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ieee symposium on computational intelligence in image and signal processing

作者： Chiang, Chun-Chung Hsueh, Yu-Hung Liu, Damon Shing-Min Natl Chung Cheng Univ Dept Comp Sci & Informat Engn Chiayi Taiwan

ISBN: (纸本)9781424407071

Silhouette is an important research issue in the field of Non-Photorealistic Rendering (NPR) and it is also a popular drawing feature in illustrations and line-drawing artworks. In this paper, we present a real-time image-based stylized rendering system. First, we project a 3D model to image-space. Then we extract edges in the image-space data. We perform edge-detection algorithms on GPU (Graphics processing Units) for speedup. GPU is good at floating-points calculating and processing with parallelism. Both features match the property of most image processing tasks. Our system can run at an interactive frame rate when combining our edge-detection algorithms with graphic hardware architecture. We demonstrate that this system performance can reach real-time and render images in good NPR style.

关键词： Computer graphics

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computational intelligence for automated keg identification and deformation detection

Computational intelligence for automated keg identification ...

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ieee symposium on computational intelligence in image and signal processing

作者： Campbell, Duncan Keir, Andrew Lees, Michael Queensland Univ Technol Sch Engn Syst Brisbane Qld Australia Yatala Operat Fosters Yatala Australia

ISBN: (纸本)9781424407071

A machine vision based keg inspection system can allow cost effective keg tracking and preventative maintenance programs to be implemented, leading to substantial savings for breweries with large keg fleets. A robust keg serial number recognition and keg condition assessment process is required to cater for different keg brands and a range of keg ages in the fleet It has been demonstrated that the proposed image processing methodology, and neural network based number recognition system, successfully located and identified keg serial numbers with a 92% digit accuracy. Furthermore, the vision system allowed the concurrent assessment of the keg condition by assessing deformity of the keg rim, and that of the filler valve. A correlation coefficient, generated using a template matching process, proved to be a suitable metric which adequately indicated rims within and outside acceptable deformity bounds.

关键词： neural networks OCR keg deformation detection keg tracking

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AN EFFICIENT ARCHITECTURE FOR HARDWARE IMPLEMENTATIONS OF image processing ALGORITHMS

AN EFFICIENT ARCHITECTURE FOR HARDWARE IMPLEMENTATIONS OF IM...

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ieee symposium on computational intelligence in image and signal processing

作者： Khalvati, Farzad Tizhoosh, Hamid R. Univ Waterloo Dept Syst Design Engn Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781424427604

This work presents a new performance improvement technique for hardware implementations of non-recursive convolution based image processing algorithms. It combines an advanced data flow technique (instruction reuse) proposed in modern microprocessor design with the value locality of image data to develop a method, window memoization, that increases the throughput with minimal cost in area and accuracy. We implement window memoization as a 2-wide superscalar pipeline such that it consumes significantly less area than conventional 2-wide superscalar pipelines. As a case study, we have applied window memoization to Kirsch edge detector. The average speedup factor was 1.76 with only 25% extra hardware.

关键词： Pipelines

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Progressive image reconstruction based on multi-scale edge model

Progressive image reconstruction based on multi-scale edge m...

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ieee symposium on computational intelligence in image and signal processing

作者： Bao, Paul Zhang, Xianjun Univ S Florida Tampa FL 33620 USA E China Normal Univ Shanghai Peoples R China

ISBN: (纸本)9781424407071

In this paper, we present a progressive image reconstruction scheme based on the semantically scalable multi-scale edge representation of images, with the resolution and visual quality scalable to various bitrate requirements. In the multi-scale edge representation an image is decomposed into its multi-scale primal sketch and the background where the multi-scale primal sketch preserves the structural semantics of images, and the background represents the smooth locale. Edge compensation is performed to smoothly remove edges at each scale. The multi-scale edges are then embedded encoded using the GFA modeling. The image reconstruction is progressively achieved by synthesizing multi-scale edges on the reconstructed image obtained from previous scale. As edge synthesis is performed at consecutive scales, the visual quality of the reconstructed image is progressively enhanced. Experiment shows that the proposed scheme performs well at low bit-rate multiresoultion representation and progressive reconstruction.

关键词： image reconstruction

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Bayesian Deep Learning for image Reconstruction: From structured sparsity to uncertainty estimation

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ieee signal processing MAGAZINE 2023年第1期40卷 73-84页

作者： Dong, Weisheng Wu, Jinjian Li, Leida Shi, Guangming Li, Xin Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Nanyang Technol Univ Singapore Singapore Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Rapid Rich Object Search Lab Singapore Singapore Xidian Univ Sch Elect Engn Xian Peoples R China Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Univ Sci & Technol China Elect Engn & informat Sci Hefei Peoples R China West Virginia Univ Lane Dept Comp Sci & Elect Engn Morgantown WV 26506 USA

Conventional wisdom in model-based computational imaging incorporates physics-based imaging models, noise characteristics, and image priors into a unified Bayesian framework. Rapid advances in deep learning have inspired a new generation of data-driven computational imaging systems with performances even better than those of their model-based counterparts. However, the design of learning-based algorithms for computational imaging often lacks transparency, making it difficult to optimize the entire imaging system in a complete manner.

关键词： Deep learning Uncertainty image coding computational modeling Imaging signal processing algorithms Network architecture

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Towards image retrieval by texture segmentation with genetic programming

Towards image retrieval by texture segmentation with genetic...

