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检索条件"任意字段=International Conference on Computer Vision and Image Processing, CVIP 2016"

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A new fuzzy clustering algorithm by incorporating constrained class uncertainty-based entropy for brain MR image segmentation 4th

A new fuzzy clustering algorithm by incorporating constraine...

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4th international conference on computer vision and image processing, cvip 2019

作者： Mahata, Nabanita Sing, Jamuna Kanta Department of Computer Science and Engineering Jadavpur University Kolkata700032 India

ISBN: (纸本)9789811540141

We propose a new fuzzy clustering algorithm by incorporating constrained class uncertainty-based entropy for brain MR image segmentation. Due to deficiencies of MRI machines, the brain MR images are affected by noise and intensity inhomogeneity (IIH), resulting unsharp tissue boundaries with low resolution. As a result, standard fuzzy clustering algorithms fail to classify pixels properly, especially using only pixel intensity values. We mitigate this difficulty by introducing entropy that measures constrained class uncertainty for each pixel. The value of this entropy is more for the pixels in the unsharp tissue boundaries. Apart from using the fuzzy membership function, we also define the similarity as the complement of a measure characterized by a Gaussian density function in non-Euclidean space to reduce the affect of noise and IIH. By introducing a regularization parameter, the trade-off between the fuzzy membership function and class uncertainty-based measure is resolved. The proposed algorithm is assessed both in qualitatively and quantitatively on several brain MR images of a benchmark database and two clinical data. The simulation results show that the proposed algorithm outperforms some of the fuzzy-based state-of-the-art methods devised in recent past when evaluated in terms of cluster validity functions, segmentation accuracy and Dice coefficient. © Springer Nature Singapore Pte Ltd 2020.

关键词： Entropy

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Semantic 3D Obstacle Detection Using an Enhanced Probabilistic Voxel Octree Representation 16

Semantic 3D Obstacle Detection Using an Enhanced Probabilist...

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IEEE 16th international conference on Intelligent computer Communication and processing (ICCP)

作者： Baciu, Sorana Oniga, Florin Nedevschi, Sergiu Tech Univ Cluj Napoca Comp Sci Dept Cluj Napoca Romania

ISBN: (数字)9781728190808

ISBN: (纸本)9781728190808

In this paper we present a solution to overcome specific drawbacks of 3D obstacles detection and image based semantic segmentation, by combining the two approaches into an enhanced 3D octree. The 3D obstacle detection will he performed on the enhanced octree. Semantic information is added to the voxel octree representation in a probabilistic fashion, during the building step of the octree. Using the voxels' semantic labels and probabilities, more accurate obstacle primitives are extracted during octree traversal. Later, the semantic information helps for a better strategy in merging the 3D primitives. A better detection bounding box and pixel wise accuracy is achieved;as well a higher precision rate, as it allows easy introduction of 3D properties filtering. The evaluation metric is applied on KITTI's 3D object detection dataset. Semantic values are obtained using an already trained R-CNN model, while the 3D point cloud is generated with the semi-global block snatching algorithm.

关键词： semantic environment perception stereo vision obstacle detection 3D voxels octree clustering

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Extreme weather prediction using 2-phase deep learning pipeline 4th

Extreme weather prediction using 2-phase deep learning pipel...

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4th international conference on computer vision and image processing, cvip 2019

作者： Oza, Vidhey Thesia, Yash Rasalia, Dhananjay Thakkar, Priyank Dube, Nitant Garg, Sanjay Computer Science and Engineering Department Institute of Technology Nirma University AhmedabadGujarat India Space Application Centre Indian Space Research Organisation AhmedabadGujarat India

ISBN: (纸本)9789811540172

Weather nowcasting is a problem pursued by scientists for a long time. Accurate short-term forecasting is helpful for detecting weather patterns leading to extreme weather events. Adding the dimension of nowcasting to extreme weather prediction increases the ability of models to look for preliminary patterns ahead in time. In this paper, we propose a two-stage deep learning pipeline that fuses the usability of nowcasting to the high value of extreme events prediction. Our experiments are performed on INSAT-3D satellite data from MOSDAC, SAC-ISRO. We show that our pipeline is modular, and many events can be predicted in the second phase based on the availability of the relevant data from the first phase. Testing for extreme events like the Chennai floods of 2015 and Mumbai floods of 2017 validates the efficacy of our approach. © Springer Nature Singapore Pte Ltd 2020.

关键词： Pipelines

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Classification of effusion and cartilage erosion affects in osteoarthritis knee MRI images using deep learning model 4th

Classification of effusion and cartilage erosion affects in ...

