The article presents a concept of the analysis of mechanical wear of prisms in the in-pavement airport lamps. The solution is based on imageprocessing technique that requires an appropriate selection of parameters du...
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ISBN:
(数字)9788362065424
ISBN:
(纸本)9788362065424
The article presents a concept of the analysis of mechanical wear of prisms in the in-pavement airport lamps. The solution is based on imageprocessing technique that requires an appropriate selection of parameters due to the specificity of the objects. During the experimental tests, a database consisting of 316 photos of IDM airport lamps mounted in the airport areas was used. The proposed solution using an artificialneural network allows for the classification of lamps with an efficiency of 81.4%.
We focus on defending against adversarial attacks in deep neuralnetworks using signal analysis technology. The method employs a novel signal processing theory as a defense to adversarial perturbations. The method nei...
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ISBN:
(数字)9781510626782
ISBN:
(纸本)9781510626782
We focus on defending against adversarial attacks in deep neuralnetworks using signal analysis technology. The method employs a novel signal processing theory as a defense to adversarial perturbations. The method neither modifies the protected network nor requires knowledge of the process for generating adversarial examples. Extensive evaluation experiments demonstrate the efficiency and effectiveness of the proposed adversarial defending method.
We investigate the use of a Differential Vector Quantizer (DVQ) architecture for the coding of digital images. An artificialneural Network (ANN) is used to develop entropy-based codebooks which yield substantial data...
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ISBN:
(纸本)0819412015
We investigate the use of a Differential Vector Quantizer (DVQ) architecture for the coding of digital images. An artificialneural Network (ANN) is used to develop entropy-based codebooks which yield substantial data compression while retaining insensitivity to transmission channel errors. Two methods are presented for variable bit-rate coding using the described DVQ algorithm. In the first method, both the encoder and the decoder have multiple codebooks of different sizes. In the second, variable bit-rates are achieved by encoding using subsets of one fixed codebook. We compare the performance of these approaches under conditions of error-free and error-prone channels.
A collection of related N by M images, such asa set of faces, may be modeled by a manifold embedded in an NM-dimensional Euclidean space called an image manifold. With the modeling of image spaces as manifolds, geomet...
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ISBN:
(纸本)0819427470
A collection of related N by M images, such asa set of faces, may be modeled by a manifold embedded in an NM-dimensional Euclidean space called an image manifold. With the modeling of image spaces as manifolds, geometric properties of image manifolds can be studied either theoretically or experimentally A practical result of the investigation of image manifolds provides an insight into image source entropy (i.e., image compressibility), a subject about which, oddly, little is known. The investigation begins with the most basic properties of a manifold, its dimension and its curvature. The study of dimensionality reveals a high embedding ratio, which gives promise of very high compression rates. The curvature of image manifolds is shown to be large indicating that application of traditional Linear transform techniques may not fulfill this promise.
It is seen that during image acquisition, storage, retrieval or transmission, images get degraded due to presence of noise. With different varieties of noise and its extent, de-noising becomes challenging. Traditional...
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ISBN:
(纸本)9781479928668
It is seen that during image acquisition, storage, retrieval or transmission, images get degraded due to presence of noise. With different varieties of noise and its extent, de-noising becomes challenging. Traditionally, a host of techniques have considered spatial, statistical and multiple domain approaches for de-noising. Yet, the scope always exist for exploring innovative means of performing de-noising for enhancing image quality. In the proposed work, we present an approach to de-noise images by combining the features of multilevel Discrete Wavelet Transform (DWT) and Feed Forward artificialneural Network (FF ANN). We apply our algorithm to de-noise the images corrupted by a kind of multiplicative noise known as speckle noise. The results show that the proposed method proves effective for a range of variations and is suitable for critical applications.
For the automatic inspection for printed labels, which are covered with rubber-like coatings and Curl, we have developed a camera-based portable inspection system. In this paper, we explained the developed system, and...
