Showing papers on "Edge detection published in 2008"

Minimization of Region-Scalable Fitting Energy for Image Segmentation

Abstract: Intensity inhomogeneities often occur in real-world images and may cause considerable difficulties in image segmentation. In order to overcome the difficulties caused by intensity inhomogeneities, we propose a region-based active contour model that draws upon intensity information in local regions at a controllable scale. A data fitting energy is defined in terms of a contour and two fitting functions that locally approximate the image intensities on the two sides of the contour. This energy is then incorporated into a variational level set formulation with a level set regularization term, from which a curve evolution equation is derived for energy minimization. Due to a kernel function in the data fitting term, intensity information in local regions is extracted to guide the motion of the contour, which thereby enables our model to cope with intensity inhomogeneity. In addition, the regularity of the level set function is intrinsically preserved by the level set regularization term to ensure accurate computation and avoids expensive reinitialization of the evolving level set function. Experimental results for synthetic and real images show desirable performances of our method.

Groups of Adjacent Contour Segments for Object Detection

Abstract: We present a family of scale-invariant local shape features formed by chains of k connected roughly straight contour segments (kAS), and their use for object class detection. kAS are able to cleanly encode pure fragments of an object boundary without including nearby clutter. Moreover, they offer an attractive compromise between information content and repeatability and encompass a wide variety of local shape structures. We also define a translation and scale invariant descriptor encoding the geometric configuration of the segments within a kAS, making kAS easy to reuse in other frameworks, for example, as a replacement or addition to interest points (IPs). Software for detecting and describing kAS is released at http://lear.inrialpes.fr/software. We demonstrate the high performance of kAS within a simple but powerful sliding-window object detection scheme. Through extensive evaluations, involving eight diverse object classes and more than 1,400 images, we (1) study the evolution of performance as the degree of feature complexity k varies and determine the best degree, (2) show that kAS substantially outperform IPs for detecting shape-based classes, and (3) compare our object detector to the recent state-of-the-art system by Dalal and Triggs (2005).

Using contours to detect and localize junctions in natural images

Abstract: Contours and junctions are important cues for perceptual organization and shape recognition. Detecting junctions locally has proved problematic because the image intensity surface is confusing in the neighborhood of a junction. Edge detectors also do not perform well near junctions. Current leading approaches to junction detection, such as the Harris operator, are based on 2D variation in the intensity signal. However, a drawback of this strategy is that it confuses textured regions with junctions. We believe that the right approach to junction detection should take advantage of the contours that are incident at a junction; contours themselves can be detected by processes that use more global approaches. In this paper, we develop a new high-performance contour detector using a combination of local and global cues. This contour detector provides the best performance to date (F=0.70) on the Berkeley Segmentation Dataset (BSDS) benchmark. From the resulting contours, we detect and localize candidate junctions, taking into account both contour salience and geometric configuration. We show that improvements in our contour model lead to better junctions. Our contour and junction detectors both provide state of the art performance.

Adaptive Bilateral Filter for Sharpness Enhancement and Noise Removal

Abstract: In this paper, we present the adaptive bilateral filter (ABF) for sharpness enhancement and noise removal. The ABF sharpens an image by increasing the slope of the edges without producing overshoot or undershoot. It is an approach to sharpness enhancement that is fundamentally different from the unsharp mask (USM). This new approach to slope restoration also differs significantly from previous slope restoration algorithms in that the ABF does not involve detection of edges or their orientation, or extraction of edge profiles. In the ABF, the edge slope is enhanced by transforming the histogram via a range filter with adaptive offset and width. The ABF is able to smooth the noise, while enhancing edges and textures in the image. The parameters of the ABF are optimized with a training procedure. ABF restored images are significantly sharper than those restored by the bilateral filter. Compared with an USM based sharpening method-the optimal unsharp mask (OUM), ABF restored edges are as sharp as those rendered by the OUM, but without the halo artifacts that appear in the OUM restored image. In terms of noise removal, ABF also outperforms the bilateral filter and the OUM. We demonstrate that ABF works well for both natural images and text images.

