Detection, counting and characterization of bubbles, that is, transparent objects in a liquid, is important in many industrial applications. These applications include monitoring of pulp delignification and multiphase dispersion processes common in the chemical, pharmaceutical, and food industries. Typically the aim is to measure the bubble size distribution. In this paper, we present a comprehensive comparison of bubble detection methods for challenging industrial image data. Moreover, we compare the detection-based methods to a direct bubble size distribution estimation method that does not require the detection of individual bubbles. The experiments showed that the approach based on a convolutional neural network (CNN) outperforms the other methods in detection accuracy. However, the boosting-based approaches were remarkably faster to compute. The power spectrum approach for direct bubble size distribution estimation produced accurate distributions and it is fast to compute, but it does not provide the spatial locations of the bubbles. Selecting the most suitable method depends on the specific application.

We present an object detector coupled with pose estimation directly in a single compact and simple model, where the detector shares extracted image features with the pose estimator. The output of the classification of each candidate window consists of both object score and likelihood map of poses. This extension introduces negligible overhead during detection so that the detector is still capable of real time operation. We evaluated the proposed approach on the problem of vehicle detection. We used existing datasets with viewpoint/pose annotation (WCVP, 3D objects, KITTI). Besides that, we collected a new traffic surveillance dataset COD20k which fills certain gaps of the existing datasets and we make it public. The experimental results show that the proposed approach is comparable with state-of-the-art approaches in terms of accuracy, but it is considerably faster - easily operating in real time (Matlab with C++ code). The source codes and the collected COD20k dataset are made public along with the paper.

Augmented reality does not make any sense for fixed cameras. Or does it? In this work, we are dealing with static cameras and their usability for interactive augmented reality applications. Knowing that the camera does not move makes camera pose estimation both less and more difficult — one does not have to deal with pose change in time, but on the other hand, obtaining some level of understanding of the scene from a single viewpoint is challenging. We propose several ways how to gain advantage from the camera being static and a pipeline of a system for broadcasting a video stream enriched by information needed for its interactive visual augmenting — Interactive Camera Streams, INCAST. We present a proof-of-concept system showing the usability of INCASt on several use-cases — non-interactive demos and simple AR games.

This paper deals with automatic calibration of roadside surveillance cameras. We focus on parameters necessary for measurements in traffic-surveillance applications. Contrary to the existing solutions, our approach requires no a priori knowledge, and it works with a very wide variety of road settings (number of lanes, occlusion, quality of ground marking), as well as with practically unlimited viewing angles. The main contribution is that our solution works fully automatically-without any percamera or per-video manual settings or input whatsoever-and it is computationally inexpensive. Our approach uses tracking of local feature points and analyzes the trajectories in a manner based on cascaded Hough transform and parallel coordinates. An important assumption for the vehicle movement is that at least a part of the vehicle motion is approximately straight-we discuss the impact of this assumption on the applicability of our approach and show experimentally that this assumption does not limit the usability of our approach severely. We efficiently and robustly detect vanishing points, which define the ground plane and vehicle movement, except for the scene scale. Our algorithm also computes parameters for radial distortion compensation. Experiments show that the obtained camera parameters allow for measurements of relative lengths (and potentially speed) with ~2% mean accuracy. The processing is performed easily in real time, and typically, a 2-min-long video is sufficient for stable calibration.

This paper deals with the detection of orthogonal vanishing points. The aim is to efficiently cope with the clutter edges in real-life images and to determine the camera orientation in the Manhattan world reliably. We are using a modified scheme of the Cascaded Hough Transform where only one Hough space is accumulated – the space of the vanishing points. The parameterization of the vanishing points – the “diamond space” – is based on the PClines line parameterization and it is defined as a mapping of the whole real projective plane to a finite space. Our algorithm for detection of vanishing points operates directly on edgelets detected by an edge detector, skipping the common step of grouping edges into straight lines or line segments. This decreases the number of configuration parameters and reduces the complexity of the algorithm. Evaluated on the York Urban DB, our algorithm yields 98.04 % success rate at 10◦ angular error tolerance, which outperforms comparable existing solutions. Our parameterization of vanishing points is in all aspects linear; it involves no goniometric or other non-linear operations and thus it is suitable for implementation in embedded chips and circuitry. The iterative search scheme allows for finding orthogonal triplets of vanishing points with high accuracy and low computational costs. At the same time, our approach can be used without the orthogonality constraint.

We introduce here an improved design of the Uniform Marker Fields and an algorithm for their fast and reliable detection. Our concept of the marker field is designed so that it can be detected and recognized for camera pose estimation: in various lighting conditions, under a severe perspective, while heavily occluded, and under a strong motion blur. Our marker field detection harnesses the fact that the edges within the marker field meet at two vanishing points and that the projected planar grid of squares can be defined by a detectable mathematical formalism. The modules of the grid are grey scale and the locations within the marker field are defined by the edges between the modules. The assumption that the marker field is planar allows for a very cheap and reliable camera pose estimation in the captured scene. The detection rates and accuracy are slightly better compared to state-of-the-art marker-based solutions. At the same time, and more importantly, our detector of the marker field is several times faster and the reliable real-time detection can be thus achieved on mobile and low-power devices. We show three targeted applications where the planarity is assured and where the presented marker field design and detection algorithm provide a reliable and extremely fast solution.

In this article, we propose an instant matchmoving solution for green screen. It uses a recent technique of planar uniform marker fields. Marker fields are an extension of planar markers used in augmented reality, offering better reliability and performance suitable for our task: tolerance to occlusion, speed of detection, and use of arbitrary low-contrast colors. We show that marker fields of shades of green (or blue or other color) can be used to obtain an instant and effortless camera pose estimation. We provide exemplar applications of the presented technique: virtual camera/simulcam and live storyboarding or shot prototyping. The matchmoving technique based on marker fields of shades of green is very computationally efficient - our measurements show that the matchmoving preview and living storyboard editing and recording can be easily done on today's ultramobile devices. Our technique is thus available to anyone at low cost and with easy setup, opening space for new levels of filmmakers' creative expression.

One limitation of existing fiduciary markers is that the camera motion is tightly limited: the marker (one of the markers) must be visible and it must be observed at a proper scale. This paper introduces a fractal structure of markers similar to matrix codes (such as QR-code or Data Matrix): the Fractal Marker Field. The FMF allows for embedding markers of a virtually unlimited number of scales. At the same time, for each of the scales it guarantees a constant density of markers at that scale over the whole marker field's surface. The Fractal Marker Field can provide unprecedented freedom of motion to camera-based augmented reality applications.

In various applications, a wider area needs to be covered by fiduciary markers but a large marker cannot be used because only a fraction of the area is to be viewed by the camera. Such an area can be covered by a number of small markers with unique identifiers. However, with the camera freely moving in the scene and with occluders present, it is difficult to ensure that at least one of the individual markers is completely visible, unless the markers are small and numerous. In that case, the markers are not recognizable from larger distances.

Detection of lines in raster images is often performed using Hough transform. This paper presents a new parameterization of lines and a modification of the Hough transform–PClines. PClines are based on parallel coordinates, a coordinate system used mostly or solely for high-dimensional data visualization. The PClines algorithm is described in the paper; its accuracy is evaluated numerically and compared to the commonly used line detectors based on the Hough transform. The results show that PClines outperform the existing approaches in terms of accuracy. Besides, PClines are computationally extremely efficient, require no floating-point operations, and can be easily accelerated by different hardware architectures.