Showing papers on "Corner detection published in 2012"

Interesting Interest Points

Abstract: Not all interest points are equally interesting. The most valuable interest points lead to optimal performance of the computer vision method in which they are employed. But a measure of this kind will be dependent on the chosen vision application. We propose a more general performance measure based on spatial invariance of interest points under changing acquisition parameters by measuring the spatial recall rate. The scope of this paper is to investigate the performance of a number of existing well-established interest point detection methods. Automatic performance evaluation of interest points is hard because the true correspondence is generally unknown. We overcome this by providing an extensive data set with known spatial correspondence. The data is acquired with a camera mounted on a 6-axis industrial robot providing very accurate camera positioning. Furthermore the scene is scanned with a structured light scanner resulting in precise 3D surface information. In total 60 scenes are depicted ranging from model houses, building material, fruit and vegetables, fabric, printed media and more. Each scene is depicted from 119 camera positions and 19 individual LED illuminations are used for each position. The LED illumination provides the option for artificially relighting the scene from a range of light directions. This data set has given us the ability to systematically evaluate the performance of a number of interest point detectors. The highlights of the conclusions are that the fixed scale Harris corner detector performs overall best followed by the Hessian based detectors and the difference of Gaussian (DoG). The methods based on scale space features have an overall better performance than other methods especially when varying the distance to the scene, where especially FAST corner detector, Edge Based Regions (EBR) and Intensity Based Regions (IBR) have a poor performance. The performance of Maximally Stable Extremal Regions (MSER) is moderate. We observe a relatively large decline in performance with both changes in viewpoint and light direction. Some of our observations support previous findings while others contradict these findings.

Hierarchical Filtered Motion for Action Recognition in Crowded Videos

Abstract: Action recognition with cluttered and moving background is a challenging problem. One main difficulty lies in the fact that the motion field in an action region is contaminated by the background motions. We propose a hierarchical filtered motion (HFM) method to recognize actions in crowded videos by the use of motion history image (MHI) as basic representations of motion because of its robustness and efficiency. First, we detect interest points as the two-dimensional Harris corners with recent motion, e.g., locations with high intensities in the MHI. Then, a global spatial motion smoothing filter is applied to the gradients of the MHI to eliminate isolated unreliable or noisy motions. At each interest point, a local motion field filter is applied to the smoothed gradients of the MHI by computing structure proximity between any pixel in the local region and the interest point. Thus, the motion at a pixel is enhanced or weakened based on its structure proximity with the interest point. To validate its effectiveness, we characterize the spatial and temporal features by histograms of oriented gradient in the intensity image and the MHI, respectively, and use a Gaussian-mixture-model-based classifier for action recognition. The performance of the proposed approach achieves the state-of-the-art results on the KTH dataset that has clean background. More importantly, we perform cross-dataset action classification and detection experiments, where the KTH dataset is used for training, while the microsoft research (MSR) action dataset II that consists of crowded videos with people moving in the background is used for testing. Our experiments show that the proposed HFM method significantly outperforms existing techniques.

Performance Comparisons of Contour-Based Corner Detectors

Abstract: Corner detectors have many applications in computer vision and image identification and retrieval. Contour-based corner detectors directly or indirectly estimate a significance measure (e.g., curvature) on the points of a planar curve, and select the curvature extrema points as corners. While an extensive number of contour-based corner detectors have been proposed over the last four decades, there is no comparative study of recently proposed detectors. This paper is an attempt to fill this gap. The general framework of contour-based corner detection is presented, and two major issues—curve smoothing and curvature estimation, which have major impacts on the corner detection performance, are discussed. A number of promising detectors are compared using both automatic and manual evaluation systems on two large datasets. It is observed that while the detectors using indirect curvature estimation techniques are more robust, the detectors using direct curvature estimation techniques are faster.

