The MIN PFS problem and piecewise linear model estimation

Image Analysis and Computer Vision: 1998☆

Analysis of aerial images has been an important research topic for urban analysis The classification and measurement for geometrical objects of a city will be simplified if its road network could be automatically extracted from aerial images In this paper we present an automatic method to extract road network from high-resolution aerial images These roads are found through the use of a consecutive linear features detection algorithm that is mainly based on the three following steps: first, pre-processing, a local selective window method followed by median filter are combined to remove noise and possibly irrelevant small scale details; which is followed by a cross entropy segmentation algorithm in variable overlapping window according to some rules, thus generating a binary edge map; in order to detect straight lines, a Hough transform is applied to the binary edge map to get the parameters of candidate lines The experiment shows that the proposed method can be used to correctly extract road network from aerial images

Automatic extraction of road network from aerial images

In this paper we present a new class of algorithms for detecting lines in digital images. The approach is based on a general formulation of a combinatorial optimization problem. It aims at estimating piecewise linear models. A linear system is constructed with the coordinates of all contour points in the image as coefficients and the line parameters as unknowns. The resulting linear system is then partitioned into a close-to-minimum number of consistent subsystems using a greedy strategy based on a thermal variant of the perceptron algorithm. While the partition into consistent subsystems yields the classification of the corresponding image points into a close-to-minimum number of lines. A comparison with the standard Hough Transform and the Randomized Hough Transform shows the considerable advantages of our combinatorial optimization approach in terms of memory requirements, time complexity, robustness with respect to noise, possibility of introducing "a priori" knowledge, and quality of the solutions regardless of the algorithm parameter settings.

/pdf/fast-line-detection-algorithms-based-on-combinatorial-2euna95v04.pdf

Fast Line Detection Algorithms Based on Combinatorial Optimization

In this paper, a new methodology for speeding up edge and line detection algorithms is presented, achieving improved performance over the state of the art software library OpenCV (speedup from 1.35 up to 2.22) and other conventional implementations, in both general and embedded processors, by reducing the number of load/store and arithmetic instructions, the number of data cache accesses and data cache misses in memory hierarchy and the algorithm memory size. This is achieved by fully exploiting the combination of the software and hardware parameters which are considered simultaneously as one problem and not separately. Furthermore, the edge and line detection algorithms have been simplified for a computer vision application in a Virtex-5 Xilinx FPGA using Microblaze soft processor (detection and measurement of flow fronts in a microfluid device); it achieves speedup up to 660 times in comparison with conventional software implementations.

A methodology for speeding up edge and line detection algorithms focusing on memory architecture utilization

In this paper we present a new algorithm for detecting lines in digital images. The algorithm is based on a general combinatorial optimization approach for estimating piecewise linear models that we introduced in Mattavelli and Amaldi. A linear system is constructed with the coordinates of all contour points in the image as coefficients and the parameters of the line as unknowns. The resulting linear system is then partitioned into a close-to-minimum number of consistent subsystems using a greedy strategy based on a thermal variant of the perceptron algorithm. While the partition into consistent subsystems yields the classification of the corresponding image points into a close-to-minimum number of lines, the solution of each subsystem provides the parameters of the line. An extensive comparison with the standard Hough transform and the randomized Hough transform shows the consistent advantages of our combinatorial optimization approach in terms of memory requirements, computational complexity, robustness with respect to noise, and quality of the solution independently from parameter settings.

An efficient line detection algorithm based on a new combinational optimization formulation

In this paper we present a new algorithm for detecting lines in digital images. The algorithm is based on a general combinatorial optimization approach for estimating piecewise linear models that we introduced previously (Mattavelli et al., 1997). A linear system is constructed with the coordinates of all contour points in the image as coefficients and the line parameters as unknowns. The resulting linear system is then partitioned into a close-to-minimum number of consistent subsystems using a greedy strategy based on a thermal variant of the perceptron algorithm. While the partition into consistent subsystems yields the classification of the corresponding image points into a close-to-minimum number of lines, the solution of each subsystem provides the parameters of those lines. An extensive comparison with the standard Hough transform and the randomized Hough transform shows the considerable advantages of our combinatorial optimization approach in terms of memory requirements, time complexity, robustness with respect to noise, and quality of the solution regardless of the parameter settings.

A new approach for fast line detection based on combinatorial optimization

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An efficient line detection algorithm based on a new combinational optimization formulation

A new approach for fast line detection based on combinatorial optimization