Line Detection

Line detection is a very basic, yet important problem in image processing. It is an approach based on whether sets of pixels lie on curves of a specified shape. Once detected, these curves form the edges of interest. Most of the shape information of an image is enclosed in edges. So first detect these edges in an image and by using these filters and then by enhancing those areas of image. Which contains edges, sharpness of the image will increase and image will become clearer. However the output from an edge detector is still an image described by its pixels. If lines, ellipses and so forth could be defined by their characteristic equations, the amount of data would be reduced even more. Line detection is an algorithm that takes a collection of an edge points and finds all the lines on which these edge points lie. The most popular line detectors are the Hough transform and convolution based techniques. While edges (i.e. boundaries between regions with relatively distinct gray levels) are by far the most common type of discontinuity in an image, instances of thin lines in an image occur frequently enough that it is useful to have a separate mechanism for detecting them. Here present a convolution based technique which produces an image description of the thin lines in an input image. Note that the Hough transform can be used to detect lines; however, in that case, the output is a parametric description of the lines in an image.

Cracks are typical line structures that are of interest in many computer-vision applications. In practice, many cracks, e.g., pavement cracks, show poor continuity and low contrast, which brings great challenges to image-based crack detection by using low-level features. In this paper, we propose DeepCrack-an end-to-end trainable deep convolutional neural network for automatic crack detection by learning high-level features for crack representation. In this method, multi-scale deep con-volutional features learned at hierarchical convolutional stages are fused together to capture the line structures. More detailed representations are made in larger-scale feature maps and more holistic representations are made in smaller-scale feature maps. We build DeepCrack net on the encoder-decoder architecture of SegNet, and pairwisely fuse the convolutional features generated in the encoder network and in the decoder network at the same scale. We train DeepCrack net on one crack dataset and evaluate it on three others. The experimental results demonstrate that DeepCrack achieves F-Measure over 0.87 on the three challenging datasets in average and outperforms the current state-of-the-art methods.

The onset of Unmanned Ground Vehicles (UGVs) date back to World War II. These full autonomous robots or remote-controlled robots provide many services for military purposes. The deployment of UGVs in battlefield keeps the soldiers safe from harm, navigates the target points and works as a path tracker. As time goes by, researchers are encouraged to apply UGVs in other domains as in the industrial, road services and the urban domains. The autonomy of UGVs comes from the collective sensory resources and the manipulators that are used to perform specialized tasks. This paper presents the design of a fully autonomous vehicle called “E500”, which has been implemented to compete in the 22nd Intelligent Ground Vehicle Competition (IGVC), held at Oakland University, Rochester, Michigan in June 2014. The E500's body and chassis are custom made. Its power plant is based on two scooter electric motors that are driven through Pulse Width Modulation (PWM). It receives the information from a camera, few ultrasonic range finding sensors and global positioning system (GPS) receiver. The unmanned vehicle also incorporates vision and navigation systems. They are implemented to meet the design requirements of the IGVC competition. The E500's vision system acquires the images through a Microsoft camera then processes them on an onboard laptop. The vehicle was able to extract the features of the road, detecting the white lines and the position of obstacles then figure out the best path to avoid collisions. A navigation algorithm has been developed to achieve high accuracy up to 10 cm using a Samsung mobile phone running android. The algorithms were tested in a green area with two white lines and some obstacles distributed in a random way.

This paper describes a document recognition system for the modern neume based notation of Byzantine music. We propose algorithms for page segmentation, lyrics removal, syntactical symbol grouping and the determination of characteristic page dimensions. All algorithms are experimentally evaluated on a variety of printed books for which we also give an optimal feature set for a nearest neighbour classifier. The system is based on the Gamera framework for document image analysis. Given that we cover all aspects of the recognition process, the paper can also serve as an illustration how a recognition system for a non standard document type can be designed from scratch.