Novel Approaches to Improve Robustness, Accuracy and Rapidity of Iris Recognition Systems

Abstract: Iris authentication is one of the most successful applications in video analysis and image processing. In this paper, several novel approaches are proposed to improve the overall performance of iris recognition systems. First, this paper proposes a new eyelash detection algorithm based on directional filters, which achieves a low rate of eyelash misclassification. Second, a multiscale and multidirection data fusion method is introduced to reduce the edge effect of wavelet transformation produced by complex segmentation algorithms. Finally, an iris indexing method on the basis of corner detection is presented to accelerate exhausted the 1: N search in a huge iris database. The performance evaluations are carried out on two popular iris databases, and the test results are experimentally more robust and accurate with less elapsed time compared with most existing methods.

An automatic satellite image registration technique based on Harris corner detection and Random Sample Consensus (RANSAC) outlier rejection model

Abstract: Automatic satellite image registration is a challenging task of overlaying two images for geometric conformity aligning common features by establishing a transformation model using distinguishable feature points collected simultaneously in both the images in a completely un assisted manner. Remote sensed images capture terrain features in a natural condition subjected to seasonal changes, sun illumination conditions, and cloud presence. The critical steps in image registration are collection of feature points and estimating a spatial transformation especially when outliers are present besides feature matching and resampling the slave image to the master image geometry. In this paper, the details and merit of employing automatic Harris corner detection and building a transformation model using Random Sample Consensus (RANSAC) algorithm is brought out while registering a pair of LISS-3 or AWIFS images from Indian Remote Sensing Satellite (IRS) platform. Potential available with this approach for performing large scale image registration tasks such as time series processing are highlighted.

Image Mosaicing Using Corner Techniques

Abstract: Image Mosaicing algorithm based on random corner method is proposed. An image mosaic is a method of assembling multiple overlapping images of same scene into a larger one. The output of image mosaic will be the union of two input images. In this paper we have to use three step automatic image mosaic methods. The first step is taking two input images and finding out the corners in both the images, second step is removing out the false corner in both the images and then by using homography we find its matched corner pair and we get final output mosaic. The experimental results show the proposed algorithm produces an improvement in mosaic accuracy, efficiency and robustness.

A Session Based Blind Watermarking Technique within the NROI of Retinal Fundus Images for Authentication Using DWT, Spread Spectrum and Harris Corner Detection

Abstract: Digital Retinal Fundus Images helps to detect various ophthalmic diseases by detecting morphological changes in optical cup, optical disc and macula. Present work proposes a method for the authentication of medical images based on Discrete Wavelet Transformation (DWT) and Spread Spectrum. Proper selection of the Non Region of Interest (NROI) for watermarking is crucial, as the area under concern has to be the least required portion conveying any medical information. Proposed method discusses both the selection of least impact area and the blind watermarking technique. Watermark is embedded within the High-High (HH) sub band. During embedding, watermarked image is dispersed within the band using a pseudo random sequence and a Session key. Watermarked image is extracted using the session key and the size of the image. In this approach the generated watermarked image having an acceptable level of imperceptibility and distortion is compared to the Original retinal image based on Peak Signal to Noise Ratio (PSNR) and correlation value.

Fully automatic vertebra detection in x-ray images based on multi-class SVM

Abstract: Automatically detecting vertebral bodies in X-Ray images is a very complex task, especially because of the noise and the low contrast resulting in that kind of medical imagery modality. Therefore, the contributions in the literature are mainly interested in only 2 medical imagery modalities: Computed Tomography (CT) and Magnetic Resonance (MR). Few works are dedicated to the conventional X-Ray radiography and propose mostly semi-automatic methods. However, vertebra detection is a key step in many medical applications such as vertebra segmentation, vertebral morphometry, etc. In this work, we develop a fully automatic approach for the vertebra detection, based on a learning method. The idea is to detect a vertebra by its anterior corners without human intervention. To this end, the points of interest in the radiograph are firstly detected by an edge polygonal approximation. Then, a SIFT descriptor is used to train an SVM-model. Therefore, each point of interest can be classified in order to detect if it belongs to a vertebra or not. Our approach has been assessed by the detection of 250 cervical vertebrae on radiographs. The results show a very high precision with a corner detection rate of 90.4% and a vertebra detection rate from 81.6% to 86.5%.

