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6th International conference on Imaging, Signal processing and Communications (ICISPC)

作者： Wu, Chenhao Ma, Huijuan Jiang, Hongqi Huang, Zirui Cai, Zhengyue Zheng, Ziying Wong, Chin-Hong Fuzhou Univ Maynooth Int Engn Coll Fujian Peoples R China

ISBN: (纸本)9781665454803

Edge detection is widely used in biological vision and computer vision. Canny edge detection is a common method to locate the sharp intensity changes and seek object boundaries. Nowadays, there are several improvement designs for Canny edge detection algorithms. In this paper, the Gaussian filtering is replaced by median filtering to increase the noise robustness of Canny edge detection. the inhabitant of the false edge is achieved by implementing an improved Sobel operator and iterative threshold filter method before the non-maximum suppression of the Canny operator. the final image is obtained after threshold filtering and binarization. the results show that the proposed algorithm is more robust to noise and can preserve more useful edge information by suppressing more false edges than traditional Canny edge detection.

关键词： median filtering iterative algorithm canny operator image processing computer vision

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Speeding up SIFT using GPU

Speeding up SIFT using GPU

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2013 4th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2013

作者： Aniruddha Acharya, K. Venkatesh Babu, R. Video Anaytics Laboratory Supercomputer Education and Research Center Indian Institute of Science Bangalore India

ISBN: (纸本)9781479915880

Scale Invariant Feature Transform (SIFT) is one of the widely used interest point features. It has been successfully applied in various computer vision algorithms like object detection, object tracking, robotic mapping and large scale image retrieval. Although SIFT descriptors are highly robust towards scale and rotation variations, the high computational complexity of the SIFT algorithm inhibits its use in applications demanding real time response, and in algorithms dealing with very large scale databases. this paper presents a parallel implementation of SIFT on a GPU, where we obtain a speed of around 55 fps for a 640×480 image. One of the main contributions of our work is the novel combined kernel optimization that has led to a significant improvement of 21.79% in the execution speed. We compare our results with the existing implementations in the literature that accelerate SIFT, and find that our implementation has better speedup than the most of them. © 2013 IEEE.

关键词： graphics processing unit

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On Improved CS-SS image Watermark Detection Over Radio Mobile Channel 16

On Improved CS-SS Image Watermark Detection Over Radio Mobil...

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Bose, Anirban Maity, Santi P. IIEST Dept Informat Technol Sibpur 711103 Howrah India

ISBN: (纸本)9781450347532

Recently compressed sensing or compressive sampling (CS), apart from its intrinsic applications of sub-sample signal reconstruction, is explored a lot in the design of bandwidth preserving-energy efficient wireless networks. At the same time, due to open nature of wireless channel, digital data (media) transmission needs their protection from unauthorized access and digital watermarking has been devised as one form of potential solution over the years. Among the various methods, spread spectrum (SS) watermarking is found to be efficient due to its improved robustness and imperceptibility. SS watermarking on digital images in presence of additive and multiplicative noise is studied a lot. To the best of knowledge, CS-SS watermarking in presence of both multiplicative (fading channel) and additive noise is not explored much in the existing literature. To address this problem, a wireless communication theoretic model is suggested here to develop an improved detection scheme on additive SS image watermark framework. System model considers sub sample (CS) transmission of the watermarked image over both non-fading and fading channel. then a diversity assisted weighted combining scheme for the improved watermark detection is developed. An optimization problem is formulated where the weight for the individual link is calculated through eigen filter approach to maximize the watermark detection probability for a fixed false alarm rate under the constraint of an embedding power (strength). A large set of simulation results validate the mathematical model of the diversity assisted compressive watermark detector.