Edge Detection Techniques: Evaluations and Comparison

Abstract: Edge detection is one of the most commonly used operations in image analysis, and there are probably more algorithms in the literature for enhancing and detecting edges than any other single subject. The reason for this is that edges form the outline of an object. An edge is the boundary between an object and the background, and indicates the boundary between overlapping objects. This means that if the edges in an image can be identified accurately, all of the objects can be located and basic properties such as area, perimeter, and shape can be measured. Since computer vision involves the identification and classification of objects in an image, edge detections is an essential tool. In this paper, we have compared several techniques for edge detection in image processing. We consider various well-known measuring metrics used in image processing applied to standard images in this comparison.

Corner detector based on global and local curvature properties

Abstract: This paper proposes a curvature-based corner detector that detects both fine and coarse features accurately at low computational cost. First, it extracts contours from a Canny edge map. Second, it com- putes the absolute value of curvature of each point on a contour at a low scale and regards local maxima of absolute curvature as initial corner candidates. Third, it uses an adaptive curvature threshold to remove round corners from the initial list. Finally, false corners due to quantiza- tion noise and trivial details are eliminated by evaluating the angles of corner candidates in a dynamic region of support. The proposed detector was compared with popular corner detectors on planar curves and gray- level images, respectively, in a subjective manner as well as with a fea- ture correspondence test. Results reveal that the proposed detector per- forms extremely well in both fields. © 2008 Society of Photo-Optical

Discriminative Sparse Image Models for Class-Specific Edge Detection and Image Interpretation

Abstract: Sparse signal models learned from data are widely used in audio, image, and video restoration. They have recently been generalized to discriminative image understanding tasks such as texture segmentation and feature selection. This paper extends this line of research by proposing a multiscale method to minimize least-squares reconstruction errors and discriminative cost functions under ?0 or ?1 regularization constraints. It is applied to edge detection, category-based edge selection and image classification tasks. Experiments on the Berkeley edge detection benchmark and the PASCAL VOC'05 and VOC'07 datasets demonstrate the computational efficiency of our algorithm and its ability to learn local image descriptions that effectively support demanding computer vision tasks.

A Parallel Hardware Architecture for Scale and Rotation Invariant Feature Detection

Abstract: This paper proposes a parallel hardware architecture for image feature detection based on the scale invariant feature transform algorithm and applied to the simultaneous localization and mapping problem. The work also proposes specific hardware optimizations considered fundamental to embed such a robotic control system on-a-chip. The proposed architecture is completely stand-alone; it reads the input data directly from a CMOS image sensor and provides the results via a field-programmable gate array coupled to an embedded processor. The results may either be used directly in an on-chip application or accessed through an Ethernet connection. The system is able to detect features up to 30 frames per second (320times240 pixels) and has accuracy similar to a PC-based implementation. The achieved system performance is at least one order of magnitude better than a PC-based solution, a result achieved by investigating the impact of several hardware-orientated optimizations on performance, area and accuracy.

An ant colony optimization algorithm for image edge detection

Abstract: Ant colony optimization (ACO) is an optimization algorithm inspired by the natural behavior of ant species that ants deposit pheromone on the ground for foraging. In this paper, ACO is introduced to tackle the image edge detection problem. The proposed ACO-based edge detection approach is able to establish a pheromone matrix that represents the edge information presented at each pixel position of the image, according to the movements of a number of ants which are dispatched to move on the image. Furthermore, the movements of these ants are driven by the local variation of the imagepsilas intensity values. Experimental results are provided to demonstrate the superior performance of the proposed approach.

Canny edge detection on NVIDIA CUDA

Abstract: The Canny edge detector is a very popular and effective edge feature detector that is used as a pre-processing step in many computer vision algorithms. It is a multi-step detector which performs smoothing and filtering, non-maxima suppression, followed by a connected-component analysis stage to detect ldquotruerdquo edges, while suppressing ldquofalserdquo non edge filter responses. While there have been previous (partial) implementations of the Canny and other edge detectors on GPUs, they have been focussed on the old style GPGPU computing with programming using graphical application layers. Using the more programmer friendly CUDA framework, we are able to implement the entire Canny algorithm. Details are presented along with a comparison with CPU implementations. We also integrate our detector in to MATLAB, a popular interactive simulation package often used by researchers. The source code will be made available as open source.

