23 research outputs found

    Face recognition using skin texture

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    In today's society where information technology is depended upon throughout homes, educational establishments and workplaces the challenge of identity management is ever growing. Advancements in image processing and biometric feature based identification have provided a means for computer software to accurately identify individuals from increasingly vast databases of users. In the quest to improve the performance of such systems in varying environmental conditions skin texture is here proposed as a biometric feature. This thesis presents and discusses a hypothesis for the use of facial skin texture regions taken from 2-dimensional photographs to accurately identify individuals using three classifiers (neural network, support vector machine and linear discriminant). Gabor wavelet filters are primarily used for feature extraction and arc supported in later chapters by the grey-level cooccurrence probability matrix (GLCP) to strengthen the system by providing supplementary high-frequency features. Various fusion techniques for combining these features are presented and their perfonnance is compared including both score and feature fusion and various permutations of each. Based on preliminary results from the BioSecure Multimodal Database (BMDB) , the work presented indicates that isolated texture regions of the human face taken from under the eye may provide sufficient information to discriminately identify an individual with an equal error rate (EER) of under 1% when operating in greyscale. An analysis of the performance of the algorithm against image resolution investigates the systems performance when faced with lower resolution training images and discusses optimal resolutions for classifier training. The system also shows a good degree of robustness when the probe image resolution is reduced indicating that the algorithm provides some level of scale invariance. Scope for future work is laid out and a review of the evaluation is also presented

    An interest point based illumination condition matching approach to photometric registration within augmented reality worlds

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    With recent and continued increases in computing power, and advances in the field of computer graphics, realistic augmented reality environments can now offer inexpensive and powerful solutions in a whole range of training, simulation and leisure applications. One key challenge to maintaining convincing augmentation, and therefore user immersion, is ensuring consistent illumination conditions between virtual and real environments, so that objects appear to be lit by the same light sources. This research demonstrates how real world lighting conditions can be determined from the two-dimensional view of the user. Virtual objects can then be illuminated and virtual shadows cast using these conditions. This new technique uses pairs of interest points from real objects and the shadows that they cast, viewed from a binocular perspective, to determine the position of the illuminant. This research has been initially focused on single point light sources in order to show the potential of the technique and has investigated the relationships between the many parameters of the vision system. Optimal conditions have been discovered by mapping the results of experimentally varying parameters such as FoV, camera angle and pose, image resolution, aspect ratio and illuminant distance. The technique is able to provide increased robustness where greater resolution imagery is used. Under optimal conditions it is possible to derive the position of a real world light source with low average error. An investigation of available literature has revealed that other techniques can be inflexible, slow, or disrupt scene realism. This technique is able to locate and track a moving illuminant within an unconstrained, dynamic world without the use of artificial calibration objects that would disrupt scene realism. The technique operates in real-time as the new algorithms are of low computational complexity. This allows high framerates to be maintained within augmented reality applications. Illuminant updates occur several times a second on an average to high end desktop computer. Future work will investigate the automatic identification and selection of pairs of interest points and the exploration of global illuminant conditions. The latter will include an analysis of more complex scenes and the consideration of multiple and varied light sources.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Boosting for Generic 2D/3D Object Recognition

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    Generic object recognition is an important function of the human visual system. For an artificial vision system to be able to emulate the human perception abilities, it should also be able to perform generic object recognition. In this thesis, we address the generic object recognition problem and present different approaches and models which tackle different aspects of this difficult problem. First, we present a model for generic 2D object recognition from complex 2D images. The model exploits only appearance-based information, in the form of a combination of texture and color cues, for binary classification of 2D object classes. Learning is accomplished in a weakly supervised manner using Boosting. However, we live in a 3D world and the ability to recognize 3D objects is very important for any vision system. Therefore, we present a model for generic recognition of 3D objects from range images. Our model makes use of a combination of simple local shape descriptors extracted from range images for recognizing 3D object categories, as shape is an important information provided by range images. Moreover, we present a novel dataset for generic object recognition that provides 2D and range images about different object classes using a Time-of-Flight (ToF) camera. As the surrounding world contains thousands of different object categories, recognizing many different object classes is important as well. Therefore, we extend our generic 3D object recognition model to deal with the multi-class learning and recognition task. Moreover, we extend the multi-class recognition model by introducing a novel model which uses a combination of appearance-based information extracted from 2D images and range-based (shape) information extracted from range images for multi-class generic 3D object recognition and promising results are obtained

    3D panoramic imaging for virtual environment construction

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    The project is concerned with the development of algorithms for the creation of photo-realistic 3D virtual environments, overcoming problems in mosaicing, colour and lighting changes, correspondence search speed and correspondence errors due to lack of surface texture. A number of related new algorithms have been investigated for image stitching, content based colour correction and efficient 3D surface reconstruction. All of the investigations were undertaken by using multiple views from normal digital cameras, web cameras and a ”one-shot” panoramic system. In the process of 3D reconstruction a new interest points based mosaicing method, a new interest points based colour correction method, a new hybrid feature and area based correspondence constraint and a new structured light based 3D reconstruction method have been investigated. The major contributions and results can be summarised as follows: • A new interest point based image stitching method has been proposed and investigated. The robustness of interest points has been tested and evaluated. Interest points have been proved robust to changes in lighting, viewpoint, rotation and scale. • A new interest point based method for colour correction has been proposed and investigated. The results of linear and linear plus affine colour transforms have proved more accurate than traditional diagonal transforms in accurately matching colours in panoramic images. • A new structured light based method for correspondence point based 3D reconstruction has been proposed and investigated. The method has been proved to increase the accuracy of the correspondence search for areas with low texture. Correspondence speed has also been increased with a new hybrid feature and area based correspondence search constraint. • Based on the investigation, a software framework has been developed for image based 3D virtual environment construction. The GUI includes abilities for importing images, colour correction, mosaicing, 3D surface reconstruction, texture recovery and visualisation. • 11 research papers have been published.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Pose estimation for objects with planar surfaces using eigenimage and range data analysis

