DepthCut: Improved Depth Edge Estimation Using Multiple Unreliable Channels

In the context of scene understanding, a variety of methods exists to estimate different information channels from mono or stereo images, including disparity, depth, and normals. Although several advances have been reported in the recent years for these tasks, the estimated information is often imprecise particularly near depth discontinuities or creases. Studies have however shown that precisely such depth edges carry critical cues for the perception of shape, and play important roles in tasks like depth-based segmentation or foreground selection. Unfortunately, the currently extracted channels often carry conflicting signals, making it difficult for subsequent applications to effectively use them. In this paper, we focus on the problem of obtaining high-precision depth edges (i.e., depth contours and creases) by jointly analyzing such unreliable information channels. We propose DepthCut, a data-driven fusion of the channels using a convolutional neural network trained on a large dataset with known depth. The resulting depth edges can be used for segmentation, decomposing a scene into depth layers with relatively flat depth, or improving the accuracy of the depth estimate near depth edges by constraining its gradients to agree with these edges. Quantitatively, we compare against 15 variants of baselines and demonstrate that our depth edges result in an improved segmentation performance and an improved depth estimate near depth edges compared to data-agnostic channel fusion. Qualitatively, we demonstrate that the depth edges result in superior segmentation and depth orderings.Comment: 12 page

Shadows Aren't So Dangerous After All: A Fast and Robust Defense Against Shadow-Based Adversarial Attacks

Robust classification is essential in tasks like autonomous vehicle sign recognition, where the downsides of misclassification can be grave. Adversarial attacks threaten the robustness of neural network classifiers, causing them to consistently and confidently misidentify road signs. One such class of attack, shadow-based attacks, causes misidentifications by applying a natural-looking shadow to input images, resulting in road signs that appear natural to a human observer but confusing for these classifiers. Current defenses against such attacks use a simple adversarial training procedure to achieve a rather low 25\% and 40\% robustness on the GTSRB and LISA test sets, respectively. In this paper, we propose a robust, fast, and generalizable method, designed to defend against shadow attacks in the context of road sign recognition, that augments source images with binary adaptive threshold and edge maps. We empirically show its robustness against shadow attacks, and reformulate the problem to show its similarity to $\varepsilon$ perturbation-based attacks. Experimental results show that our edge defense results in 78\% robustness while maintaining 98\% benign test accuracy on the GTSRB test set, with similar results from our threshold defense. Link to our code is in the paper.Comment: This is a draft version - our core results are reported, but additional experiments for journal submission are still being ru

Seguimiento de puntos en imágenes diagnósticas

Seguir puntos en secuencias de imágenes médicas es una tarea que presenta dificultades solucionables por diferentes métodos determinísticos como KLT, o estadísticos como Kalman que presentan ventajas y desventajas que en ciertas situaciones producen mejores o peores resultados en términos de precisión y consistencia, esta investigación se enfocó en encontrar y mejorar tres métodos de seguimiento de objetos usando imágenes médicas y representando objetos con uno o más puntos en su lugar.Track points in medical image sequences is a difficult task that can be solved using statistical methods like Kalman filtering, or deterministically with Kanade-Lucas-Tomasi (KLT), both have advantages and disadvantages that in certain circumstances produce better or worst results at tracking in terms of precision and consistency, this research focused on finding and improving three different object tracking methods in order to perform better results using medical images and representing objects with one or more points instead.Ingeniero (a) de SistemasPregrad

Identification and three-dimensional positioning of urban energy lines from optical images to aid a teleoperated pruning robot

