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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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Deep Learning Based Novel Cascaded Approach for Skin Lesion Analysis 7th

Deep Learning Based Novel Cascaded Approach for Skin Lesion ...

引用

7th international conference on computer vision and image processing, cvip 2022

作者： Innani, Shubham Dutande, Prasad Baheti, Bhakti Baid, Ujjwal Talbar, Sanjay Center of Excellence Signal and Image Processing Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India

ISBN: (纸本)9783031314063

Patients diagnosed with skin cancer like melanoma are prone to a high mortality rate. Automatic lesion analysis is critical in skin can¬cer diagnosis and ensures effective treatment. The computer-aided diag¬nosis of such skin cancer in dermoscopic images can significantly reduce the clinicians’ workload and help improve diagnostic accuracy. Although researchers are working extensively to address this problem, early detec¬tion and accurate identification of skin lesions remain challenging. This research focuses on a two-step framework for skin lesion segmentation followed by classification for lesion analysis. We explored the effective¬ness of deep convolutional neural network (CNN) based architectures by designing an encoder-decoder architecture for skin lesion segmenta¬tion and CNN based classification network. The proposed approaches are evaluated quantitatively in terms of the Accuracy, mean Intersection over Union(mIoU) and Dice Similarity Coefficient. Our cascaded end-to-end deep learning-based approach is the first of its kind, where the classification accuracy of the lesion is significantly improved because of prior segmentation. The code is available at https://***/shubhaminnani/skin/lesion. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： image enhancement

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HetroCV: Auto-tuning Framework and Runtime for image processing and computer vision Applications on Heterogeneous Platform 44

HetroCV: Auto-tuning Framework and Runtime for Image Process...

引用

44th Annual international conference on Parallel processing Workshops (ICPPW)

作者： Wang, Daihou Foran, David J. Qi, Xin Parashar, Manish Rutgers State Univ Rutgers Discovery Informat Inst Piscataway NJ 08854 USA Rutgers Canc Inst New Jersey New Brunswick NJ USA

ISBN: (纸本)9781467375894

With the wide adoption of high-performance processors and accelerators, large-scale computer vision applications have gained great performance improvement. However, it often requires extensive experiments and expertise to achieve optimal performance from manually-tuned programs, and the programs often need to be re-tuned when transplanted to a different platform, or using a different system configuration. To overcome this problem, in this paper we proposed HetroCV, a programmer-directed auto-tuning framework and runtime for computer vision applications on heterogeneous CPU-MIC platform. In HetroCV auto-tuning framework, computation units in the application pipeline are categorized in to one of three patterns: Map, Stencil and MapReduce, and program statistics are extracted from units' meta-information. Machine learning is adopted to train models for each pattern using the tuned parameters and program statistics from trial-run sets, so that when a new unit is presented, HetroCV autotuner can use the corresponding trained model to generate optimized tuning parameters. In HetroCV runtime, performance models for processor and co-processor are built to predict the prospective execution time of each computation unit in the application pipeline. We adopted the maximum-throughput mapping strategy, thus each unit would be mapped dynamically to the processor/co-processor queue, which would generate the minimum overall execution time. Experiments on two medical image processing applications running on heterogeneous platform composed of Intel Xeon CPU and Intel Phi co-processor showed advanced performance over naive OpenMP tuning and Genetic Algorithm (GA) based heuristic tuning.

关键词： Online auto-tuning runtime heterogeneous platform Intel MIC architecture image processing

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Volumetric segmentation

Volumetric segmentation

引用

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

作者： Burdescu, Dumitru Dan Brezovan, Marius Stănescu, Liana Spahiu, Cosmin Stoica Ebâncă, Daniel Costin Computers and Information Technology Department Faculty of Automatics Computers and Electronics University of Craiova Dolj Romania

ISBN: (纸本)1601324421

image segmentation plays a crucial role in effective understanding of digital images, planar or volumetric images. The current research in graph based methods is oriented towards producing approximate solution (or sub-optimal solution) for such graph matching problem to reduce processing time. We are introducing an algorithm for volumetric segmentation based on virtual tree-hexagonal structure (prisms) constructed on the image voxels to improve the speed of segmentation. Here, a graph-based theoretic framework is considered by modeling image segmentation as a graph partitioning problem using input spatial graph. Then we can use the graph facilities and their related algorithms and computational complexity can be viewed as slow as the fundamental graph algorithms. The key to the whole algorithms of volumetric segmentation method is the prism cells as vertices. Volumetric segmentation algorithm contains many other algorithms but only segmentation algorithm is presented based on the limited space of paper. © CSREA Press.

