Jiří Havel

PClines — Line detection using parallel coordinates

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.

Real-time line detection using accelerated high-resolution Hough transform

Hough transform is a well-known and popular algorithm for detecting lines in raster images. The standard Hough transform is rather slow to be usable in real-time, so different accelerated and approximated algorithms exist. This paper proposes a modified accumulation scheme for the Hough transform, which makes it suitable for computer systems with small but fast read-write memory - such as the todays GPUs. The proposed algorithm is evaluated both on synthetic binary images and on complex high resolution real-world photos. The results show that using todays commodity graphics chips, the Hough transform can be computed at interactive frame rates even with a high resolution of the Hough space and with the Hough transform fully computed.

Real-time precise detection of regular grids and matrix codes

The traditional approach in detecting sets of concurrent and/or parallel lines is to first detect lines in the image and then find such groups of them which meet the concurrence condition. The Hough Transform can be used for detecting the lines and variants of HT such as the Cascaded Hough Transform can be used to detect the vanishing points. However, these approaches disregard much of the information actually accumulated to the Hough space. This article proposes using the Hough space as a 2D signal instead of just detecting the local maxima and processing them. On the example of QRcode detection, it is shown that this approach is computationally cheap, robust, and accurate. The proposed algorithm can be used for efficient and accurate detection and localization of matrix codes (QRcode, Aztec, DataMatrix, etc.) and chessboard-like calibration patterns.

Real-time detection of lines using parallel coordinates and CUDA

The Hough transform is a well-known and popular algorithm for detecting lines in raster images. The standard Hough transform is rather slow to be usable in real time, so different accelerated and approximated algorithms exist. This study proposes a modified accumulation scheme for the Hough transform, using a new parameterization of lines “PClines”. This algorithm is suitable for computer systems with a small but fast read-write memory, such as today’s graphics processors. The algorithm requires no floating-point computations or goniometric functions. This makes it suitable for special and low-power processors and special-purpose chips. The proposed algorithm is evaluated both on synthetic binary images and on complex real-world photos of high resolutions. The results show that using today’s commodity graphics chips, the Hough transform can be computed at interactive frame rates, even with a high resolution of the Hough space and with the Hough transform fully computed.

Vanishing points in point-to-line mappings and other line parameterizations

Some variants of the Hough transform can be used for detecting vanishing points and groups of concurrent lines. This article addresses a common misconception that in the polar line parameterization the vanishing point is represented by a line. The numerical error caused by this inaccuracy is then estimated. The article studies in detail point-to-line-mappings (PTLMs) - a class of line parameterizations which have the property that the vanishing point is represented by a line (and thus can be easily searched for). When a PTLM parameterization is used for the straight line detection by the Hough transform, a pair or a triplet of complementary PTLMs has to be used in order to obtain a limited Hough space. The complementary pairs and triplets of PTLMs are formalized and discussed in this article.

Efficient tree construction for multiscale image representation and processing

With the continuous growth of sensor performances, image analysis and processing algorithms have to cope with larger and larger data volumes. Besides, the informative components of an image might not be the pixels themselves, but rather the objects they belong to. This has led to a wide range of successful multiscale techniques in image analysis and computer vision. Hierarchical representations are thus of first importance, and require efficient algorithms to be computed in order to address real-life applications. Among these hierarchical models, we focus on morphological trees (e.g., min/max-tree, tree of shape, binary partition tree, α-tree) that come with interesting properties and already led to appropriate techniques for image processing and analysis, with a growing interest from the image processing community. More precisely, we build upon two recent algorithms for efficient α-tree computation and introduce several improvements to achieve higher performance. We also discuss the impact of the data structure underlying the tree representation, and provide for the sake of illustration several applications where efficient multiscale image representation leads to fast but accurate techniques, e.g., in remote sensing image analysis or video segmentation.

Real-time detection of lines using parallel coordinates and OpenGL

Line detection in raster images is frequently performed using the Hough Transform. Hough Transform for line detection is difficult to accelerate using the GPU because it essentially requires rasterization of sinusoids into a high-resolution raster of accumulators, which is not a suitable task for GPU. This paper presents a GPU implementation of the PClines -- a new parameterization of lines for the Hough Transform. PClines are a point-to-line-mapping and thus the detection of lines uses the graphics processor to rasterize lines into a rectangular frame buffer which is a task very natural and effective on the GPU. The OpenGL 3.3 pipeline is used to efficiently perform the whole of the PClines-based Hough Transform on the GPU. Experimental evaluation shows that even for high-resolution input images with complicated content, the line detector performs easily in real time, which allows for different practical applications.

Efficient Schemes for Computing α-tree Representations

Hierarchical image representations have been addressed by various models by the past, the max-tree being probably its best representative within the scope of Mathematical Morphology. However, the max-tree model requires to impose an ordering relation between pixels, from the lowest values (root) to the highest (leaves). Recently, the α-tree model has been introduced to avoid such an ordering. Indeed, it relies on image quasi-flat zones, and as such focuses on local dissimilarities. It has led to successful attempts in remote sensing and video segmentation. In this paper, we deal with the problem of α-tree computation, and propose several efficient schemes which help to ensure real-time (or near-real time) morphological image processing.

Front-to-back blending with early fragment discarding

Alpha-blending is a frequently used technique implemented in graphics hardware to achieve effects like transparency, volume rendering, displaying particle systems and others. A common approach to using blending is to sort objects from the furthest to closest (back-to-front) and render them in this order. However, the inverse order can be used with some small modifications to the blending process. This paper introduces an approach to use the front-to-back rendering order to discard fragments of rendered polygons that would not influence the visual result and thus speed the rendering up. The speed gain and visual compromises are measured in several scenarios simulating practical situations and the measured results are discussed and conclusions are drawn.