In this paper, we propose a low computing complexity architecture design of 2D-to-3D image converter. The presented approach establishes database of slope pattern to analyze the 2D image. According to slope and inters...
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ISBN:
(纸本)9781479948512
In this paper, we propose a low computing complexity architecture design of 2D-to-3D image converter. The presented approach establishes database of slope pattern to analyze the 2D image. According to slope and intersection, the scheme predicts vanishing points of 2D image. Then, depth map is reconstructed using vanishing points information. This approach focuses on fundamental structure analysis to reduce computing complexity and performs low computing complexity architecture of 2D-to-3D image converter.
This paper presents a Dynamic Local Contrast Enhancement (DLCE) method, which can strengthen the image quality in most of inclement weather conditions. This improves unnatural image over-enhancement, reduce noise, mak...
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ISBN:
(纸本)9781479948512
This paper presents a Dynamic Local Contrast Enhancement (DLCE) method, which can strengthen the image quality in most of inclement weather conditions. This improves unnatural image over-enhancement, reduce noise, make image more saturated, and can be applied on foggy day and night. DLCE reaches 50 fps D1 resolution and 120 fps CIF resolution on ATOM 1.6GHz in TREK-668 embedded platform.
The proceedings contain 26 papers. The topics discussed include: development of a low-cost accurate phase measurement system;the construction of sequences for identification of digital circuits using simulated anneali...
ISBN:
(纸本)9781479969500
The proceedings contain 26 papers. The topics discussed include: development of a low-cost accurate phase measurement system;the construction of sequences for identification of digital circuits using simulated annealing (SA);fuzzy model for multicriteria decision making;affixal approach for Arabic decomposable word recognition : a validation on the multi-font printed script;detection of the thickness of scale on the inner surface of water pipes by infrared thermography;arabic handwritten word recognition with large vocabulary based on explicit segmentation;printed/handwritten Arabic script identification using local features and GMMs;the use of web 2.0 and online virtual communities to develop marketing strategies;the k-unobservability: a new privacy protection guarantee for e-service systems;e-learning and entrepreneurship: is it the perfect match?;fabrics defects detecting using imageprocessing and neural networks;advanced hybrid tracking through neural network regression;nearest cluster center decision in hierarchical classification process;proposition to distinguish machine-printed from handwritten Arabic and Latin words;robust and blind watermarking of avatar faces;and remote laboratories across the Mediterranean, EOLES project, a case study and a point of start.
This paper addresses issues in multi-class visual object classification, where sequential learning and sensor fusion are exploited in a unified framework. We adopt a novel method for head pose classification using RGB...
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ISBN:
(纸本)9781479928668
This paper addresses issues in multi-class visual object classification, where sequential learning and sensor fusion are exploited in a unified framework. We adopt a novel method for head pose classification using RGB and depth images. The main contribution of this paper is a multi-class AdaBoost classification framework where information obtained from RGB and depth modalities interactively complement each other. This is achieved by learning weak hypotheses for RGB and depth modalities independently with the same sampling weight in the boosting structure, and then fusing them through learning a sub-ensemble. Experiments are conducted on a Kinect RGB-D face image dataset containing 4098 face images in 5 different poses. Results have shown good performance in obtaining high classification rate (99.76%) with low false alarms on the dataset.
This paper presents a machine vision system for defect inspection disposable glucose teststrips. The proposed system first lightened and captured image of flawless glucose teststrip while offline training. The captu...
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ISBN:
(纸本)9781479948512
This paper presents a machine vision system for defect inspection disposable glucose teststrips. The proposed system first lightened and captured image of flawless glucose teststrip while offline training. The captured image was processed through imageprocessing technology to establish standard projection data of silver paste and carbon rubber in flawless glucose teststrips. Then the measured blood glucose teststrips were processed with the same lighting and imageprocessing procedures when online detection. The defects of the measured blood glucose teststrips were inspected by the projection data comparison. Experiments show that the proposed system can effectively detect three main defects on glucose teststrips, i.e., silver exposing, short circuit of carbon paste electrode and unglued carbon plastic electrode.
Automation of Electroencephalogram (EEG) analysis can significantly help the neurologist during the diagnosis of epilepsy. During last few years lot of work has been done in the field of computer assisted analysis to ...