关键词： image enhancement

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ON THE GENERALITY OF NEURAL image FEATURES 23

ON THE GENERALITY OF NEURAL IMAGE FEATURES

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23rd IEEE international conference on image processing (ICIP)

作者： Venkatesan, Ragav Gatupalli, Vijetha Li, Baoxin Arizona State Univ Sch Comp Informat & Decis Syst Engn Tempe AZ 85281 USA

ISBN: (纸本)9781467399616

Often the filters learned by Convolutional Neural Networks (CNNs) from different image datasets appear similar. This similarity of filters is often exploited for the purposes of transfer learning. This is also being used as an initialization technique for different tasks in the same dataset or for the same task in similar datasets. Off-the-shelf CNN features have capitalized on this idea to promote their networks as best transferable and most general and are used in a cavalier manner in day-to-day computer vision tasks. While the filters learned by these CNNs are related to the atomic structures of the images from which they are learnt, all datasets learn similar looking low-level filters. With the understanding that a dataset that contains many such atomic structures learn general filters and are therefore useful to initialize other networks with, we propose a way to analyse and quantify generality. We applied this metric on several popular character recognition, natural image and a medical image dataset, and arrive at some interesting conclusions. On further experimentation we also discovered that particular classes in a dataset themselves are more general than others.

关键词： Character recognition

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image DESCRIPTION THROUGH FUSION BASED RECURRENT MULTI-MODAL LEARNING 23

IMAGE DESCRIPTION THROUGH FUSION BASED RECURRENT MULTI-MODAL...

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23rd IEEE international conference on image processing (ICIP)

作者： Oruganti, Ram Manohar Sah, Shagan Pillai, Suhas Ptucha, Raymond Rochester Inst Technol Comp Engn Rochester NY 14603 USA Rochester Inst Technol Ctr Imaging Sci Rochester NY USA Rochester Inst Technol Comp Sci Rochester NY USA

ISBN: (纸本)9781467399616

Current research in computer vision and machine learning has demonstrated some great abilities at detecting and recognizing objects in natural images. The promising results in these areas have inspired research towards solving more complex multi-modal learning problems in the image/video domains such as automatic annotation, segmentation, labelling, and generic understanding. Although solutions have been provided for one or more of these problems, their approaches have been application specific. This paper introduces an end-to-end trainable Fusion-based Recurrent Multi-Modal (FRMM) model to address multi-modal applications. FRMM allows each input modality to be independent in terms of architecture, parameters and length of input sequences. FRMM image description models seamlessly blend convolutional neural net work feature descriptors with sequential language data in a recurrent framework. For training and testing we used the Flickr30K and MSCOCO datasets, demonstrating state-of-the-art description results.

关键词： image Captioning CNN LSTM Recurrent Neural Networks

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Accelerating DCT-based color image watermarking on GPUs

Accelerating DCT-based color image watermarking on GPUs

引用

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

作者： Fan, Hong Huang, Miaoqing Lai, Chenggang Yu, Jinming Xu, Wujun School of Information Science and Technology Donghua University Shanghai China Department of Computer Science and Computer Engineering University of Arkansas AR United States

ISBN: (纸本)1601324421

— Watermarking is widely used to protect information authenticity and integrity. In this work we propose to add invisible watermark into one channel of color RGB images in the frequency domain. Using Discrete Cosine Transform (DCT) in watermarking can result in better watermark quality and robustness due to the use of uncorrelated coefficients that operate in the frequency domain. The watermarking process consists of three steps, i.e., DCT, adding watermark, inverse DCT (IDCT). Applying DCT/IDCT on large images using CPUs typically takes quite long time, which prevents the online integration of invisible watermarks. GPUs can significantly improve the performance of the watermarking process. In our experiments, it is found that Nvidia Tesla K20 GPU is able to add watermark to one 512×512 image in less than 0.1 ms, which is 170 times faster than the Intel Core i7-3820 CPU. The energy-efficient GPU on Tegra TK1 SOC can achieve more than 20× speedup than the ARM CPU. © CSREA Press.

关键词： Graphics processing unit

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Structure-Texture Decomposition-Based Enhancement Framework for Weakly Illuminated images 6th

Structure-Texture Decomposition-Based Enhancement Framework ...