Robust Image Corner Detection Based on the Chord-to-Point Distance Accumulation Technique

Abstract: Many contour-based image corner detectors are based on the curvature scale-space (CSS). We identify the weaknesses of the CSS-based detectors. First, the ldquocurvaturerdquo itself by its ldquodefinitionrdquo is very much sensitive to the local variation and noise on the curve, unless an appropriate smoothing is carried out beforehand. In addition, the calculation of curvature involves derivatives of up to second order, which may cause instability and errors in the result. Second, the Gaussian smoothing causes changes to the curve and it is difficult to select an appropriate smoothing-scale, resulting in poor performance of the CSS corner detection technique. We propose a complete corner detection technique based on the chord-to-point distance accumulation (CPDA) for the discrete curvature estimation. The CPDA discrete curvature estimation technique is less sensitive to the local variation and noise on the curve. Moreover, it does not have the undesirable effect of the Gaussian smoothing. We provide a comprehensive performance study. Our experiments showed that the proposed technique performs better than the existing CSS-based and other related methods in terms of both average repeatability and localization error.

Evaluating Pavement Cracks with Bidimensional Empirical Mode Decomposition

Abstract: Crack evaluation is essential for effective classification of pavement cracks. Digital images of pavement cracks have been analyzed using techniques such as fuzzy set theory and neural networks. Bidimensional empirical mode decomposition (BEMD), a new image analysis method recently developed, can potentially be used for pavement crack evaluation. BEMD is an extension of the empirical mode decomposition (EMD), which can decompose nonlinear and nonstationary signals into basis functions called intrinsic mode functions (IMFs). IMFs are monocomponent functions that have well-defined instantaneous frequencies. EMD is a sifting process that is nonparametric and data driven; it does not depend on an a priori basis set. It is able to remove noise from signals without complicated convolution processes. BEMD decomposes an image into two-dimensional IMFs. The present paper explores pavement crack detection using BEMD together with the Sobel edge detector. A number of images are filtered with BEMD to remove noise, and the residual image analyzed with the Sobel edge detector for crack detection. The results are compared with results from the Canny edge detector, which uses a Gaussian filter for image smoothing before performing edge detection. The objective is to qualitatively explore how well BEMD is able to smooth an image for more effective edge detection with the Sobel method.

Statistical Models of Shape: Optimisation and Evaluation

Abstract: The goal of image interpretation is to convert raw image data into me- ingful information Images are often interpreted manually In medicine, for example, a radiologist looks at a medical image, interprets it, and tra- lates the data into a clinically useful form Manual image interpretation is, however, a time-consuming, error-prone, and subjective process that often requires specialist knowledge Automated methods that promise fast and - jective image interpretation have therefore stirred up much interest and have become a signi?cant area of research activity Early work on automated interpretation used low-level operations such as edge detection and region growing to label objects in images These can p- ducereasonableresultsonsimpleimages,butthepresenceofnoise,occlusion, andstructuralcomplexity oftenleadstoerroneouslabelling Furthermore,- belling an object is often only the ?rst step of the interpretation process In order to perform higher-level analysis, a priori information must be incor- rated into the interpretation process A convenient way of achieving this is to use a ?exible model to encode information such as the expected size, shape, appearance, and position of objects in an image The use of ?exible models was popularized by the active contour model, or snake [98] A snake deforms so as to match image evidence (eg, edges) whilst ensuring that it satis?es structural constraints However, a snake lacks speci?city as it has little knowledge of the domain, limiting its value in image interpretation

Building detection from aerial images using invariant color features and shadow information

Abstract: Robust detection of buildings is an important part of the automated aerial image interpretation problem. Automatic detection of buildings enables creation of maps, detecting changes, and monitoring urbanization. Due to the complexity and uncontrolled appearance of the scene, an intelligent fusion of different methods gives better results. In this study, we present a novel approach for building detection using multiple cues. We benefit from segmentation of aerial images using invariant color features. Besides, we use the edge and shadow information for building detection. We also determine the shape of the building by a novel method.