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ieee symposium on computational intelligence in image and signal processing

作者： Ciesielski, Vic Kurniawan, Djaka Song, Andy RMIT Univ Sch Comp Sci & Informat Technol Melbourne Vic 3001 Australia

ISBN: (纸本)9781424407071

This paper examines the feasibility of an approach to image retrieval from a heterogeneous collection based on texture. For each texture of interest (T), a T-vs-other classifier is evolved for small n x n windows using genetic programming. The classifier is then used to segment the images in the collection. If there is a significant contiguous area of T in an image, it is considered to contain that texture for retrieval purposes. We have experimented with sky and grass textures in the Corel Volume 12 image set Experiments with a single image indicate that classifiers for the two textures can be learned to a high accuracy. Experiments with a test set of 714 Corel images gave a retrieval accuracy of 84% for both sky and grass textures. These results suggest that the use of texture could enhance retrieval accuracy in content based image retrieval systems.

关键词： image retrieval

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An automated GA-based fuzzy image enhancement method

An automated GA-based fuzzy image enhancement method

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ieee symposium on computational intelligence in image and signal processing

作者： Khayat, Omid Razjouyan, Javad Aghvami, Mina Shahdoosti, Hamid Reza Loni, Babak

ISBN: (纸本)9781424427604

This paper presents an automated algorithm for image enhancement. A novel parametric indices of fuzziness (PIF) is introduced, which serves as the optimization criterion of the contrast enhancement procedure. The proposed PIT comprises the Sugeno class of involutive fuzzy complements and the first order fuzzy moment of the image. The PIT as the measure of fuzziness should be maximized, and the maximum of PIT is tuned based on the first-order fuzzy moment of the image. The parameters of the transformation function are found by the genetic algorithm aiming to maximize the PIF. Finally, several experiments are made to demonstrate the efficiency of the proposed method.

关键词： image enhancement Measure of fuzziness Fuzzy complements Parametric indices of fuzziness Genetic algorithm

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image segmentation using a weighted kernel PCA approach to spectral clustering

Image segmentation using a weighted kernel PCA approach to s...

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ieee symposium on computational intelligence in image and signal processing

作者： Alzate, Carlos Suykens, Johan A. K. Katholieke Univ Leuven Dept Elect Engn ESAT SCD SISTA B-3001 Louvain Belgium

ISBN: (纸本)9781424407071

In classical graph-based image segmentation, a data-driven matrix is constructed representing similarities between every pair of pixels. The eigenvectors of such matrices contain relevant information about the clusters present on the image. An approach to image segmentation using spectral clustering with out-of-sample extensions is presented. This approach is based on the weighted kernel PCA framework. An advantage of the proposed method is the possibility to train and validate the clustering model on subsampled parts of the image to be segmented. The cluster indicators for the remaining pixels can then be inferred using the out-of-sample extension. This subsampling scheme can be used to reduce the computation time of the segmentation. Simulation results with grayscale and color images show improvements in terms of computation times together with visually appealing clusters.

关键词： image segmentation

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Identification of dynamic nonlinear systems using computational intelligence techniques

Identification of dynamic nonlinear systems using computatio...

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ieee symposium on computational intelligence in image and signal processing

作者： Turchetti, Claudio Gianfelici, Francesco Univ Politecn Marche Dipartimento Elettron Intelligenza Artificiale & I-60131 Ancona Italy

ISBN: (纸本)9781424407071

A novel approach based on computational intelligence techniques for the identification of nonlinear dynamic systems is presented in this paper. The technique encompasses both the properties of the Karhunen-Loeve Transform in representing stochastic processes and the approximation capabilities of multi-layer neural networks. Experimental results on nonlinear systems governed by difference equations demonstrate the effectiveness of the proposed approach that is based on a real-time learning algorithm. Exhaustive experimentation on specific case studies was performed and some experimental results were compared with other existing techniques such as the Lee-Schetzen method, Least Mean Square (LMS), Recursive Least Square (RLS) and Normalized Least Mean Square (NLMS) algorithms. A better identification-accuracy was also achieved, and a reduction of some orders of magnitude in training-times compared with the well-known Lee-Schetzen method was obtained, thus making the proposed methodology one of the current best practices in this field.

关键词： system identification multi-layer neural networks Karhunen-Loeve Transform nonlinear dynamic systems

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Exploiting automatic image segmentation to human detection and depth estimation

Exploiting automatic image segmentation to human detection a...

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symposium Series on computational intelligence, ieee SSCI 2011 - 2011 ieee symposium on computational intelligence for Multimedia, signal and Vision processing, CIMSIVP 2011

作者： Tseng, Hsiao-Chun Shyu, Jia-Jye Chang, Jyh-Yeong Lin, Chin-Teng Department of Electrical Engineering National Chiao Tung University Hsinchu Taiwan

ISBN: (纸本)9781424499151

In this paper, we combine image segmentation techniques and face detection methods to extract the human from scenes. Firstly, skin regions are detected and an ellipse fitting method is employed to detect the face region and consequently locate the human position. Then we propose an improved automatic seeded region growing algorithm to segment the image. The initial seeds are generated automatically, and the remaining pixels are classified to the nearest region. After the region growing procedure, two neighboring regions with high similarity are merged. The human body is determined by confining semantic human body region in segmented regions, and those belonging to the human face and human body are merged afterward. Lastly, we will detect the human vertical y-coordinate values in the image, and the depths can then be estimated according to the depth look-up tables of the camera. © 2011 ieee.

关键词： image segmentation

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