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ISBN:
(纸本)9783642024801
For the automatic inspection for printed labels, which are covered with rubber-like coatings and Curl, we have developed a camera-based portable inspection system. In this paper, we explained the developed system, and especially discuss the inspection method of the spread and chip of the printed labels using neuralnetworks. The experimental results confirm the validity of the proposed method for the spread and chip of alphanumerics.
Automated annotation and analysis of video sequences requires efficient methods to abstract video information. The identification of shots in video sequences is an important step for summarizing the content of the vid...
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ISBN:
(纸本)0819427470
Automated annotation and analysis of video sequences requires efficient methods to abstract video information. The identification of shots in video sequences is an important step for summarizing the content of the video. In general, video shots need to be clustered to form more semantically significant units, such as scenes and sequences. In this paper, we describe a neural network based technique for automatic clustering of video frame signatures. The proposed technique utilizes Self Organizing Map (SOM) and/or Parallel Collision Control Network (PCC) to automatically produce a set of prototype vectors useful in the following process of scene segmentation. Results presented in this paper show that the SOM network perform efficiently, operating without requiring "a priori" knowledge about the number of shot present in the video. When we require the segmentation of a video composed by similar shots, the PCC network is suitable for its capability to preserve the acquired information.
Two types of artificialneuralnetworks are introduced for the robust classification of spatio- temporal sequences. The first network is the Adaptive Spatio-Temporal Recognizer (ASTER), which adaptively estimates the ...
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ISBN:
(纸本)0819412015
Two types of artificialneuralnetworks are introduced for the robust classification of spatio- temporal sequences. The first network is the Adaptive Spatio-Temporal Recognizer (ASTER), which adaptively estimates the confidence that a (variable length) signal of a known class is present by continuously monitoring a sequence of feature vectors. If the confidence for any class exceeds a threshold value at some moment, the signal is considered to be detected and classified. The nonlinear behavior of ASTER provides more robust performance than the related dynamic time warping algorithm. ASTER is compared with a more common approach wherein a self-organizing feature map is first used to map a sequence of extracted feature vectors onto a lower dimensional trajectory, which is then identified using a variant of the feedforward time delay neural network. The performance of these two networks is compared using artificial sonograms as well as feature vectors strings obtained from short-duration oceanic signals.
The quantization of deep neuralnetworks (QDNNs) has been actively studied for deployment in edge devices. Recent studies employ the knowledge distillation (KD) method to improve the performance of quantized networks....
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ISBN:
(纸本)9781577358664
The quantization of deep neuralnetworks (QDNNs) has been actively studied for deployment in edge devices. Recent studies employ the knowledge distillation (KD) method to improve the performance of quantized networks. In this study, we propose stochastic precision ensemble training for QDNNs (SPEQ). SPEQ is a knowledge distillation training scheme;however, the teacher is formed by sharing the model parameters of the student network. We obtain the soft labels of the teacher by randomly changing the bit precision of the activation stochastically at each layer of the forward-pass computation. The student model is trained with these soft labels to reduce the activation quantization noise. The cosine similarity loss is employed, instead of the KL-divergence, for KD training. As the teacher model changes continuously by random bit-precision assignment, it exploits the effect of stochastic ensemble KD. SPEQ outperforms the existing quantization training methods in various tasks, such as image classification, question-answering, and transfer learning without the need for cumbersome teacher networks.
In this paper we apply neural network techniques and physically based models to determine the surface shape of chips from scanning electronic microscopy images. Deducing some specific feature's vertical cross-sect...
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ISBN:
(纸本)0819439835
In this paper we apply neural network techniques and physically based models to determine the surface shape of chips from scanning electronic microscopy images. Deducing some specific feature's vertical cross-section within an integrated circuit from two-dimensional top down scanning electron microscope images of the feature surface is a difficult "inverse problem" which arises in semiconductor fabrication. This paper refines our previous work on the reconstruction of semiconductor wafer surface shapes from top down electron microscopy images. One of the approaches we have developed directly maps from the CD-SEM intensity waveforms to line profiles. The other novel method we describe is based on an approximate physical model, where we assume a simplified mathematical representation of the physical process that produces the SEM image from the electron beam's interaction with the feature surface. Our results are illustrated with a variety of real data sets.
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