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Moving object segmentation using depth and optical flow in car driving sequences

Moving object segmentation using depth and optical flow in c...

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IEEE international conference on image processing

作者： Jiun-Yu Kao Dong Tian Hassan Mansour Anthony Vetro Antonio Ortega Department of Electrical Engineering University of Southern California Los Angeles CA USA Mitsubishi Electric Research Labs (MERL) Cambridge MA USA

Segmentation of moving objects in a scene is difficult for non-stationary cameras, and especially challenging in the presence of fast and unstable egomotion, e.g., as encountered with car-mounted cameras or wearable devices. Based on an analysis of motion vanishing points of the scene and estimated depth, a geometric model that relates extracted 2D motion to a 3D motion field relative to the camera is derived. Observing that the 3D motion field is piece-wise smooth, a constrained optimization problem that considers group sparsity is formulated to recover the 3D motion field from the 2D motion. The recovered 3D motion field is then clustered to provide the segmentation of moving objects. Experiments are performed using the KITTI vision Benchmark Suite and demonstrate that the proposed framework provides a dense segmentation of moving objects that is robust to the challenging conditions inherent with car driving sequences.

关键词： Three-dimensional displays Motion segmentation Cameras Two dimensional displays computer vision Trajectory Solid modeling

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Circle detection based on hough transform and Mexican Hat filter

Circle detection based on hough transform and Mexican Hat fi...

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international conference on computer, Control, Informatics and Its Applications (IC3INA)

作者： Nova Hadi Lestriandoko Rifki Sadikin Research Center for Informatics Indonesian Institute of Sciences

ISBN: (纸本)9781509023240

Circles detection is an important part of object recognition in image processing and computer vision. In this paper, we propose an adaptive method based on Hough transform to detect the circle shapes in digital image. The Mexican Hat filter derived from edge filter is used to concentrate the peaks of Hough local maxima. So, the circle center and its radius can be extracted easily and accurate. The comparison of adaptive method and traditional Hough transform will be analyzed. Experimental results over several digital images with varying ranges of complexity showed the efficiency of proposed method that more efficient than traditional Hough transform. Finally, The development of circles detection is still needed to get the best results, especially for circles shape with any noise.

关键词： Transforms image edge detection Shape Robustness Convolution Informatics Noise measurement

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Blind image deconvolution based on robust stable edge prediction

Blind image deconvolution based on robust stable edge predic...

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IEEE EMBS international conference on Information Technology Applications in Biomedicine (ITAB)

作者： Wei Li Xuenan Cui Van Huan Nguyen Trung Dung Do Iju Park Hakil Kim Laboratory of Computer Vision School of Information and Communication Eng. Inha University South Korea Laboratory of Computer Vision School of Information and Communication Eng. Inha University Incheon Incheon KR

This paper proposes a robust blind deconvolution method for removing a uniform blur from microscopy images. For the estimation of the kernel - point spread function (PSF) - the stable edge is estimated using a fuzzy edge prediction method. Based on the estimated stable edges, optimizing a blurring objective function leads to a closed form for the estimation of a kernel and latent image. In comparison with existing deconvolution methods based on iterative optimization, the proposed method with a closed-form solution produces a significant decrease in processing time, which is an important barrier in applying deconvolution methods to real-world applications. Experimental results demonstrate the robustness and high efficiency of the proposed method for diverse microscopy images.

关键词： image edge detection Kernel Deconvolution Estimation Robustness Prediction methods Microscopy

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LOW COMPLEXITY OPTICAL FLOW USING NEIGHBOR-GUIDED SEMI-GLOBAL MATCHING

LOW COMPLEXITY OPTICAL FLOW USING NEIGHBOR-GUIDED SEMI-GLOBA...