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4th international conference on computer vision and image processing, cvip 2019

作者： Singh, Pankaj Pratap Prasad, Shitala Chaudhary, Anil Kumar Patel, Chandan Kumar Debnath, Manisha Department of Computer Science and Engineering Central Institute of Technology Kokrajhar BTAD KokrajharAssam India CYSREN NTU Singapore Singapore

ISBN: (纸本)9789811540172

In today’s digital world, the major working culture is shifted to electronic devices such as computers and smartphone causing minimal physical exercise. Due to this reason, at old age people suffer many kinds of health issues such as Osteoarthritis (OA) disease. OA is the most common chronic condition of joints, which is also called as degenerative arthritis or degenerative joint disease. To identify this disease level an automated detection and classification method is required. This also requires expert knowledge person in this area. In this regard some other support is also required which is related to expert person of this area. The current strategy for OA identification includes clinical investigation and medical imaging techniques. In this paper, we detect and classify OA disease in knee from medical images using deep features. As we know, in medical imaging noise has a major role and therefore in this paper we focus on denoising medical images for accurate detection and classification. This paper also focuses on handling huge amount of image data by utilizing some High Performance Computing (HPC). The dataset used in this paper for detection and classification is a keen MRI image. Thus, an integrated discussion of various detection techniques regarding OA is done in a scientific way. © Springer Nature Singapore Pte Ltd 2020.

关键词： Classification (of information)

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Discovering cricket stroke classes in trimmed telecast videos 4th

Discovering cricket stroke classes in trimmed telecast vide...

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4th international conference on computer vision and image processing, cvip 2019

作者： Gupta, Arpan Karel, Ashish Sakthi Balan, M. Department of Computer Science and Engineering The LNM Institute of Information Technology Jaipur India

ISBN: (纸本)9789811540172

Activity recognition in sports telecast videos is challenging, especially, in outdoor field events, where there is a lot of camera motion. Generally, camera motions like zoom, pan, and tilt introduce noise in the low-level motion features, thereby, effecting the recognition accuracy, but in some cases, such camera motion can have a pattern which can be useful for action recognition in trimmed videos. In this work, we experimentally discover the types of strokes in a Cricket strokes dataset using direction information of dense optical flow. We use trimmed videos of Cricket strokes taken from Cricket telecast videos of match highlights. The predominant direction of motion is found by summing up the histograms of optical flow directions, taken for significant pixels, over the complete Cricket stroke clip. We show that such a quantized representation of the optical flow direction for the complete video clip can be used for Cricket stroke recognition. Our method uses an unsupervised K-Means clustering of the extracted clip feature vectors and we evaluate our results for 3-cluster K-Means by manually annotating the clusters as Left strokes, Right strokes and Ambiguous strokes. The accuracy for different set of feature vectors was obtained by varying the bin granularity of the histogram and the optical flow magnitude threshold. The best result we obtained, for 3-cluster K-Means and 562 stroke instances, was 87.72%. © Springer Nature Singapore Pte Ltd 2020.

关键词： Optical flows

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Efficient Objects Identification for a KidSize Humanoid Soccer Robot: Using Feedforward Neural Networks with image Segmentation 16

Efficient Objects Identification for a KidSize Humanoid Socc...

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16th IEEE international conference on Control, Automation, Robotics and vision (ICARCV)

作者： Jeronimo, Gilmar Correia Botelho, Wagner Tanaka Pimentel, Edson Pinheiro Maia dos Santos, Thaynara Cristina Fed Univ ABC UFABC Ctr Engn Modelling & Appl Social Sci CECS Santo Andre SP Brazil Fed Univ ABC UFABC Ctr Math Computat & Cognit CMCC Santo Andre SP Brazil

ISBN: (纸本)9781728177090

One of the biggest problems faced in robot soccer tournaments is related to object identification in the soccer field. The ball, field marks, crossbars of the goal, the opponent players or teammates are considered objects and should be identified by the robot soccer player in the category of KidSize RoboCup Humanoid League. The main target of this paper is to present a robotic architecture composed of the Software and Hardware Modules responsible for identifying these objects. The Software Module was composed of a DataBase (DB) with images of the objects to be identified and a Feedforward Neural Network (FNN) trained by the Neural Network Toolbox (NNT) of the MATrix LABoratory (MATLAB). In order to integrate the identification process with the Hardware Module, it was necessary to develop the NeuralNet library. This library was implemented in JAVA, making it compatible with multiple platforms. The purpose of this library was to transfer the FNN already trained by the NNT to the Raspberry Pi 3B. The Raspberry Pi 3B was responsible for processing the images captured by the vision System of the robotic player. Also, all the objects in the field were identified through the trained FNN and the Open Source computer vision (OpenCV) library. The results showed an efficiency of about 82% in ball identification, 92% in field marks, 81% in crossbars of the goal, and 93% in opponent players or teammates.

关键词： Object recognition Neural networks vision for robots

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Proceedings of the 2016 international conference on image processing, computer vision, and Pattern Recognition, IPCV 2016

Proceedings of the 2016 International Conference on Image Pr...

