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检索条件"任意字段=15th Brazilian Symposium on Computer Graphics and Image Processing"

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Experimental Feasibility Analysis of ROI-Based Hough Transform for Real-Time Line Tracking in Auto-Landing of UAV

Experimental Feasibility Analysis of ROI-Based Hough Transfo...

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IEEE International symposium on Object/Component/Service-Oriented Real-Time Distributed Computing Workshops (ISORCW)

作者： Tyan Vladimir Dongwoon Jeon Doo-Hyun Kim Chun-Hyon Chang Jungguk Kim Department of Interner and Multimedia Engineering Konkuk University Seoul South Korea Department of Computer Engineering University of Konkuk Seoul South Korea College of Information and Industrial Engineering Hankuk University of Foreign Studies South Korea

Autonomous landing for fixed-wing aircraft is one of issues in smart Unmanned Aerial Vehicles (UAV). In this paper, we present a robust real-time line tracking algorithm for UAV using image processing. In the proposed algorithm, we first reduce computation region, and then transform to Hough space to detect lines in frame. Finally, the line tracking is performed. Experimental model has been designed in MATLAB Simulink environment and evaluated with several landing video clips. We took tolerance boundary test to check robustness. Experimental results indicate this approach is applicable in real uses.

关键词： Distributed computing Real time systems Conferences

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Improving Non-local Video Denoising with Local Binary Patterns and image Quantization

Improving Non-local Video Denoising with Local Binary Patter...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Welinton A. Contato Tiago S. Nazare Gabriel B. Paranhos da Costa Moacir Ponti João E. S. Batista Neto Instituto de Ciências Matemáticas e de Computação (ICMC) Universidade de São Paulo (USP) São Carlos SP Brazil Inst. de Cienc. Mat. e de Comput. (ICMC) Universidade de Sao Paulo Sao Paulo SÃ£o Paulo BR

the most challenging aspect of video and image denoising is to preserve texture and small details, while filtering out noise. To tackle such problem, we present two novel variants of the 3D Non-Local Means (NLM3D) which are suitable for videos and 3D images. the first proposed algorithm computes texture patterns for each pixel by using the LBP-TOP descriptor to modify the NLM3D weighting function. It also uses MSB (Most Significant Bits) quantization to improve robustness to noise. the second proposed algorithm filters homogeneous and textured regions differently. It analyses the percentage of non-uniform LBP patterns of a region to determine whether or not the region exhibits textures and/or small details. Quantitative and qualitative experiments indicate that the proposed approaches outperform well known methods for the video denoising task, especially in the presence of textures and small details.

关键词： Noise reduction three-dimensional displays Gray-scale Histograms Quantization (signal) Two dimensional displays image denoising

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Transfer Learning for Human Action Recognition

Transfer Learning for Human Action Recognition

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Ana Paula Lopes Elerson Santos Eduardo Valle Jussara Almeida Arnaldo Araujo Depart. of Exact and Tech. Sciences - Universidade Estadual de Santa Cruz (UESC) Ilhéus BA Brazil Depart. of Computer Science - Universidade Federal de Minas Gerais (UFMG) Belo Horizonte MG Brazil Institute of Computation - Universidade Estadual de Campinas (UNICAMP) Campinas SP Brazil

To manually collect action samples from realistic videos is a time-consuming and error-prone task. this is a serious bottleneck to research related to video understanding, since the large intra-class variations of such videos demand training sets large enough to properly encompass those variations. Most authors dealing with this issue rely on (semi-) automated procedures to collect additional, generally noisy, examples. In this paper, we exploit a different approach, based on a Transfer Learning (TL) technique, to address the target task of action recognition. More specifically, we propose a framework that transfers the knowledge about concepts from a previously labeled still image database to the target action video database. It is assumed that, once identified in the target action database, these concepts provide some contextual clues to the action classifier. Our experiments with Caltech256 and Hollywood2 databases indicate: (a) the feasibility of successfully using transfer learning techniques to detect concepts and, (b) that it is indeed possible to enhance action recognition with the transferred knowledge of even a few concepts. In our case, only four concepts were enough to obtain statistically significant improvements for most actions.