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IEEE international conference on image processing

作者： Jiang Xiang Ziyun Li David Blaauw Hun Seok Kim Chaitali Chakrabarti School of Electrical Computer and Energy Engineering Arizona State University Department of Electrical Engineering and Computer Science University of Michigan

ISBN: (纸本)9781467399623

This paper presents Neighbor-Guided Semi-Global Matching (NG-fSGM), a new method for optical flow. It is based on SGM, a popular dynamic programming algorithm for stereo vision, where the disparity of each pixel is calculated by aggregating local matching costs over the entire image to resolve local ambiguity in texture-less and occluded regions. Unlike conventional SGM, NG-fSGM operates on a subset of the search space that has been aggressively pruned based on neighboring pixels' information. Our proposed method achieves a fast approximation of SGM with significantly simpler cost aggregation and flow computation. Compared to a prior SGM extension for optical flow, the proposed NG-fSGM provides about 9x reduction in the number of computations and 5× reduction in the memory requirement with only 0.17% accuracy degradation when evaluated with Middlebury benchmark test cases.

关键词： Optical flow SGM FSGM Low complexity Optic Flow memory requirements pixel Stereoviews reduction

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Copy-Move Forgery Detection Using Color Space and Moment Invariants-Based Features

Copy-Move Forgery Detection Using Color Space and Moment Inv...

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Proceedings of the Digital image Computing: Technqiues and Applications (DICTA)

作者： Rafsanjany Kushol Md Sirajus Salekin Md Hasanul Kabir Ashraful Alam Khan Department of Computer Science and Engineering Islamic University of Technology Dhaka Bangladesh

Due to the availability of different image processing tools, now-a-days Copy-move forgery is emerging immensely. Addressing this problem is being more deterrent because of different types of image post processing techniques. In this paper, we propose a robust approach to detect copy-move forgery detection which ensembles human vision information from color space of the image and moment invariants features to represent the objects of the images. Integration of color perception and object representation generates a powerful feature vector of reasonable size which ensures detection against different types of challenges. Experiments on benchmark dataset demonstrate the efficiency and robustness of the proposed approach on different image post processing methods.

关键词： Feature extraction image color analysis Forgery Digital images Robustness image coding Transform coding

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Discriminant color texture descriptors for diabetic retinopathy recognition

Discriminant color texture descriptors for diabetic retinopa...

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

作者： Holly H. Vo Abhishek Verma Department of Computer Science California State University Fullerton CA USA

ISBN: (纸本)9781509039005

Diabetic retinopathy (DR) is a common eye disease that could lead to irreversible vision loss but hard to be noticed by carriers in early stages. Instead of isolating DR signs for DR recognition, this paper examines discriminant texture features obtained by color multi-scale uniform local binary pattern (LBPs) descriptors on five common color spaces and two proposed hybrid color spaces. The extracted features are evaluated by the enhanced Fisher linear discriminant, EFM. Experiments are done on a large dataset of 35,126 training images and 53,576 testing images that have been taken by different devices with high variance in dimensions, quality and luminance. The best performance is above 71.45% by HSI-LBPs, a*SI-LBPs, and bSI-LBPs descriptors.

关键词： image color analysis Retina Feature extraction Retinopathy Blood vessels Biomedical imaging Diabetes

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Fast and high-quality bilateral filtering using Gauss-Chebyshev approximation

Fast and high-quality bilateral filtering using Gauss-Chebys...

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international conference on Signal processing and Communications, SPCOM

作者： Sanjay Ghosh Kunal N. Chaudhury Department of Electrical Engineering Indian Institute of Science Bangalore India

ISBN: (纸本)9781509017478

The bilateral filter is an edge-preserving smoother that has diverse applications in image processing, computer vision, computer graphics, and computational photography. The filter uses a spatial kernel along with a range kernel to perform edge-preserving smoothing. In this paper, we consider the Gaussian bilateral filter where both the kernels are Gaussian. A direct implementation of the Gaussian bilateral filter requires O(σ s 2 ) operations per pixel, where σ s is the standard deviation of the spatial Gaussian. In fact, it is well-known that the direct implementation is slow in practice. We present an approximation of the Gaussian bilateral filter, whereby we can cut down the number of operations to O(1) per pixel for any arbitrary σ s , and yet achieve very high-quality filtering that is almost indistinguishable from the output of the original filter. We demonstrate that the proposed approximation is few orders faster in practice compared to the direct implementation. We also demonstrate that the approximation is competitive with existing fast algorithms in terms of speed and accuracy.

