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Context-Supported Road Information for Background Modeling

Context-Supported Road Information for Background Modeling

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Marcelo Mendonça Luciano Oliveira Federal University of Bahia (UFBA) Salvador Brazil

Background subtraction methods commonly suffers from incompleteness and instability over many situations. If one treats fast updating when objects run fast, it is not reliable to modeling the background while objects stop in the scene, as well, it is easy to find examples where the contrary is also true. In this paper we propose a novel method - designated Context-supported Road Information (CRON) for unsupervised background modeling, which deals with stationary foreground objects, while presenting a fast background updating. Differently from general-purpose methods, our method was specially conceived for traffic analysis, being stable in several challenging circumstances in urban scenarios. To assess the performance of the method, a thorough analysis was accomplished, comparing the proposed method with many others, demonstrating promising results in our favor.

关键词： Roads image color analysis Adaptation models Automobiles Analytical models Surveillance

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Exploring the Use of Leaf Shape Frequencies for Plant Classification

Exploring the Use of Leaf Shape Frequencies for Plant Classi...

引用

brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Ana Carolina Quintão Siravenha Schubert R. Carvalho Federal University of Para Vale Institute of Technology Belem Brazil Vale Institute of Technology Belem Brazil

Plant identification and classification play an important role in ecology, but the manual process is cumbersome even for experimented taxonomists. Technological advances allows the development of strategies to make these tasks easily and faster. In this context, this paper describes a methodology for plant identification and classification based on leaf shapes, that explores the discriminative power of the contour-centroid distance in the Fourier frequency domain in which some invariance (e.g. Rotation and scale) are guaranteed. In addition, it is also investigated the influence of feature selection techniques regarding classification accuracy. Our results show that by combining a set of features vectors - in the principal components space - and a feed forward neural network, an accuracy of 97.45% was achieved.

关键词： Shape Accuracy Feature extraction Decision trees Vegetation image color analysis Training

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IFT-SLIC: A General Framework for Superpixel Generation Based on Simple Linear Iterative Clustering and image Foresting Transform

IFT-SLIC: A General Framework for Superpixel Generation Base...

引用

brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Eduardo Barreto Alexandre Ananda Shankar Chowdhury Alexandre Xavier Falcao Paulo A. Vechiatto Miranda University of São Paulo (USP) São Paulo SP Brazil Jadavpur University Kolkata India University of Campinas SP Brazil Univ. of Campinas Brazil

image representation based on super pixels has become indispensable for improving efficiency in computer Vision systems. Object recognition, segmentation, depth estimation, and body model estimation are some important problems where super pixels can be applied. However, super pixels can influence the efficacy of the system in positive or negative manner, depending on how well they respect the object boundaries in the image. In this paper, we improve super pixel generation by extending a popular algorithm -- Simple Linear Iterative Clustering (SLIC) -- to consider minimum path costs between pixel and cluster centers rather than their direct distances. This creates a new image Foresting Transform (IFT) operator that naturally defines super pixels as regions of strongly connected pixels by choice of the most suitable path-cost function for a given application. Non-smooth connectivity functions are also explored in our IFT-SLIC approach leading to improved performance. Experimental results indicate better super pixel extraction using the proposed approach as compared to that of SLIC.

关键词： image segmentation Transforms Clustering algorithms Vegetation Accuracy image color analysis Shape

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Embedding Color Watermarks Into Halftoning images Using Minimum-Distance Binary Patterns

Embedding Color Watermarks Into Halftoning Images Using Mini...