关键词： Videos Databases Training Kernel Feature extraction Support vector machines Visualization

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Superpixel-Based Interactive Classification of Very High Resolution images

Superpixel-Based Interactive Classification of Very High Res...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： John E. Vargas Priscila T. M. Saito Alexandre X. Falcao Pedro J. De Rezende Jefersson A. Dos Santos Institute of Computing University of Campinas Campinas Brazil Federal Technological University of Parana Cornelio Procopio Brazil Inst. of Comput. Universidade Estadual de Campinas Campinas SP BR Universidade Federal de Minas Gerais Belo Horizonte MG BR

ISBN: (纸本)9781479942572

Very high resolution (VHR) images are large datasets for pixel annotation -- a process that has depended on the supervised training of an effective pixel classifier. Active learning techniques have mitigated this problem, but pixel descriptors are limited to local image information and the large number of pixels makes the response time to the user's actions impractical, during active learning. To circumvent the problem, we present an active learning strategy that relies on superpixel descriptors and a priori dataset reduction. Firstly, we compare VHR image annotation using superpixel- and pixel-based classifiers, as designed by the same state-of-the-art active learning technique -- Multi-Class Level Uncertainty (MCLU). Even with the dataset reduction provided by the superpixel representation, MCLU remains unfeasible for user interaction. therefore, we propose a technique to considerably reduce the superpixel dataset for active learning. Moreover, we subdivide the reduced dataset into a list of subsets with random sample rearrangement to gain both speed and sample diversity during the active learning process.

关键词： Training Uncertainty image color analysis Accuracy Clustering algorithms Support vector machines Histograms

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Unsupervised Dual-Layer Aggregation for Feature Fusion on image Retrieval Tasks

Unsupervised Dual-Layer Aggregation for Feature Fusion on Im...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Ademir Moreno Daniel Carlos Guimarães Pedronette Department of Statistics Applied Mathematics and Computing São Paulo State University (UNESP) Rio Claro Brazil

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

the revolutionary advances in image representation have led to impressive progress in many image understanding-related tasks, primarily supported by Convolutional Neural Networks (CNN) and, more recently, by Transformer models. Despite such advances, assessing the similarity among images for retrieval in unsupervised scenarios remains a challenging task, mostly grounded on traditional pairwise measures, such as the Euclidean distance. the scenario is even more challenging when different visual features are available, requiring the selection and fusion of features without any label information. In this paper, we propose an Unsupervised Dual-Layer Aggregation (UDLA) method, based on contextual similarity approaches for selecting and fusing CNN and Transformer-based visual features trained through transfer learning. In the first layer, the selected features are fused in pairs focused on precision. A sub-set of pairs is selected for a second layer aggregation focused on recall. An experimental evaluation conducted in different public datasets showed the effectiveness of the proposed approach, which achieved results significantly superior to the best-isolated feature and also superior to a recent fusion approach considered as baseline.

关键词： Visualization image retrieval Transfer learning Estimation Euclidean distance image representation Transformers Convolutional neural networks Optimization

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Graph Based Characterization of Microcirculation in Sepsis Using Sidestream Dark Field Imaging

Graph Based Characterization of Microcirculation in Sepsis U...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： J. M. Zoghbi L. T. De-La-Cruz M. A. G. V. Galarreta Marcel P. Jackowski J. C. F. Vieira A. M. A. Liberatore Ivan H. J. Koh Inst. of Math. & Stat. Universidade de Sao Paulo Sao Paulo SÃ£o Paulo BR Inst. of Math. & Stat Universidade de Sao Paulo Sao Paulo SÃ£o Paulo BR Institute of Mathematics and Statistics University of São Paulo São Paulo Brazil Universidade Federal de Sao Paulo Sao Paulo SÃ£o Paulo BR

