Vector quantization (VQ) is widely used in many high-quality and high-rate data compression applications such as speech coding, audio coding, image coding and video coding. When the size of a VQ codebook is large, the...
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Vector quantization (VQ) is widely used in many high-quality and high-rate data compression applications such as speech coding, audio coding, image coding and video coding. When the size of a VQ codebook is large, the computational complexity for the full codeword search method is a significant problem for many applications. A number of complexity reduction algorithms have been proposed and investigated using such properties of the codebook as the triangle inequality. This paper proposes a new fast VQ search algorithm that is based on a multi-stage structure for searching for the best codeword. Even using only two stages, a significant complexity reduction can be obtained without any loss of quality.
image segmentation gained popularity recently due to numerous applications in many fields such as computer vision, medical imaging. From its name, segmentation is interested in partitioning the image into separate reg...
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ISBN:
(纸本)9781479973491
image segmentation gained popularity recently due to numerous applications in many fields such as computer vision, medical imaging. From its name, segmentation is interested in partitioning the image into separate regions where one of them is of special interest. Such region is called the Region of Interest (RoI) and it is very important for many medical imaging problems. Clustering is one of the segmentation approaches typically used on medical images despite its long running time. In this work, we propose to leverage the power of the Graphics processing Unit (GPU) to improve the performance of such approaches. Specifically, we focus on the Fuzzy C-Means (FCM) algorithm and its more recent variation, the Type-2 Fuzzy C-Means (T2FCM) algorithm. We propose a hybrid CPU-GPU implementation to speed up the execution time without affecting the algorithm's accuracy. The experiments show that such an approach reduces the execution time by up to 80% for FCM and 74% for T2FCM.
For a binary image containing only curves (and lines) in a background infested by binary noises, (e.g., salt-and-pepper noise,) a very efficient way to extract the image data, and to save them in a very compact file f...
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ISBN:
(纸本)0819456489
For a binary image containing only curves (and lines) in a background infested by binary noises, (e.g., salt-and-pepper noise,) a very efficient way to extract the image data, and to save them in a very compact file for an accurate and complete image recall later, is very attractive to many imageprocessing and pattern recognition systems. This paper reports the data-extraction method we developed recently for inputting a binary image to a special neural-network pattern recognition system, the noniterative, real-time learning system. We use an adaptive/tracking window to track the direction of a continuous curve in the binary image, and record the xycoordinates of all points on this curve until the window hits an end point, or a branch point, or the original starting point. By scanning this tracking window across the whole image frame, we can then segment the original binary image into many single curves. The xy's of points on each curve can then be analyzed by a curve fitting process, and the analytic data can be stored very compactly in an analog data file. This data file can be recalled very efficiently to reconstruct the original binary image, or can be used directly for inputting to a special neural network and for carrying out an extremely fast pattern learning process. This paper reports the image-processing steps, the programming algorithm, and the experimental results on this novel image extraction technique. It will be verified in each experiment by reconstructing the original image from the compactly extracted analog data file.
In this paper, we propose a novel method for detecting and recognizing the text from the blurred images. Text detection in natural scenery images is an important issue in the processing stage. All the previously propo...
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ISBN:
(纸本)9788132226710;9788132226697
In this paper, we propose a novel method for detecting and recognizing the text from the blurred images. Text detection in natural scenery images is an important issue in the processing stage. All the previously proposed methods use different algorithms to detect text in images;however, they suffer from poor performance while performing detection in blurred images. The proposed algorithm is capable of removing blur with an iterative deconvolution method and a linear invariant filter. The proposed method can achieve detection and recognition of the text with a time complexity of 4.53 s. Experiments show our method achieves a better text detection than the other existing methods.
In social networks, Secret image Sharing (SIS) provides an effective way to protect secret images. However, most existing SIS schemes only support limited access policies, which are not flexible enough for lots of sce...
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ISBN:
(纸本)9783030953911;9783030953904
In social networks, Secret image Sharing (SIS) provides an effective way to protect secret images. However, most existing SIS schemes only support limited access policies, which are not flexible enough for lots of scenarios. In this work, we propose an SIS scheme to solve this defect. The core contributions of our scheme can be summarized as follows. Firstly, we propose a Secret Matrix Sharing Scheme (SMSS), which is extended from the traditional Linear Secret Sharing Scheme (LSSS). Different from LSSS, SMSS shares a secret matrix instead of a single secret value. Secondly, based on our SMSS, we propose an SIS scheme named LM-SIS, which supports monotonous access policies. Compared with other SIS schemes, our scheme has advantages in flexibility and efficiency. Furthermore, the LM-SIS scheme is compact, which is reflected in its shadow size ratio is approximately 1/k, where k denotes the root threshold of the access tree. Finally, our scheme provides lossless or approximately lossless recovery, and experimental results show that the PSNR of the recovered images is always greater than 30 dB and the SSIM usually exceeds 0.99. By sacrificing a little storage cost, our LM-SIS scheme can achieve lossless recovery.
