Canny Edge Detector is the most widely used operator for edge detection. The problem with Canny is that it outputs a binary edge map, where an edge pixel (edgel) is marked (e.g., its value in the edge map is 255) and a non-edge pixel is unmarked (e.g., its value in the edge map is 0). A typical binary edge map is of low quality, consisting of gaps, notch-like structures, ragged and multi-pixel wide edgels. To clean up Canny's binary edge maps, fill up one pixel-wide gaps between the edgels, and to return the map as a set of edge segments, each of which is a one-pixel wide, contiguous chain of pixels, we employ the Smart Routing (SR) algorithm from our recently proposed Edge Segment Detection Algorithm, the Edge Drawing (ED). The proposed algorithm, called Canny Smart Routing (CannySR), runs Canny to obtain a binary edge map, and uses the Canny edgels as anchors for SR to convert them to edge segments. The produced edge segments can then be used in many applications such as line, arc, circle, ellipse, corner detection and other similar higher level object detection applications. We qualitatively evaluate the effectiveness of the proposed algorithm on some sample images and conclude that CannySR visibly improves the modal quality of Canny's binary edge maps although ED seems to produce the best results.

We present a parallel implementation of the widely-used entropy encoding algorithm, the Huffman coder, on the NVIDIA CUDA architecture. After constructing the Huffman codeword tree serially, we proceed in parallel by generating a byte stream where each byte represents a single bit of the compressed output stream. The final step is then to combine each consecutive 8 bytes into a single byte in parallel to generate the final compressed output bit stream. Experimental results show that we can achieve up to 22× speedups compared to the serial CPU implementation without any constraint on the maximum codeword length or data entropy.

In this study, we present a new contour-based corner detection method based on the turning angle curvature computed from the contour gradients of the image. In general, curvature is computed with the pixel locations of the extracted image contours. In most contour extraction methods, the image gradient information is already computed. The proposed algorithm makes use of this available information to compute the curvature function and takes local extremums as potential corner candidates. Afterwards, the candidates are validated by a novel validation algorithm which tries to approximate the local geometric structure of the contour with an iterative least squares estimation algorithm. Thus, we not only eliminate the false detected corners; but also estimate the corner strength precisely in terms of degrees. The experiments show that the detected corners with gradient-based turning angle curvature are more durable to affine transformations according to the ACU and LE criterions.

We propose a real-time, parameter-free circle detection algorithm that produces accurate results with very few false positives. The algorithm makes use of the contiguous (connected) set of edge segments produced by our novel edge segment detector, the Edge Drawing (ED) algorithm; hence the name EDCircles. The proposed algorithm first fits line segments to ED's edge segments and then processes these line segments to detect circles in a given image. We show through experimentation that EDCircles works real-time, produces good results, and is very suitable for the next generation real-time automation applications including automatic inspection of manufactured products, human eye iris and pupil detection, circular traffic sign detection, etc.

We present a high-speed contour detector, which we name EDContours, that works by running our real-time parameter-free edge segment detector, Edge Drawing Parameter Free (EDPF), at different scale-space representations of an image. Combining the edge segments detected by EDPF at different scales, EDContours generates a soft contour map for a given image. EDContours works on gray-scale images, is parameter-free, runs very fast, and results in an F-measure score of 0.62 on the Berkeley Segmentation Dataset (BSDS300).

