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Histogram-based segmentation of quantum images

THEORETICAL COMPUTER SCIENCE 2014年 529卷 46-60页

作者： Caraiman, Simona Manta, vasile I. Tech Univ Iasi Dept Comp Engn Iasi Romania

In this paper we investigate the use of quantum computing systems in the field of image processing. We consider histogram-based image processing operations and develop quantum algorithms for histogram computation and threshold-based segmentation. The underlying principle used for constructing the proposed quantum algorithms is to reformulate them in order to exploit the performance of the quantum Fourier transform and of quantum amplitude amplification. We show that, compared to the classical correspondents, a significant speedup can be achieved by expressing parts of the computational process in terms of problems that can be solved using these quantum techniques.(C) 2013 Elsevier B.v. All rights reserved.

关键词： Quantum information processing Quantum algorithms Quantum image processing Quantum image segmentation Quantum amplitude amplification Quantum Fourier transform

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Active learning based image annotation

Active learning based image annotation

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National Conference on Computer vision, Pattern Recognition, image processing and Graphics (NCvPRIPG)

作者： Priyam Bakliwal C. v. Jawahar International Institute of Information Technology Hyderabad Gachibowli Telangana IN

Automatic image annotation is the computer vision task of assigning a set of appropriate textual tags to a novel image. The aim is to eventually bridge the semantic gap of visual and textual representations with the help of these tags. This also has applications in designing scalable image retrieval systems and providing multilingual interfaces. Though a wide varieties of powerful machine learning algorithms have been explored for the image annotation problem in the recent past, nearest neighbor techniques still yield superior results to them. A challenge ahead of the present day annotation schemes is the lack of sufficient training data. In this paper, an active Learning based image annotation model is proposed. We leverage the image-to-image and image-to-tag similarities to decide the best set of tags describing the semantics of an image. The advantages of the proposed model includes: (a). It is able to output the variable number of tags for images which improves the accuracy. (b). It is effectively able to choose the difficult samples that needs to be manually annotated and thereby reducing the human annotation efforts. Studies on Corel and IAPR TC-12 datasets validate the effectiveness of this model.

关键词： Training data Prediction algorithms Training Semantics Hidden Markov models vocabulary Uncertainty

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image SUPER-RESOLUTION FROM COMPRESSED SENSING OBSERvATIONS

IMAGE SUPER-RESOLUTION FROM COMPRESSED SENSING OBSERVATIONS

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IEEE International Conference on image processing

作者： Wael Saafin Miguel vega Rafael Molina Aggelos K. Katsaggelos Dept. of Computer Science and Artificial Intellegence University of Granada Dept. of Languages and Information Systems University of Granada Dept. of Electrical Engineering and Computer Science Northwestern University

ISBN: (纸本)9781479983407

In this work we propose a novel framework to obtain High Resolution (HR) images from Compressed Sensing (CS) imaging systems capturing multiple Low Resolution (LR) images of the same scene. The proposed CS Super Resolution (SR) approach combines existing CS reconstruction algorithms with an LR to HR approach based on the use of a Super Gaussian (SG) regularization term. The reconstruction is formulated as a constrained optimization problem which is solved using the Alternate Direction Methods of Multipliers (ADMM). The image estimation subproblem is solved using Majorization-Minimization (MM) while the CS reconstruction becomes an l_1-minimization subject to a quadratic constraint. The performed experiments show that the proposed method compares favorably to classical SR methods at compression ratio 1, obtaining excellent SR reconstructions at ratios below one.

关键词： Super resolution Compressed sensing image sampling image reconstruction image enhancement super-resolution image samples Compressed sensing image enhancement image reconstruction images CONTAINMENT SPRAY carbon monosulfide Cesium SAvANNAH RIvER OPERATIONS OFFICE Strontium COMPRESSED

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DETECTING REPETITIvE ELEMENTS WITH ACCURATE LOCATIONS AND SHAPES FROM URBAN FACADE

DETECTING REPETITIVE ELEMENTS WITH ACCURATE LOCATIONS AND SH...