Feature point detection under extreme lighting conditions

Abstract: This paper evaluates the suitability of High Dynamic Range (HDR) imaging techniques for feature point detection under extreme lighting conditions. The conditions are extreme in respect to the dynamic range of the lighting within the test scenes used. This dynamic range cannot be captured using standard low dynamic range imagery techniques without loss of detail. Four widely used feature point detectors are used in the experiments: Harris corner detector, Shi-Tomasi, FAST and Fast Hessian. Their repeatability rate is studied under changes of camera viewpoint, camera distance and scene lighting with respect to the image formats used. The results of the experiments show that HDR imaging techniques improve the repeatability rate of feature point detectors significantly.

Human-centric panoramic imaging stitching

Abstract: We introduce a novel image mosaicing algorithm to generate 360° landscape images while also taking into account the presence of people at the boundaries between stitched images. Current image mosaicing techniques tend to fail when there is extreme parallax caused by nearby objects or moving objects at the boundary between images. This parallax causes ghosting or unnatural discontinuities in the image. To address this problem, we present an image mosaicing algorithm that is robust to parallax and misalignment, and is also able to preserve the important human-centric content, specifically faces. In particular, we find an optimal path between the boundary of two images that preserves color continuity and peoples' faces in the scene. Preliminary results show promising results of preserving close-up faces with parallax while also being able to generate a perceptually plausible 360° panoramic image.

A Vector SIFT Detector for Interest Point Detection in Hyperspectral Imagery

Abstract: This paper presents an algorithm for the extraction of interest points in hyperspectral images. Interest points are spatial features of the image that capture information from their neighbors, are distinctive and stable under transformations such as translation and rotation, are helpful in data reduction, and reduce the computational burden of various algorithms such as image registration by replacing an exhaustive search over the entire image domain by a probe into a concise set of highly informative points. Interest points have been applied to problems in computer vision, including image matching, recognition, 3-D reconstruction, and change detection. Interest point operators for monochromatic images were proposed more than a decade ago and have extensively been studied. An interest point operator seeks out points in an image that are structurally distinct, invariant to imaging conditions, and stable under geometric transformations. An extension of Lowe's scale-invariant feature transform (SIFT) to vector images is proposed here. The approach takes the vectorial nature of the hyperspectral images into account. Furthermore, the multiscale representation of the image is generated by vector nonlinear diffusion, which leads to improved detection, because it better preserves edges in the image as opposed to Gaussian blurring, which is used in Lowe's original approach. Experiments with hyperspectral images of the same and different resolutions that were collected with the Airborne Hyperspectral Imaging System (AISA) and Hyperion sensors are presented. Evaluation of the proposed approach using repeatability criterion and image registration is carried out. Comparisons with other approaches that were described in the literature are presented.

JUDOCA: JUnction Detection Operator Based on Circumferential Anchors

Abstract: In this paper, we propose an edge-based junction detector. In addition to detecting the locations of junctions, this operator specifies their orientations as well. In this respect, a junction is defined as a meeting point of two or more ridges in the gradient domain into which an image can be transformed through Gaussian derivative filters. To accelerate the detection process, two binary edge maps are produced; a thick-edge map is obtained by imposing a threshold on the gradient magnitude image, and another thin-edge map is obtained by calculating the local maxima. Circular masks are centered at putative junctions in the thick-edge map, and the so-called circumferential anchors or CA points are detected in the thin map. Radial lines are scanned to determine the presence of junctions. Comparisons are made with other well-known detectors. This paper proposes a new formula for measuring the detection accuracy. In addition, the so-called junction coordinate systems are introduced. Our operator has been successfully used to solve many problems such as wide-baseline matching, 3-D reconstruction, camera parameter enhancing, and indoor and obstacle localization.

