EDLines: A real-time line segment detector with a false detection control

We propose a linear time line segment detector that gives accurate results, requires no parameter tuning, and runs up to 11 times faster than the fastest known line segment detection algorithm in the literature; namely, the LSD by Gioi et al. The proposed algorithm also includes a line validation step due to the Helmholtz principle, which lets it control the number of false detections. Our detector makes use of the clean, contiguous (connected) chain of edge pixels produced by our novel edge detector, the Edge Drawing (ED) algorithm; hence the name EDLines. With its accurate results and blazing speed, EDLines will be very suitable for the next generation real-time computer vision applications.

Real-time image and video processing applications require skilled architects, and recent trends in the hardware platform make the design and implementation of these applications increasingly complex. Many frameworks and libraries have been proposed or commercialized to simplify the design and tuning of real-time image processing applications. However, they tend to lack flexibility because they are normally oriented towards particular types of applications, or they impose specific data processing models such as the pipeline. Other issues include large memory footprints, difficulty for reuse and inefficient execution on multicore processors. This paper presents a novel software architecture for real-time image and video processing applications which addresses these issues. The architecture is divided into three layers: the platform abstraction layer, the messaging layer, and the application layer. The platform abstraction layer provides a high level application programming interface for the rest of the architecture. The messaging layer provides a message passing interface based on a dynamic publish/subscribe pattern. A topic-based filtering in which messages are published to topics is used to route the messages from the publishers to the subscribers interested in a particular type of messages. The application layer provides a repository for reusable application modules designed for real-time image and video processing applications. These modules, which include acquisition, visualization, communication, user interface and data processing modules, take advantage of the power of other well-known libraries such as OpenCV, Intel IPP, or CUDA. Finally, we present different prototypes and applications to show the possibilities of the proposed architecture.

EDCircles: A real-time circle detector with a false detection control

We propose a new edge detection algorithm that works by computing a set of anchor edge points in an image and then linking these anchor points by drawing edges between them. The resulting edge map consists of perfect contiguous, one pixel wide edges. The performance tests show that our algorithm is up to 16% faster than the fastest known edge detection algorithm, i.e., OpenCV implementation of the Canny edge detector. We believe that our edge detector is a novel step in edge detection and would be very suitable for the next generation real-time image processing and computer vision applications.

Line segment detection plays a cornerstone role in computer vision tasks. Among numerous detection methods that have been recently proposed, the ones based on edge drawing attract increasing attention owing to their excellent detection efficiency. However, the existing methods are not robust enough due to the inadequate usage of image gradients for edge drawing and line segment fitting. Based on the observation that the line segments should locate on the edge points with both consistent coordinates and level-line information, i.e., the unit vector perpendicular to the gradient orientation, this paper proposes a level-line guided edge drawing for robust line segment detection (GEDRLSD). The level-line information provides potential directions for edge tracking, which could be served as a guideline for accurate edge drawing. Additionally, the level-line information is fused in line segment fitting to improve the robustness. Numerical experiments show the superiority of the proposed GEDRLSD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> algorithm compared with state-of-the-art methods.

Nowadays, approximate computing is an emerging trend widely applied in image processing, signal processing, and computer vision applications. Approximation is suitable for applications which are error tolerant. In this work, approximate computing is used in the Gaussian filtering stage of edge detection. In computer vision applications like medical imaging, transportation etc., Edge detection plays a crucial role. In edge detection algorithms, the Gaussian filtering block is a fundamental module. This block involves power-consuming addition operations, resulting in increased computational complexity, power consumption, and delay. To address these trade-offs, imprecise computation is applied in the Gaussian filter module. The aim of the work is to reduce computational complexity, reduction in usage of power and delay in the Gaussian filtering module using an approximate compressor-based architecture. The proposed Gaussian filter is designed using newly developed approximate compressors. Simulation outcome indicates that the Gaussian filter implemented with proposed newly developed compressors achieves decline in power and delay compared to exact and existing implementations in literature. Error analysis and Edge Detection is performed using different metrics in MATLAB, and the results demonstrate good PSNR values for the proposed compressors, confirming their suitability for edge detection in image processing applications.

PEL: A Predictive Edge Linking algorithm

The purpose of this article is to present some of the fundamental principles of filter banks, wavelets and frames and their connections, with special emphasis on applications in computer vision and image processing. This is a vast field and we can only give a glimpse of it. We start with a short historical review and a rather broad discussion of filter banks, wavelets and frames. It is discussed how filter banks and wavelets are connected via multiresolution. Some of the most important structures and properties are presented but hardly no mathematical details are given. We focus especially on directional filter banks and wavelets, on analysis and extraction of directional features in images and image sequences. A system for motion estimation (estimation of optical flow) is presented.

