Constellation Of Landmarks Research Articles

The use of Z-scores to facilitate morphometric comparisons between African Plio-Pleistocene hominin fossils: An example of method

South Africa and East Africa each have a rich palaeoanthropological heritage, but the taxonomy of fossil hominins from these regions is controversial. In this study, two morphometric methods related to the quantification of variability in morphology have been applied to pairwise comparisons of linear measurements of hominoid crania and mandibles. The log-transformed standard error of the m-coefficient (‘log sem’) is calculated from linear regressions. Like Procrustes Distances (PD), log sem statistics can serve to quantify variation in the shape of a cranium or mandible in the context of a constellation of landmarks. In this study, PD and log sem statistics are integrated and standardised using Z-scores, and applied probabilistically to Plio-Pleistocene hominins. As a test case, OH 7 and OH 24 as reference specimens of Homo habilis are compared to fossils representing other taxa. There is a wide spectrum of variation in Z-scores for specimens attributed to early Homo dated within the period between circa 1.8 Ma and 2 Ma. In terms of morphometric variation predating 1.8 Ma, Z-scores (Z<2) for Australopithecus afarensis, A. africanus and Homo habilis display a small range of variability. This study serves as a demonstration of a method whereby log sem and PD can be used together to facilitate an objective assessment of morphological variability, applicable in palaeontological contexts.Significance: Using a probabilistic approach, two morphometric methods are integrated to quantify morphological variability in Plio-Pleistocene African hominin mandibles and crania. Two Tanzanian specimens of Homo habilis (the OH 7 mandible of the holotype specimen, and the OH 24 skull) can be used as reference material for morphometric comparisons with other fossils (mandibles or crania) attributed to Australopithecus africanus, A. afarensis, H. erectus and H. rudolfensis. The results of these comparisons are expressed as standardised probabilistic Z-scores such that these statistics for skulls and mandibles can be expressed on a common scale.

Landmark constellation models for medical image content identification and localization.

Many medical imaging tasks require the detection and localization of anatomical landmarks, for example for the initialization of model-based segmentation or to detect anatomical regions present in an image. A large number of landmark and object localization methods have been described in the literature. The detection of single landmarks may be insufficient to achieve robust localization across a variety of imaging settings and subjects. Furthermore, methods like the generalized Hough transform yield the most likely location of an object, but not an indication whether or not the landmark was actually present in the image. For these reasons, we developed a simple and computationally efficient method combining localization results from multiple landmarks to achieve robust localization and to compute a localization confidence measure. For each anatomical region, we train a constellation model indicating the mean relative locations and location variability of a set of landmarks. This model is registered to the landmarks detected in a test image via point-based registration, using closed-form solutions. Three different outlier suppression schemes are compared, two using iterative re-weighting based on the residual landmark registration errors and the third being a variant of RANSAC. The mean weighted residual registration error serves as a confidence measure to distinguish true from false localization results. The method is optimized and evaluated on synthetic data, evaluating both the localization accuracy and the ability to classify good from bad registration results based on the residual registration error. Two application examples are presented: the identification of the imaged anatomical region in trauma CT scans and the initialization of model-based segmentation for C-arm CT scans with different target regions. The identification of the target region with the presented method was in 96% of the cases correct. The presented method is a simple solution for combining multiple landmark localization results. With appropriate parameters, outlier suppression clearly improves the localization performance over model registration without outlier suppression. The optimum choice of method and parameters depends on the expected level of noise and outliers in the application at hand, as well as on the focus on localization, classification, or both. The method allows detecting and localizing anatomical fields of view in medical images and is well suited to support a wide range of applications comprising image content identification, anatomical navigation and visualization, or initializing the pose of organ shape models.

Towards an Optical Aircraft Navigation System

Navigation by means that are fully self contained, without the weight and cost of high performance inertial navigation units is highly desirable in many applications both military and civilian. In this paper we introduce a suite of sensors and behaviors that include: the means to reduce lateral drift due to wind using optical flow, detection of a constellation of landmarks using a machine vision system, and a polarization compass that is reliable at extreme latitudes based on polarization. In a series of flight trials and detailed simulations we have demonstrated that a combination of these functions achieves purely optical navigation with simplicity and robustness.

