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Speed Bump Detection Through Inertial Sensors and Deep Learning in a Multi-contextual Analysis

SN computer Science 2023年第1期4卷 18页

作者： Menegazzo, Jeferson von Wangenheim, Aldo Graduate Program in Computer Science (PPGCC) Image Processing and Computer Graphics Lab (LAPIX) Brazilian National Institute for Digital Convergence (INCoD) Department of Informatics and Statistics (INE) Federal University of Santa Catarina (UFSC)-Florianópolis Santa Catarina Brazil

Speed bumps are vertical raisings of the road pavement used to force drivers to slow down to ensure greater safety in traffic. However, these obstacles have disadvantages in terms of efficiency and safety, where the presence of speed bumps can affect travel time and fuel consumption, cause traffic jams, delay emergency vehicles, and cause vehicle damage or accidents when not properly signaled. Due to these factors, the availability of geolocation information for these obstacles can benefit several applications in Intelligent Transportation System (ITS), such as Advanced Driver Assistance Systems (ADAS) and autonomous vehicles, allowing to trace more efficient routes or alert the driver of the presence of the obstacle ahead. Speed bump detection applications described in the literature employ cameras or inertial sensors, represented by accelerometers and gyroscopes. While camera-based solutions are mature with evaluation in different contextual conditions, those based on inertial sensors do not offer multi-contextual analyses, being mostly simple applications of proof of concept, not applicable in real-world scenarios. For this reason, in this work, we propose the development of a reliable speed bump detection model based on inertial sensors, capable of operating reliably in contextual variations: different vehicles, driving styles, and environments in which vehicles can travel to. For the model development and validation, we collect nine datasets with contextual variations, using three different vehicles, with three different drivers, in three different environments, in which there are three different surface types, in addition to variations in conservation state and the presence of obstacles and anomalies. The speed bumps are present in two different pavement types, asphalt and cobblestone. We use the collected data in experiments to evaluate aspects such as the influence of the placement of the sensors for vehicle data collection and the data window size. Afterwar

关键词： Autonomous vehicles Deep learning Inertial sensors Intelligent transport systems Road conditions Speed bump

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A fast gabor filter approach for multi-channel texture feature discrimination 19

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19th Iberoamerican Congress on Pattern Recognition, CIARP 2014

作者： Sobieranski, Antonio Carlos Linhares, Rodrigo T. F. Comunello, Eros von Wangenheim, Aldo Federal University of Santa Catarina Laboratory of Image Processing and Computer Graphics Brazil Federal University of Parana Department of Informatics Brazil

ISBN: (纸本)9783319125671

Texture is a very important concept for many image understanding and pattern classification applications. The analysis of texture can be performed by the multi-channel filtering theory, a classical theory for texture perception based on the early stages of human visual system. This approach decomposes an image into a set of responses given by a bank of Gabor filters, that nearly covers in an uniformly manner the spatial-frequency domain. This approach relies on the image dimensions, and the number of kernels in a bank of Gabor filters varies according to the number of combinations between frequencies and orientations. In many practical applications, this large number of combinations makes quickly unfeasible the computation of the whole bank of filters. To ease this problem, in this paper we propose a multi-channel filtering where the Gabor bank for texture discrimination is computed in parallel in a graphics processing unit (GPU). Experimental results show an improvement of 8.78 times for feature extraction when compared against the corresponding CPU-based approach. © Springer International Publishing Switzerland 2014.

关键词： Gabor filters

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A multi-layered development framework for medical imaging applications

A multi-layered development framework for medical imaging ap...

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Annual IEEE Symposium on computer-Based Medical Systems

作者： Andre Ferreira Bem Silva Diego Dias Bispo Carvalho Tiago H. C. Nobrega Renan Teston Inacio Aldo von Wangenheim LAPIX-Laboratory of Image Processing and Computer Graphics UFSC Federal University of Santa Catarina Florianopolis Santa Catarina Brazil

computer aided diagnostic comprises wide research areas with stability and reliability software requirements. We propose a set of libraries aiming to provide a development framework for medical applications. This framework's libraries, mostly written from scratch (e.g Core library), were developed for both medical imaging systems and general physical simulations. Their internal interactions tend to be transparent to the developer and easy to use at the same time while providing flexibility to interact with other codes. The robustness of the code is shown in complex applications like the medical workstation here exemplified, while its flexibility and fast prototyping characteristics are exemplified with some simple demonstrations.

关键词： Biomedical imaging Software libraries Application software computer applications Stability Medical services Biomedical equipment Computational modeling Medical simulation Robustness

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Learning a nonlinear color distance metric for the identification of skin immunohistochemical staining

Learning a nonlinear color distance metric for the identific...