Fast motion detection from airborne videos using graphics processing unit

Abstract: DCM Research Resources LLC, Germantown, Maryland 20874Abstract. In our previous work, we proposed a joint optical flow and principal componentanalysis (PCA) approach to improve the performance of optical flow based detection, wherePCA is applied on the calculated two-dimensional optical flow image, and motion detectionis accomplished by a metric derived from the two eigenvalues. To reduce the computationaltime when processing airborne videos, parallel computing using graphic processing unit(GPU) is implemented on NVIDIA GeForce GTX480. Experimental results demonstrate thatour approach can efficiently improve detection performance even with dynamic background,and processing time can be greatly reduced with parallel computing on GPU.

An Object Recognition and Identification System Using the Harris Corner Detection Method

Abstract: This paper presents an object recognition and identification system using the Harris Corner Detection method. The process starts from imported images into the system by webcam, detected image edge by canny edge detection, recognized the object by Harris Corner Detection, and separated the objects by the robot arm. Three object types; triangle, rectangular and, rigid circle are used. The results showed that the objects can be isolated 94%, 95%, and 99% correct for triangular, rectangular, and rigid circle respectively. Index Terms—Harris Corner Detection, canny edge detection,

Characterization of 3-D Volumetric Probabilistic Scenes for Object Recognition

Abstract: This paper presents a new volumetric representation for categorizing objects in large-scale 3-D scenes reconstructed from image sequences. This work uses a probabilistic volumetric model (PVM) that combines the ideas of background modeling and volumetric multi-view reconstruction to handle the uncertainty inherent in the problem of reconstructing 3-D structures from 2-D images. The advantages of probabilistic modeling have been demonstrated by recent application of the PVM representation to video image registration, change detection and classification of changes based on PVM context. The applications just mentioned, operate on 2-D projections of the PVM. This paper presents the first work to characterize and use the local 3-D information in the scenes. Two approaches to local feature description are proposed and compared: 1) features derived from a PCA analysis of model neighborhoods; and 2) features derived from the coefficients of a 3-D Taylor series expansion within each neighborhood. The resulting description is used in a bag-of-features approach to classify buildings, houses, cars, planes, and parking lots learned from aerial imagery collected over Providence, RI. It is shown that both feature descriptions explain the data with similar accuracy and their effectiveness for dense-feature categorization is compared for the different classes. Finally, 3-D extensions of the Harris corner detector and a Hessian-based detector are used to detect salient features. Both types of salient features are evaluated through object categorization experiments, where only features with maximal response are retained. For most saliency criteria tested, features based on the determinant of the Hessian achieved higher classification accuracy than Harris-based features.

A Study of feature extraction algorithms for optical flow tracking

Abstract: Sparse optical flow algorithms, such as the Lucas-Kanade approach, provide more robustness to noise than dense optical flow algorithms and are the preferred approach in many scenarios. Sparse optical flow algorithms estimate the displacement for a selected number of pixels in the image. These pixels can be chosen randomly. However, pixels in regions with more variance between the neighbours will produce more reliable displacement estimates. The selected pixel locations should therefore be chosen wisely. In this study, the suitability of Harris corners, Shi-Tomasi's “Good features to track", SIFT and SURF interest point extractors, Canny edges, and random pixel selection for the purpose of frame-by-frame tracking using a pyramidical Lucas-Kanade algorithm is investigated. The evaluation considers the important factors of processing time, feature count, and feature trackability in indoor and outdoor scenarios using ground vehicles and unmanned aerial vehicles, and for the purpose of visual odometry estimation.

A Comparative Study between Moravec and Harris Corner Detection of Noisy Images Using Adaptive Wavelet Thresholding Technique

Abstract: In this paper a comparative study between Moravec and Harris Corner Detection has been done for obtaining features required to track and recognize objects within a noisy image. Corner detection of noisy images is a challenging task in image processing. Natural images often get corrupted by noise during acquisition and transmission. As Corner detection of these noisy images does not provide desired results, hence de-noising is required. Adaptive wavelet thresholding approach is applied for the same.