Detection of edge in image is a fundamental requirement involved in computer vision and image processing applications. In this paper, the performance of traditional edge detectors is compared with Grunwald-Letnikov(G-L) based Fractional Order Derivative (FOD) based edge detector. The performance is measured for both types of detectors under noise free and noisy conditions on fish images. Image quality assessment (IQA) parameters Mean Square Error (MSE), Peak Signal-to-Noise-Ratio (PSNR), Structural Similarity Index (SSIM) and Feature Similarity Index (FSIM) are used for quantitative comparison of the edge detection. From the experimental results, it is observed that FOD based edge detector shows better results than the traditional edge detectors under noisy conditions either in terms of quality or quantity.

In computer vision and image processing, edge detection concerns the localization of significant variations of the grey level image and the identification of the physical phenomena that originated them. It is difficult to design a general edge detection algorithm which performs well in many contexts and captures the requirements of subsequent processing stages. Consequently, over the history of digital image processing, a variety of edge detectors have been devised which differ in their mathematical and algorithmic properties. In this paper, we briefly describe some existing edge detectors. We investigate the wavelet transform with direction which is related to the Canny and Marr–Hildreth's edge detectors. This wavelet transform has a remarkable feature that plays an important role in image processing applications. We discuss its applications to edge and target detections. The direction effect enables the wavelets to analyze the directional features of images. Algorithms and experiments for edge and target detections have been developed based on the theory. And, very promising results have been shown in image processing.

The fields of computer vision and image processing are constantly evolving as new research and applications in these areas emerge. Staying abreast of the most up-to-date developments in this field is necessary in order to promote further research and apply these developments in real-world settings. Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies features timely and informative research on the design and development of computer vision and image processing applications in intelligent agents as well as in multimedia technologies. Covering a diverse set of research in these areas, this publication is ideally designed for use by academicians, technology professionals, students, and researchers interested in uncovering the latest innovations in the field.

With the rapid development of artificial intelligence technology, optimizing the convolutional operation of convolutional neural network (hereinafter referred to as CNN) to adapt to the resource constraints of embedded systems has become one of the current research hotspots. In this paper, we explain the basic connotation of CNN and embedded platform Zynq, and optimize the Im2col-Gemm algorithm based on Darknet framework, so as to further optimize the CNN model. The CNN before and after optimization under different hardware configurations are compared through acceleration tests, and the average time spent on each layer and the total time of CNN operations are recorded, which clearly concludes that the Zynq combining the optimized CNN can achieve 658.12 and 23.18 times acceleration with respect to CPU and GPU, respectively. Through the character recognition detection and traffic sign detection, Zynq’s character recognition with optimized CNN achieves 220FPS with less than 4.5W power consumption, and it only takes about 4.5ms to recognize a picture. Meanwhile, the traffic sign recognition has a high recognition rate of 97.8% on average and a low leakage rate of 8.28%, which verifies that Zynq with optimized CNN is fast and consumes low power, which is advantageous for applications in real-time image processing. Optimizing CNN based on embedded systems helps promote the continuous upgrading of artificial intelligence.

Cellular neural networks are widely used with real-time image processing's applications. Such systems can be efficiently realized using macro enriched field-programmable gate-arrays. This paper explores the benefits of packet switching and discusses its advantages over a current design based on circuit switching. The implementation is performed using Xilinx Virtex-II Pro P30 and handles around 500 Mpixels per second using 128 parallel processing nodes.

In this paper are presented solutions to develop algorithms for digital image processing focusing particularly on edge detection. Edge detection is one of the most important phases used in computer vision and image processing applications and also in human image understanding. In this chapter, implementation of classical edge detection algorithms it is presented and also implementation of algorithms based on the theory of Cellular Automata (CA). This work is totally related to the idea of understanding the impact of the inherently local information processing of CA on their ability to perform a managed computation at the global level. If a suitable encoding of a digital image is used, in some cases, it is possible to achieve better results in comparison with the solutions obtained by means of conventional approaches. The software application which is able to process images in order to detect edges using both conventional algorithms and CA based ones is written in C# programming language and experimental results are presented for images with different sizes and backgrounds.

Snakes or active contours are used extensively in computer vision and image processing applications, particularly to locate object boundaries. The traditional snake was sensitive to initialization of contour; also one snake was able to detect one object only. In presented research work we have developed improved active contour model for detection of multiple objects well as edge detection in satellite images. We have proposed modified gradient vector flow as external force to make edge detection insensitive to initialization and to exact contour on edges. Method of capturing of relevant control points, so as to neglecting extra control points is introduced to get proper edges. For satellite images new techniques as pre processing to enhance edges by removing noise, double thresholding based method for deleting of excess control points, and average based thresholding for obtaining continuous edges by eliminating need of complex interpolation is developed. The algorithm is tested on variety of images and cases. Both internal and external initialization of contours gives satisfactory edges. Snake work efficiently for both noise free as well as noisy images. Algorithm also outperforms in terms of time complexity as compare to other edge detection algorithm such as canny edge detector.