Alzheimer's disease (AD) is a neurodegenerative condition that deteriorates brain cells and impairs a patient's memory. It is progressive and incurable. Early identification can shield the patient from more br...
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ISBN:
(纸本)9798350391558;9798350379990
Alzheimer's disease (AD) is a neurodegenerative condition that deteriorates brain cells and impairs a patient's memory. It is progressive and incurable. Early identification can shield the patient from more brain cell damage and, as a result, help them avoid irreversible memory loss. The scientific community has employed a number of deep learning algorithms to automatically identify Alzheimer's patients. These comprise binary classification of patient scans into stages of AD as well as moderate cognitive impairment (MCI). Limited research has been done on the multiclass classification of Alzheimer's disease (AD) up to six distinct stages. This research proposes novel technique in Alzheimer disease detection with severity level analysis utilizing deep learning (DL) model. Input is collected as MRI brain images and processed for noise removal and smoothening. Then processed image classification and disease stage is detected using pre-trained multi-layer convolutional residual transfer Random Forest with InceptionV3 model. Experimental analysis is carried out in terms of training accuracy, mean average mean average precision, sensitivity, AUC for various MRI brain image dataset. Training accuracy attained by proposed technique is 96%, mean average precision of 93%, sensitivity of 95%, AUC of 90%.
In todays digital prepress workflow images are most often stored in the CMYK color representation. Besides the fact that 32 bits per pixel are needed to store the color information, these images often have a large spa...
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In todays digital prepress workflow images are most often stored in the CMYK color representation. Besides the fact that 32 bits per pixel are needed to store the color information, these images often have a large spatial resolution. This indicates that lossy compression can be considered to reduce disk capacity problems and to limit the transmission times needed to transmit images from the designer to the prepress shop. Lossy compression techniques are mostly evaluated by calculating pixelwise distortion measures such as the root-mean-square-error (RMSE) or the peak-signal-to-noise-ratio (PSNR). Since we are aware of the weaknesses these distortion measures have, an experiment is designed in which the subjects are able to evaluate several lossy compression schemes in a subjective way. Among the used compression methods are some popular algorithms such as the standard coder for lossy compression JPEG. They are evaluated on three typical CMYK images and for each compression method four different quality factors, ranging from no visual image degradation to clearly distinguishable artifacts, are used. The method of evaluation is the paired comparison method. All subjects are presented a number of image pairs and their task is to decide which of the two images looks most pleasant to them. From the results it is shown that for low compression ratios there is not very much difference in the performance of the different algorithms. For the highest compression ratios a wavelet coder and an algorithm that tends to reduce the blocking artifacts are considered best by the subjects. In general one will see similar results from the judgements between experts and non-experts although the experts are more determined in the results of their observations.
A demonstrator for OFDM transmission based on a programmable DSP (TMS320C6201) is described. It turns out that the realized rather moderate sampling rates up to 10 Msamples/s still represent quite a challenge for stat...
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ISBN:
(纸本)0780370414
A demonstrator for OFDM transmission based on a programmable DSP (TMS320C6201) is described. It turns out that the realized rather moderate sampling rates up to 10 Msamples/s still represent quite a challenge for state-of-the-art DSPs in terms of the required computational power but also the synchronization of the internal processing with the I/O interface to a real-time environment. It is illustrated that SW development under stringent resource constraints requires analysis and partitioning of the algorithms in a manner very similar to the mapping strategies necessary in an ASIC design for either cost-sensitive or extremely challenging applications. Therefore, the demonstrator development provides a sound basis for a subsequent design of such a kind of ASICs.
image representation has always been an important and interesting topic in imageprocessing and pattern recognition. In 1999, Bribiesca introduced a new two dimensional chain code scheme called Vertex Chain Code (VCC)...
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This work is aimed at comparing the classification algorithms and methods of machine learning with various methods of preliminary processing of radar images. Preprocessing step includes speckle noise filtering and obj...
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In Hadoop's big data processingsystems, YARN is responsible for resource management and job scheduling. The built-in job scheduling algorithms in YARN are simple to execute, but have some limitations such as job ...
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The vision evolved not only as a recognition system, but also as a sensory system for reaching, grasping and other motion activities. In advanced creatures, it has become a component of prediction functions, allowing ...
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ISBN:
(纸本)081945155X
The vision evolved not only as a recognition system, but also as a sensory system for reaching, grasping and other motion activities. In advanced creatures, it has become a component of prediction functions, allowing the creation of environmental models and activity planning. Fast information processing and decision making requires reduction of informational and computational complexities. The brain achieves this goal using symbolic coding, hierarchical compression, and selective processing of visual information. Network-Symbolic representation, where both systematic structural/logical methods and neural/statistical methods are the parts of a single mechanism, is the most feasible for such models. It converts visual information into the relational Network-Symbolic structures, instead of precise computations of 3-dimensional models. Narrow foveal vision provides separation of figure from ground, object identification, semantic analysis, and precise control of actions. Rough wide peripheral vision identifies and tracks salient motion, guiding foveal system to salient objects. It also provides scene context. Objects and other stable systems have coherent relational structures. Network-Symbolic transformations derive more abstract structures that allow invariably recognize a particular structure as an exemplar of class. Robotic systems, equipped with such smart vision, will be able to navigate in any environment, understand situation, and act accordingly.
To understand emotion and make machine emotion is one of the goals of affective computing. In order to understand language from interface of machine, both the meaning and the emotion are necessary to be interpreted co...
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Many forms of programmable matter have been proposed for various tasks. We use an abstract model of self-organizing particle systems for programmable matter which could be used for a variety of applications, including...
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ISBN:
(数字)9783319924359
ISBN:
(纸本)9783319924359;9783319924342
Many forms of programmable matter have been proposed for various tasks. We use an abstract model of self-organizing particle systems for programmable matter which could be used for a variety of applications, including smart paint and coating materials for engineering or programmable cells for medical uses. Previous research using this model has focused on shape formation and other spatial configuration problems (e.g., coating and compression). In this work we study foundational computational tasks that exceed the capabilities of the individual constant size memory of a particle, such as implementing a counter and matrix-vector multiplication. These tasks represent new ways to use these self-organizing systems, which, in conjunction with previous shape and configuration work, make the systems useful for a wider variety of tasks. They can also leverage the distributed and dynamic nature of the self-organizing system to be more efficient and adaptable than on traditional linear computing hardware. Finally, we demonstrate applications of similar types of computations with self-organizing systems to imageprocessing, with implementations of image color transformation and edge detection algorithms.
This paper presents optical laboratory data on a number of new optical filter systems for: rank-order morphological filtering, morphological ternary phase amplitude filters, morphological hit-miss detection filters, G...
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ISBN:
(纸本)0819416215
This paper presents optical laboratory data on a number of new optical filter systems for: rank-order morphological filtering, morphological ternary phase amplitude filters, morphological hit-miss detection filters, Gabor detection filters and distortion-invariant detection filters. All filters and processing are performed on the same optical correlator architecture. This provides a general purpose multi-functional optical image processor for general scene analysis, capable of low-level vision, detection, and image enhancement operations.
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