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Computer Vision, IET

Issue 1 • Date March 2010

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Displaying Results 1 - 6 of 6
  • Shape from shading using wavelets and weighted smoothness constraints

    Page(s): 1 - 11
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (610 KB)  

    A new method that allows capturing shapes from an input image using an optimisation-based approach is presented. An objective function is designed by introducing two terms: the first term is used to minimise the difference between the shading of the reconstructed shape and the input image, and the second term is to apply smoothness constraints to the reconstructed shape. To achieve shape reconstruction in high quality, the authors propose weighted smoothness constraint, which is designed to be anti-proportional to the intensity gradients in the input image. Under this constraint, flat image areas make more contribution towards the smoothness of the reconstructed shape, while the fine details from the image areas with large intensity gradients are preserved in the reconstructed result. Given the objective function, wavelets are used to obtain the solution effectively. Since wavelets accurately preserve high-frequency data, they can be used to solve the objective function with the advantage of allowing for a good recovery of fine details from the input image. The authors have chosen to use the Daubechies wavelets, which are orthonormal and compactly supported. Here the formulation of the algorithm based on the mathematical details is provided. Finally, the authors present experimental results on a number of different images and compare them against some well-known methods and ground truth (where available). The comparison shows that the method is effective and offers good results. View full abstract»

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  • Hierarchical pose classification based on human physiology for behaviour analysis

    Page(s): 12 - 24
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (911 KB)  

    This study presents a new approach to classify human body poses by using angular constraints and variations of body joints. Although different classifications of the poses have been previously made, the proposed approach attempts to create a more comprehensive, accurate and extensible classification by integrating all possible poses based on angles of movement in human joints. The angular variations in all body joints can determine any possible poses. The joint angles from the body axis are computed in the three-dimensional space. In order to train and classify the pose in an automated manner, support vector machines (SVMs) were used. Experiments were carried out on both benchmark (CMU dataset) and in-house simulated (POSER dataset) poses to evaluate the performance of the proposed classification scheme. View full abstract»

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  • Gait recognition using active shape model and motion prediction

    Page(s): 25 - 36
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (962 KB)  

    This study presents a novel, robust gait recognition algorithm for human identification from a sequence of segmented noisy silhouettes in a low-resolution video. The proposed recognition algorithm enables automatic human recognition from model-based gait cycle extraction based on the prediction-based hierarchical active shape model (ASM). The proposed algorithm overcomes drawbacks of existing works by extracting a set of relative model parameters instead of directly analysing the gait pattern. The feature extraction function in the proposed algorithm consists of motion detection, object region detection and ASM, which alleviate problems in the baseline algorithm such as background generation, shadow removal and higher recognition rate. Performance of the proposed algorithm has been evaluated by using the HumanID Gait Challenge data set, which is the largest gait benchmarking data set with 122 objects with different realistic parameters including viewpoint, shoe, surface, carrying condition and time. View full abstract»

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  • Environment classification and hierarchical lane detection for structured and unstructured roads

    Page(s): 37 - 49
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (854 KB)  

    This study presents a hierarchical lane detection system with the ability to deal with both structured and unstructured roads. The proposed system classifies the environment first before applying suitable algorithms for different types of roads. Instead of dealing with all situations with one complicated algorithm, this hierarchical architecture makes it possible to achieve high accuracy with relatively simple and efficient lane detection algorithms. For environment classification, pixels with lane-marking colours are extracted as feature points. Eigenvalue decomposition regularised discriminant analysis is utilised in model selection and maximum likelihood estimation of Gaussian parameters in high-dimensional feature space. For structured roads, the extracted feature points are reused for lane detection. Moving vehicles that have the same colours as the lane markings are eliminated from the feature points before the authors perform angles of inclination and turning points searching to locate the lane boundaries. For unstructured roads, mean-shift segmentation is applied to divide the scene into regions. Possible candidate pairs for road boundaries are elected from the region boundaries, and Bayes rule is used to choose the most probable candidate pairs as the lane boundaries. The experimental results have shown that the classification mechanism can effectively choose the correct lane detection algorithm according to the current environment setting, and the system is able to robustly find the lane boundaries on different types of roads in various weather conditions. View full abstract»

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  • Spatio-temporal motion-based foreground segmentation and shadow suppression

    Page(s): 50 - 60
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (708 KB)  

    A relevant problem in computer vision is how to detect and track moving objects from video sequences efficiently. Some algorithms require manual calibration in terms of specification of parameters or some hypotheses. A novel method is developed to extract moving objects through multi-scale wavelet transform across background subtraction. The optimal selection of threshold is automatically determined which does not require any complex supervised training or manual calibration. The proposed approach is efficient in detecting moving objects with low contrast against the background and the detection is less affected by the presence of moving objects in the scene. The developed method combines region connectivity with chromatic consistency to overcome the aperture problem. Ghosts are removed by the proposed background update function, which efficiently prevents undesired corruption of background model and does not consider adaptation coefficient. The mentioned approach is scene-independent and the capacity to extract moving object and suppress cast shadow is high. The developed algorithm is flexible and computationally cost-effective. Experiments show that the proposed approach is robust and efficient in segmenting foreground and suppressing shadow by comparison. View full abstract»

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  • Generic approach to 3D elastic model fitting to volume data

    Page(s): 61 - 72
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (756 KB)  

    The authors address here the problem of fitting a generic model to 3D volume data and present a method that embeds the model inside a geometric block and uses the mechanical analogy of springs to fit the model to the data. The authors work out the equations that connect the deformation of the block with the deformation of the shape embedded in it and then apply the desired transformation to the block in order to deform the embedded generic model until it fits the data. Two example applications are shown: fitting a jaw bone model to 3D MRI head data and fitting a scanned face model to MRI volume data. Both applications are akin to the development of a tool box for maxillofacial plastic surgery planning, with anatomically correct structures and realistic face appearance adapted to the individual patient. View full abstract»

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Aims & Scope

IET Computer Vision seeks original research papers in a wide range of areas of computer vision. The vision of the journal is to publish the highest quality research work that is relevant and topical to the field, but not forgetting those works that aim to introduce new horizons and set the agenda for future avenues of research in Computer Vision.

Full Aims & Scope