ao veículo. Um pequeno circuito de testes foi construído, para avaliar o trabalho. Os resultados dos testes indicaram que é possível manter autonomamente o veículo no caminho simulado, com diferentes tipos de terreno e em condições de variabilidade na iluminação natural, porém ainda ocorrem falhas e melhorias (hardware e software) devem ser feitas