A framework for robust object multi-detection with a vote aggregation and a cascade filtering

This paper presents a framework designed for the multi-object detection purposes and adjusted for the application of product search on the market shelves. The framework uses a single feedback loop and a pattern resizing mechanism to demonstrate the top effectiveness of the state-of-the-art local features. A high detection rate with a low false detection chance can be achieved with use of only one pattern per object and no manual parameters adjustments. The method incorporates well known local features and a basic matching process to create a reliable voting space. Further steps comprise of metric transformations, graphical vote space representation, two-phase vote aggregation process and a cascade of verifying filters

FastRIFE: Optimization of Real-Time Intermediate Flow Estimation for Video Frame Interpolation

The problem of video inter-frame interpolation is an essential task in the field of image processing. Correctlyincreasing the number of frames in the recording while maintaining smooth movement allows to improve thequality of played video sequence, enables more effective compression and creating a slow-motion recording. Thispaper proposes the FastRIFE algorithm, which is some speed improvement of the RIFE (Real-Time IntermediateFlow Estimation) model. The novel method was examined and compared with other recently published algorithms.All source codes are available at:https://gitlab.com/malwinq/interpolation-of-images-for-slow-motion-videos

Supervised Learning for Makeup Style Transfer

This paper addresses the problem of using deep learning for makeup style transfer. For solving this problem, we propose a new supervised method. Additionally, we present a technique for creating a synthetic dataset for makeup transfer used to train our model. The obtained results were compared with six popular methods for makeup transfer using three metrics. The tests were carried out on four available data sets. The proposed method, in many respects, is competitive with the methods used in the literature. Thanks to images of faces with generated synthetic makeup, the proposed method learns to better transfer details, and the learning process is significantly accelerated

Fractional Derivatives Application to Image Fusion Problems

In this paper, an analysis of the method that uses a fractional order calculus to multispectral images fusion is presented. We analyze some correct basic definitions of the fractional order derivatives that are used in the image processing context. Several methods of determining fractional derivatives of digital images are tested, and the influence of fractional order change on the quality of fusion is presented. Results achieved are compared with the results obtained for methods where the integer order derivatives were used

Synthetic Image Translation for Football Players Pose Estimation

In this paper, we present an approach for football players pose estimation on very low-resolution images. The camera recording the football match is far away from the pitch in order to register at least half of it. As a result, even using very high resolution cameras, the image area presenting every single player is very small. Additionally, variable weather conditions or shadows and reflections, make this aim very hard. Such images are very hard to annotate by human. In our research we assume lack of manually annotated training data from our target distribution. Instead of manual annotation of large dataset, we create simple python script for rendering synthetic images with perfect annotations. Then we train vanilla CycleGAN (Cycle-consistent Generative Adversarial Networks) for transformation of raw synthetic images into more realistic. We use transformed images to train CPN (Cascaded Pyramid Networks) model. Without bells and whistles, we achieve similar precision on our images as the same CPN model trained with COCO (Common Objects in Context) keypoints dataset

Identification of the Fractional-Order Systems: A Frequency Domain Approach

The paper deals with a comparison of different optimization methods to identification of fractional order dynamical systems. The fractional models of the examples of physical systems- ultracapacitors- are established. Then different real frequency responses data from a laboratory setup of the processes are collected and the comparison of identification methods based on least squares and total least squares are presented. The accuracy of the methods is discussed using the frequency responses of the identified model and the theoretical one

Modelling heat transfer in heterogeneous media using fractional calculus

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