引用

brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Pedro Garcia Freitas Mylène C.Q. Farias Aletéia P.F. de Araújo Department of Computer Science University of Brasília Brazil Department of Electrical Engineering University of Brasília Brazil

This paper presents a half toning-based watermarking method. This method enables the embedding of a color image into a binary black-and-white halftone, while maintaining the image quality. The proposed technique is capable of embedding watermarks of three color channels into a binary halftone. To achieve high quality halftones, the method maps colors to halftone channels with homogeneous dot patterns which in turn use different binary texture orientations to carry the watermark. They are obtained by solving a minimization problem in which the objective function is the binary distance between the original binary halftone and the available patterns. To restore the color information, we scan the printed halftone image and compute the inverse information (considering the dot pattern). Using the mapped information, we restore the original color channels from the halftone images using a high-quality inverse half toning algorithm. Experimental results show that the method produces restorations with a superior quality than other methods found in the literature and increases the embedding capacity.

关键词： image color analysis Watermarking image restoration Gray-scale Color Yttrium Distortion

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BoWFire: Detection of Fire in Still images by Integrating Pixel Color and Texture Analysis

BoWFire: Detection of Fire in Still Images by Integrating Pi...

引用

brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Daniel Y. T. Chino Letricia P. S. Avalhais Jose F. Rodrigues Agma J. M. Traina Institute of Mathematics and Computer Science University of Sao Paulo Sao Carlos Brazil

Emergency events involving fire are potentially harmful, demanding a fast and precise decision making. The use of crowd sourcing image and videos on crisis management systems can aid in these situations by providing more information than verbal/textual descriptions. Due to the usual high volume of data, automatic solutions need to discard non-relevant content without losing relevant information. There are several methods for fire detection on video using color-based models. However, they are not adequate for still image processing, because they can suffer on high false-positive results. These methods also suffer from parameters with little physical meaning, which makes fine tuning a difficult task. In this context, we propose a novel fire detection method for still images that uses classification based on color features combined with texture classification on super pixel regions. Our method uses a reduced number of parameters if compared to previous works, easing the process of fine tuning the method. Results show the effectiveness of our method of reducing false-positives while its precision remains compatible with the state-of-the-art methods.

关键词： image color analysis Feature extraction Videos Training Clustering algorithms Color Proposals

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Double Noise Filtering in CT: Pre- and Post-Reconstruction

Double Noise Filtering in CT: Pre- and Post-Reconstruction

引用

brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Vinicius C. Assis Denis H.P. Salvadeo Nelson D.A. Mascarenhas Alexandre L.M. Levada Department of Statistics Applied Mathematics and Computing State University of São Paulo Rio Claro SP Brazil Faculdade Campo Limpo Paulista Graduate Program in Computer Science SP Brazil Department of Computing Federal University of São Carlos São Carlos SP Brazil

Motivated by the ALARA (As Low As Reasonably Achievable) principle, this paper proposes to denoise Computed Tomography (CT) images by using a double-filtering approach. First, projection data were filtered using methods to filter Poisson noise (pre-filtering step). Then the filtered back projection (FBP) algorithm was applied to image reconstruction. After, the reconstructed images were denoised by using suitable methods for filtering Gaussian noise (post-filtering step). Finally, known metrics of image quality evaluation (such as SSIM and PSNR) were used to compare the filtered images with the ones considered ideal images in various combinations of filters. The results lead to the conclusion that a second filtering applied on image domain can improve the CT denoising quality from pre-filtering step. Thus, CT double-filtering strategy achieved a better balance between noise reduction and details preservation.

关键词： Noise Noise measurement Computed tomography Noise reduction image reconstruction Correlation Standards

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Exploded View Diagrams of 3D Grids

Exploded View Diagrams of 3D Grids

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Zamir Martins Filho Emilio Vital Brazil Mario Costa Sousa University of Calgary Calgary Canada

We present a system for creating interactive exploded view diagrams in generalized 3D grids. The primary difference between our approach and existing ones is that our technique neither requires geometrical information of the whole model nor any information regarding the relationship among model parts, instead our implementation depends on which grid cells are considered as object of interest, and which view angle to use. To achieve this, we introduce the Explosion Tree, a data structure closely related to a BSP tree, which supports the explosion view diagrams technique based on the relationship between disjoint convex polygons. In this paper we discuss the application of this technique to Corner-Point Grid which has been extensively used for geological modeling and flow simulation. All the data presented in this work consists of real data currently used in the industry.

