In the field of defect detection, imageprocessingalgorithms and feature extraction algorithms have some limitations, owing to their necessity for extracting a large number of different features of diverse products i...
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In the field of defect detection, imageprocessingalgorithms and feature extraction algorithms have some limitations, owing to their necessity for extracting a large number of different features of diverse products images. Meanwhile, the images of defective products are less and various. Aiming at these problems, we presented a One-Class classifier based on deep convolution neural network to detect the defect images in this paper. We design a loss function with the penalty term based on Euclidean distance to train the deep convolution neural network model. A hypersphere is used as classification decision surface after setting an appropriate hypersphere radius according to the inspection accuracy. It maps the non-defective products into a hypersphere in a high dimensional feature space, while the defect images are mapped somewhere far from the center of hypersphere. Thus, a One-Class classifier based on convolutional neural network(CNN) model is proposed to detect the defects. Experiments show that the proposed method, with less number of iteration, help build the classifier for image defect detection with high generalization ability and high detection precision. (C) 2017 The Authors. Published by Elsevier B.v.
Clustering is one of the most important steps in the data processing pipeline. Of all the clustering techniques, perhaps the most widely used technique is K-Means. However, K-Means does not necessarily result in clust...
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Clustering is one of the most important steps in the data processing pipeline. Of all the clustering techniques, perhaps the most widely used technique is K-Means. However, K-Means does not necessarily result in clusters which are spatially connected and hence the technique remains unusable for several remote sensing, geoscience and geographic information science (GISci) data. In this article, we propose an extension of K-Means algorithm which results in spatially connected clusters. We empirically verify that this indeed is true and use the proposed algorithm to obtain most significant group of waterbodies mapped from multispectral image acquired by IRS LISS-III satellite.
This paper introduces a texture representation suitable for image synthesis of textured surfaces. An efficient representation for natural images is of fundamental importance in imageprocessing and analysis. The autom...
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Recent advances in microcopy and improvements in imageprocessingalgorithms have allowed the development of computer-assisted analytical approaches in cell identification. Several applications could be mentioned in t...
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A new technique of Mueller-matrix mapping of polycrystalline structure of histological sections of biological tissues is suggested. The algorithms of reconstruction of distribution of parameters of linear and circular...
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ISBN:
(数字)9781510612501
ISBN:
(纸本)9781510612501;9781510612495
A new technique of Mueller-matrix mapping of polycrystalline structure of histological sections of biological tissues is suggested. The algorithms of reconstruction of distribution of parameters of linear and circular birefringence of prostate histological sections are found. The interconnections between such distributions and parameters of linear and circular birefringence of prostate tissue histological sections are defined. The comparative investigations of coordinate distributions of phase anisotropy parameters formed by fibrillar networks of prostate tissues of different pathological states (adenoma and carcinoma) are performed. The values and ranges of change of the statistical (moments of the 1st - 4th order) parameters of coordinate distributions of the value of linear and circular birefringence are defined. The objective criteria of cause of Benign and malignant conditions differentiation are determined.
The text deals with the problem of detecting anomalies on a background of multi-dimensional images. We synthesized a detection algorithm based on the use of doubly stochastic models of random fields and which requires...
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The text deals with the problem of detecting anomalies on a background of multi-dimensional images. We synthesized a detection algorithm based on the use of doubly stochastic models of random fields and which requires pre-filtering the image. We propose to use the modified Kalman filter. We also investigated an efficiency of extended signals detection on real images. It is shown that the resulting algorithm has a higher efficiency than the known algorithms which based on the traditional autoregressive image description. The gain is explained by more adequate description of the real inhomogeneous material using doubly stochastic models.
A new technique of Mueller-matrix mapping of polycrystalline structure of histological sections of biological tissues is suggested. The algorithms of reconstruction of distribution of parameters of linear and circular...
详细信息
ISBN:
(数字)9781510612501
ISBN:
(纸本)9781510612501;9781510612495
A new technique of Mueller-matrix mapping of polycrystalline structure of histological sections of biological tissues is suggested. The algorithms of reconstruction of distribution of parameters of linear and circular dichroism of histological sections liver tissue of mice with different degrees of severity of diabetes are found. The interconnections between such distributions and parameters of linear and circular dichroism of liver of mice tissue histological sections are defined. The comparative investigations of coordinate distributions of parameters of amplitude anisotropy formed by Liver tissue with varying severity of diabetes (10 days and 24 days) are performed. The values and ranges of change of the statistical (moments of the 1st - 4th order) parameters of coordinate distributions of the value of linear and circular dichroism are defined. The objective criteria of cause of the degree of severity of the diabetes differentiation are determined.
Background Estimation is a common computer vision task, used for segmenting moving objects in video streams. This can be useful as a pre-processing step, isolating regions of interest for more complicated algorithms p...
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Background Estimation is a common computer vision task, used for segmenting moving objects in video streams. This can be useful as a pre-processing step, isolating regions of interest for more complicated algorithms performing detection, recognition, and identification tasks, in order to reduce overall computation time. This is especially important in the context of embedded systems like smart cameras, which may need to process images with constrained computational resources. This work focuses on accelerating SuperBE, a superpixel-based background estimation algorithm that was designed for simplicity and reducing computational complexity while maintaining state-of-the-art levels of accuracy. We explore both software and hardware acceleration opportunities, converting the original algorithm into a greyscale, integer-only version, and using Hardware/Software Co-design to develop hardware acceleration components on FPGA fabric that assist a software processor. We achieved a 4.4x speed improvement with the software optimisations alone, and a 2x speed improvement with the hardware optimisations alone. When combined, these led to a 9x speed improvement on a Cyclone v System-on-Chip, delivering almost 38 fps on 320 x 240 resolution images.
In recent years, multisensor information fusion has been widely used in military field, automatic aircraft navigation, robotics, imageprocessing and other related fields. This paper mainly introduces the concept and ...
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In recent years, multisensor information fusion has been widely used in military field, automatic aircraft navigation, robotics, imageprocessing and other related fields. This paper mainly introduces the concept and architecture model of multi-sensor information fusion. The most frequently used algorithms are also illustrated such as weighted average method, Kalman filter, particle filter, Bayes estimation and neural network. Furthermore, the main applications of multisensor information fusion are demonstrated in the military, industrial field, navigation and robotics. The prospects of multisensor information fusion is discussed in the paper as well.
A biometric system acquires biometric features from an individual and compare these features with other features stored in the database. Iris recognition system is a reliable and an accurate biometric system. Localiza...
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A biometric system acquires biometric features from an individual and compare these features with other features stored in the database. Iris recognition system is a reliable and an accurate biometric system. Localization of the iris boundaries in an eye image is considered to be the most vital step in the iris recognition process. There exist many algorithms to segment the iris. One of the segmentation methods, that is used in many commercial iris biometric systems is an algorithm known as a Daugman's algorithm. Especially it focuses on image segmentation and feature extraction for iris recognition process but Daugman uses more processing time. This paper implements the proposed algorithm to achieve best performance in terms of accuracy and time. The implemented algorithm was tested on UBIRIS v.1 database which includes 15 individuals from both Right and Left eyes resulting in 45 classes in total. The proposed algorithm attains an overall accuracy of 95% with robust performance.
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