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IEEE International Conference on image processing

作者： Yongjian Lian Xukun Shen State Key Laboratory of Virtual Reality Technology and Systems Beihang University

ISBN: (纸本)9781479983407

This paper proposes a novel algorithm to automatically detect the repetitive elements with accurate shapes, locations and sizes from single facade image. Unlike other algorithms, our algorithm is not entirely dependent on the extracted feature points, edges and symmetric information. Our algorithm mainly includes following steps: First, we combine the clustering method with the repetitive characteristic curve to derive templates and to detect repetitive elements matched with derived templates. Moreover, a global repetition-based optimization framework is proposed to derive occluded repetitive elements and determine the number of all the repetitive elements with the accurate locations, shapes and sizes. Experiment results demonstrate that the proposed algorithm improves the accuracy, robustness and efficiency on facade databases compared with the state-of-the-art methods.

关键词： Repetitive Elements Detection Urban Facade Candidate Templates Derivation Repetitive Characteristic Curve Repetition-based Optimization Repetitive Sequences Elements Repetitive Clustering methods jigs(templates) algorithms Shape

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A Fast and Secure image Encryption Algorithm Using Number Theoretic Transforms and Discrete Logarithms

A Fast and Secure Image Encryption Algorithm Using Number Th...

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IEEE International Conference on Signal processing, Informatics, Communication and Energy systems

作者： Jeyamala Chandrasekaran Thiruvengadam S. Jayaraman Department of Information Technology Thiagarajar College of Engineering Madurai India

Many of the Internet applications such as video conferencing, military image databases, personal online photograph albums and cable television require a fast and efficient way of encrypting images for storage and transmission. In this paper, discrete logarithms are used for generation of random keys and Number Theoretic Transform (NTT) is used as a transformation technique prior to encryption. The implementation of NTT is simple as it uses arithmetic for real sequences. Encryption and decryption involves the simple and reversible XOR operation of image pixels with the random keys based on discrete logarithms generated independently at the transmitter and receiver. Experimental results with the standard bench mark test images proposed in the USC-SIPI data base confirm the enhanced key sensitivity and strong resistivity of the algorithm against brute force attack and statistical crypt analysis. The computational complexity of the algorithm in terms of number of operations and number of rounds is very small in comparison with the other image encryption algorithms. The randomness of the keys generated has been tested and is found in accordance with the statistical test suite for security requirements of cryptographic modules as recommended by National Institute of Standards and Technology (NIST).

关键词： Encryption Ciphers Transforms Chaotic communication Correlation

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Comparison of Citrus fruit diseases revealing with Data Mining and image processing Techniques based on assorted parameters

Comparison of Citrus fruit diseases revealing with Data Mini...

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International Conference on Computer Communication and Informatics (ICCCI)

作者： M. Renuka Devi v. Kavitha SNS College of Technology Coimbatore India Bharathiar University Coimbatore India

Machine vision has become a key technology in the area of quality control. “vision systems” is primarily focused on computer vision in the context of inspection of the products such as food, pharmaceuticals. The system can consist of a number of cameras all capturing, interpreting and signaling individually with a control system related to some predefined algorithms. The analysis of citrus fruits using various assorted parameters revealing the diseases afflicting Citrus fruits and isolation of the same using image processing and Data Mining Techniques is the core area discussed here with.

关键词： image color analysis Accuracy Diseases Machine vision Shape Lesions

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An Adaptive Algorithm for Public Transport Arrival Time Prediction Based on Hierarhical Regression

An Adaptive Algorithm for Public Transport Arrival Time Pred...

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International IEEE Conference on Intelligent Transportation systems

作者： Anton Agafonov vladislav Myasnikov Samara State Aerospace University (SSAU) Image Processing Systems Institute of the Russian Academy of Sciences (IPSI RAS) Samara Russia

ISBN: (纸本)9781467365970

In this paper we consider a problem of public transport arrival time prediction for a large city in real time. We propose a new prediction algorithm based on a model of an adaptive combination of elementary prediction algorithms, each of which is characterized by a small number of adjustable parameters. Adaptability means that parameters of the constructed combination depend on a number of control parameters of the model, which includes the following factors: weather conditions, traffic density, driving dynamics, prediction horizon, and others. Adaptability is achieved by the use of a hierarchical regression (similar to a regression tree). The proposed arrival prediction algorithm has been tested with the data of all the public transport routes in Samara, Russia.