Calibration for high-definition camera rigs with marker chessboard

Abstract: The geometrical calibration of a high-definition camera rig is an important step for 3D film making and computer vision applications. Due to the large amount of image data in high-definition, maintaining execution speeds appropriate for on-set, on-line adjustment procedures is one of the biggest challenges for machine vision based calibration methods. Our aims are to provide a low-cost, fast and accurate system to calibrate both the intrinsic and extrinsic parameters of a stereo camera rig. We first propose a novel calibration target that we call marker chessboard to speed up the corner detection. Then we develop an automatic key frame selection algorithm to optimize frames used in calibration. We also propose a bundle adjustment method to overcome the geometrical inaccuracy of the chessboard. Finally we introduce an online stereo camera calibration system based on the above improvements.

Satellite and Aerial Image Mosaicing - A Comparative Insight

Abstract: Image registration or image stitching is a central operation in many useful and important tasks in image processing like maps construction, scanning large documents and panoramic photos creation. In particular image mosaicing is used to assemble several overlapping images in order to constitute the global frame. We will focus on a feature-point matching method to perform the mosaicing. The SIFT algorithm is used to extract the feature points in both images. The mosaicing result is obtained after transforming the sensed or target image to align to the reference image. Performing a mosaicing operation is not sufficient to claim reaching the goal. Objective metrics must be used to evaluate the resulting mosaic. In this paper we present a complete mosaicing system named EsiReg and give a brief comparative insight on results of stitching satellite and aerial images using well known performance metrics.

Real time corner detection for miniaturized electro-optical sensors onboard small unmanned aerial systems.

Abstract: This paper describes the target detection algorithm for the image processor of a vision-based system that is installed onboard an unmanned helicopter. It has been developed in the framework of a project of the French national aerospace research center Office National d’Etudes et de Recherches Aerospatiales (ONERA) which aims at developing an air-to-ground target tracking mission in an unknown urban environment. In particular, the image processor must detect targets and estimate ground motion in proximity of the detected target position. Concerning the target detection function, the analysis has dealt with realizing a corner detection algorithm and selecting the best choices in terms of edge detection methods, filtering size and type and the more suitable criterion of detection of the points of interest in order to obtain a very fast algorithm which fulfills the computation load requirements. The compared criteria are the Harris-Stephen and the Shi-Tomasi, ones, which are the most widely used in literature among those based on intensity. Experimental results which illustrate the performance of the developed algorithm and demonstrate that the detection time is fully compliant with the requirements of the real-time system are discussed.

SIFT-based low complexity keypoint extraction and its real-time hardware implementation for full-HD video

Abstract: Scale-Invariant Feature Transform (SIFT) has lately attracted attention in computer vision as a robust keypoint detection algorithm which is invariant for scale, rotation and illumination change. However, its computational complexity is too high to apply practical real-time applications. This paper proposes a low complexity keypoint extraction algorithm based on SIFT descriptor and utilization of the database, and its real-time hardware implementation for Full-HD resolution video. The proposed algorithm computes SIFT descriptor on the keypoint obtained by corner detection and selects a scale from the database. It is possible to parallelize the keypoint detection and descriptor computation modules in the hardware. These modules do not depend on each other in the proposed algorithm in contrast with SIFT that computes a scale. The processing time of descriptor computation in this hardware is independent of the number of keypoints because its descriptor generation is pipelining structure of pixel. Evaluation results show that the proposed algorithm on software is 12 times faster than SIFT. Moreover, the proposed hardware on FPGA is 427 times faster than SIFT and 61 times faster than the proposed algorithm on software. The proposed hardware performs keypoint extraction and matching at 60 fps for Full-HD video.