Evaluations of both academic face recognition algorithms and commercial systems have shown that the recognition performance degrades significantly due to the variation of illumination. Previous methods for illuminatio...
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ISBN:
(纸本)9780819493057
Evaluations of both academic face recognition algorithms and commercial systems have shown that the recognition performance degrades significantly due to the variation of illumination. Previous methods for illumination robust face recognition usually involve computationally expensive 3D model transformations or optimization base reconstruction using multiple gallery face images, making them infeasible in practical large scale face identification applications. In this paper, we propose an alternative face identification framework, in which one image per person is used for enrollment as is commonly practiced in real life applications. Several probe images captured under different illumination conditions are synthesized to imitate the illumination condition of the enrolled gallery face image. We assume Lambertian reflectance of human faces and use the harmonic representations of lighting. We demonstrate satisfactory performance on the Yale B database, both visually and quantitatively. The proposed method is of very low complexity when linear facial feature are used, and is therefore scalable for large scale applications.
This two volume set LNCS 7016 and LNCS 7017 constitutes the refereed proceedings of the 11th International conference on algorithms and Architectures for Parallel processing, ICA3PP 2011, held in Melbourne, Australia,...
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ISBN:
(数字)9783642246692
ISBN:
(纸本)9783642246685
This two volume set LNCS 7016 and LNCS 7017 constitutes the refereed proceedings of the 11th International conference on algorithms and Architectures for Parallel processing, ICA3PP 2011, held in Melbourne, Australia, in October 2011. The second volume includes 37 papers from one symposium and three workshops held together with ICA3PP 2011 main conference. These are 16 papers from the 2011 International Symposium on Advances of Distributed Computing and Networking (ADCN 2011), 10 papers of the 4th IEEE International Workshop on Internet and Distributed Computing systems (IDCS 2011), 7 papers belonging to the iii International Workshop on Multicore and Multithreaded Architectures and algorithms (M2A2 2011), as well as 4 papers of the 1st IEEE International Workshop on Parallel Architectures for Bioinformatics systems (HardBio 2011).
DAS (Distributed Apertures Sensors) system can acquire panoramic images from a distributed number of sensors, whose performance is mainly determined by the stitching algorithm. In DAS, the direction angle between sens...
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To cope with the high level of ambiguity faced in domains such as Computer Vision or Natural Language processing, robust prediction methods often search for a diverse set of high-quality candidate solutions or proposa...
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To cope with the high level of ambiguity faced in domains such as Computer Vision or Natural Language processing, robust prediction methods often search for a diverse set of high-quality candidate solutions or proposals. In structured prediction problems, this becomes a daunting task, as the solution space (image labelings, sentence parses, etc.) is exponentially large. We study greedy algorithms for finding a diverse subset of solutions in structured-output spaces by drawing new connections between submodular functions over combinatorial item sets and High-Order Potentials (HOPs) studied for graphical models. Specifically, we show via examples that when marginal gains of submodular diversity functions allow structured representations, this enables efficient (sub-linear time) approximate maximization by reducing the greedy augmentation step to inference in a factor graph with appropriately constructed HOPs. We discuss benefits, trade-offs, and show that our constructions lead to significantly better proposals.
Human detection in digital videos is used in a variety of real time scenarios such as unusual activity detection in crowded places, gender identification and age determination and people detection in heavy traffic. Ev...
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ISBN:
(纸本)9781538611227
Human detection in digital videos is used in a variety of real time scenarios such as unusual activity detection in crowded places, gender identification and age determination and people detection in heavy traffic. Every human detection algorithm detects all the moving objects in the initial stage. The next step is to differentiate the moving object as a human being (or) non-human being. This classification is based on shape, texture and motion features. The objective of this work is to compare the performance of two different human detection algorithms. One is the human detection based on shape and another one is human detection based on Daubechies wavelet Transform. The shape based detection uses the shape information of human body to classify the moving objects. Daubechies wavelet transform is shift invariant in nature. Thus, the algorithm is able to detect even small hand or head movements. The performance of these two algorithms are compared in terms of detection accuracy, Precision and Recall. Experimental results have shown promising results for the wavelet transform based approach.
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