Parsing radiographs by integrating landmark set detection and multi-object active appearance models.

This work addresses the challenging problem of parsing 2D radiographs into salient anatomical regions such as the left and right lungs and the heart. We propose the integration of an automatic detection of a constellation of landmarks via rejection cascade classifiers and a learned geometric constellation subset detector model with a multi-object active appearance model (MO-AAM) initialized by the detected landmark constellation subset. Our main contribution is twofold. First, we propose a recovery method for false positive and negative landmarks which allows to handle extreme ranges of anatomical and pathological variability. Specifically we (1) recover false negative (missing) landmarks through the consensus of inferences from subsets of the detected landmarks, and (2) choose one from multiple false positives for the same landmark by learning Gaussian distributions for the relative location of each landmark. Second, we train a MO-AAM using the true landmarks for the detectors and during test, initialize the model using the detected landmarks. Our model fitting allows simultaneous localization of multiple regions by encoding the shape and appearance information of multiple objects in a single model. The integration of landmark detection method and MO-AAM reduces mean distance error of the detected landmarks from 20.0mm to 12.6mm. We assess our method using a database of scout CT scans from 80 subjects with widely varying pathology.

Goal seeking in honeybees: matching of optic flow snapshots?

Visual landmarks guide humans and animals including insects to a goal location. Insects, with their miniature brains, have evolved a simple strategy to find their nests or profitable food sources; they approach a goal by finding a close match between the current view and a memorised retinotopic representation of the landmark constellation around the goal. Recent implementations of such a matching scheme use raw panoramic images ('image matching') and show that it is well suited to work on robots and even in natural environments. However, this matching scheme works only if relevant landmarks can be detected by their contrast and texture. Therefore, we tested how honeybees perform in localising a goal if the landmarks can hardly be distinguished from the background by such cues. We recorded the honeybees' flight behaviour with high-speed cameras and compared the search behaviour with computer simulations. We show that honeybees are able to use landmarks that have the same contrast and texture as the background and suggest that the bees use relative motion cues between the landmark and the background. These cues are generated on the eyes when the bee moves in a characteristic way in the vicinity of the landmarks. This extraordinary navigation performance can be explained by a matching scheme that includes snapshots based on optic flow amplitudes ('optic flow matching'). This new matching scheme provides a robust strategy for navigation, as it depends primarily on the depth structure of the environment.

Automated Hybrid TACT® Volume Reconstructions

Summary Objectives: To design, implement in Java™, and evaluate a method and means for the automated localization of artificial landmarks in optical images for tuned-aperture computed tomography® (TACT®) that allows the replacement of radiographic with optical landmarks. Methods: Circular, colored, optical landmarks were designed to provide flexibility with regard to landmark constellation, imaging equipment, and lighting conditions. The landmark detection was based on Hough transforms (HT) for ellipses and lines. The HT for ellipses was extended to enable selective detection of bright ellipses on a dark background and vice versa, and the number of irrelevant votes in the accumulator arrays was reduced. An experiment was performed in vitro to test the automated landmark localization scheme, verify registration accuracy, and measure the required computation time. Results: A visual evaluation of the tomographic slices that were produced using the new method revealed good registration accuracy. A comparison to tomographic slices similarly produced by means of conventional TACT showed identical results. The algorithm ran sufficiently fast on standard hardware to allow landmark localization in “real time” during successive image acquisition in clinical applications. Conclusions: The proposed method provides robust automated localization of landmarks in optical images. Using a hybrid imaging system, TACT can now be clinically applied without manual interaction of a human operator and without radiopaque landmarks, which might cover anatomic details of diagnostic interest.