A Vision-Based Method to Find Fingertips in a Closed Hand

Abstract: Hand gesture recognition is an important area of research in the field of Human Computer Interaction (HCI). The geometric attributes of the hand play an important role in hand shape reconstruction and gesture recognition. That said, fingertips are one of the important attributes for the detection of hand gestures and can provide valuable information from hand images. Many methods are available in scientific literature for fingertips detection with an open hand but very poor results are available for fingertips detection when the hand is closed. This paper presents a new method for the detection of fingertips in a closed hand using the corner detection method and an advanced edge detection algorithm. It is important to note that the skin color segmentation methodology did not work for fingertips detection in a closed hand. Thus the proposed method applied Gabor filter techniques for the detection of edges and then applied the corner detection algorithm for the detection of fingertips through the edges. To check the accuracy of the method, this method was tested on a vast number of images taken with a webcam. The method resulted in a higher accuracy rate of detections from the images. The method was further implemented on video for testing its validity on real time image capturing. These closed hand fingertips detection would help in controlling an electro-mechanical robotic hand via hand gesture in a natural way.

Comparison and Study of Classic Feature Point Detection Algorithm

Abstract: Detection base on feature points contains the characteristics of the whole image, this method is widely used in the field of computer vision. Several popular feature points detection algorithms are discussed, including SIFT feature points detection method and the corner detection methods like Forstner, Harris and SUSAN. In this paper, SIFT, Forstner, Harris and SUSAN are compared by a number of experiments that the invariance to scale, rotation and illumination and the anti-noise ability to Gaussian. We can compare the resules of feature point extraction and analysis of the stability and anti-noise ability of the feature point extraction algorithm on image.

Image based obstacle detection for automatic train supervision

Abstract: This paper focuses on the track obstacle detection based on image processing, analyzes and improves the Image gray-scale algorithm, Image binarization algorithm, Canny edge detection algorithm, and designs the detection window to rule out the useless information. As a result, we come up with three obstacle detection algorithms based on image feature extraction and feature analysis: Method based on Gray Level Histogram, Method based on the Proportion of Black and White Pixels, Method based on the integrity of the Rails and Sleepers. Finally, we take use of the server/client distributed model to design an automatically track detection system, which uses wireless communications. Through the experiment, the desired results have been obtained.

Low-complexity pruning for accelerating corner detection

Abstract: In this paper, we present a novel and computationally efficient pruning technique to speed up the Shi-Tomasi and Harris corner detectors. The proposed technique quickly prunes non-corners and selects a small corner candidate set by approximating the complex corner measure of Shi-Tomasi and Harris. The actual corner measure is then applied only to the reduced candidate set. Experimental results on the NiOS-II platform show that the proposed technique achieves an average execution time savings of 90% for Shi-Tomasi and 70% for Harris detectors for 500 corners with no loss in accuracy.

Scale-space color blob and ridge detection

Abstract: Feature detection in color images frequently consists in image conversion from color to grayscale and then one of grayscale detectors application. This approach has a few disadvantages: some features become indistinguishable in grayscale and features ordering based on response of grayscale detector do not accord with features order of importance from human's perception point of view. There are two essential contributions in this paper. First, the method for direct detection of blobs and ridges in color images is proposed. Second, for scale-space ridge detection we introduce a 3D non maxima suppression procedure (in two orthogonal directions) which makes ridge detection simple and easy programmable in contrast to Lindeberg's automatic scale selection approach. The proposed algorithms also produce estimates of blobs sizes and ridges width.

Efficient exploitation of heterogeneous platforms for images features extraction

Abstract: Image processing algorithms present a necessary tool for various domains related to computer vision, such as video surveillance, medical imaging, pattern recognition, etc. However, these algorithms are hampered by their high consumption of both computing power and memory, which increase significantly when processing large sets of images. In this work, we propose a development scheme enabling an efficient exploitation of parallel (GPU) and heterogeneous platforms (Multi-CPU/Multi-GPU), for improving performance of single and multiple image processing algorithms. The proposed scheme allows a full exploitation of hybrid platforms based on efficient scheduling strategies. It enables also overlapping data transfers by kernels executions using CUDA streaming technique within multiple GPUs. We present also parallel and heterogeneous implementations of several features extraction algorithms such as edge and corner detection. Experimentations have been conducted using a set of high resolution images, showing a global speedup ranging from 5 to 30, by comparison with CPU implementations.