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Annual IEEE Symposium on computer-Based Medical Systems

作者： Antonio Carlos Sobieranski Sylvio Luiz Mantelli Neto Leandro Coser Eros Comunello Aldo von Wangenheim Eduardo Cargnin-Ferreira Gabriella Di Giunta LAPIX - Laboratory of Image Processing and Computer Graphics UFSC - Federal University of Santa Catarina Pathology Service UFSC - Federal University of Santa

This paper presents a semiautomatic method for the identification of immunohistochemical (IHC) staining in digitized samples. The user trains the system by selecting on a sample image some typical positive stained regions that will be used as a reference for the construction of a distance metric. In this learning process, the global optimum is obtained by induction employing higher polynomial terms of the Mahalanobis distance, extracting nonlinear features of the IHC pattern distributions. The results of the proposed method showed a high correlation to a pathologist's manual analysis, which was used as a golden standard, presenting a more robust discrimination between stained and non-stained areas with little bias.

关键词： Skin image color analysis Immune system Proteins image processing Weather forecasting computer graphics Extraterrestrial measurements Knowledge engineering computer science

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Using Acceleration Data from Smartphones to Interact with 3D Medical Data

Using Acceleration Data from Smartphones to Interact with 3D...

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Brazilian Symposium on computer graphics and image processing (SIBGRAPI)

作者： Mateus de Souza Diego Dias Bispo Carvalho Peter Barth Jeferson Vieira Ramos Eros Comunello Aldo von Wangenheim LAPIX - Laboratory for Image Processing and Computer Graphics Federal University of Santa Catarina Florianópolis Brazil Hochschule RheinMain Wiesbaden Germany

Accelerometers integrated in modern smartphones pave the way to intuitively use gestures for collaboratively controlling interactive applications. Using and holding smartphones has become natural and ensures user acceptance as well as intuitive handling. We focus on using accelerators in several smartphones at the same time to interactively control a medical imaging solution. To this end, we introduce a framework to collect, modify, and distribute acceleration sensor data from multiple smartphones and integrate it with a medical imaging system which results in an environment suitable for e.g. doctors reviewing and explaining diagnostic findings. We performed some experiments to evaluate the usability of this approach and present an ongoing research in adapting the smartphone interface to physical simulation applications.

关键词： Smart phones Acceleration Biomedical imaging Medical services Three dimensional displays Skull Mice

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Real-Time Fine-Tuned Adjustment of Fiber Tracking Parameters

Real-Time Fine-Tuned Adjustment of Fiber Tracking Parameters

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Annual IEEE Symposium on computer-Based Medical Systems

作者： Adiel Mittmann Eros Comunello Aldo von Wangenheim Image Processing and Computer Graphics Lab-LAPIX Federal University of Santa Catarina-UFSC Florianopolis SC Brazil

Fiber tracking is a mature technique that aids neurosurgeons by providing the location of neuronal fibers in a patient's brain. Although the interactivity of tools have been typically limited, the exploitation of the parallel nature of fiber tracking is rapidly changing this landscape. The parallel environment offered by GPUs and modern CPUs enables fiber tracking to be executed very quickly on consumer-grade computers. The adjustment of fiber tracking parameters can currently be made on the fly, and the new results can be both computed and displayed in real-time on the screen. In order to further improve the interactivity of fiber tracking tools, this article proposes the application of histogram equalization, a traditional digital image processing procedure, to the fiber tracking parameters. The non-linear scale obtained by this process allows the user to interactively explore sensitive regions of the parameter space. Results show that this scale can be computed in real-time and that it can be cleanly integrated into fiber tracking tools.

关键词： Histograms Trajectory Indexes Visualization graphics processing units Real-time systems Anisotropic magnetoresistance

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Color skin segmentation based on non-linear distance metrics 19

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19th Iberoamerican Congress on Pattern Recognition, CIARP 2014

作者： Sobieranski, Antonio Carlos Chiarella, Vito F. Barreto-Alexandre, Eduardo Linhares, Rodrigo T. F. Comunello, Eros von Wangenheim, Aldo Federal University of Santa Catarina Laboratory of Image Processing and Computer Graphics Brazil Federal University of Parana Department of Informatics Brazil University of Sao Paulo Department of Computer Science Brazil

ISBN: (纸本)9783319125671

In this paper we present a semiautomatic method for skin identification in video sequences. The user trains the system by selecting in a frame some typical positive skin pixels, that will be used as a reference for the construction of a nonlinear distance metric. In this learning process the global optimum is obtained by induction employing higher polynomial terms of the Mahalanobis distance, extracting nonlinear features of the skin pattern distributions. These nonlinear features are then used to classify the frames captured from the camera, identifying all skin and non-skin regions on the scene. We adopt an estrategy which enables this method to run in real-time after some iteractions. We also compare our classification method against vector norm (L2) and Mahalanobis distance, showing a better classification for the skin patterns. © Springer International Publishing Switzerland 2014.

