Building Detection on Aerial Images Using U-NET Neural Networks

This article presents research results of two convolutional neural networks for building detection on satellite images of Planet database. To analyze the quality of developed algorithms, there was used Sorensen-Dice coefficient of similarity which compares results of algorithms with tagged masks. The masks were generated from json files and sliced on smaller parts together with respective images before the training of algorithms. This approach allows to cope with the problem of segmentation for aerial high-resolution images efficiently and effectively. The problem of building detection on satellite images can be put into practice for urban planning, building control, etc

Full-focused image fusion in the presence of noise

The implementation and analysis of the algorithm for the full-focused image fusion in the presence of noise are presented. Three methods of combining noisy images are considered: without pre-processing and post-processing, using prefiltration of original images, using post-filtering of the fused image. The database of test scenes created by the authors was used for testing the proposed algorithm for full-focused image fusion. Additive white Gaussian noise was considered as an noise model. Two-stage digital image processing scheme, based on principal components analysis was used as a filtering algorithm. Quantitative and visual results are shown and demonstrate the main features of the proposed algorithm

Application for video analysis based on machine learning and computer vision algorithms

An application for video data analysis based on computer vision methods is presented. The proposed system consists of five consecutive stages: face detection, face tracking, gender recognition, age classification and statistics analysis. AdaBoost classifier is utilized for face detection. A modification of Lucas and Kanade algorithm is introduced on the stage of tracking. Novel gender and age classifiers based on adaptive features and support vector machines are proposed. All the stages are united into a single system of audience analysis. The proposed software complex can find its applications in different areas, from digital signage and video surveillance to automatic systems of accident prevention and intelligent human-computer interfaces

Eye center localization on a facial image based on multi-block local binary patterns

In this paper an eye center localization algorithm based on multi-block local binary patterns is described. Performance of the suggested algorithm is compared to another methods based on Bayesian approach and image gradients. Visual examples of eye center localization results are provided

Gender classification for real-time audience analysis system

The system allowing to extract all the possible information about depicted people from the input video stream is discussed. As reported previously, the proposed system consists of five consecutive stages: face detection, face tracking, gender recognition, age classification and statistics analysis. The crucial part of the system is gender classifier construction on the basis of machine learning methods. We propose a novel algorithm consisting of two stages: adaptive feature extraction and support vector machine classification. Both training technique of the proposed algorithm and experimental results acquired on a large image dataset are presented. More than 90% accuracy of viewer's gender recognition is achieved

Evaluation of Interest Point Detectors and Feature Descriptors for Visual SLAM

ln this paper we present comparison of feature detectors and descriptors in the visual feature-based simultaneous localization and mapping (SLAM) context. Feature mraction concept is widely used in computer vision and imaee processing algori1hms which are SLAM, panorama stitching, object detection, etc. Since features are used as the starting point and main primitives for subsequent algorithms, the overall algori1hm will often only be as good as its feature detector. Identifying of detected features is provided with the help of descriptor that distinguish it from the rest features. In tum, descriptor should provide invariance when finding the matches between the specific points relative to the image transformation. In this study, we evaluate the performance of well-known detectors and descriptors under the effects of JPEG compression, mom and rotation, blur, viewpoint and illumination variation. Performance parameters of the descriptors have investigated in terms of precision and recall values

Face detection algorithm based on a cascade of ensembles of decision trees

Face detection algorithm based on a cascade of ensembles of decision trees (CEDT) is presented. The new approach allows detecting faces other than the front position through the use of multiple classifiers. Each classifier is trained for a specific range of angles of the rotation head. The results showed a high rate of productivity for CEDT on images with standard size. The algorithm increases the area under the ROC-curve of 13% compared to a standard Viola-Jones face detection algorithm. To test the applicability of the algorithm in the real world have been conducted research on a robustness. Robustness research shown that the algorithm based on the CEDT show that Gaussian noise, impulsive “salt-and-pepper” noise exert a strong influence on the algorithm (in the worst case decrease in the area under the ROC-curve of 21.2% with a decrease in PSNR metric to 17.99 dB). At the same time blurring, JPEG-compression and JPEG2000 algorithms distortion have little effect on the proposed face detection algorithm (reduction of the area under the ROC-curve by 3.5% while reducing PSNR metric to 21.58 dB)

Convolutional Neural Network for Satellite Imagery

Information extracted from aerial photographs has found applications in different areas including urban planning, crop and forest management, disaster relief, and climate modeling. In many cases information extraction is still performed by human experts, making the process slow, costly, and error prone. The goal of this investigation is to develop methods for automatically extracting the locations of objects such as water resource, forest and urban areas from aerial images. We analyze patterns in land using large-scale satellite imagery data which is available worldwide from third-party providers. For training, given the limited availability of standard benchmarks for remotesensing data, we obtain ground truth land use class labels carefully sampled from open-source surveys, in particular the Urban Atlas land classification dataset of 20 land use classes across 300 European cities. The developed algorithms are based on the implementation of a relatively new approach in the field of deep machine learning - a convolutional neural network. We show how deep neural networks implemented on modern GPUs can be used to efficiently learn highly discriminative image features

Cross-Platform App Development for Blended Learning Courses

The survey data suggest a high technical and sufficient psychological readiness of Yaroslavl State University students to use mobile devices in learning. Nevertheless, it is obvious that there is a need in popularizing mobile learning among students by organizing explanatory talks and encouraging on the part of the teaching staff. The developed mobile application Study24Seven is cross-platform and is suitable for modern versions of the iOS and Android operating systems. This result is achieved both through the application architecture and the use of PhoneGap iOnic technology. The development of appropriate methods of the blended learning system will increase the efficiency of teaching the Humanities to students of technical specialties