No-reference image quality assessment (NR-IQA) is significant for image processing and yet very challenging, especially for real-time application and big image data processing. Traditional NR-IQA metrics usually train...
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ISBN:
(纸本)9783662485705;9783662485699
No-reference image quality assessment (NR-IQA) is significant for image processing and yet very challenging, especially for real-time application and big image data processing. Traditional NR-IQA metrics usually train complex models such as support vector machine, neural network, and probability graph, which result in long training and testing time and lack robustness. Hence, this paper proposed a novel no-reference image quality via hash code (NRHC). First, the image is divided into some overlapped patches and the features of blind/referenceless image spatial quality evaluator (BRISQUE) are extracted for each patch. Then the features are encoded to produce binary hash codes via an improved iterative quantization (IITQ) method. Finally, comparing the hash codes of the test image with those of the original undistorted images, the final image quality can be obtained. Thorough experiments on standard databases, e.g. LIVE II, show that the proposed NRHC obtains promising performance for NR-IQA. And it has high computational efficiency and robustness for different databases and different distortions.
Most mainstream object-oriented languages provide a notion of equality between objects which can be customized to be weaker than reference equality, and which is coupled with the customizable notion of object hash cod...
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ISBN:
(纸本)9798400702464
Most mainstream object-oriented languages provide a notion of equality between objects which can be customized to be weaker than reference equality, and which is coupled with the customizable notion of object hash code. This feature is so pervasive in object-oriented code that incorrect redefinition or use of equality and hash code may have a serious impact on software reliability and safety. Despite redefinition of equality and hash code in mutable classes is unsafe, many widely used API libraries do that in Java and other similar languages. When objects of such classes are used as keys in hash tables, programs may exhibit unexpected and unpredictable behavior. In this paper we propose a runtime verification solution to avoid or at least mitigate this issue. Our proposal uses RML, a rewriting-based domain specific language for runtime verification which is independent from code instrumentation and the programming language used to develop the software to be verified.
Human gesture recognition allows for a more natural human machine interface eliminating expensive training for human's to get accustomed to the machines and avoid costly mistakes that follow till one becomes an ex...
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ISBN:
(纸本)9781479904006;9781479903979
Human gesture recognition allows for a more natural human machine interface eliminating expensive training for human's to get accustomed to the machines and avoid costly mistakes that follow till one becomes an experienced user. With advances in technology embedded devices with additional processing power and memory are becoming available. This is making our machines more capable and complex to operate, though the cost of human error is even higher. Hand gesture recognition offers a solution, but it still remains a very time and space complex problem when most non statistical methods are employed. Thus most embedded systems with limited space and processing power are unable to support hand gesture recognition. The paper introduces a statistical method which converts image contour to orientation based hash codes in-order to project it to a 3D-address space bounded by hamming distance. The main objectives are to reduce time, space complexity along with complete rotation invariance and online scalability. The implemented method proved to be 82.1% accurate against 1000 images comprising of 10 distinct static hand gesture sets.
This paper proposes to learn binary hash codes within a statistical learning framework, in which an upper bound of the probability of Bayes decision errors is derived for different forms of hash functions and a rigoro...
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ISBN:
(纸本)9781479928392
This paper proposes to learn binary hash codes within a statistical learning framework, in which an upper bound of the probability of Bayes decision errors is derived for different forms of hash functions and a rigorous proof of the convergence of the upper bound is presented. Consequently, minimizing such an upper bound leads to consistent performance improvements of existing hash code learning algorithms, regardless of whether original algorithms are unsupervised or supervised. This paper also illustrates a fast hash coding method that exploits simple binary tests to achieve orders of magnitude improvement in coding speed as compared to projection based methods.
Human gesture recognition allows for a more natural human machine interface eliminating expensive training for human's to get accustomed to the machines and avoid costly mistakes that follow till one becomes an ex...
详细信息
ISBN:
(纸本)9781479903979
Human gesture recognition allows for a more natural human machine interface eliminating expensive training for human's to get accustomed to the machines and avoid costly mistakes that follow till one becomes an experienced user. With advances in technology embedded devices with additional processing power and memory are becoming available. This is making our machines more capable and complex to operate, though the cost of human error is even higher. Hand gesture recognition offers a solution, but it still remains a very time and space complex problem when most non statistical methods are employed. Thus most embedded systems with limited space and processing power are unable to support hand gesture recognition. The paper introduces a statistical method which converts image contour to orientation based hash codes in-order to project it to a 3D-address space bounded by hamming distance. The main objectives are to reduce time, space complexity along with complete rotation invariance and online scalability. The implemented method proved to be 82.1% accurate against 1000 images comprising of 10 distinct static hand gesture sets.
Introduced is a hash coding method based on fixed-point division rather than multiplication or logical operations. This new method allows the hash table to have almost any length. Also a new method of handling collisi...
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Secondary clustering as a cause of hash code inefficiency is discussed, and a new hashing method based on its eliminiation is presented. Comparisons with previous methods are made both analytically and empirically. &#...
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