In the present work new methods and algorithms for selecting features using deep learning techniques based on autoencoders will be proposed to provide high informativeness with low within-class and high between-class variance. The performance of the proposed methods in real indoor environments is presented and discussed.
The problem at solving which the project is aimed consists in the development of methods of constructing a threedimensional combined dense map are of the accessible environment and determining a position of a robot in a relative coordinate system based on a history of camera positions and the robot's motions, symbolic (semantic) tags.
Computer vision tasks are remaining very important for the last couple of years. One of the most complicated problems in computer vision is face recognition that could be used in security systems to provide safety and to identify person among the others. There is a variety of different approaches to solve this task, but there is still no universal solution that would give adequate results in some cases. Current paper presents following approach. Firstly, we extract an area containing face, then we use Canny edge detector. On the next stage we use convolutional neural networks (CNN) to finally solve face recognition and person identification task.
The present work will propose a new heuristic algorithms for path planning of a mobile robot in an unknown dynamic space that have theoretically approved estimates of computational complexity and are approbated for solving specific applied problems.
A new method will be developed in the present work of the detection of a robot's position in a relative coordinate system based on a history of camera positions and the robot's movement, symbolic tags and on combining obtained three-dimensional depth maps that account for accuracy of their superimposition and geometric relationships between various images of the same scene. It is expected that this approach will enable one to develop a fast and accurate algorithm for localization in unknown dynamic environment.
In this work we present an algorithm of fusing thermal infrared and visible imagery to identify persons. The proposed
face recognition method contains several components. In particular this is rigid body image registration. The rigid
registration is achieved by a modified variant of the iterative closest point (ICP) algorithm. We consider an affine
transformation in three-dimensional space that preserves the angles between the lines. An algorithm of matching is
inspirited by the recent results of neurophysiology of vision. Also we consider the ICP minimizing error metric stage for
the case of an arbitrary affine transformation. Our face recognition algorithm also uses the localized-contouring
algorithms to segment the subject’s face; thermal matching based on partial least squares discriminant analysis. Thermal
imagery face recognition methods are advantageous when there is no control over illumination or for detecting disguised
faces. The proposed algorithm leads to good matching accuracies for different person recognition scenarios (near
infrared, far infrared, thermal infrared, viewed sketch). The performance of the proposed face recognition algorithm in
real indoor environments is presented and discussed.
Face recognition is one of the most important tasks in computer vision and pattern recognition. Face recognition is useful
for security systems to provide safety. In some situations it is necessary to identify the person among many others. In this
case this work presents new approach in data indexing, which provides fast retrieval in big image collections. Data
indexing in this research consists of five steps. First, we detect the area containing face, second we align face, and then
we detect areas containing eyes and eyebrows, nose, mouth. After that we find key points of each area using different
descriptors and finally index these descriptors with help of quantization procedure. The experimental analysis of this
method is performed. This paper shows that performing method has results at the level of state-of-the-art face
recognition methods, but it is also gives results fast that is important for the systems that provide safety.
Proc. SPIE. 9599, Applications of Digital Image Processing XXXVIII
KEYWORDS: Visualization, Clouds, Cameras, RGB color model, 3D image processing, Detection and tracking algorithms, Distance measurement, Associative arrays, Information visualization, Information fusion
Recently various algorithms for building of three-dimensional maps of indoor environments have been proposed. In this work we use a Kinect camera that captures RGB images along with depth information for building three-dimensional dense maps of indoor environments. Commonly mapping systems consist of three components; that is, first, spatial alignment of consecutive data frames; second, detection of loop-closures, and finally, globally consistent alignment of the data sequence. It is known that three-dimensional point clouds are well suited for frame-to-frame alignment and for three-dimensional dense reconstruction without the use of valuable visual RGB information. A new fusion algorithm combining visual features and depth information for loop-closure detection followed by pose optimization to build global consistent maps is proposed. The performance of the proposed system in real indoor environments is presented and discussed.