Hippocampectomized rats can use a constellation of landmarks to recognize a place

The hippocampus, one of the most studied regions of the mammalian forebrain, plays some well-established roles in topographic navigation. For two decades, one widely accepted explanation for the observed impairment of hippocampectomized rats in spatial navigation has been an inability to form place representations. In this report, we present a direct experimental evidence that animals with hippocampal lesions can learn to recognize places using the constellation of distinct landmarks. The extrahippocampal implementation of all three basic constituents of topographic orientation — guidance, vector navigation, and place recognition — shows that the hippocampus, and its place cells, serve a much more specialized cognitive function than previously thought. We propose that this function includes multi-place and multi-vector topographic integration.

Time-courses of memory decay in vector-based and landmark-based systems of navigation in desert ants, Cataglyphis fortis

In foraging and homing, desert ants of the genus Cataglyphis employ two different systems of navigation: a vector-based or dead-reckoning mechanism, depending on angles steered and distances travelled, and a landmark-based piloting mechanism. In these systems the ants use either celestial or terrestrial visual information, respectively. In behavioural experiments we investigated how long these types of information are preserved in the ant's memory, i.e. how long the ants are able to orient properly in either way. To answer this question, ants were tested in specific dead-reckoning and piloting situations, whereby the two vector components, direction and distance, were examined separately. The ability to follow a particular vector course vanishes rapidly. Information about a given homing direction is lost from the 6th day on (the time constant of the exponential memory decay function is τ = 4.5 days). The homing distances show a significantly higher dispersion from the 4th day on (τ = 2.5 days). Having learned a constellation of landmarks positioned at the corners of an equidistant triangle all ants were oriented properly after 10 days in captivity, and 64% of the ants exhibited extremely precise orientation performances even when tested after 20 days. Thus, the memory decay functions have about the same short time-course for information on distance and direction, i.e. information used for dead-reckoning. In contrast, landmark-based information used in pinpointing the nest entrance is stored over the entire lifetime of a Cataglyphis forager.

Evidence against instantaneous transfer of spatial knowledge in the house mouse (Mus musculus)

Perceptual vector deduction (instantaneous transfer) is the ability of a displaced animal to deduce the vector toward home from a novel location solely on the basis of its current perception of a previously learned constellation of landmarks. Different groups of house mice, Mus musculus, subjected to different treatments, were tested for such an ability. The tests were done on a circular arena, 1.5 m in diameter, that had a nest attached to the periphery. Mice that were confined to the nest for 24 h, with a full view of the room, failed to home accurately when deprived of the opportunity of integrating the path from the nest to the center of the arena. Similar failures were obtained when the mice were tested after they had freely explored an adjacent arena for 24 h, exposed to the same distal landmarks. However, the mice were able to home after being passively transported from the nest to the center of the arena. Free exploration of the test arena for 24 h improved homing in terms of directness and speed of locomotion toward the nest. Shuttling the mice 10 times between the nest and the release point in the center of the arena, in addition to 24-h free exploration, did not improve subsequent homing accuracy over that obtained from 24-h free exploration. In conclusion, the mice were unable to perceptually deduce a homing vector, and homing was improved by movement through the part of the environment that was used for homing.

Mobile robot visual mapping and localization: A view-based neurocomputational architecture that emulates hippocampal place learning

We propose a real-time, view-based neurocomputational architecture for unsupervised 2-D mapping and localization within a 3-D environment defined by a spatially distributed set of visual landmarks. This architecture emulates place learning by hippocampal place cells in rats, and draws from anatomy of the primate object (“What”) and spatial (“Where”) processing streams. It extends by analogy, principles for learning characteristic views of 3-D objects (i.e., “aspects”), to learning characteristic views of environments (i.e., “places”). Places are defined by the identities and approximate poses (the What) of landmarks, as provided by visible landmark aspects. They are also defined by prototypical locations (the Where) within the landmark constellation, as indicated by the panoramic spatial distribution of landmark gaze directions. Combining these object and spatial definitions results in place nodes whose activity profiles define decision boundaries that parcel a 2-D area of the environment into place regions. These profiles resemble the spatial firing patterns over hippocampal place fields observed in rat experiments. A realtime demonstration of these capabilities on the binocular mobile robot MAVIN (the mobile adaptive visual navigator) illustrates the potential of this approach for qualitative mapping and fine localization.