Lane detection algorithms constitute a basis for intelligent vehicle systems such as lane tracking and involuntary lane departure detection. In this paper, we propose a simple and video-based lane detection algorithm that uses a fast vanishing point estimation method. The first step of the algorithm is to extract and validate the line segments from the image with a recently proposed line detection algorithm. In the next step, an angle based elimination of line segments is done according to the perspective characteristics of lane markings. This basic operation removes many line segments that belong to irrelevant details on the scene and greatly reduces the number of features to be processed afterwards. Remaining line segments are extrapolated and superimposed to detect the image location where majority of the linear edge features converge. The location found by this efficient operation is assumed to be the vanishing point. Subsequently, an orientation-based removal is done by eliminating the line segments whose extensions do not intersect the vanishing point. The final step is clustering the remaining line segments such that each cluster represents a lane marking or a boundary of the road (i.e. sidewalks, barriers or shoulders). The properties of the line segments that constitute the clusters are fused to represent each cluster with a single line. The nearest two clusters to the vehicle are chosen as the lines that bound the lane that is being driven on. The proposed algorithm works in an average of 12 milliseconds for each frame with 640×480 resolution on a 2.20 GHz Intel CPU. This performance metric shows that the algorithm can be deployed on minimal hardware and still provide real-time performance.

We propose a real-time, parameter-free edge/edge segment detection algorithm based on our novel edge/edge segment detector, the edge drawing (ED) algorithm; hence the name edge drawing parameter free (EDPF). EDPF works by running ED with ED's parameters set at their extremes. This produces all edge segments in a given image with numerous false detections. The detected edge segments are then validated by an "a contrario" validation step due to the Helmholtz principle, which eliminates invalid detections leaving only "meaningful" edge segments.

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.

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.

Enabling peer-to-peer (P2P) communication for hosts behind network address translation (NAT) boxes is an important and difficult problem. Existing proposals, for example, UPnP, MIDCOM, TURN, STUN, STUNT, P2PNAT, NATBlaster among others, offer only partial, limited and non-deterministic solutions. A framework that offers a complete solution to the P2P communication problem is presented. The proposed framework is based on the use of IPv4+4 addresses and the standard IPv4 Loose Source Record Route (LSRR) option and requires no changes whatsoever to end-host protocol stacks and Internet routers. The only requirement is a simple upgrade of border routers with a new LSRR-based packet-forwarding algorithm for the P2P traffic. The implementation of a Linux-based border router that runs the proposed forwarding algorithm is detailed, and how P2P applications can benefit from this framework is described.

Repository for continuous media data differs from that of the traditional text-based data both in storage space and streaming bandwidth requirements. The file systems used for continuous media streams need to support large volumes and high bandwidth. We propose a scalable distributed continuous media file system built using autonomous disks. Autonomous disks are attached directly to the network and are able to perform lightweight processing. We discuss different ways to realize the autonomous disk, and describe a prototype implementation on a Linux platform using PC-based hardware. We present the basic requirements of the continuous media file system and present the design methodology and a prototype Linux-based implementation of the distributed file system that supports the requirements. We present experimental results on the performance of the proposed file system prototyped using autonomous disks. We show that the performance of the file system scales linearly with the number of disks and the number of clients. The file system performs much superior to NFS running on the same hardware platform and can deliver higher raw disk bandwidth to the applications. We also present bandwidth and time sensitive read/write procedures for the file system and show that the file system can provide strict bandwidth guarantees for continuous media streams.

With the proliferation of network attached appliances and inexpensive computing devices there is an increasing demand for inter-networking among these devices in the home, in small offices or even on an ad-hoc basis. While administration may be necessary in today's large complex IP networks, it is unacceptable for emerging networks such as home networks, i.e., networking of consumer electronic devices such as TV, VCR, phone, fax, printer, PC, etc., small office home office (SOHO) networks, automobile networks, airplane networks, ad-hoc networks at conferences, emergency relief stations, and many others. These networks require zero manual administration and configuration. This demand recently initiated a new paradigm of IT networking called zero-configuration (zeroconf) networking. Zeroconf networks are a class of IP networks that do not require any manual configuration or administration. In this paper we examine name-to-address translation in zeroconf networks. We suggest requirements for name-to-address translation algorithms, review the existing solutions, extend these solutions, and show that the proposed solutions satisfy the suggested requirements.

