14 research outputs found

    Corner detector based on global and local curvature properties

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    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 computes 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 quantization 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 feature correspondence test. Results reveal that the proposed detector performs extremely well in both fields. © 2008 Society of Photo-Optical Instrumentation Engineers.published_or_final_versio

    Fast feature matching for simultaneous localization and mapping

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    BakaláƙskĂĄ prĂĄce se zabĂœvĂĄ rychlĂœm vyhledĂĄvĂĄnĂ­m lokĂĄlnĂ­ch obrazovĂœch vlastnostĂ­ v rozsĂĄhlĂœch databĂĄzĂ­ch pro simultĂĄnnĂ­ lokalizaci a mapovĂĄnĂ­ prostƙedĂ­. SoučástĂ­ prĂĄce je krĂĄtkĂœ pƙehled detektorĆŻ a deskriptorĆŻ invariantnĂ­ch vƯči rotaci, translaci, změně měƙítka a affinitě. Pro ƙadu aplikacĂ­ z oblasti počítačovĂ©ho viděnĂ­ (SLAM, object retrieval, wide–robust baseline stereo, tracking, . . . ) je odezva reĂĄlnĂ©m čase naprosto nezbytnĂĄ. Jako ƙeĆĄenĂ­ sublineĂĄrnĂ­ časovĂ© nĂĄročnosti vyhledĂĄvĂĄnĂ­ v databĂĄzĂ­ch bylo navrĆŸeno pouĆŸitĂ­ vĂ­cenĂĄsobnĂœch nĂĄhodně generovanĂœch KD–stromĆŻ. DĂĄle je pƙedklĂĄdĂĄn novĂœ zpĆŻsob dělenĂ­ dat do vĂ­cenĂĄsobnĂœch KD–stromĆŻ. NavĂ­c byl navrĆŸen novĂœ, obecně pouĆŸitelnĂœ vyhodnocovacĂ­ software (podporovĂĄny jsou KD–stromy, BBD-stromy a k-means stromy.)The thesis deals with the fast feature matching for simultaneous localization and mapping. A brief description of local features invariant to scale, rotation, translation and affine transformations, their detectors and descriptors are included. In general, real–time response for matching is crucial for various computer vision applications (SLAM, object retrieval, wide–robust baseline stereo, tracking, . . . ). We solve the problem of sub–linear search complexity by multiple randomised KD–trees. In addition, we propose a novel way of splitting dataset into the multiple trees. Moreover, a new evaluation package for general use (KD–trees, BBD–trees, k–means trees) was developed.

    BEMDEC: An Adaptive and Robust Methodology for Digital Image Feature Extraction

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    The intriguing study of feature extraction, and edge detection in particular, has, as a result of the increased use of imagery, drawn even more attention not just from the field of computer science but also from a variety of scientific fields. However, various challenges surrounding the formulation of feature extraction operator, particularly of edges, which is capable of satisfying the necessary properties of low probability of error (i.e., failure of marking true edges), accuracy, and consistent response to a single edge, continue to persist. Moreover, it should be pointed out that most of the work in the area of feature extraction has been focused on improving many of the existing approaches rather than devising or adopting new ones. In the image processing subfield, where the needs constantly change, we must equally change the way we think. In this digital world where the use of images, for variety of purposes, continues to increase, researchers, if they are serious about addressing the aforementioned limitations, must be able to think outside the box and step away from the usual in order to overcome these challenges. In this dissertation, we propose an adaptive and robust, yet simple, digital image features detection methodology using bidimensional empirical mode decomposition (BEMD), a sifting process that decomposes a signal into its two-dimensional (2D) bidimensional intrinsic mode functions (BIMFs). The method is further extended to detect corners and curves, and as such, dubbed as BEMDEC, indicating its ability to detect edges, corners and curves. In addition to the application of BEMD, a unique combination of a flexible envelope estimation algorithm, stopping criteria and boundary adjustment made the realization of this multi-feature detector possible. Further application of two morphological operators of binarization and thinning adds to the quality of the operator

    3D Laser Scanner Development and Analysis

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    Design and Development of Robotic Part Assembly System under Vision Guidance

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    Robots are widely used for part assembly across manufacturing industries to attain high productivity through automation. The automated mechanical part assembly system contributes a major share in production process. An appropriate vision guided robotic assembly system further minimizes the lead time and improve quality of the end product by suitable object detection methods and robot control strategies. An approach is made for the development of robotic part assembly system with the aid of industrial vision system. This approach is accomplished mainly in three phases. The first phase of research is mainly focused on feature extraction and object detection techniques. A hybrid edge detection method is developed by combining both fuzzy inference rule and wavelet transformation. The performance of this edge detector is quantitatively analysed and compared with widely used edge detectors like Canny, Sobel, Prewitt, mathematical morphology based, Robert, Laplacian of Gaussian and wavelet transformation based. A comparative study is performed for choosing a suitable corner detection method. The corner detection technique used in the study are curvature scale space, Wang-Brady and Harris method. The successful implementation of vision guided robotic system is dependent on the system configuration like eye-in-hand or eye-to-hand. In this configuration, there may be a case that the captured images of the parts is corrupted by geometric transformation such as scaling, rotation, translation and blurring due to camera or robot motion. Considering such issue, an image reconstruction method is proposed by using orthogonal Zernike moment invariants. The suggested method uses a selection process of moment order to reconstruct the affected image. This enables the object detection method efficient. In the second phase, the proposed system is developed by integrating the vision system and robot system. The proposed feature extraction and object detection methods are tested and found efficient for the purpose. In the third stage, robot navigation based on visual feedback are proposed. In the control scheme, general moment invariants, Legendre moment and Zernike moment invariants are used. The selection of best combination of visual features are performed by measuring the hamming distance between all possible combinations of visual features. This results in finding the best combination that makes the image based visual servoing control efficient. An indirect method is employed in determining the moment invariants for Legendre moment and Zernike moment. These moments are used as they are robust to noise. The control laws, based on these three global feature of image, perform efficiently to navigate the robot in the desire environment

    Bio-signal data gathering, management and analysis within a patient-centred health care context

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    The healthcare service is under pressure to do more with less, and changing the way the service is modelled could be the key to saving resources and increasing efficacy. This change could be possible using patient-centric care models. This model would include straightforward and easy-to-use telemonitoring devices and a flexible data management structure. The structure would maintain its state by ingesting many sources of data, then tracking this data through cleaning and processing into models and estimates to obtaining values from data which could be used by the patient. The system can become less disease-focused and more health-focused by being preventative in nature and allowing patients to be more proactive and involved in their care by automating the data management. This work presents the development of a new device and a data management and analysis system to utilise the data from this device and support data processing along with two examples of its use. These are signal quality and blood pressure estimation. This system could aid in the creation of patient-centric telecare systems

    Remote Sensing of Earth Resources: A literature survey with indexes (1970 - 1973 supplement). Section 1: Abstracts

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    Abstracts of reports, articles, and other documents introduced into the NASA scientific and technical information system between March 1970 and December 1973 are presented in the following areas: agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, oceanography and marine resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis
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