In this paper we propose a new and efficient texture feature extraction method: the Segmentation-based Fractal Texture Analysis, or SFTA. The extraction algorithm consists in decomposing the input image into a set of ...
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In this paper we propose a new and efficient texture feature extraction method: the Segmentation-based Fractal Texture Analysis, or SFTA. The extraction algorithm consists in decomposing the input image into a set of binary images from which the fractal dimensions of the resulting regions are computed in order to describe segmented texture patterns. The decomposition of the input image is achieved by the Two-Threshold Binary Decomposition (TTBD) algorithm, which we also propose in this work. We evaluated SFTA for the tasks of content-based image retrieval (CBIR) and image classification, comparing its performance to that of other widely employed feature extraction methods such as Haralick and Gabor filter banks. SFTA achieved higher precision and accuracy for CBIR and image classification. Additionally, SFTA was at least 3.7 times faster than Gabor and 1.6 times faster than Haralick with respect to feature extraction time.
Accuracy in image object detection has been usually achieved at the expense of much computational load. Therefore a trade-off between detection performance and fast execution commonly represents the ultimate goal of a...
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Accuracy in image object detection has been usually achieved at the expense of much computational load. Therefore a trade-off between detection performance and fast execution commonly represents the ultimate goal of an object detector in real life applications. In this present work, we propose a novel method toward that goal. The proposed method was grounded on a multi-scale spectral residual (MSR) analysis for saliency detection. Compared to a regular sliding window search over the images, in our experiments, MSR was able to reduce by 75% (in average) the number of windows to be evaluated by an object detector. The proposed method was thoroughly evaluated over a subset of Label Me dataset (person images), improving detection performance in most cases.
Recently, investigations on medical imaging have been indicating a strong correlation between cases of cancers and the increasing number of Computed Tomography (CT) exams, mainly due to high radiation doses to which p...
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Recently, investigations on medical imaging have been indicating a strong correlation between cases of cancers and the increasing number of Computed Tomography (CT) exams, mainly due to high radiation doses to which patients are exposed during the data acquisition process. Thus, there is a need to reduce the radiation doses whereas still keeping satisfactory quality images for diagnosis. In this paper, we propose to filter noise in CT images using contextual versions of Wiener Filter such as Generalized Wiener Filter (GWF) and Non Local Means approach for parameter estimation. Experiments show that the proposed methods are promising, since they provide good results with no significant increase in the computational cost.
Recognizing different kinds of fruits and vegetables is a common task in supermarkets. This task, however, poses several challenges as it requires the identification of different species of a particular produce and al...
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Recognizing different kinds of fruits and vegetables is a common task in supermarkets. This task, however, poses several challenges as it requires the identification of different species of a particular produce and also its variety. Usually, existing computer-based recognition approaches are not automatic and demand long-term and laborious prior training sessions. This paper presents a novel framework for classifier fusion aiming at supporting the automatic recognition of fruits and vegetables in a supermarket environment. The objective is to provide an effective mechanism for combining low-cost classifiers trained for specific classes of interest. The experiments performed demonstrate that the proposed framework yields better results than several related work found in the literature and represents a step forward automatic produce recognition in cashiers of supermarkets.
The efficiency in image classification tasks can be improved using combined information provided by several sources, such as shape, color, and texture visual properties. Although many works proposed to combine differe...
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The efficiency in image classification tasks can be improved using combined information provided by several sources, such as shape, color, and texture visual properties. Although many works proposed to combine different feature vectors, we model the descriptor combination as an optimization problem to be addressed by evolutionary-based techniques, which compute distances between samples that maximize their separability in the feature space. The robustness of the proposed technique is assessed by the Optimum-Path Forest classifier. Experiments showed that the proposed methodology can outperform individual information provided by single descriptors in well-known public datasets.
Proxy servers of scalable video distribution systems must perform not only efficient memory management but also adopt video protection mechanisms. This work proposes an adaptive mix-grained parallelization of AES ciph...
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This paper presents a methodology for creating 3D non-structured meshes for industrial applications, which honor all the boundaries of the domain. The input for the system is a multi-region 3D non-manifold model, poss...
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This paper presents a methodology for creating 3D non-structured meshes for industrial applications, which honor all the boundaries of the domain. The input for the system is a multi-region 3D non-manifold model, possibly containing dangling faces, which poses a number of difficulties for standard 3D triangulation frameworks. The main contribution of this work consists of a detailed description of how these difficulties were overcome in a real implementation.
Many virtual mirror and telepresence applications require novel viewpoint synthesis with little latency to user motion. image-based visual hull (IBVH) rendering is capable of rendering arbitrary views from segmented i...
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A flexible biomedical visualization framework implemented with Java, OpenGL, and OpenCL performs efficient volume rendering with large, multi-modal datasets. The framework takes advantage of the parallel processing po...
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A flexible biomedical visualization framework implemented with Java, OpenGL, and OpenCL performs efficient volume rendering with large, multi-modal datasets. The framework takes advantage of the parallel processing power on modern graphics hardware with novel OpenCL and GLSL shading language implementations. The Java and GPU environment provide portable advanced biomedical image visualization applications. Several applications built on top of the GPU framework are also presented to show the extensibility of the application. These include multi-surface rendering, stereoscopic rendering, image fusion, and diffusion tensor visualization.
In this paper, we propose an error concealment technique for H.264 coded videos. The algorithm is targeted at compressed videos degraded after packet losses caused by transmission over an unreliable channel. We use a ...
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In this paper, we propose an error concealment technique for H.264 coded videos. The algorithm is targeted at compressed videos degraded after packet losses caused by transmission over an unreliable channel. We use a combination of watermarking and half toning (dithering) techniques. At the encoder side, a dithered version of each video frame is embedded into the video using a watermarking technique. The watermarking technique used by the proposed algorithm is a modified version of the Quantization Index Modulation (QIM) algorithm, which provides a good data hiding capacity. At the decoder side, the algorithm identifies which packets of the video were lost, extracts the corresponding mark, and applies an inverse half toning technique to estimate the original content. The algorithm is fast and has a good performance, being able to restore content with a better quality than the default H.264 error concealment algorithm.
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