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6th international conference on computer vision and image processing, cvip 2021

作者： Haritha, K.M. Sreeni, K.G. Zacharias, Joseph Jeena, R.S. Department of Electronics and Communication Engineering College of Engineering Trivandrum Thiruvananthapuram India

ISBN: (纸本)9783031113451

images acquired in poor illumination conditions are characterized by low brightness and considerable noise which constrain the performance of computer vision systems. image enhancement thus remains crucial for improving the efficiency of such systems. To improve the visibility of low-light images, a novel image enhancement framework based on the structure-texture decomposition is proposed in this paper. Firstly, the low-light image is split into structure and texture layers using the total-variation (TV) based image decomposition approach. The structure layer is initially diffused using Perona-Malik (PM) diffusion model and the local and global luminance enhancement is incorporated in the structure-pathway using an expanded model of biological normalization for visual adaptation. In the texture pathway, the suppression of local noise and the enhancement of image details are attained with the estimation of the local energy of the texture layer and the strategy of energy weighting. Eventually, the final enhanced image of improved quality is obtained by merging the modified structure and texture layers. The effectiveness of the proposed framework is validated using no-reference image quality metrics including IL-NIQE, BRISQUE, PIQE, and BLIINDS-II. The experimental results show that the proposed method outperforms the state-of-the-art approaches. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image enhancement

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An Unsupervised Approach for Estimating Depth of Outdoor Scenes from Monocular image 5th

An Unsupervised Approach for Estimating Depth of Outdoor Sce...

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5th international conference on computer vision and image processing, cvip 2020

作者： Mitra, Shankhanil Dutta, H. Pallab Jyoti Bhuyan, M.K. Department of Electrical and Communication Engineering IISc Bengaluru560012 India Department of Electronics and Electrical Engineering IIT Guwahati Guwahati781039 India

ISBN: (纸本)9789811611025

Majority of the existing deep learning based depth estimation approaches employed for finding depth from monocular image need very accurate ground truth depth information to train a supervised decision framework. However, it is not always possible to get an accurate depth information particularly for diverse outdoor scenes. To address this, a convolutional network architecture is proposed, which comprises of two encoder-decoders for utilizing stereo matching criterion for training. The image reconstruction error measure is employed for optimization of network parameters instead of ground truth depth information. To estimate an accurate disparity map in low textured and occluded regions, a cross based cost-aggregation loss term is proposed along with a novel occlusion detection and filling method in the post-processing stage. The proposed method achieves an improvement of 6.219% RMS error for a depth cap of 80m and 6.15% RMS error for a depth cap of 50 m among the unsupervised approaches on KITTI 2015 dataset. The importance of channel-wise descriptor for training a deep neural network is also established through the performance measure. © 2021, Springer Nature Singapore Pte Ltd.

关键词： image reconstruction

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computer vision based Assistive Technology For Blind and Visually Impaired People 16

Computer Vision based Assistive Technology For Blind and Vis...

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7th international conference on Computing, Communication and Networking Technologies (ICCCNT)

作者： Sivan, Shankar Darsan, Gopu Sree Buddha Coll Engn Alappuzha Kerala India

ISBN: (纸本)9781450341790

The computer vision based assistive technology for the blind and visually impaired is a developing area. The assistive technology helps the visually impaired by providing them with a greater independence. By enabling them with their day-to-day activities like indoor and outdoor navigation, obstacle detection, locating the doors and lost objects, etc. Even though different assistive technologies are available for the blind, most of them have complex designs which are developed for a specific purpose and are expensive for the commercial production. Rather than depending on a traditional white cane, the blind and visually impaired people can make use of the cheaper assistive device proposed in this paper. The proposed system incorporates several assistance features in a device which will be an asset for them according to their needs.

关键词： Assistive technology computer vision image processing electronic travel aids wearable systems

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Epipolar Geometry Comparison of SAR and Optical Camera 2

Epipolar Geometry Comparison of SAR and Optical Camera

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2nd ISPRS international conference on computer vision in Remote Sensing (CVRS)

作者： Li, Dong Zhang, Yunhua Chinese Acad Sci Natl Space Sci Ctr Key Lab Microwave Remote Sensing 1 Nanertiao Beijing 100190 Peoples R China

ISBN: (纸本)9781510601543

In computer vision, optical camera is often used as the eyes of computer. If we replace camera with synthetic aperture radar (SAR), we will then enter a microwave vision of the world. This paper gives a comparison of SAR imaging and camera imaging from the viewpoint of epipolar geometry. The imaging model and epipolar geometry of the two sensors are analyzed in detail. Their difference is illustrated, and their unification is particularly demonstrated. We hope these may benefit researchers in field of computer vision or SAR image processing to construct a computer SAR vision, which is dedicated to compensate and improve human vision by electromagnetically perceiving and understanding the images.

关键词： computer vision computer SAR vision epipolar geometry imaging model optical camera synthetic aperture radar (SAR) 3D reconstruction

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