IP hosts and network infrastructure have historically been difficult to configure, requiring network services such as DHCP and DNS servers, and relying on highly trained network administrators. This need for administration has prevented IP networks from being used in many environments such as in homes, in small businesses, in impromptu networks established at conferences, construction sites, emergency relief stations, etc. With the proliferation of IP-enabled network-attached appliances and inexpensive computing devices, the demand to enable plug-and-play, easy-to-use IP networking has increased. This demand has initiated a new paradigm of IP networking called zero configuration, or zeroconf, networking. The goal is to develop a set of zeroconf configuration protocols to enable IP networking without manual configuration or administration. Such IP networks are called zeroconf networks. We examine IP address configuration in zeroconf networks involving two network topologies: (1) A routerless network, which consists of several hosts attached to a segment having no router, (2) A single-router network, which consists of a router joining several segments together into a star-shaped topology. We suggest the requirements for IP address configuration, review the existing solutions, and propose new algorithms to address their deficiencies. Finally, we present a comparison of our algorithms to existing solutions and describe when a particular configuration method should be used.

Zero-configuration (zeroconf) networks are a particular class of IP networks that do not require any user administration for correct operation. IP address configuration in zeroconf networks is an important problem. While there are a few proposals for IP host configuration, a general solution for IP router configuration, an important problem in multi-router zeroconf networks, does not yet exist. In a single-router zeroconf network, the router can easily configure by creating unique IP subnets over each of its directly attached segments. But when several such self-configuring routers are interconnected together to form a multi-router network, there is a need for (1) dynamic exchange of routing information among the routers and (2) consistent assignment of IP subnets in the network, i.e., an IP subnet can not be assigned to different segments. As new routers are added to the zeroconf network, any IP subnet conflicts must be detected and resolved. No solutions for IP address auto-configuration of multi-router networks exist. This paper suggests the requirements for IP address configuration of multi-router zeroconf networks, and proposes IP host and router configuration algorithms to satisfy these requirements. Among the proposed algorithms is a novel routing algorithm designed by augmenting the basic distance vector routing algorithm that can solve both the problem of dynamic routing and consistent IP subnet assignment in multi-router zeroconf networks. We also show how the popular routing information protocol (RIP) can be augmented to implement the proposed routing algorithm, called the zeroconf routing information protocol (ZRIP).

Estimating the pose of a camera (virtual or real) in which some augmentation takes place is one of the most important parts of an augmented reality (AR) system. The availability of powerful processors and fast frame grabbers has made the use of vision-based trackers commonplace due to their accuracy as well as flexibility and ease of use. Current vision-based trackers are based on tracking of markers. The use of markers increases robustness and reduces computational requirements. However, their use can be very complicated, as they require maintenance. Direct use of scene features for tracking, therefore, is desirable. To this end, we describe a general system that tracks the position and orientation of a camera observing a scene without visual markers. Our method is based on a two-stage process. In the first stage, a set of features is learned with the help of an external tracking system during use. The second stage uses these learned features for camera tracking when the system in the first stage decides that it is possible to do so. The system is very general so that it can employ any available feature tracking and pose estimation system for learning and tracking. We experimentally demonstrate the viability of the method in real-life examples.

Repositories for multimedia data differ from those for traditional text-based data both in terms of storage space and streaming bandwidth requirements. The file systems used in the multimedia environment need to support large volumes and high bandwidth. In this paper, we propose a novel scalable distributed file system built using autonomous disks. Autonomous disks are attached directly to the network and are able to perform lightweight processing. We discuss different ways to realize an autonomous disk, and describe a prototype implementation on a Linux platform using PC-based hardware. We present the design methodology and a prototype Linux-based implementation of the distributed file system that supports such disks. We detail experimental results on the performance of the proposed file system prototyped using autonomous disks. We show that the performance of the file system scales linearly with the number of disks and the number of clients. The file system's performance is much superior to NFS running on the same hardware platform, and it can deliver higher raw disk bandwidth to the applications. We also show that the file system can provide strict bandwidth guarantees for multimedia streams.