关键词： Artificial intelligence

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Guided-YNet: Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network

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computers, Materials & Continua 2024年第9期80卷 4813-4832页

作者： Tao Zhou Yunfeng Pan Huiling Lu Pei Dang Yujie Guo Yaxing Wang School of Computer Science and Engineering North Minzu UniversityYinchuan750021China School of Medical Information&Engineering Ningxia Medical UniversityYinchuan750004China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission NorthMinzuUniversityYinchuan750021China

Multimodal lung tumor medical images can provide anatomical and functional information for the same *** as Positron Emission Computed Tomography(PET),Computed Tomography(CT),and *** to utilize the lesion anatomical and functional information effectively and improve the network segmentation performance are key *** solve the problem,the Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network(Guide-YNet)is proposed in this ***,a double-encoder single-decoder U-Net is used as the backbone in this model,a single-coder single-decoder U-Net is used to generate the saliency guided feature using PET image and transmit it into the skip connection of the backbone,and the high sensitivity of PET images to tumors is used to guide the network to accurately locate ***,a Cross Scale Feature Enhancement Module(CSFEM)is designed to extract multi-scale fusion features after ***,a Cross-Layer Interactive Feature Enhancement Module(CIFEM)is designed in the encoder to enhance the spatial position information and semantic ***,a Cross-Dimension Cross-Layer Feature Enhancement Module(CCFEM)is proposed in the decoder,which effectively extractsmultimodal image features through global attention and multi-dimension local *** proposed method is verified on the lung multimodal medical image datasets,and the results showthat theMean Intersection overUnion(MIoU),Accuracy(Acc),Dice Similarity Coefficient(Dice),Volumetric overlap error(Voe),Relative volume difference(Rvd)of the proposed method on lung lesion segmentation are 87.27%,93.08%,97.77%,95.92%,89.28%,and 88.68%,*** is of great significance for computer-aided diagnosis.

关键词： Medical image segmentation U-Net saliency feature guidance cross-modal feature enhancement cross-dimension feature enhancement

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Named Entity Recognition in Chinese Judicial Domain Based on Self-attention mechanism and IDCNN 8

Named Entity Recognition in Chinese Judicial Domain Based on...

引用

8th International Conference on Digital Home, ICDH 2020

作者： Huang, Wenming Zhang, Juan Xiao, Yannan Han, Zheng Deng, Zhenrong Guilin University of Electronic Technology School of Computer Science and Information Security Guilin China Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin China Guilin Bank Guilin China

ISBN: (纸本)9781728192345

Chinese named entity recognition (CNER) in the judicial domain is an important basic task for intelligent analysis and processing of massive documents. This domain entity has more complicated structure than the common named entity, and its entity category is more abundant. However, the general method can not solve the problem of domain specific identification. In this paper, we combine self-attention mechanism and iteration dilated convolution neural network (IDCNN) for CNER in judicial domain. The bidirectional gate recurrent unit (BiGRU) model is used to automatically learn the context semantic information of the text and solve the long-distance dependence of the sequence. The model introduce the IDCNN to extract the key features of context semantic information, and capture finer-grained semantic information in underlying texts. The self-attention mechanism is used to analyze the relationship between characters, and the problem of long sequence semantic dilution is effectively solved by means of dynamic weight, and the optimal tag sequence is calculated by integrating conditional random fields (CRF), which further improves the recognition ability of the model. Finally, by analyzing the characteristics of legal documents, the new data set is annotated and the fine-grained named entity recognition is realized. The experimental results on our corpus show that the proposed method can effectively identify the entities in legal documents, and improve performance in the judicial field. © 2020 IEEE.

关键词： Semantics

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Fast two-step segmentation of natural color scenes using hierarchical region-growing and a color-gradient network

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Journal of the Brazilian computer Society 2008年第4期14卷 29-40页

作者： von Wangenheim, Aldo Bertoldi, Rafael F. Abdala, Daniel D. Richter, Michael M. Priese, Lutz Schmitt, Frank Lab for Image Processing and Computer Graphics - LAPIX Department of Computer Sciences - INE Universidade Federal de Santa Catarina - UFSC CEP 88049-200 Florianopolis - SC Brazil University of Münster Department of Computer Science Einsteinstrasse 62 D-48149 Münster Germany Department of Computer Science University of Calgary AB Canada Institut für Computervisualistik Universität Koblenz-Landau Postfach 201 602 56016 Koblenz Germany

We present evaluation results with focus on combined image and efficiency performance of the Gradient Network Method to segment color images, especially images showing outdoor scenes. A brief review of the techniques, Gradient Network Method and Color Structure Code, is also presented. Different region-growing segmentation results are compared against ground truth images using segmentation evaluation indices Rand and Bipartite Graph Matching. These results are also confronted with other well established segmentation methods (EDISON and JSEG). Our preliminary results show reasonable performance in comparison to several state-of-art segmentation techniques, while also showing very promising results comparatively in the terms of efficiency, indicating the applicability of our solution to real time problems.

关键词： Efficiency

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