This paper describes quality of airborne laser scanning concerning analysis methods, digital terrain model quality, quality of building extraction and possibilities for laser scanning based change detection. Analysis methods include the use of portable brightness targets for intensity calibration, and co-registration of laser points with image data using an interactive orientation method. A quality of DTMs derived both from first

Laundering e-mail spam through open-proxies or compromised PCs is a widely-used trick to conceal real spam sources and reduce spamming cost in the underground e-mail spam industry. Spammers have plagued the Internet by exploiting a large number of spam proxies. The facility of breaking spam laundering and deterring spamming activities close to their sources, which would greatly benefit not only e-mail users but also victim ISPs, is in great demand but still missing. In this article, we reveal one salient characteristic of proxy-based spamming activities, namely packet symmetry, by analyzing protocol semantics and timing causality. Based on the packet symmetry exhibited in spam laundering, we propose a simple and effective technique, DBSpam, to online detect and break spam laundering activities inside a customer network. Monitoring the bidirectional traffic passing through a network gateway, DBSpam utilizes a simple statistical method, Sequential Probability Ratio Test, to detect the...

Abstract Document analysis is done to analyze entire forms (eg intelligent form analysis, table detection) or to describe the layout/structure of a document. In this paper document analysis is applied to snippets of torn documents to calculate features that can be used for ...

We propose the “Line Space” as a novel
parameterization for lines in 2D images. The
approach has similarities to the well known Hough
Transform; however, we use a linear
parameterization instead of angular representations
leading to a better quality and less redundancy. The
Line Space is very well suited for GPU
implementation since all potential lines in an image
are captured through rasterization. In addition, we
improve the efficiency by introducing the term
“Cascaded Line Space”, where the image is
subdivided into smaller Line Spaces which are
finally merged to the global Line Space. We
implemented our approaches exploiting modern
GPU facilities (i.e. compute shader) and we will
describe the details in this paper. Finally, we will
discuss the enormous potential of the Line Space for
further extensions.

Biochemical detection (BCD) methods are commonly used to screen plant extracts for specific biological activities in batch assays. Traditionally, bioactives in the most active extracts were identified through time-consuming bio-assay guided fractionation until single active compounds could be isolated. Not only are isolation procedures often tedious, but they could also lead to artifact formation. On-line coupling of BCD assays to high performance liquid chromatography (HPLC) is gaining ground as a high resolution screening technique to overcome problems associated with pre-isolation by measuring the effects of compounds post-column directly after separation. To date, several on-line HPLC-BCD assays, applied to whole plant extracts and mixtures, have been published. In this review the focus will fall on enzyme-based, receptor-based and antioxidant assays.

This paper proposes an active contour algorithm for spectrogram track detection. It extends upon previously published work in a number of areas, previously published internal and potential energy models are refined and theoretical motivations for these changes are offered. These refinements offer a marked improvement in detection performance, including a notable reduction in the probability of false positive detections. The result is feature extraction at signal-to-noise ratios as low as− 1dB in the frequency domain.

Lines, thin regions of approximately-uniform width
and colour, are important in the visual interpretation
of information. Lines are a primary feature of many
objects, and are used to convey semantic
information in the human world, e.g. road markings.
In order to interpret such information, an efficient
method is desired for the detection of lines under
this basic definition, independent of the colour and
shape of the lines to be detected. A method of
obtaining the centre-line and width of arbitrarilycoloured and -curving salient lines is described in
this paper. However, this method alone is shown to
be inadequate for its originally-intended purpose,
the detection of wires in the disassembly of end-oflife electronics, due to complications such as
twisted wires and the large number of other lines
present in images taken during assembly. Examples
are also provided of the application of the described
method to other contexts.

This paper investigates the detection of parametric bridging and delay faults affecting the functional block of CMOS self-checking circuits (SCCs). As far as these faults are concerned, classical definitions are shown to become ambiguous because they are entirely based on logic considerations. Thus, new definitions are proposed here to consider the analog and dynamic effects of such faults, and to

We propose a new content-aware image resizing scheme, Stream Carving, which is based on the well-known seam carving method. Our algorithm may introduce larger seams in the retargeted image, i.e. seams with a width larger than one pixel, that we call “streams”. The resulting holes are then recovered using an inpainting method. Our retargeting algorithm is also more related to human perception by exploiting an adaptive importance map that merges several features like gradient magnitude, saliency, face, edge and straight line detection. Our approach induces an increase in the quality of the retargeted image when compared to the original seam carving method and provides similar or better results than other actual image retargeting techniques.