An Extension to Hough Transform Based on Gradient Orientation

The Hough transform is one of the most common methods for line detection. In this paper we propose a novel extension of the regular Hough transform. The proposed extension combines the extension of the accumulator space and the local gradient orientation resulting in clutter reduction and yielding more prominent peaks, thus enabling better line identification. We demonstrate benefits in applications such as visual quality inspection and rectangle detection.Comment: Part of the Proceedings of the Croatian Computer Vision Workshop, CCVW 2015, Year

Text Line Segmentation of Historical Documents: a Survey

There is a huge amount of historical documents in libraries and in various National Archives that have not been exploited electronically. Although automatic reading of complete pages remains, in most cases, a long-term objective, tasks such as word spotting, text/image alignment, authentication and extraction of specific fields are in use today. For all these tasks, a major step is document segmentation into text lines. Because of the low quality and the complexity of these documents (background noise, artifacts due to aging, interfering lines),automatic text line segmentation remains an open research field. The objective of this paper is to present a survey of existing methods, developed during the last decade, and dedicated to documents of historical interest.Comment: 25 pages, submitted version, To appear in International Journal on Document Analysis and Recognition, On line version available at http://www.springerlink.com/content/k2813176280456k3

Design of automatic vision-based inspection system for solder joint segmentation

Purpose: Computer vision has been widely used in the inspection of electronic components. This paper proposes a computer vision system for the automatic detection, localisation, and segmentation of solder joints on Printed Circuit Boards (PCBs) under different illumination conditions. Design/methodology/approach: An illumination normalization approach is applied to an image, which can effectively and efficiently eliminate the effect of uneven illumination while keeping the properties of the processed image the same as in the corresponding image under normal lighting conditions. Consequently special lighting and instrumental setup can be reduced in order to detect solder joints. These normalised images are insensitive to illumination variations and are used for the subsequent solder joint detection stages. In the segmentation approach, the PCB image is transformed from an RGB color space to a YIQ color space for the effective detection of solder joints from the background. Findings: The segmentation results show that the proposed approach improves the performance significantly for images under varying illumination conditions. Research limitations/implications: This paper proposes a front-end system for the automatic detection, localisation, and segmentation of solder joint defects. Further research is required to complete the full system including the classification of solder joint defects. Practical implications: The methodology presented in this paper can be an effective method to reduce cost and improve quality in production of PCBs in the manufacturing industry. Originality/value: This research proposes the automatic location, identification and segmentation of solder joints under different illumination conditions

A parallel windowing approach to the Hough transform for line segment detection

In the wide range of image processing and computer vision problems, line segment detection has always been among the most critical headlines. Detection of primitives such as linear features and straight edges has diverse applications in many image understanding and perception tasks. The research presented in this dissertation is a contribution to the detection of straight-line segments by identifying the location of their endpoints within a two-dimensional digital image. The proposed method is based on a unique domain-crossing approach that takes both image and parameter domain information into consideration. First, the straight-line parameters, i.e. location and orientation, have been identified using an advanced Fourier-based Hough transform. As well as producing more accurate and robust detection of straight-lines, this method has been proven to have better efficiency in terms of computational time in comparison with the standard Hough transform. Second, for each straight-line a window-of-interest is designed in the image domain and the disturbance caused by the other neighbouring segments is removed to capture the Hough transform buttery of the target segment. In this way, for each straight-line a separate buttery is constructed. The boundary of the buttery wings are further smoothed and approximated by a curve fitting approach. Finally, segments endpoints were identified using buttery boundary points and the Hough transform peak. Experimental results on synthetic and real images have shown that the proposed method enjoys a superior performance compared with the existing similar representative works

A review of hough transform and line segment detection approaches

In a wide range of image processing and computer vision problems, line segment detection is one of the most critical challenges. For more than three decades researchers have contributed to build more robust and accurate algorithms with faster performance. In this paper we review the main approaches and in particular the Hough transform and its extensions, which are among the most well-known techniques for the detection of straight lines in a digital image. This paper is based on extensive practical research and is organised into two main parts. In the first part, the HT and its major research directions and limitations are discussed. In the second part of the paper, state-of-the-art line segmentation techniques are reviewed and categorized into three main groups with fundamentally distinctive characteristics. Their relative advantages and disadvantages are compared and summarised in a table