关键词： public transport arrival time prediction combination of algorithms hierarchical regression Public Transit control parameter Prediction algorithms composite algorithm arrival time Adaptive algorithms metropolis Traffic density Regression (Psychology)

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Decision Tree for Corner Detection

Decision Tree for Corner Detection

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IEEE International Conference on Signal processing, Informatics, Communication and Energy systems

作者： Dipen Somani Shanmuganathan Raman Electrical Engineering Indian Institute of Technology Gandhinagar

ISBN: (纸本)9781479918249

Corner detection is a important task in low level vision. Detecting corners helps one to establish similarity between two or more images. Traditional approaches for corner detection involve finding significant variation around a pixel neighbourhood in two different directions. In this work, we have developed a novel framework to detect corners in a given image by learning corners from images corresponding to the same object category. We detect extrema of the intensity and second derivative neighbourhood around a given pixel location to identify possible corners. We build a decision tree using the learned parameters and also employ the intensity variation in the local neighbourhood in order to detect corners accurately. We show that the performance of the proposed approach compares well with the standard corner detection algorithms and the other learning based approach for corner detection.

关键词： Detectors Eigenvalues and eigenfunctions Training Decision trees image edge detection Computer vision Spatial resolution

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Proposed algorithms for Effective Real Time Stream Analysis in Big Data

Proposed Algorithms for Effective Real Time Stream Analysis ...

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International Conference on image Information processing

作者： Nishant Agnihotri Aman Kumar Sharma Department of Computer Science Himachal Pradesh University

ISBN: (纸本)9781509001491

Big data is emerging in all the fields of science. Scope of data analysis is not limited to the analysis of archival data, rather is it more concerned towards giving better decisions on the bases of visualization of analytic reports. Traditional systems are only dealing with 2 v's of big data, i.e. volume and variety. In order to make decisions more fast 3rd v i.e. velocity of data is more effective and convenient characteristic for analysis. Big data analytics is helping businesses with millions of customers to identify customer needs by bringing unstructured data into the arena. Data Analytics techniques can help organizations make sense of the data gain competitive advantage. This paper gives a method of improving speed of decision making by analyzing real time streams for effective Business Intelligence with traditional system and to give fast results for improvised decision making.

关键词： Big Data Business Intelligence 5 v's of big data Analysis tools visualization Real time analysis big data Business intelligence real-time analysis visualization

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Segmenting MRI brain images using evolutionary computation technique

Segmenting MRI brain images using evolutionary computation t...

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International Conference on Cognitive Computing and Information processing

作者： S.v. Aruna Kumar B.S. Harish D.S. Guru Sri Jayachamarajendra College of Engg. India Sri Jayachamarajendra College of Engg. Mysuru India DOS in Computer Science University of Mysore Mysuru India

Medical image segmentation is a fundamental preprocessing step in most systems that supports diagnosis or planning of surgical operations. The traditional Fuzzy c means clustering algorithm performs well in the absence of noise. Traditional FCM leads to its non robust result mainly due to 1. Not utilizing the spatial information in the image. 2. Use of Euclidean distance. These limitations can be addressed by using robust spatial kernel FCM (RSKFCM). RSKFCM consider the spatial information and uses Gaussian kernel function to calculate the distance between the center and data points. Though RSKFCM gives good result, the main drawback behind this method is the inability of generating global minima for the objective function. To improve the efficiency of RSKFCM method, in this paper we proposed the genetic algorithm based RSKFCM. By using the genetic algorithm, RSKFCM initializes the cluster centers and reaches the global minima of the objective function. Experimentation is carried out on the standard brain image dataset. Experimental result reveals that the proposed genetic algorithm based RSKFCM outperforms other FCM methods with the use of various cluster validity functions.

关键词： image segmentation Genetic algorithms Kernel Clustering algorithms Robustness Biomedical imaging Brain

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