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    MARS - Models of Appearance, Registration and Segmentation: Explanations

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    Registration Assessment Without Ground Truth Proven Most Sensitive
    Explanations N recent weeks we have worked towards a batch of publication on our recent work. That work is primarily focused on assessement of registration with and without ground truth. Below is a short summary of the work. More details are yet to be published in this site soon.
    A diverse collection or methods exist for the problem of non-rigid registration, whereby a set of images is to be aligned. We perceive a deficiency, however, in the ways such registrations are validated or even evaluated. We devised two methods for evaluating non-rigid registration. One of the methods requires ground-truth solutions to be provided a priori, yet the other does not. We have collected results, which confirm that both methods are valid and proceeded to calculating their sensitivities. We find that the method which requires ground-truth solutions is not as sensitive as the method which need not have anything but the raw images and the corresponding deformation fields.
    It is worth mentioning that, for the time being, segmentation is not considered. We are aware that it has great potential for being incorporated into the framework and, moreover, we have observed results that served as 'proof of concept' for this contention. We are yet to do some similar work on sets of faces if time permits.

    Posted by admin on Wednesday, October 19 @ 04:36:28 BST (6626 reads)
    Read More... | 240 comments | Score: 0

    Evaluation of Appearance Models
    Explanations There is another project that parallels with MARS. Evaluation of models, which involved a long series of experiments, have shown considerable promise. An algorithm was devised for assessing the quality of non-rigid registration and for evaluating appearance models with application to face and brain data.
    Posted by admin on Thursday, July 07 @ 15:40:26 BST (10577 reads)
    Read More... | 215 comments | Score: 0

    Label Propagation
    Explanations We are approaching a framework whereby labels (segmentation) can be bound to a single image and then automatically be used to infer the labelling in other images. This has the potential of saving a great deal of manual work which traditionally goes into segmenting images. Furthermore, since data from a group of images is used, there ought to be added robustness to noise. This helps in surpassring stand-alone segmentation, which is typically handled by investigating a single image at a time.

    The framework, which relies on non-rigid registration, is illustrated schematically below.

    Posted by admin on Monday, July 04 @ 04:07:32 BST (8269 reads)
    Read More... | 308 comments | Score: 0

    Segmentation Propagation from Model
    Explanations The next advancement to demonstrate is that segmentation of of one image can be propagated to an entire group of similar images.

    The mechanism which enables the propagation is model construction, which has the notion of correspondences. For each point that resides in a segment, a corresponding point can be found in another image. From this, an analogous segment in other images can be identified. If the correspondence is dense (i.e. incorporates all points in a continuous space), then entire labels can be propagated from one image to similar image in the entire group. Non-rigid registration provides that dense correspondence which is necessary.

    An advantage of this approach, which is worth pointing out, is that use of a group of images for segmentation compensates for noise in the images. By using information possessed by the image group, robustness can be gained.

    Posted by admin on Sunday, July 03 @ 12:13:03 BST (8457 reads)
    Read More... | 235 comments | Score: 0

    Mean of Squared Differences (MSD) Unstable at Registration
    Explanations There is one key problem that must be resolved somehow. Transformation in MARS is handled using clamped-plate splines (CPS) and an MSD-based objective function does not identify its minimum value when it is forced to lie in the correct solution.

    The little, fine bits on the top of bumps are unwanted
    artefacts that result in improved MSD evaluation.

    Posted by admin on Friday, July 01 @ 09:59:02 BST (6883 reads)
    Read More... | 235 comments | Score: 5

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