Real-time detection of sepsis on a video data is a new aboard technique that aids the septic patient and decreases the high mortality rate. the progressive impairment of the micro-circulation associated with increased systemic inflammatory response in sepsis has been considered the origin of the multiple organ dysfunction syndrome that often leads to death. However, despite the recognized importance of the micro-circulatory dysfunction, analysis methods able to correlate the severity of sepsis with the degree of impairment of micro-hemodynamic captured by portable microscope Side-stream Dark Field Imaging (SDF) are rarely used. Hence, the classification of the severity of sepsis by analyzing the micro-circulatory dysfunction would be of great assistance in diagnosing severity and therapeutic management. In this context, the aim of this work is to propose a new computational methodology based on image processing to obtain graph metrics for determining the degree of micro-vascular and tissue commitment due to sepsis.

关键词： Taxonomy Blood vessels Biomedical imaging image edge detection Mice image segmentation

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Learning to Annotate Clothes in Everyday Photos: Multi-modal, Multi-label, Multi-instance Approach

Learning to Annotate Clothes in Everyday Photos: Multi-modal...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Adriano Alonso Veloso Jefersson A. Dos Santos Keiller Nogueira Universidade Federal de Minas Gerais Belo Horizonte Brazil Universidade Federal de Minas Gerais Belo Horizonte MG BR

ISBN: (纸本)9781479942572

In this paper, we present an effective algorithm to automatically annotate clothes in everyday photos posted in online social networks, such as Facebook and Instagram. Specifically, clothing annotation can be informally stated as predicting, as accurately as possible, the garment items appearing in the target photo. this task not only poses interesting challenges for existing vision and recognition algorithms, but also brings huge opportunities for recommender and e-commerce systems. We formulate the annotation task as a multi-modal, multi-label and multi-instance classification problem: (i) both image and textual content (i.e., comments about the image) are available for learning classifiers, (ii) the classifiers must predict a set of labels (i.e., a set of garment items), and (iii) the decision on which labels to predict comes from a bag of instances that are used to build a function, which separates labels that should be predicted from those that should not be. Under this setting, we propose a classification algorithm which employs association rules in order to build a prediction model that combines image and textual information, and adopts an entropy-minimization strategy in order to the find the best set of labels to predict. We conducted a systematic evaluation of the proposed algorithm using everyday photos collected from two major fashion-related social networks, namely *** and ***. Our results show that the proposed algorithm provides improvements when compared to popular first choice multi-label algorithms that range from 2% to 40% in terms of accuracy.

关键词： Clothing Prediction algorithms Entropy Visualization image color analysis Social network services Feature extraction

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K-means Clustering for Multispectral images Using Floating-Point Divide

K-means Clustering for Multispectral Images Using Floating-P...

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Annual IEEE symposium on Field-Programmable Custom Computing Machines (FCCM)

作者： Xiaojun Wang Miriam Leeser Department of Electrical and Computer Engineering Northeastern University Boston MA USA

Many signal processing algorithms can be accelerated using reconfigurable hardware. To achieve a good speedup compared to running software on a general purpose processor, fine-grained control over the bitwidth of each component in the datapath is desired. this goal can be achieved by using NU's variable precision floating-point library. To analyze the usefulness of the floating-point divide unit, we incorporate it into our previous implementation of the Kmeans clustering algorithm applied to multispectral satellite images. With the lack of a floating-point divide hardware implementation, the mean updating step in each iteration of the K-means algorithm had to be moved to the host computer for calculation. the new means calculated on the host then had to be moved back to the FPGA board for the next iteration of the algorithm. this added data transfer overhead between the host and the FPGA board. In this work, we use the new fp div module to implement the mean updating step in FPGA hardware. this greatly reduces the communication overhead between host and FPGA board and further accelerates run time. the Kmeans clustering example illustrates the use of the fp div, fix2float and float2fix modules seamlessly assembled together in a real application. It is the first implementation that has the complete K-means computation done in FPGA hardware. Our results show that the hardware implementation achieves a speedup of over 2150x for core computation time and about 11x for total run time including data transfer time. they also show that the divide in FPGA hardware is 100 times faster than in software. Moreover, implementing divide in the FPGA frees the host to work on other tasks concurrently with K-means clustering, thus providing further speedup by allowing the image analyst to exploit this coarse grained parallelism.