关键词： Spread spectrum watermarking compressive transmission

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Color Restoration in Turbid Medium 16

Color Restoration in Turbid Medium

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Nimisha, T. M. Seemakurthy, Karthik Rajagopalan, A. N. Vedachalam, Narayanaswamy Raju, Ramesh IIT Madras Madras Tamil Nadu India Natl Inst Ocean Technol Madras Tamil Nadu India

ISBN: (纸本)9781450347532

Light scattering and color distortions are two major issues with underwater imaging. Scattering occurs due to turbidity of the medium and color distortions are caused by differential attenuation of wavelengths as a function of depth. As a result, underwater images taken in a turbid medium have low contrast, color cast, and color loss. the main objective of this work is color restoration of underwater images i.e, produce its equivalent image as seen outside of the water surface. As a first step, we account for low contrast by employing dark channel prior based dehazing. these images are then color corrected by learning a mapping function between a pair of color chart images, one taken inside water and another taken outside. the mapping thus learned is with respect to a reference distance from the water surface. We also propose a color modulation scheme that is applied prior to color mapping to accommodate the same mapping function for different depths as well. Color restoration results are given on several images to validate the efficacy of the proposed methodology.

关键词： Color mapping color modulation white balancing boosting dehazing

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A Rapid Non-Linear Diffusion Compressed Sensing parallel MR image Reconstruction 11

A Rapid Non-Linear Diffusion Compressed Sensing parallel MR ...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Joy, Ajin Paul, Joseph Suresh Indian Inst Informat Technol & Management Kerala Trivandrum Kerala India

ISBN: (纸本)9781450366151

Optimization of the tradeoff between computation time and image quality is essential for reconstructing high-quality magnetic resonance image (MRI) from a limited number of acquired samples in a short time using compressed sensing (CS) algorithms. In this paper, we achieve this for the edge preserving non-linear diffusion reconstruction (NLDR) which eliminates the critical step-size tuning of the total variation (TV) based CS-MRI. Based on optimization of contrast parameter that controls noise and signal in sensitivity modulated channel images, we propose an a-switching NLDR technique for a faster approximation of reconstruction image without affecting the image quality. Proposed algorithm exploits the difference in the extent of undersampling artifacts in signal-background regions of the channel images to arrive at different estimates of contrast parameter, leading to an effective optimization of speed and quality. While maintaining better image quality as compared to conventional TV reconstruction, the switched NLDR also achieves 25-35% gain in convergence time over NLDR without switching. this makes the switched NLDR a better candidate for fast reconstruction over traditional TV and NLDR approaches. In the detailed numerical experiments, we have compared and optimized the tradeoff for various state-of-the-art choices of contrast parameter.

关键词： parallel MRI nonlinear diffusion contrast parameter

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Automated delineation of regions of interest through adaptive segmentation and interpretation of images in view of dimensional evaluation of manufactured parts

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JOURNAL OF ELECTRONIC IMAGING 2004年第3期13卷 462-473页

作者： Lallement, A Khemmar, R Hirsch, E Univ Strasbourg 1 Ecole Natl Super Phys Strasbourg Lab Sci Image Informat & Teledetect CNRSUMR 7005 F-67412 Illkirch Graffenstaden France

Industrial applications of computer vision, such as dimensional inspection, require, among other things, making automated procedures available for the analysis of gray-level image to derive application-dependent 3-D descriptions of their contents. In view of the quantitative 3-D reconstruction of an imaged object and to fully automate the evaluation process, we describe two segmentation and interpretation methods for the automated delineation of regions of interest belonging to the object and associated with free-form surfaces. the selection of the procedure to apply, either a mean shift based or level set based analysis, depends essentially on available a priori information relative to the localization and the shape of the object in the scene. the two approaches are part of a 3-D vision-based on-line inspection system. Results on images of manufactured parts acquired under realistic conditions illustrate the use of these two approaches. (C) 2004 SPIE and IST.

