The inspection and monitoring of the wear of grinding tools is essential to ensure the quality of the grinding tool surface and the finished product Most of the current methods for examining a grinding tool surface re...
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The inspection and monitoring of the wear of grinding tools is essential to ensure the quality of the grinding tool surface and the finished product Most of the current methods for examining a grinding tool surface rely on dismounting the grinding tool. Often, the state of the grinding tool surface is checked indirectly by evaluating the quality of the workpiece. We describe the application of imageprocessing, which offers an effective means for in situ inspection and monitoring. It yields more detailed information about the surface and the kind of wear observed than the common methods. By using multidirectional illumination and image fusion, an image with a high degree of relevant information is generated that is then segmented using the wavelet transform (multiscale analysis) and classified to distinguish grains and cavities on the surface. Results of the application of the algorithms for a high-performance grinding tool with CBN grains embedded in a resin base are presented. (C) 2004 SPIE and IST.
At the time of image acquisition, professional photographers apply many rules of thumb to improve the composition of their photographs. This paper develops a joint optical-digital processing framework for automating c...
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At the time of image acquisition, professional photographers apply many rules of thumb to improve the composition of their photographs. This paper develops a joint optical-digital processing framework for automating composition rules during image acquisition for photographs with one main subject. Within the framework, we automate three photographic composition rules: repositioning the main subject, making the main subject more prominent, and making objects that merge with the main subject less prominent. The idea is to provide to the user alternate pictures obtained by applying photographic composition rules in addition to the original picture taken by the user. The proposed algorithms do not depend on prior knowledge of the indoor/outdoor setting or scene content. The proposed algorithms are also designed to be amenable to software implementation on fixed-point programmable digital signal processors available in digital still cameras.
Due to the critical importance of underwater pipeline integrity, particularly in the oil and gas transportation sector. This paper addresses the significance of applying low-rank matrix and sparse representation theor...
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Extracting a human silhouette from an image is the enabling step for many high-level vision processing tasks, such as human tracking and activity analysis. Although there are a number of silhouette extraction algorith...
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ISBN:
(纸本)9780780394889
Extracting a human silhouette from an image is the enabling step for many high-level vision processing tasks, such as human tracking and activity analysis. Although there are a number of silhouette extraction algorithms proposed in the Uterature, most approaches work efficiently only in constrained environments where the background is relatively simple and static. In a previous paper, we addressed some of the challenges in silhouette extraction and human tracking in a real-world unconstrained environment where the background is complex and dynamic. We extracted features from image regions, accumulated the feature information over time, fused high-level knowledge with low-level features, and built a time-varying background model. A problem with our system is that by adapting the background model, objects moved by a human are difficult to handle. In order to reinsert them into the background, we run the risk of cutting off part of the human silhouette, such as in a quick arm movement. In this paper, we develop a fuzzy logic inference system to detach the silhouette of a moving object from the human body. Our experimental results demonstrate that the fuzzy inference system is very efficient and robust.
Performance measures of image enhancement are traditionally subjective and have difficulty quantifying the improvement made by the algorithm. In this paper, we present the image enhancement measures and show how utili...
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ISBN:
(纸本)0819461040
Performance measures of image enhancement are traditionally subjective and have difficulty quantifying the improvement made by the algorithm. In this paper, we present the image enhancement measures and show how utilizing logarithmic arithmetic based addition, subtraction, and multiplication provides better results than previously used measures. In addition, for illustration of the performance of developed measures, we present a comprehensive study of several image enhancement algorithms from all three domains, including spatial, transform, and logarithmic algorithms.
The development of target detection and recognition algorithms in the field of imageprocessing has promoted the development of automatic image conversion systems for digital musical scores and related algorithms. Thi...
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ADAS (Advanced Driver Assistance systems) algorithms increasingly use heavy imageprocessing operations. To embed this type of algorithms, semiconductor companies offer many heterogeneous architectures. These SoCs (Sy...
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ISBN:
(纸本)9781479989379
ADAS (Advanced Driver Assistance systems) algorithms increasingly use heavy imageprocessing operations. To embed this type of algorithms, semiconductor companies offer many heterogeneous architectures. These SoCs (System on Chip) are composed of different processing units, with different capabilities, and often with massively parallel computing unit. Due to the complexity of these SoCs, predicting if a given algorithm can be executed in real time on a given architecture is not trivial. In fact it is not a simple task for automotive industry actors to choose the most suited heterogeneous SoC for a given application. Moreover, embedding complex algorithms on these systems remains a difficult task due to heterogeneity, it is not easy to decide how to allocate parts of a given algorithm on the different computing units of a given SoC. In order to help automotive industry in embedding algorithms on heterogeneous architectures, we propose a novel approach to predict performances of imageprocessingalgorithms applicable on different types of computing units. Our methodology is able to predict a more or less wide interval of execution time with a degree of confidence using only high level description of algorithms, and a few characteristics of computing units.
Smart painting software and cross-border fusion based on digital imageprocessingalgorithms is studied in this paper. Early non-deep methods use shallow network model architectures, which are difficult to effectively...
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Digital image and video forensics is to detect the authenticity of digital images/videos. So far, existing algorithms are designed by exploring one or several special features, which usually result in limited performa...
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ISBN:
(纸本)9781424465880
Digital image and video forensics is to detect the authenticity of digital images/videos. So far, existing algorithms are designed by exploring one or several special features, which usually result in limited performance and applicability. With the perspective of entire procedure including image acquisition and imageprocessing, a theory of general blind image forensics is proposed in this paper. Then a new blind image forensic method is designed based on this theory to identify camera source and processing history of cropped compressed digital images. In this method, tensor decomposition analysis is applied to extract features of nonlinear operations, which come from both algorithms embedded within camera and operations done by post-software. Then, the Support Vector Machine is utilized to classify whether the target image is captured by the claimed camera and underwent the declared processing history. Experimental results show that this method has high detection accuracy, which demonstrated that our theory is correct.
Markov Random Field modeling is a powerful parallel processing paradigm which can appropriately deal with the huge amount of data in the domain of low-level imageprocessing problems. This paper describes a novel comb...
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Markov Random Field modeling is a powerful parallel processing paradigm which can appropriately deal with the huge amount of data in the domain of low-level imageprocessing problems. This paper describes a novel combined Simulation and semiconductor-technology independent VLSI design environment for Markov Random Field based processing models and systems. The concepts of this novel combined Simulation- and VLSI Design-Environment are experimentally demonstrated and proved by simulation results and detailed chip-layouts of a special Markov Random Field, which simultaneously solves the imageprocessing problem of noise removing, intensity-level preserving and intensity histogram based segmentation.
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