Електронний науковий архів

Національного університету "Львівська політехніка"

Архів зберігає опубліковані наукові матеріали переважно працівників Університету. Також доступна можливість "самоархівування"

 

Communities in DSpace

Select a community to browse its collections.

Now showing 1 - 3 of 3

Recent Submissions

Item
Підсистеми температурного контролю кіберфізичних систем
(Національний університет «Львівська політехніка», 2022) Мідик , Андрій-Володимир Володимирович; Національний університет «Львівська політехніка»
Дисертація присвячена дослідженню та подальшому розвитку кібер-фізичних систем, а саме їх температурних підсистем, для потреб сільського господарства та його переробної промисловості.
Item
Research of content-based image retrieval algorithms
(Lviv Politechnic Publishing House, 2021) Yakubchyk, Eduard; Yurchak, Iryna; Lviv Polytechnic National University
Finding similar images on a visual sample is a difficult AI task, to solve which many works are devoted. The problem is to determine the essential properties of images of low and higher semantic level. Based on them, a vector of features is built, which will be used in the future to compare pairs of images. Each pair always includes an image from the collection and a sample image that the user is looking for. The result of the comparison is a quantity called the visual relativity of the images. Image properties are called features and are evaluated by calculation algorithms. Image features can be divided into low-level and high-level. Low-level features include basic colors, textures, shapes, significant elements of the whole image. These features are used as part of more complex recognition tasks. The main progress is in the definition of high-level features, which is associated with understanding the content of images. In this paper, research of modern algorithms is done for finding similar images in large multimedia databases. The main problems of determining high-level image features, algorithms of overcoming them and application of effective algorithms are described. The algorithms used to quickly determine the semantic content and improve the search accuracy of similar images are presented. The aim: The purpose of work is to conduct comparative analysis of modern image retrieval algorithms and retrieve its weakness and strength.
Item
Software implementation of the algorithm for recognizing protective elements on the face
(Lviv Politechnic Publishing House, 2021) Voloshyn, Mykola; Vavruk, Yevhenii; Lviv Polytechnic National University
The quarantine restrictions introduced during COVID-19 are necessary to minimize the spread of coronavirus disease. These measures include a fixed number of people in the room, social distance, wearing protective equipment. These restrictions are achieved by the work of technological control workers and the police. However, people are not ideal creatures, quite often the human factor makes its adjustments. That is why in this work we have developed software for determining the protective elements on the face in real time using the Python scripting language, the open software libraries OpenCV v4.5.4, TensorFlow v2.6.0, Keras v2.6.0 and MobileNetV2 using the camera. The training program uses a prepared set of photos from KAGGLE – with a mask and without a mask. This set has been expanded by the authors to include different types of masks and their location. Using TensorFlow, Keras, MobileNetV2, a model is created to study the neural net work by analyzing images. The generated neural network uses a model to determine the masks. You can preview the learning result of the network – it is presented as a graphic file. A program that uses the connected camera is then launched and the user can test the operation. This model can be easily deployed on embedded systems such as Raspberry Pi, Google Coral, and become a hardware and software automated system that can be used in crowded places – airports, shopping malls, stadiums, government agencies and more.
Item
Detection of stego images with adaptively embedded data by component analysis methods
(Lviv Politechnic Publishing House, 2021) Progonov, Dmytro; Igor Sikorsky Kyiv Polytechnic Institute
Ensuring the effective protection of personal and corporate sensitive data is topical task today. The special interest is taken at sensitive data leakage prevention during files transmission in communication systems. In most cases, these leakages are conducted by usage of advance adaptive steganographic methods. These methods are aimed at minimizing distortions of cover files, such as digital images, during data hiding that negatively impact on detection accuracy of formed stego images. For overcoming this shortcoming, it was proposed to pre-process (calibrate) analyzed images for increasing stego-to-cover ratio. The modern paradigm of image calibration is based on usage of enormous set of high-pass filters. However, selection of filter(s) that maximizes the probability of stego images detection is non-trivial task, especially in case of limited a prior knowledge about embedding methods. For solving this task, we proposed to use component analysis methods for image calibration, namely principal components analysis. Results of comparative analysis of novel maxSRMd2 cover rich model and proposed solution showed that principal component analysis allows increasing detection accuracy up to 1.5 % even in the most difficult cases (low cover image payload and absence of cover-stego images pairs in training set).
Item
Methods to increase the contrast of the image with preserving the visual quality
(Lviv Politechnic Publishing House, 2021) Maksymiv, Mykola; Rak , Taras; Lviv Polytechnic National University; IT STEP University
Contrast enhancement is a technique for increasing the contrast of an image to obtain better image quality. As many existing contrast enhancement algorithms typically add too much contrast to an image, maintaining visual quality should be considered as a part of enhancing image contrast. This paper focuses on a contrast enhancement method that is based on histogram transformations to improve contrast and uses image quality assessment to automatically select the optimal target histogram. Improvements in contrast and preservation of visual quality are taken into account in the target histogram, so this method avoids the problem of excessive increase in contrast. In the proposed method, the optimal target histogram is the weighted sum of the original histogram, homogeneous histogram and Gaussian histogram. Structural and statistical metrics of “naturalness of the image” are used to determine the weights of the corresponding histograms. Contrast images are obtained by matching the optimal target histogram. Experimentsshow that the proposed method gives better results compared to other existing algorithms for increasing contrast based on the transformation of histograms.