This paper describes algorithms for detection and evaluation of trajectory parameters of small, spatially moving objects by passive optical and radio thermal vision system. The algorithms are based on spatial and temp...
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ISBN:
(数字)9781510630321
ISBN:
(纸本)9781510630321
This paper describes algorithms for detection and evaluation of trajectory parameters of small, spatially moving objects by passive optical and radio thermal vision system. The algorithms are based on spatial and temporal imageprocessing. During spatial processing, a system of equations representing a sufficient condition for the conjugation of direction vectors to objects in stereo pairs is solved. During temporal processingvectors of the directions on accessory to objects in a sequence of the periods of supervision are distributed. The results of the algorithms theoretical and experimental examination are given. They are showing the advantage of the joint application of the two approaches.
Melanoma is a deadly form of skin cancer that is often undiagnosed or misdiagnosed as a benign skin lesion. Its early detection is extremely important, since the life of patients with melanoma depends on accurate and ...
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The availability and sophistication of Advanced Driver Assistance System (ADASs) are becoming increasingly important for customers when making purchasing decisions and thus also for the manufacturers of such systems. ...
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ISBN:
(纸本)9783030186562;9783030186555
The availability and sophistication of Advanced Driver Assistance System (ADASs) are becoming increasingly important for customers when making purchasing decisions and thus also for the manufacturers of such systems. The increased demands on functionality have also increased the demands in computing power and today's standard processors in automotive Electronic Control Unit (ECUs) struggle to provide enough computing power for those tasks. Here, heterogeneous systems, for example consisting of Central processing Unit (CPUs), embedded Graphics processing Unit (GPUs), and Field-Programmable Gate Array (FPGAs) provide a remedy. These heterogeneous systems, however, increase the development effort and the development costs enormously. In this paper, we analyze the extent to which it is possible to automatically generate code with the help of a Domain-Specific Language (DSL) for typical algorithms in the field of environment perception and environment mapping. We show that with the Heterogeneous imageprocessing Acceleration (Hipacc) framework it is possible to generate program code for CPUs, GPUs, and FPGAs. After that, we compare for selected algorithms the execution times of the automatically generated code with hand-written variants from the literature.
In gravel roads management systems (GRMS), the need for a holistic approach for detecting the dust amounts on gravel roads has enabled the development of a solution that works based on one of the subdisciplines of art...
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In gravel roads management systems (GRMS), the need for a holistic approach for detecting the dust amounts on gravel roads has enabled the development of a solution that works based on one of the subdisciplines of artificial intelligence (AI). Recently, machine learning is one of the most widely used algorithms to train data to optimize systems. The advances in machine learning has enabled us to develop a complex application. This paper demonstrates the ability of using one of the most popular machine learning frameworks TensorFlow to build an image classifier. This classifier has the ability to classify the dust amounts on gravel roads into four major levels (None, Low, Medium, and High). This classifier is based on the aspect of optimizing one of the deep neural networks models Inception-v3 model. This model contains a pretrained package used to extract and recognize dust patterns from dust images automatically. In this paper, a data set of 4,000 images of gravel roads were collected. For training, 80% of the data set was used, and 20% was used for testing. Furthermore, a prediction accuracy plot was generated, and it was found that this classifier achieves a prediction accuracy of 72%.
In the paper, we propose two watermarking algorithms for semi-fragile data hiding in JPEG 2000 lossy compressed images. Both algorithms are based on the concept of quantization index modulation. These methods have a p...
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Method is proposed to improve the visual quality of images based on algorithms based on histogram equalization. The work examined widely used algorithms of histograms equalization, described their advantages and disad...
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ISBN:
(数字)9781728149448
ISBN:
(纸本)9781728149455
Method is proposed to improve the visual quality of images based on algorithms based on histogram equalization. The work examined widely used algorithms of histograms equalization, described their advantages and disadvantages. The classification of images with contrast violation has been introduced. A focus of the work is the development of a new method of imageprocessing with different types of contrast violations. This approach provides improved images quality, regardless of classification and characteristics.
We present a simple method for the diagnosis of urinary schistosomiasis using an in-line lensless holographic microscope combined with flow cytometry technique. Using simple imageprocessingalgorithms and binary imag...
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This research addresses topical issues of creating industrial monitoring systems for chips, which are formed when materials are cut, based on light field-based optical detectors. A proposed scheme uses a light field (...
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This research addresses topical issues of creating industrial monitoring systems for chips, which are formed when materials are cut, based on light field-based optical detectors. A proposed scheme uses a light field (LF) method to register formed chips with an optical detector. An algorithms and monitoring methods for key geometric parameters of chip shapes are developed based on the analysis of images obtained from LF cameras. They feature digital image capturing and morphological analysis. For practical implementation of developed algorithms, National Instruments software platform is used. This research shows that the result accuracy in determining geometric characteristics of chips using the proposed solutions makes it possible to run diagnostics for cutting and material processing equipment.
Single-pixel camera (SPC) is a low-cost compressive imaging architecture that obtains random projections of the scenes using binary coded apertures. After the acquisitions, image reconstructions are usually obtained b...
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ISBN:
(纸本)9781728108247
Single-pixel camera (SPC) is a low-cost compressive imaging architecture that obtains random projections of the scenes using binary coded apertures. After the acquisitions, image reconstructions are usually obtained by nonlinear and relatively expensive optimization-based algorithms. Recent works have focused on designing the binary coded apertures to improve the speed of the reconstruction algorithms and the sampling complexity of the compressed sensing systems. However, it has been shown that image recovery is not necessary for image classification from compressive measurements, where only specific features of the images are required. This work proposes a deep learning approach for image classification directly from SPC measurements. In this approach, a neural network is trained to simultaneously learn the linear binary sensing matrix and the non-linear classification parameters, considering the constraints imposed by the SPC. Specifically, the first layer learns the sensing matrix, and subsequent layers perform the classification directly on the compressed measurements. Simulation results from two image datasets validate the proposed method, which provides the best classification accuracy along with a binary sensing matrix.
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