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ISBN:
(纸本)9781479952083
Automation of Electroencephalogram (EEG) analysis can significantly help the neurologist during the diagnosis of epilepsy. During last few years lot of work has been done in the field of computer assisted analysis to detect an epileptic activity in an EEG. still there is a significant amount of need to make these computer assisted EEG analysis systems more convenient and informative for a neurologist. After briefly discussing some of the existing work we have suggested an approach which can make these systems more helpful, detailed and precise for the neurologist. In our proposed approach we have handled each epoch of each channel for each type of epileptic pattern exclusive to each other. In our approach feature extraction starts with an application of multilevel Discrete Wavelet Transform (DWT) on each 1 sec non-overlapping epochs. Then we apply Principal Component Analysis (PCA) to reduce the effect of redundant and noisy data. Afterwards we apply Support Vector machine (SVM) to classify these epochs as Epileptic or not. In our system a user can mark any mistakes he encounters. The concept behind the inclusion of the retraining is that, if there is more than one example with same attributes but different labels, the classifier is going to get trained to the one with most population. These corrective marking will be saved as examples. On retraining the classifier will improve its classification, hence it will tries to adapt the user. In the end we have discussed the results we have acquired till now. Due to limitation in the available data we are only able to report the classification performance for generalised absence seizure. The reported accuracy is resulted on very versatile dataset of 21 patients from Punjab Institute of Mental Health (PIMH) and 21 patients from Children Hospital Boston (CHB) which have different number of channel and sampling frequency. This usage of the data proves the robustness of our algorithm.
Differential Evolution (DE) algorithm represent an adaptive search process for solving engineering and machinelearning optimization problems. This paper presents an attempt to demonstrate its adaptability and effecti...
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ISBN:
(纸本)9781479928668
Differential Evolution (DE) algorithm represent an adaptive search process for solving engineering and machinelearning optimization problems. This paper presents an attempt to demonstrate its adaptability and effectiveness for searching global optimal solutions to enhance the contrast and detail in a gray scale image. In this paper contrast enhancement of an image is performed by gray level modification using parameterized intensity transformation function that is considered as an objective function. The task of DE is to adapt the parameters of the transformation function by maximizing the objective fitness criterion. Experimental results are compared with other enhancement techniques, viz. histogram equalization, contraststretching and particle swarm optimization (PSO) based image enhancement techniques.
In this current paper, a sensor array based on WO3 gas sensors has been used to detect O-3. Comparing to the classical analysis methods, metal oxide sensors present the advantages of low cost, short response time and ...
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ISBN:
(纸本)9781479948888
In this current paper, a sensor array based on WO3 gas sensors has been used to detect O-3. Comparing to the classical analysis methods, metal oxide sensors present the advantages of low cost, short response time and versatility. However, the use of these sensors is limited due to their lack of selectivity. Representative variable as gas response and time response have been extracted from gas response base. Considering concentration range from 50 ppb to 400 ppb, database is established for four gas sensor. Then, the variables are grouped in database and tested with principal component analysis (PCA) to evaluate the contribution to the classification to enhance the sensors selectivity.
Classification is a common task in patternrecognition. Classifiers used in embedded intelligent devices need a good trade-off between prediction accuracy, resource consumption and prediction speed. Support vector mac...
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ISBN:
(纸本)9783319126401
Classification is a common task in patternrecognition. Classifiers used in embedded intelligent devices need a good trade-off between prediction accuracy, resource consumption and prediction speed. Support vector machine(SVM) is accurate but its run-time complexity is higher due to the large number of support vectors. A new separating hyperplane method (NSHM) for the binary classification task was proposed. NSHM allows fast classification. However, NSHM is order-sensitive and this affects its classification accuracy. Inspired by NSHM, we propose CSHM, a combining separating hyperplane method. CSHM combines all optimal separating hyperplanes found by NSHM. Experimental results on UCI machinelearning Repository show that, compared with NSHM and SVM, CSHM achieves a better trade-off between prediction accuracy, resource consumption and prediction speed.
A discriminative dictionary-based approach to supporting the classification of 3D Optical Coherence Tomography (OCT) retinal images, so as to determine the presence of Age-related Macular Degeneration (AMD), is descri...
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ISBN:
(纸本)9783319089799;9783319089782
A discriminative dictionary-based approach to supporting the classification of 3D Optical Coherence Tomography (OCT) retinal images, so as to determine the presence of Age-related Macular Degeneration (AMD), is described. AMD is one of the leading causes of blindness in people aged over 50 years. The proposed approach is founded on the concept of a uniform 3D image decomposition into a set of sub-volumes where each sub-volume is described in terms of a "spatial gradient" histogram, which in turn is used to define a set of feature vectors (one per sub-volume). Feature selection is conducted using the maximum sum of the squared values of each feature vector for each sub-volume. After that, a "coding-pooling" framework is applied so that each image is represented as a single feature vector. The "coding-pooling" framework generates a representative subset of feature vectors called a dictionary, and then use this dictionary as a guide for the generation of a single feature vectors for each volume. Experiments conducted using the proposed approach, in comparison with range of alternatives, indicated that the approach outperformed other existing methods with an accuracy of 95.2%, sensitivity of 95.7% and specificity of 94.6%.
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