关键词： Multispectral imaging Field programmable gate arrays Hardware Signal processing algorithms Clustering algorithms Acceleration image analysis Process control Software libraries Algorithm design and analysis

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Combining Dialectical Optimization and Gradient Descent Methods for Improving the Accuracy of Straight Line Segment Classifiers

Combining Dialectical Optimization and Gradient Descent Meth...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Rosario Medina Rodriguez Ronaldo Hashimoto Institute of Mathematics and Statistics IME Department of Computer Science University of Sao Paulo Sao Paulo Brazil

A recent published pattern recognition technique called Straight Line Segment (SLS) uses two sets of straight line segments to classify a set of points from two different classes and it is based on distances between hese points and each set of straight line segments. It has been demonstrated that, using this technique, it is possible to generate classifiers which can reach high accuracy rates for supervised pattern classification. However, a critical issue in this technique is to find the optimal positions of the straight line segments given a training data set. this paper proposes a combining method of the dialectical optimization method (DOM) and the gradient descent technique for solving this optimization problem. the main advantage of DOM, such as any evolutionary algorithm, is the capability of escaping from local optimum by multi-point stochastic searching. On the other hand, the strength of gradient descent method is the ability of finding local optimum by pointing the direction that maximizes the objective function. Our hybrid method combines the main characteristics of these two methods. We have applied our combining approach to several data sets obtained from artificial distributions and UCI databases. these experiments show that the proposed algorithm in most cases has higher classification rates with respect to single gradient descent method and the combination of gradient descent with genetic algorithms.

关键词： Training Optimization methods Probability density function Pattern recognition Training data Vectors

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Histograms of Optical Flow Orientation and Magnitude to Detect Anomalous Events in Videos

Histograms of Optical Flow Orientation and Magnitude to Dete...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Rensso Victor Hugo Mora Colque Carlos Antônio Caetano Júnior William Robson Schwartz Universidade Federal de Minas Gerais Belo Horizonte MG BR Computer Science Department Universidade Federal de Minas Gerais Belo Horizonte Brazil

Modeling human behavior and activity patterns for recognition or detection of anomalous events has attracted significant research interest in recent years, particularly among the video surveillance community. An anomalous event might be characterized as an event that deviates from the normal or usual, but not necessarily in an undesirable manner, e.g., An anomalous event might just be different from normal but not a suspicious event from the surveillance stand point. One of the main challenges of detecting such events is the difficulty to create models due to their unpredictability. therefore, most works model the expected patterns on the scene, instead, based on video sequences where anomalous events do not occur. Assuming images captured from a single camera, we propose a novel spatiotemporal feature descriptor, called Histograms of Optical Flow Orientation and Magnitude (HOFM), based on optical flow information to describe the normal patterns on the scene, so that we can employ a simple nearest neighbor search to identify whether a given unknown pattern should be classified as an anomalous event. Our descriptor captures spatiotemporal information from cuboids (regions with spatial and temporal support) and encodes both magnitude and orientation of the optical flow separately into histograms, differently from previous works, which are based only on the orientation. the experimental evaluation demonstrates that our approach is able to detect anomalous events with success, achieving better results than the descriptor based only on optical flow orientation and outperforming several state-of-the-art methods on one scenario (Peds2) of the well-known UCSD anomaly data set, and achieving comparable results in the other scenario (Peds1).

关键词： Optical imaging Histograms Feature extraction Hidden Markov models image motion analysis computer vision Nearest neighbor searches

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