43 research outputs found

    Color-contrast landmark detection and encoding in outdoor images

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    International Conference on Computer Analysis of Images and Patterns (CAIP), 2005, Versalles (Francia)This paper describes a system to extract salient regions from an outdoor image and match them against a database of previously acquired landmarks. Region saliency is based mainly on color contrast, although intensity and texture orientation are also taken into account. Remarkably, color constancy is embedded in the saliency detection process through a novel color ratio algorithm that makes the system robust to illumination changes, so common in outdoor environments. A region is characterized by a combination of its saliency and its color distribution in chromaticity space. The newly acquired landmarks are compared with those already stored in a database, through a quadratic distance metric of their characterizations. Experimentation with a database containing 68 natural landmarks acquired with the system yielded good recognition results, in terms of both recall and rank indices. However, the discrimination between landmarks should be improved to avoid false positives, as suggested by the low precision index.This work was supported by the project 'Sistema reconfigurable para la navegación basada en visión de robots caminantes y rodantes en entornos naturales.' (00).Peer Reviewe

    Detecting salient cues through illumination-invariant color ratios

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    This work presents a novel technique for embedding color constancy into a saliency-based system for detecting potential landmarks in outdoor environments. Since multiscale color opponencies are among the ingredients determining saliency, the idea is to make such opponencies directly invariant to illumination variations, rather than enforcing the invariance of colors themselves. The new technique is compared against the alternative approach of preprocessing the images with a color constancy procedure before entering the saliency system. The first procedure used in the experimental comparison is the well-known image conversion to chromaticity space, and the second one is based on successive lighting intensity and illuminant color normalizations. The proposed technique offers significant advantages over the preceding two ones since, at a lower computational cost, it exhibits higher stability in front of illumination variations and even of slight viewpoint changes, resulting in a better correspondence of visual saliency to potential landmark elements.This work was supported by the project 'Sistema reconfigurable para la navegación basada en visión de robots caminantes y rodantes en entornos naturales.' (00). The authors would like to the support obtained from the Forschungszentrum Informatik and Institut für Prozessrechentechnik, Automation und Robotik , Karlsruhe University, Germany. This work is partially supported by the Spanish Science and Technology Directorate, in the scope of the project “Reconfigurable system for vision-based navigation of legged and wheeled robots in natural environments (SIRVENT)”, grant DPI2003-05193-C02-01.Peer Reviewe

    Outdoor view recognition based on landmark grouping and logistic regression

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    Vision-based robot localization outdoors has remained more elusive than its indoors counterpart. Drastic illumination changes and the scarceness of suitable landmarks are the main difficulties. This paper attempts to surmount them by deviating from the main trend of using local features. Instead, a global descriptor called landmark-view is defined, which aggregates the most visually-salient landmarks present in each scene. Thus, landmark co-occurrence and spatial and saliency relationships between them are added to the single landmark characterization, based on saliency and color distribution. A suitable framework to compare landmark-views is developed, and it is shown how this remarkably enhances the recognition performance, compared against single landmark recognition. A view-matching model is constructed using logistic regression. Experimentation using 45 views, acquired outdoors, containing 273 landmarks, yielded good recognition results. The overall percentage of correct view classification obtained was 80.6%, indicating the adequacy of the approach.Peer ReviewedPostprint (author’s final draft

    Can Giraffes Become Birds? An Evaluation of Image-to-image Translation for Data Generation

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    There is an increasing interest in image-to-image translation with applications ranging from generating maps from satellite images to creating entire clothes' images from only contours. In the present work, we investigate image-to-image translation using Generative Adversarial Networks (GANs) for generating new data, taking as a case study the morphing of giraffes images into bird images. Morphing a giraffe into a bird is a challenging task, as they have different scales, textures, and morphology. An unsupervised cross-domain translator entitled InstaGAN was trained on giraffes and birds, along with their respective masks, to learn translation between both domains. A dataset of synthetic bird images was generated using translation from originally giraffe images while preserving the original spatial arrangement and background. It is important to stress that the generated birds do not exist, being only the result of a latent representation learned by InstaGAN. Two subsets of common literature datasets were used for training the GAN and generating the translated images: COCO and Caltech-UCSD Birds 200-2011. To evaluate the realness and quality of the generated images and masks, qualitative and quantitative analyses were made. For the quantitative analysis, a pre-trained Mask R-CNN was used for the detection and segmentation of birds on Pascal VOC, Caltech-UCSD Birds 200-2011, and our new dataset entitled FakeSet. The generated dataset achieved detection and segmentation results close to the real datasets, suggesting that the generated images are realistic enough to be detected and segmented by a state-of-the-art deep neural network.Comment: Accepted for presentation at the Computer on the Beach (COTB'20) 202

    IDA: Improved Data Augmentation Applied to Salient Object Detection

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    In this paper, we present an Improved Data Augmentation (IDA) technique focused on Salient Object Detection (SOD). Standard data augmentation techniques proposed in the literature, such as image cropping, rotation, flipping, and resizing, only generate variations of the existing examples, providing a limited generalization. Our method combines image inpainting, affine transformations, and the linear combination of different generated background images with salient objects extracted from labeled data. Our proposed technique enables more precise control of the object's position and size while preserving background information. The background choice is based on an inter-image optimization, while object size follows a uniform random distribution within a specified interval, and the object position is intra-image optimal. We show that our method improves the segmentation quality when used for training state-of-the-art neural networks on several famous datasets of the SOD field. Combining our method with others surpasses traditional techniques such as horizontal-flip in 0.52% for F-measure and 1.19% for Precision. We also provide an evaluation in 7 different SOD datasets, with 9 distinct evaluation metrics and an average ranking of the evaluated methods.Comment: Accepted for presentation at SIBGRAPI 2020 - 33rd Conference on Graphics, Patterns and Image

    Three Wheeled Omnidirecional Mobile Robot - Design and Implementation

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    This article presents a study of the resources necessary to providemovement and localization in three wheeled omnidirectionalrobots, through the detailed presentation of the mathematical proceduresapplicable in the construction of the inverse kinematic model,the presentation of the main hardware and software componentsused for the construction of a functional prototype, and the testprocedure used to validate the assembly.The results demonstrate that the developed prototype is functional,as well as the developed kinematic equation, given the smallerror presented at the end of the validation procedure

    A Model for landing, taking off and autonomous battery recharging of a Parrot Ar.Drone 2.0 using computational vision and GPS features

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    A drone is a type of Unmanned Aerial Vehicles (UAV) that most of the times can have four propellers. They can be used in many applications, one of those is to move through places of difficult access. Besides drones practicity over other aerial vehicles, its price is way lower compared to large vehicles, which turns them attractive to many activities. Also it offers safety in dangerous situations, like fires or accidents, as it doesnt need an on-board pilot. In a system with autonomous flight the concern with its landing and recharging of the batteries, which does not last more than a few minutes, arises. Using on-board devices, like its cameras and GPS modules, it is possible to implement functions to optimize its capabilities. With the goal to present a solution to such problem, this essay proposes a model which utilizes image recognition to allow a drone to land in an autonomous system. This landing routine based on its image turns flight and landing into autonomous processes, without human intervention
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