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    In this paper we present a novel method for estimating the object pose for 3D objects with well defined planar surfaces. Specifically, we investigate the feasibility of estimating the object pose using an approach that combines the standard eigenspace analysis technique with range data analysis. In this sense, eigenspace analysis was employed to constrain one object rotation and reject surfaces that are not compatible with a model object. The remaining two object rotations are estimated by computing the normal to the surface from the range data. The proposed pose estimation scheme has been successfully applied to scenes defined by polyhedral objects and experimental results are reported

    Object recognition in infrared imagery using appearance-based methods

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    Abstract unavailable please refer to PD

    A systems engineering approach to robotic bin picking

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    In recent times the presence of vision and robotic systems in industry has become common place, but in spite of many achievements a large range of industrial tasks still remain unsolved due to the lack of flexibility of the vision systems when dealing with highly adaptive manufacturing environments. An important task found across a broad range of modern flexible manufacturing environments is the need to present parts to automated machinery from a supply bin. In order to carry out grasping and manipulation operations safely and efficiently we need to know the identity, location and spatial orientation of the objects that lie in an unstructured heap in a bin. Historically, the bin picking problem was tackled using mechanical vibratory feeders where the vision feedback was unavailable. This solution has certain problems with parts jamming and more important they are highly dedicated. In this regard if a change in the manufacturing process is required, the changeover may include an extensive re-tooling and a total revision of the system control strategy (Kelley et al., 1982). Due to these disadvantages modern bin picking systems perform grasping and manipulation operations using vision feedback (Yoshimi & Allen, 1994). Vision based robotic bin picking has been the subject of research since the introduction of the automated vision controlled processes in industry and a review of existing systems indicates that none of the proposed solutions were able to solve this classic vision problem in its generality. One of the main challenges facing such a bin picking system is its ability to deal with overlapping objects. The object recognition in cluttered scenes is the main objective of these systems and early approaches attempted to perform bin picking operations for similar objects that are jumbled together in an unstructured heap using no knowledge about the pose or geometry of the parts (Birk et al., 1981). While these assumptions may be acceptable for a restricted number of applications, in most practical cases a flexible system must deal with more than one type of object with a wide scale of shapes. A flexible bin picking system has to address three difficult problems: scene interpretation, object recognition and pose estimation. Initial approaches to these tasks were based on modeling parts using the 2D surface representations. Typical 2D representations include invariant shape descriptors (Zisserman et al., 1994), algebraic curves (Tarel & Cooper, 2000), 2 Name of the book (Header position 1,5) conics (Bolles & Horaud, 1986; Forsyth et al., 1991) and appearance based models (Murase & Nayar, 1995; Ohba & Ikeuchi, 1997). These systems are generally better suited to planar object recognition and they are not able to deal with severe viewpoint distortions or objects with complex shapes/textures. Also the spatial orientation cannot be robustly estimated for objects with free-form contours. To address this limitation most bin picking systems attempt to recognize the scene objects and estimate their spatial orientation using the 3D information (Fan et al., 1989; Faugeras & Hebert, 1986). Notable approaches include the use of 3D local descriptors (Ansar & Daniilidis, 2003; Campbell & Flynn, 2001; Kim & Kak, 1991), polyhedra (Rothwell & Stern, 1996), generalized cylinders (Ponce et al., 1989; Zerroug & Nevatia, 1996), super-quadrics (Blane et al., 2000) and visual learning methods (Johnson & Hebert, 1999; Mittrapiyanuruk et al., 2004). The most difficult problem for 3D bin picking systems that are based on a structural description of the objects (local descriptors or 3D primitives) is the complex procedure required to perform the scene to model feature matching. This procedure is usually based on complex graph-searching techniques and is increasingly more difficult when dealing with object occlusions, a situation when the structural description of the scene objects is incomplete. Visual learning methods based on eigenimage analysis have been proposed as an alternative solution to address the object recognition and pose estimation for objects with complex appearances. In this regard, Johnson and Hebert (Johnson & Hebert, 1999) developed an object recognition scheme that is able to identify multiple 3D objects in scenes affected by clutter and occlusion. They proposed an eigenimage analysis approach that is applied to match surface points using the spin image representation. The main attraction of this approach resides in the use of spin images that are local surface descriptors; hence they can be easily identified in real scenes that contain clutter and occlusions. This approach returns accurate results but the pose estimation cannot be inferred, as the spin images are local descriptors and they are not robust to capture the object orientation. In general the pose sampling for visual learning methods is a problem difficult to solve as the numbers of views required to sample the full 6 degree of freedom for object pose is prohibitive. This issue was addressed in the paper by Edwards (Edwards, 1996) when he applied eigenimage analysis to a one-object scene and his approach was able to estimate the pose only in cases where the tilt angle was limited to 30 degrees with respect to the optical axis of the sensor. In this chapter we describe the implementation of a vision sensor for robotic bin picking where we attempt to eliminate the main problem faced by the visual learning methods, namely the pose sampling problem. This paper is organized as follows. Section 2 outlines the overall system. Section 3 describes the implementation of the range sensor while Section 4 details the edge-based segmentation algorithm. Section 5 presents the viewpoint correction algorithm that is applied to align the detected object surfaces perpendicular on the optical axis of the sensor. Section 6 describes the object recognition algorithm. This is followed in Section 7 by an outline of the pose estimation algorithm. Section 8 presents a number of experimental results illustrating the benefits of the approach outlined in this chapter
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