A review of hough transform and line segment detection approaches

In a wide range of image processing and computer vision problems, line segment detection is one of the most critical challenges. For more than three decades researchers have contributed to build more robust and accurate algorithms with faster performance. In this paper we review the main approaches and in particular the Hough transform and its extensions, which are among the most well-known techniques for the detection of straight lines in a digital image. This paper is based on extensive practical research and is organised into two main parts. In the first part, the HT and its major research directions and limitations are discussed. In the second part of the paper, state-of-the-art line segmentation techniques are reviewed and categorized into three main groups with fundamentally distinctive characteristics. Their relative advantages and disadvantages are compared and summarised in a table

Automated segmentation, detection and fitting of piping elements from terrestrial LIDAR data

Since the invention of light detection and ranging (LIDAR) in the early 1960s, it has been adopted for use in numerous applications, from topographical mapping with airborne LIDAR platforms to surveying of urban sites with terrestrial LIDAR systems. Static terrestrial LIDAR has become an especially effective tool for surveying, in some cases replacing traditional techniques such as electronic total stations and GPS methods. Current state-of-the-art LIDAR scanners have very fine spatial resolution, generating precise 3D point cloud data with millimeter accuracy. Therefore, LIDAR data can provide 3D details of a scene with an unprecedented level of details. However, automated exploitation of LIDAR data is challenging, due to the non-uniform spatial sampling of the point clouds as well as to the massive volumes of data, which may range from a few million points to hundreds of millions of points depending on the size and complexity of the scene being scanned. ^ This dissertation focuses on addressing these challenges to automatically exploit large LIDAR point clouds of piping systems in industrial sites, such as chemical plants, oil refineries, and steel mills. A complete processing chain is proposed in this work, using raw LIDAR point clouds as input and generating cylinder parameter estimates for pipe segments as the output, which could then be used to produce computer aided design (CAD) models of pipes. The processing chain consists of three stages: (1) segmentation of LIDAR point clouds, (2) detection and identification of piping elements, and (3) cylinder fitting and parameter estimation. The final output of the cylinder fitting stage gives the estimated orientation, position, and radius of each detected pipe element. ^ A robust octree-based split and merge segmentation algorithm is proposed in this dissertation that can efficiently process LIDAR data. Following octree decomposition of the point cloud, graph theory analysis is used during the splitting process to separate points within each octant into components based on spatial connectivity. A series of connectivity criteria (proximity, orientation, and curvature) are developed for the merging process, which exploits contextual information to effectively merge cylindrical segments into complete pipes and planar segments into complete walls. Furthermore, by conducting surface fitting of segments and analyzing their principal curvatures, the proposed segmentation approach is capable of detecting and identifying the piping segments. ^ A novel cylinder fitting technique is proposed to accurately estimate the cylinder parameters for each detected piping segment from the terrestrial LIDAR point cloud. Specifically, the orientation, radius, and position of each piping element must be robustly estimated in the presence of noise. An original formulation has been developed to estimate the cylinder axis orientation using gradient descent optimization of an angular distance cost function. The cost function is based on the concept that surface normals of points in a cylinder point cloud are perpendicular to the cylinder axis. The key contribution of this algorithm is its capability to accurately estimate the cylinder orientation in the presence of noise without requiring a good initial starting point. After estimation of the cylinder\u27s axis orientation, the radius and position are then estimated in the 2D space formed from the projection of the 3D cylinder point cloud onto the plane perpendicular to the cylinder\u27s axis. With these high quality approximations, a least squares estimation in 3D is made for the final cylinder parameters. ^ Following cylinder fitting, the estimated parameters of each detected piping segment are used to generate a CAD model of the piping system. The algorithms and techniques in this dissertation form a complete processing chain that can automatically exploit large LIDAR point cloud of piping systems and generate CAD models

Circulant temporal encoding for video retrieval and temporal alignment

We address the problem of specific video event retrieval. Given a query video of a specific event, e.g., a concert of Madonna, the goal is to retrieve other videos of the same event that temporally overlap with the query. Our approach encodes the frame descriptors of a video to jointly represent their appearance and temporal order. It exploits the properties of circulant matrices to efficiently compare the videos in the frequency domain. This offers a significant gain in complexity and accurately localizes the matching parts of videos. The descriptors can be compressed in the frequency domain with a product quantizer adapted to complex numbers. In this case, video retrieval is performed without decompressing the descriptors. We also consider the temporal alignment of a set of videos. We exploit the matching confidence and an estimate of the temporal offset computed for all pairs of videos by our retrieval approach. Our robust algorithm aligns the videos on a global timeline by maximizing the set of temporally consistent matches. The global temporal alignment enables synchronous playback of the videos of a given scene