关键词： Chebyshev approximation Kernel Approximation algorithms image edge detection Filtering Complexity theory Hardware

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An Efficient Way for Segmentation of Bangla Characters in Printed Document Using Curved Scanning

An Efficient Way for Segmentation of Bangla Characters in Pr...

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international conference on Informatics, Electronics and vision

作者： Ahnaf Farhan Rownak Fazle Rabby Sabir Ismail Saiful Islam Dept. of Computer Science & Engineering Shahjalal University of Science & Technology Dept. of Computer Science Computer Science & Engineering Shahjalal University of Science & Technology

ISBN: (纸本)9781509012701

The preeminent reason for poor output in Optical Character Recognition (OCR) for Bangla text is introduced by segmentation related error. Different shape of characters, connected characters, modifiers in top and bottom, overlapped region between consecutive characters are the main obstacle for effective segmentation for Bangla printed text. In this paper an efficient strategy is introduced to segment characters consisting overlapped region with other characters. The proposed strategy of our research have achieved 99.8% accuracy rate in line segmentation, 99.5% accuracy in word segmentation and 99% accuracy for character segmentation. The error introduced when two consecutive characters have multiple touching points.

关键词： image processing Segmentation Character segmentation Extraction Binarization OCR Curved scanning Bangla characters

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DIRECT INFERENCE ON COMPRESSIVE MEASUREMENTS USING CONVOLUTIONAL NEURAL NETWORKS

DIRECT INFERENCE ON COMPRESSIVE MEASUREMENTS USING CONVOLUTI...

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IEEE international conference on image processing

作者： Suhas Lohit Kuldeep Kulkarni Pavan Turaga School of Electrical Computer and Energy Engineering School of Arts Media and Engineering Arizona State University

ISBN: (纸本)9781467399623

Compressive imagers, e.g. the single-pixel camera (SPC), acquire measurements in the form of random projections of the scene instead of pixel intensities. Compressive Sensing (CS) theory allows accurate reconstruction of the image even from a small number of such projections. However, in practice, most reconstruction algorithms perform poorly at low measurement rates and are computationally very expensive. But perfect reconstruction is not the goal of high-level computer vision applications. Instead, we are interested in only determining certain properties of the image. Recent work has shown that effective inference is possible directly from the compressive measurements, without reconstruction, using correlational features. In this paper, we show that convolutional neural networks (CNNs) can be employed to extract discriminative non-linear features directly from CS measurements. Using these features, we demonstrate that effective high-level inference can be performed. Experimentally, using hand written digit recognition (MNIST dataset) and image recognition (imageNet) as examples, we show that recognition is possible even at low measurement rates of about 0.1.

关键词： Compressive sensing Neural networks Compressed sensing Neural network Reconstruction algorithm perfect reconstruction images Recognition (Psychology)

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An image Dehazing Method Based On an Improved Retinex Theory

An Image Dehazing Method Based On an Improved Retinex Theory

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2016 international conference on computer Engineering,Information Science & Application Technology(ICCIA 2016)

作者： Ebtesam Mohameed Alharbi Yilin Shan Peng Ge Hong Wang School of Electronics and Information South China University of Technology Engineering Research Center for Optoelectronics of Guangdong Province School of Physics and Optoelectronics South China University of Technology Institute of Electronic and Information Hangzhou Dianzi University

ISBN: (纸本)9781510830363

image quality is often affected in many ways by the atmosphere, especially in foggy weather conditions. Dehazing is a highly demanded operation within the domain of image processing for various applications. The paper proposes a new single-image dehazing algorithm based on the single scale retinex（SSR） algorithm, combined with the theory of atmospheric scattering. Compared with the SSR algorithm, the proposed algorithm is shown to effectively take advantage of the abundance of information within an image and offers a clearer dehazing output within distant image scenes. Compared with the histogram equalization algorithm, our algorithm achieves a higher degree of natural colour restoration and hence yields a high color fidelity. Additionally, the algorithm exhibits a higher efficiency in recovering image features from objective and subjective points of view.

关键词： image processing image reconstruction-restoration Dehazing Retinex theory

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