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2016 international conference on image processing, computer vision, and Pattern Recognition, IPCV 2016

ISBN: (纸本)1601324421

The proceedings contain 67 papers. The topics discussed include: hybrid implementation of image stitching on computers with GPUs;background estimation using image processing technique;automatic damaged region detection and inpainting method for digital images;an educational tool for understanding discrete Fourier transforms;an autoencoder-based image descriptor for image matching;a new image descriptor for image retrieval;toward device assisted identification of grocery store sections and items for the visually impaired;extreme-level eliminating brightness preserving bi-histogram equalization technique for brain ischemic detection;and novel texture pattern based multi-level set segmentation in cervical cancer image analysis.

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The Good, the Bad, and the Ugly: Neural Networks Straight from JPEG

The Good, the Bad, and the Ugly: Neural Networks Straight fr...

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2020 IEEE international conference on image processing, ICIP 2020

作者： Santos, Samuel Felipe Dos Sebe, Nicu Almeida, Jurandy Instituto de Ciência e Tecnologia Universidade Federal de São Paulo - Unifesp 12247-014 São José dos Campos SP Brazil University of Trento - UniTn 38123 Dept. of Information Engineering and Computer Science TrentoTN Italy

ISBN: (纸本)9781728163956

Over the past decade, convolutional neural networks (CNNs) have achieved state-of-the-art performance in many computer vision tasks. They can learn robust representations of image data by processing RGB pixels. Since image data are often stored in a compressed format, from which JPEG is the most widespread, a preliminary decoding process is demanded. Recently, the design of CNNs for processing JPEG compressed data has gained attention from the research community. They process DCT coefficients instead of RGB pixels, saving computation for decoding JPEG images, however, at the cost of increasing the computational complexity of the network. In this paper, we examine how spatial resolution and JPEG quality impacts on the performance of a state-of-the-art CNN designed to operate directly on the JPEG compressed domain. To alleviate its computational complexity, we propose a Frequency Band Selection (FBS) technique to select the most relevant DCT coefficients before feeding them to the network. Experiments were conducted on a subset of the imageNet dataset considering both fine- and coarse-grained image classification tasks. Results show that such networks are resilient to JPEG quality but are susceptible to spatial resolution. Also, our FBS can reduce the computational complexity of the network while retaining a similar accuracy. © 2020 IEEE.

关键词： Computational complexity

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Proceedings of the international conference on Interfaces and Human computer Interaction 2016, Game and Entertainment Technologies 2016 and computer Graphics, Visualization, computer vision and image processing 2016 - Part of the Multi conference on computer Science and Information Systems 2016

Proceedings of the International Conference on Interfaces an...

引用

2016 international conference on Interfaces and Human computer Interaction, IHCI 2016, Game and Entertainment Technologies, GET 2016 and computer Graphics, Visualization, computer vision and image processing, CGVcvip 2016

ISBN: (纸本)9789898533524

The proceedings contain 52 papers. The topics discussed include: the multimodal edge of human aerobatic interaction;technology in the classroom: a pilot test with a humanoid robot;GOBBO - an alternative communication tool to students of APAE with cerebral palsy;designing applications for intellectual disability users to teach independence skills;virtual-reality based leisure experience of NINTENDO WII for elderly;the virtual reality: interface with technology, digital games and industry;kinect based 3D video generation;and contour smoothing algorithm based on contour extremes.

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Fast Ship Detection in Remote Sensing images Based on Multi-Attention Mechanism 21

Fast Ship Detection in Remote Sensing Images Based on Multi-...

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Proceedings of the 5th international conference on Algorithms, Computing and Systems

作者： Jian Huang Zhuo Chen Hao Xu Xiushan Zhang School of Electrical Engineering Naval University of Engineering China

ISBN: (纸本)9781450385084

With the continuous development of deep learning in computer vision, object detection technology is constantly employed for processing remote sensing images. Especially, ship detection has become a significant and challenging task due to complex environmental factors (strong waves, clouds interference, etc.) and object issues (orientation, scale variety, density, etc.). Current detection methods pay more attention to the detection accuracy while ignoring the detection speed. In contrast with accuracy, detection speed is more important in some cases such as marine rescue and vessel tracking. Aiming at addressing these problems, we propose an enhanced YOLOv4(C-YOLOv4) which contains the feature fusion attention module (FAM) with a channel correlation loss(C-loss). C-loss is proposed to constrain the relations between object classes and channels while maintaining the intra-class and the inter-class separability. To evaluate the effectiveness of the proposed approach, comprehensive experiments are conducted on a public dataset HRSC2016. According to the experimental results, our proposed approach outperforms the baselines.

关键词： Convolutional neural networks Object detection Remote sensing image Ship detection

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