关键词： image segmentation image processing 3D image processing Inspection Manufacturing Data acquisition image resolution 3D modeling Binary data image analysis

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image Co-segmentation using Graph Convolution Neural Network 11

Image Co-segmentation using Graph Convolution Neural Network

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Banerjee, Sayan Hati, Avik Chaudhuri, Subhasis Velmurugan, Rajbabu Indian Inst Technol Mumbai Maharashtra India

ISBN: (纸本)9781450366151

image co-segmentation is jointly segmenting two or more images sharing common foreground objects. In this paper, we propose a novel graph convolution neural network (graph CNN) based end-to-end model for performing co-segmentation. At the beginning, each input image is over-segmented into a set of superpixels. Next, a weighted graph is formed using the over-segmented images exploiting spatial adjacency and both intra-image and inter-image feature similarities among the image superpixels (nodes). Subsequently, the proposed network, consisting of graph convolution layers followed by node classification layers, classifies each superpixel either into the common foreground or its complement. During training, along with the co-segmentation network, an additional network is introduced to exploit the corresponding semantic labels, and the two networks share the same weights in graph convolution layers. the whole model is learned in an end-to-end fashion using a novel cost function comprised of a superpixel wise binary cross entropy and a multi-label cross entropy. We also use empirical class probabilities in the loss function to deal with class imbalance. Experimental results reflect that the proposed technique is very competitive with the state-of-the-art methods on two challenging datasets, Internet and Pascal-VOC.

关键词： Co-segmentation graph superpixel graph CNN convolution filter

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Retinal vessel segmentation using color image morphology and local binary patterns

Retinal vessel segmentation using color image morphology and...

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Iranian conference on Machine vision and image processing

作者： Zabihi, Seyed Mohsen Delgir, Morteza Pourreza, Hamid Reza Computer Engineering Department Ferdowsi University of Mashhad Iran Machine Vision Laboratory Eye Images Analysis Research Group Iran

ISBN: (纸本)9781424497065

In this paper, an automated retinal vessel extraction algorithm is represented. A multi-scale morphological algorithm is used for local contrast enhancement of color retinal image. this method enhances vessels not only in color image, but also in the three color components of that image. After feature extraction using LBP and spatial image processing, MLP as a classifier segments the pixels into vessels and non-vessels. Finally, in post processing step, we used binary morphologies for noise removing and smoothing. the performance of the proposed algorithm is tested on the images of DRIVE database. © 2010 IEEE.

关键词： Local binary pattern

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Exploiting image processing locality in cache pre-fetching 5

Exploiting image processing locality in cache pre-fetching

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5th International conference on High Performance Computing

作者： Cucchiara, R Piccardi, M Univ Ferrara Dipartimento Ingn I-44100 Ferrara Italy

ISBN: (纸本)0818691948

Emerging trends in computer design attempt to include specific solutions for handling images also in general-purpose computers, because of the current spread of multimedia, image processing and computer graphics applications. In this context, this paper proposes hardware pre-fetching techniques specific for caching images: the main issue we state is that most algorithms working opt images exhibit a 2D spatial locality that is not taken into account in current cache organization and data access strategies. To this aim we propose an adaptive local pre-fetching for the image data type;this technique, mirroring the two-dimensional spatial locality of image processing algorithms, results to be more efficient than other approaches, such as sequential pre-fetching and adaptive pre-fetching. Performance is evaluated on different classes of image processing algorithms, namely raster-scan and propagative algorithms, common in computer vision and multimedia applications.

关键词： General purpose computers

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Sign Language Translation 6

Sign Language Translation

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6th International conference on Advanced Computing and Communication Systems (ICACCS)

作者： Harini, R. Janani, R. Keerthana, S. Madhubala, S. Venkatasubramanian, S. Saranathan Coll Engn Dept Comp Sci & Engn Panjappur Trichy India

ISBN: (纸本)9781728151977;9781728151960

Sign language is the way of communication for hearing impaired people. there is a challenge for common people to communicate with deaf people which makes this system helpful in assisting them. this project aims at implementing computer vision which can take the sign from the users and convert them into text in real time. the proposed system contains four modules such as: image capturing, pre-processing classification and prediction. By using image processing the segmentation can be done. Sign gestures are captured and processed using OpenCV python library. the captured gesture is resized, converted to grey scale image and the noise is filtered to achieve prediction with high accuracy. the classification and predication are done using convolution neural network.

关键词： Machine learning Gesture recognition Sign language Human computer Interaction computer vision

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