关键词： Three-dimensional displays Data structures Explosions Solid modeling Data visualization image color analysis Geometry

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Simultaneously Estimation of Super-Resolution images and Depth Maps from Low Resolution Sensors

Simultaneously Estimation of Super-Resolution Images and Dep...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Daniel B. Mesquita Erickson R. Nascimento Mario F.M. Campos Computer Science Department Universidade Federal de Minas Gerais Belo Horizonte MG Brazil

The emergence of low cost sensors capable of providing texture and depth information of a scene is enabling the deployment of several applications such as gesture and object recognition and three-dimensional reconstruction of environments. However, commercially available sensors output low resolution data, which may not be suitable when more detailed information is necessary. With the purpose of increasing data resolution, at the same time reducing noise and filling the holes in the depth maps, in this work we propose a method that combines depth fusion and image reconstruction in a super-resolution framework. By joining low-resolution intensity images and depth maps in an optimization process, our methodology creates new images and depth maps of higher resolution and, at the same time, minimizes issues related with the absence of information (holes) in the depth map. Our experiments show that the proposed approach has increased the resolution of the images and depth maps without significant spawning of artifacts. Considering three different evaluation metrics, our methodology outperformed other three techniques commonly used to increase the resolution of combined images and depth maps acquired with low resolution, commercially available sensors.

关键词： Sensors image reconstruction image sensors Cameras Optimization Spatial resolution

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image Segmentation Assessment from the Perspective of a Higher Level Task

Image Segmentation Assessment from the Perspective of a High...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Mariela Atausinchi Fernandez Rubens M. Lopes Nina S.T. Hirata Institute of Mathematics and Statistics São Paulo Brazil Oceanographic Institute University of São Paulo São Paulo Brazil

image segmentation evaluation is usually performed by visual inspection, by comparing segmentation to a ground-truth, or by computing an objective function value for the segmented image. All these methods require user participation either for manual evaluation, or to define ground-truth, or to embed desired segmentation properties into the objective function. However, evaluating segmentation is a hard task if none of these three methods can be easily employed. Often, higher level tasks such as detecting or classifying objects can be performed much more easily than low level tasks such as delineating the contours of the objects. This fact can be advantageously used to evaluate algorithms for a low level task. We apply this approach to a case study on plankton classification. Segmentation methods are evaluated from the perspective of plankton classification accuracy. This approach not only helps choosing a good segmentation method but also helps detecting points where segmentation is failing. In addition, this more holistic form of segmentation evaluation better meets requirements of big data analysis.

关键词： image segmentation Accuracy Feature extraction Support vector machines Context Visualization Inspection

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BMAX: A Bag of Features Based Method for image Classification

BMAX: A Bag of Features Based Method for Image Classificatio...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Pedro Senna Isabela Neves Drummond Guilherme Sousa Bastos Instituto de Engenharia de Sistemas e Tecnologias da informação Universidade Federal de Itajubá - UNIFEI Instituto de Matemática e Computação Universidade Federal de Itajubá - UNIFEI

This work presents an image classification method based on bag of features, that needs less local features extracted for create a representative description of the image. The feature vector creation process of our approach is inspired in the cortex-like mechanisms used in "Hierarchical Model and X" proposed by Riesenhuber & Poggio. Bag of Max Features - BMAX works with the distance from each visual word to its nearest feature found in the image, instead of occurrence frequency of each word. The motivation to reduce the amount of features used is to obtain a better relation between recognition rate and computational cost. We perform tests in three public images databases generally used as benchmark, and varying the quantity of features extracted. The proposed method can spend up to 60 times less local features than the standard bag of features, with estimate loss around 5% considering recognition rate, that represents up to 17 times reduction in the running time.

关键词： Feature extraction Visualization Vocabulary Training Databases Support vector machines Histograms

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