Markov Random Field Model-Based Edge-Directed Image Interpolation

Abstract: This paper presents an edge-directed image interpolation algorithm. In the proposed algorithm, the edge directions are implicitly estimated with a statistical-based approach. In opposite to explicit edge directions, the local edge directions are indicated by length-16 weighting vectors. Implicitly, the weighting vectors are used to formulate geometric regularity (GR) constraint (smoothness along edges and sharpness across edges) and the GR constraint is imposed on the interpolated image through the Markov random field (MRF) model. Furthermore, under the maximum a posteriori-MRF framework, the desired interpolated image corresponds to the minimal energy state of a 2-D random field given the low-resolution image. Simulated annealing methods are used to search for the minimal energy state from the state space. To lower the computational complexity of MRF, a single-pass implementation is designed, which performs nearly as well as the iterative optimization. Simulation results show that the proposed MRF model-based edge-directed interpolation method produces edges with strong geometric regularity. Compared to traditional methods and other edge-directed interpolation methods, the proposed method improves the subjective quality of the interpolated edges while maintaining a high PSNR level.

Edge Enhancement Nucleus and Cytoplast Contour Detector of Cervical Smear Images

Abstract: This paper presents an edge enhancement nucleus and cytoplast contour (EENCC) detector to enable cutting the nucleus and cytoplast from a cervical smear cell image. To clean up noises from an image, this paper proposes a trim-meaning filter that can effectively remove impulse and Gaussian noises but still preserves the sharpness of object boundaries. In addition, a bigroup enhancer is proposed to make a clear-cut separation of the pixels lying in-between two objects. A mean vector difference enhancer is presented to suppress the gradients of noises and also to brighten the gradients of object contours. What is more, a relative-distance-error measure is put forward to evaluate the segmentation error between the extracted and target object contours. The experimental results show that all the aforementioned techniques proposed have performed impressively. Other than for cervical smear images, these proposed techniques can also be utilized in object segmentation of other images.

A new measure using intuitionistic fuzzy set theory and its application to edge detection

Abstract: In this paper, a new attempt has been made using Attanassov's intuitionistic fuzzy set theory for image edge detection. Intuitionistic fuzzy set takes into account the uncertainty in assignment of membership degree known as hesitation degree. Also a new distance measure, called intuitionistic fuzzy divergence, has been proposed. With this proposed distance measure, edge detection is carried out, and the results are found better with respect to the previous methods.

Multiflash Stereopsis: Depth-Edge-Preserving Stereo with Small Baseline Illumination

Abstract: Traditional stereo matching algorithms are limited in their ability to produce accurate results near depth discontinuities, due to partial occlusions and violation of smoothness constraints. In this paper, we use small baseline multiflash illumination to produce a rich set of feature maps that enable the acquisition of discontinuity preserving point correspondences. First, from a single multiflash camera, we formulate a qualitative depth map using a gradient domain method that encodes object relative distances. Then, in a multiview setup, we exploit shadows created by light sources to compute an occlusion map. Finally, we demonstrate the usefulness of these feature maps by incorporating them into two different dense stereo correspondence algorithms, the first based on local search and the second based on belief propagation. Experimental results show that our enhanced stereo algorithms are able to extract high-quality discontinuity preserving correspondence maps from scenes that are extremely challenging for conventional stereo methods. We also demonstrate that small baseline illumination can be useful to handle specular reflections in stereo imagery. Different from most existing active illumination techniques, our method is simple, inexpensive, and compact and requires no calibration of light sources.

Learning Probabilistic Models for Contour Completion in Natural Images

Abstract: Using a large set of human segmented natural images, we study the statistics of region boundaries. We observe several power law distributions which likely arise from both multi-scale structure within individual objects and from arbitrary viewing distance. Accordingly, we develop a scale-invariant representation of images from the bottom up, using a piecewise linear approximation of contours and constrained Delaunay triangulation to complete gaps. We model curvilinear grouping on top of this graphical/geometric structure using a conditional random field to capture the statistics of continuity and different junction types. Quantitative evaluations on several large datasets show that our contour grouping algorithm consistently dominates and significantly improves on local edge detection.

Parking space detection using ultrasonic sensor in parking assistance system

Abstract: This paper deals with parking space detection by using ultrasonic sensor. Using the multiple echo function, the accuracy of edge detection was increased. After inspecting effect on the multiple echo function in indoor experiment, we applied to 11 types of vehicles in real parking environment and made experiments on edge detection with various values of resolution. We can scan parking space more accurately in real parking environment. We propose the diagonal sensor to get information about the side of parking space. Our proposed method has benefit calculation and implementation is very simple.

