Line detection is an important computer vision task traditionally solved by Hough Transform. With the advance of deep learning, however, trainable approaches to line detection became popular. In this paper we propose a lightweight CNN for line detection with an embedded parameter-free Hough layer, which allows the network neurons to have global strip-like receptive fields. We argue that traditional convolutional networks have two inherent problems when applied to the task of line detection and show how insertion of a Hough layer into the network solves them. Additionally, we point out some major inconsistencies in the current datasets used for line detection.
Image registration is a problem of aligning two or more images of the same scene or object. The case when images are taken using different sensors - multimodal image registration - has applications in medical imaging and remote sensing. Unfortunately, many of the existing image registration methods operate under crude assumptions (i.e., the intensities of images are linearly correlated), which makes them inapplicable for the accurate multimodal registration. One approach to this task is to use deep learning to capture the complex intensity dependencies between images of different modalities. However, while deep learning methods produce good results, most of them are trained end-to-end and do not utilize the accumulated body of knowledge about image registration using “classic” information-theoretic and statistical methods. In this paper we consider the specific case of multimodal image registration - of optical and synthetic aperture radar (SAR) images. We use classic feature-based registration pipeline (first, corresponding feature points are found, then RANSAC is used as the transform estimator). Within this method we compare the effectiveness of various feature point detection and correspondence methods - both neural network-based and traditional. We find that Siamese network outperforms (but only slightly) the classic cross-entropy-based method for finding correspondences. Finally, we propose a hybrid method and show that it outperforms both “classic” method and an end-to-end network by a significant margin.
Porous materials are widely used in different applications, in particular they are used to create various filters. Their quality depends on parameters that characterize the internal structure such as porosity, permeability and so on. Сomputed tomography (CT) allows one to see the internal structure of a porous object without destroying it. The result of tomography is a gray image. To evaluate the desired parameters, the image should be segmented. Traditional intensity threshold approaches did not reliably produce correct results due to limitations with CT images quality. Errors in the evaluation of characteristics of porous materials based on segmented images can lead to the incorrect estimation of their quality and consequently to the impossibility of exploitation, financial losses and even to accidents. It is difficult to perform correctly segmentation due to the strong difference in voxel intensities of the reconstructed object and the presence of noise. Image filtering as a preprocessing procedure is used to improve the quality of segmentation. Nevertheless, there is a problem of choosing an optimal filter. In this work, a method for selecting an optimal filter based on attributive indicator of porous objects (should be free from "levitating stones" inside of pores) is proposed. In this paper, we use real data where beam hardening artifacts are removed, which allows us to focus on the noise reduction process.
Most modern convolutional neural networks (CNNs) are compute-intensive, making them infeasible to use in mobile or embedded devices. One of the approaches to this problem is to modify a usual deep CNN with shallow early-exit branches, appended to some convolutional layers [1]. This modification, named BranchyNet, allows to process simple input samples without performing full volume of calculations, providing a speed-up on average. In this work we consider the problem of training a BranchyNet. We exploit a cascade loss function [2], which explicitly regularizes CNN’s average computation time, and modify it to use the entropy of branches’ prediction as confidence measure. We show, that on CIFAR10 dataset the proposed loss function provides a actual speed-up increase from 43% to 47% without quality degradation, comparing with the original loss function.
Patrolling is a task of providing a uniform coverage of some area with one or several vehicles. Recent scientific developments focus on patrolling using multiple cooperative autonomous agents without a single center of command. Using a group of agents can increase the efficiency of patrolling; however, group algorithms need to govern not only individual movements, but also cooperation between agents and their distribution over the area. To achieve cooperation agents exchange data about their movements and the patrolling area. Agent's decisions can be affected by information received from other agents, however, few studies had considered how the presence of incorrect information affects the patrolling’s efficiency. In this paper we consider a novel problem of counteracting and detecting a sabotaging agent in the context of a multi-agent stochastic patrolling. We consider the modified Social Potential Fields approach, propose a model of sabotaging agent and develop two algorithms for its counteraction and detection.
With the development of Artificial Neural Networks (ANNs), they are becoming key components in many computer vision systems. However, to train ANNs or other machine learning programs it is necessary to create large and representative datasets, which can be a costly, hard and sometimes even impossible task. Another important problem with such programs is the data drift: in real-world applications input data can change with time, and the quality of a machine learning system trained on the fixed dataset may deteriorate. To combat these problems, we propose a model of ANN-based machine learning classification system that can be trained during its exploitation. The system both classifies input examples and performs training on the data gathered during its operation. We assume that besides ANN there is an external module in the system that can estimate confidence of the answers given by ANN. In this paper we consider two examples of such external module: a separate, uncorrelated classifier and a module that estimates ANN output by searching recognized words in a dictionary. We conduct numerical experiments to study the properties of the proposed system and compare it to ANNs trained offline.
Registration of images of different nature is an important technique used in image fusion, change detection, efficient information representation and other problems of computer vision. Solving this task using feature-based approaches is usually more complex than registration of several optical images because traditional feature descriptors (SIFT, SURF, etc.) perform poorly when images have different nature. In this paper we consider the problem of registration of SAR and optical images. We train neural network to build feature point descriptors and use RANSAC algorithm to align found matches. Experimental results are presented that confirm the method’s effectiveness.
An important application of autonomous robot systems is to substitute human personnel in dangerous environments to reduce their involvement and subsequent risk on human lives. In this paper we solve the problem of covertly convoying a civilian in a dangerous area with a group of unmanned ground vehicles (UGVs) using social potential fields. The novelty of our work lies in the usage of UGVs as compared to the unmanned aerial vehicles typically employed for this task in the approaches described in literature. Additionally, in our paper we assume that the group of UGVs should simultaneously solve the problem of patrolling to detect intruders on the area. We develop a simulation system to test our algorithms, provide numerical results and give recommendations on how to tune the potentials governing robots’ behaviour to prioritize between patrolling and convoying tasks.
This paper describes a method for real-time object detection based on a hybrid of a Viola-Jones cascade with a convolutional neural network. This scheme allows flexible trade-offs between detection quality and computational performance. We also propose a generalization of this method to multispectral images that effectively and efficiently utilizes information from each spectral channel. The new scheme is experimentally compared to traditional Viola-Jones, showing improved detection quality with adjustable performance.
In the paper we consider the problem of multi-agent continuous mapping of a changing, low dynamic environment. The mapping problem is a well-studied one, however usage of multiple agents and operation in a non-static environment complicate it and present a handful of challenges (e.g. double-counting, robust data association, memory and bandwidth limits). All these problems are interrelated, but are very rarely considered together, despite the fact that each has drawn attention of the researches. In this paper we devise an architecture that solves the considered problems in an internally consistent manner.
This paper proposes a method for automatic center location of objects containing concentric arcs. The method utilizes structure tensor analysis and voting scheme optimized with Fast Hough Transform. Two applications of the proposed method are considered: (i) wheel tracking in video-based system for automatic vehicle classification and (ii) tree growth rings analysis on a tree cross cut image.
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