Orientador : Prof. Dr. Leandro dos Santos CoelhoDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa: Curitiba, 30/08/2016Inclui referências : f. 138-144Área de concentraçãoResumo: Diversos fatores podem impactar a qualidade da distribuição de energia elétrica, entre eles, um dos mais impactantes é o contato de vegetação com linhas aéreas energizadas. Assim sendo, é de suma importância a poda de vegetação próxima à linhas energizadas. Visando-se aprimorar esse processo, pode-se empregar um robô teleoperado de poda, de forma que a poda possa ser realizada de maneira remota e segura. As câmeras instaladas no braço robótico permitem que o operador tenha visão da área de corte mesmo quando a visada direta do solo estiver obstruída. Um dos problemas de se visualizar a região de corte por meio de um monitor é a perda de noção de profundidade, o que pode dificultar a operação. Dessa forma, seria relevante uma técnica de visão computacional capaz de detectar as linhas de energia e seu posicionamento tridimensional (3D) a fim de auxiliar o operador. Revisando-se a literatura, avaliou-se que, no geral, os trabalhos já propostos para detecção de linhas em imagens operam em situações com fundo limpo, não urbanizado e com vista superior das linhas de energia. Assim sendo, nesse trabalho é proposta uma técnica para detecção de linhas energizadas em imagens de regiões urbanas e a obtenção de seu posicionamento 3D, fator ainda não explorado na literatura recente. Para se alcançar esse objetivo é proposta a utilização de câmeras de espectro visível posicionadas em paralelo. Assim, regiões com potencial para serem linhas de energia são selecionadas utilizando-se detecção de bordas seguidas por filtragens geométricas aplicando-se técnicas inspiradas em algoritmos de grafos e ajuste de pontos selecionados a uma curva. Após a seleção de regiões candidatas a linha de energia, o posicionamento 3D é obtido utilizando-se de visão estéreo. Para tal, a correspondência entre pontos visíveis em ambas as câmeras é encontrada e com triangulação o posicionamento 3D da linha de energia é recuperado. Com a informação 3D disponível falsos candidatos são reduzidos por um fator de aproximadamente sete vezes e finalmente as linhas são detectadas. Para avaliação do método foi criada uma base de dados contendo imagens estéreo obtidas de um cenário montado com dois postes, três linhas de energia e uma árvore entre essas, na qual foi possível atingir níveis de precisão de 98% ao término do processo de detecção, contando-se com 91% de taxa de verdadeiro positivos. As causas dos falsos negativos são evidenciadas para que trabalhos futuros possam encontrar alternativas às dificuldades apresentadas. O algoritmo aqui proposto fornece como saída um mapa de cor sobre as linhas de energia para identificação da profundidade em 2D e uma nuvem de pontos para visualização em 3D. Palavras-chave: Visão Computacional. Reconhecimento de objetos. Visão estéreo. Linhas de energia. Robô de poda.Abstract: Different factors may affect energy distribution quality, among them, one of the main causes is when vegetation gets into contact with overhead energy lines. Therefore, it is of main importance to prune vegetation close to energy lines. To improve this process it is possible to use a teleoperated robot, what allows the pruning activity to be accomplished in a remote and safe way. Cameras installed in the robot arm provide images from the pruning region to the operator even when direct sight is not an option. One of the main problems viewing the prunning region using a display is the lost of depth perception, what could make the operator unintentialy colide the robot with energy lines. Therefore, it would be of great aid a computer vision method capable of detecting energy lines and their three-dimensional (3D) positioning to aid the operator. During the state of the art review of energy line detection in images, it was perceived that, in general, the already proposed works operate in regions where the images present a clear background, not urbanized, and with the energy lines seen from above. Therefore, in this work, it is proposed a technique to detect energy lines and their 3D positioning in images taken in urban settings, factor yet unexplored in the recent literature. To reach this objective it is proposed the use of two visible spectrum cameras installed in parallel. In this way, regions with potential to be energy line are selected using edge detection followed by the geometric filtering designed using techniques inspired in graphs algorithms and curve fitting. After the regions with potential to be energy lines are found, their 3D position is obtained with stereo vision. To do so, the matching among points visible by both cameras is found and with triangulation, it is possible to recover the energy line 3D position. With the 3D information available, false positives are reduced by a factor of about seven and finally the energy lines are detected. A dataset containing stereo images of a scenario built with two power poles, three energy lines, and a tree between them was created in order to evaluate the presented method. In the commented dataset it was possible to reach accuracy of 98% at the end of the detection process, with 91% true positive rate. The causes of the false negatives cases are put in evidence in order to allow them to be overcame by future works. The algorithm proposed here outputs a colormap projected over the energy lines to inform the depth of each one in 2D and a point cloud to visualize each line in 3D. Key words: Computer vision. Object Recognition. Stereo vision. Overhead Energy Lines. Pruning Robo