Feature Extraction & Image Processing, Second Edition

Abstract: * Essential reading for engineers and students working in this cutting edge field * Ideal module text and background reference for courses in image processing and computer vision * Companion website includes worksheets, links to free software, Matlab files and new demonstrationsImage processing and computer vision are currently hot topics with undergraduates and professionals alike. Feature Extraction and Image Processing provides an essential guide to the implementation of image processing and computer vision techniques, explaining techniques and fundamentals in a clear and concise manner. Readers can develop working techniques, with usable code provided throughout and working Matlab and Mathcad files on the web.Focusing on feature extraction while also covering issues and techniques such as image acquisition, sampling theory, point operations and low-level feature extraction, the authors have a clear and coherent approach that will appeal to a wide range of students and professionals.The new edition includes:* New coverage of curvature in low-level feature extraction (SIFT and saliency) and features (phase congruency); geometric active contours; morphology; camera models* Updated coverage of image smoothing (anistropic diffusion); skeletonization; edge detection; curvature; shape descriptions (moments) * Essential reading for engineers and students working in this cutting edge field* Ideal module text and background reference for courses in image processing and computer vision* Companion website includes worksheets, links to free software, Matlab files and solutions

Improved document image binarization by using a combination of multiple binarization techniques and adapted edge information

Abstract: This paper presents a new adaptive approach for document image binarization. The proposed method is mainly based on the combination of several state- of-the-art binarization methodologies as well as on the efficient incorporation of the edge information of the gray scale source image. An enhancement step based on mathematical morphology operations is also involved in order to produce a high quality result while preserving stroke information. The proposed method demonstrated superior performance against six (6) well-known techniques on numerous degraded handwritten and machine- printed documents. The performance evaluation is based on visual criteria as well as on an objective evaluation methodology.

Segmentation of Exudates and Optic Disk in Retinal Images

Abstract: This paper proposes two efficient approaches for automatic detection and extraction of Exudates and Optic disk in ocular fundus images The localization of optic disk is composed of three steps First the centre of optic disk is estimated by finding a point that has maximum local variance The color morphology in Lab space is used to have homogeneous optic disk region The boundary of the optic disk is located using geometric active contour with variational formulation The Exudates identification involves Preprocessing, Optic disk elimination, and Segmentation of Exudates In Exudates detection the enhanced segments are extracted based on Spatially Weighted Fuzzy c-Means clustering algorithm The Spatially Weighted Fuzzy c-Means clustering algorithm is formulated by incorporating the spatial neighborhood information into the standard FCM clustering algorithm The Experimental results of both approaches are validated with ground truth images The proposed algorithm for optic disk detection produces 9253% accuracy The sensitivity and the specificity of the proposed algorithm for exudates detection are 86% and 98% respectively

Inferring spatial layout from a single image via depth-ordered grouping

Abstract: Inferring the 3D spatial layout from a single 2D image is a fundamental visual task. We formulate it as a grouping problem where edges are grouped into lines, quadrilaterals, and finally depth-ordered planes. We demonstrate that the 3D structure of planar objects in indoor scenes can be fast and accurately inferred without any learning or indexing.

An improved Canny edge detector and its realization on FPGA

Abstract: Canny edge detector treats edge detection as a signal processing problem to design an optimal edge detector and has been widely used for edge detection. However, the traditional Canny edge detector has two shortcomings. First, the threshold of the algorithm needs to be set by manual. Secondly, the algorithm is very time consuming and can not be implemented in real time. A new self-adapt threshold Canny algorithm is proposed in this paper to solve the first problem. A pipelined implementation on FPGA for this new algorithm is also designed to solve the second problem. Experiment results are also given to show the efficiency of the proposed method.

Demosaicing of Color Filter Array Captured Images Using Gradient Edge Detection Masks and Adaptive Heterogeneity-Projection

Abstract: Without demosaicing processing, this paper first proposes a new approach to extract more accurate gradient/edge information on mosaic images directly. Next, based on spectral-spatial correlation, a novel adaptive heterogeneity-projection with proper mask size for each pixel is presented. Combining the extracted gradient/edge information and the adaptive heterogeneity-projection values, a new edge-sensing demosaicing algorithm is presented. Based on 24 popular testing images, experimental results demonstrated that our proposed high-quality demosaicing algorithm has the best image quality performance when compared with several recently published algorithms.