1,172 research outputs found
Model-based object recognition from a complex binary imagery using genetic algorithm
This paper describes a technique for model-based object recognition in a noisy and cluttered environment, by extending the work presented in an earlier study by the authors. In order to accurately model small irregularly shaped objects, the model and the image are represented by their binary edge maps, rather then approximating them with straight line segments. The problem is then formulated as that of finding the best describing match between a hypothesized object and the image. A special form of template matching is used to deal with the noisy environment, where the templates are generated on-line by a Genetic Algorithm. For experiments, two complex test images have been considered and the results when compared with standard techniques indicate the scope for further research in this direction
Probabilistic Search for Object Segmentation and Recognition
The problem of searching for a model-based scene interpretation is analyzed
within a probabilistic framework. Object models are formulated as generative
models for range data of the scene. A new statistical criterion, the truncated
object probability, is introduced to infer an optimal sequence of object
hypotheses to be evaluated for their match to the data. The truncated
probability is partly determined by prior knowledge of the objects and partly
learned from data. Some experiments on sequence quality and object segmentation
and recognition from stereo data are presented. The article recovers classic
concepts from object recognition (grouping, geometric hashing, alignment) from
the probabilistic perspective and adds insight into the optimal ordering of
object hypotheses for evaluation. Moreover, it introduces point-relation
densities, a key component of the truncated probability, as statistical models
of local surface shape.Comment: 18 pages, 5 figure
Real-Time Hand Shape Classification
The problem of hand shape classification is challenging since a hand is
characterized by a large number of degrees of freedom. Numerous shape
descriptors have been proposed and applied over the years to estimate and
classify hand poses in reasonable time. In this paper we discuss our parallel
framework for real-time hand shape classification applicable in real-time
applications. We show how the number of gallery images influences the
classification accuracy and execution time of the parallel algorithm. We
present the speedup and efficiency analyses that prove the efficacy of the
parallel implementation. Noteworthy, different methods can be used at each step
of our parallel framework. Here, we combine the shape contexts with the
appearance-based techniques to enhance the robustness of the algorithm and to
increase the classification score. An extensive experimental study proves the
superiority of the proposed approach over existing state-of-the-art methods.Comment: 11 page
iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects
We address the task of 6D pose estimation of known rigid objects from single
input images in scenarios where the objects are partly occluded. Recent
RGB-D-based methods are robust to moderate degrees of occlusion. For RGB
inputs, no previous method works well for partly occluded objects. Our main
contribution is to present the first deep learning-based system that estimates
accurate poses for partly occluded objects from RGB-D and RGB input. We achieve
this with a new instance-aware pipeline that decomposes 6D object pose
estimation into a sequence of simpler steps, where each step removes specific
aspects of the problem. The first step localizes all known objects in the image
using an instance segmentation network, and hence eliminates surrounding
clutter and occluders. The second step densely maps pixels to 3D object surface
positions, so called object coordinates, using an encoder-decoder network, and
hence eliminates object appearance. The third, and final, step predicts the 6D
pose using geometric optimization. We demonstrate that we significantly
outperform the state-of-the-art for pose estimation of partly occluded objects
for both RGB and RGB-D input
Superpixel quality in microscopy images: the impact of noise & denoising
Microscopy is a valuable imaging tool in various biomedical research areas. Recent developments have made high resolution acquisition possible within a relatively short time. State-of-the-art imaging equipment such as serial block-face electron microscopes acquire gigabytes of data in a matter of hours. In order to make these amounts of data manageable, a more data-efficient representation is required. A popular approach for such data efficiency are superpixels which are designed to cluster homogeneous regions without crossing object boundaries. The use of superpixels as a pre-processing step has shown significant improvements in making computationally intensive computer vision analysis algorithms more tractable on large amounts of data. However, microscopy datasets in particular can be degraded by noise and most superpixel algorithms do not take this artifact into account. In this paper, we give a quantitative and qualitative comparison of superpixels generated on original and denoised images. We show that several advanced superpixel techniques are hampered by noise artifacts and require denoising and parameter tuning as a pre-processing step. The evaluation is performed on the Berkeley segmentation dataset as well as on fluorescence and scanning electron microscopy data
Aberrant visual pathway development in albinism: from retina to cortex
Albinism refers to a group of genetic abnormalities in melanogenesis that are associated neuronal misrouting through the optic chiasm. Previous imaging studies have shown structural alterations at different points along the visual pathway of people with albinism (PWA) including foveal hypoplasia, optic nerve and chiasm size alterations and visual cortex reorganisation, but fail to provide a holistic in-vivo characterisation of the visual neurodevelopmental alterations from retina to visual cortex. We perform quantitative assessment of visual pathway structure and function in 23 PWA and 20 matched controls using optical coherence tomography (OCT), volumetric magnetic resonance imaging (MRI), diffusion tensor imaging and visual evoked potentials (VEP). PWA had a higher streamline decussation index (percentage of total tractography streamlines decussating at the chiasm) compared to controls (Z=-2.24, p=0.025), and streamline decussation index correlated weakly significantly with inter-hemispheric asymmetry measured using VEP (r=0.484, p=0.042). For PWA, a significant correlation was found between foveal development index and total number of streamlines (r=0.662, p less than 0.001). Optic nerve (p=0.001) and tract (p=0.010) width, and chiasm width (P less than 0.001), area (p=0.006) and volume (p=0.005), were significantly smaller in PWA compared to controls. Significant positive correlations were found between peri-papillary retinal nerve fibre layer thickness and optic nerve (r=0.642, p less than 0.001) and tract (r=0.663, p less than 0.001) width. Occipital pole cortical thickness was 6.88% higher (Z=-4.10, p less than 0.001) in PWA and was related to anterior visual pathway structures including foveal retinal pigment epithelium complex thickness (r=-0.579, p=0.005), optic disc (r=0.478, p=0.021) and rim areas (r=0.597, p=0.003). We were unable to demonstrate a significant relationship between OCT-derived foveal or optic nerve measures and MRI-derived chiasm size or streamline decussation index. Non-invasive imaging techniques demonstrate aberrant development throughout the visual pathways of PWA compared to controls. Our novel tractographic demonstration of altered chiasmatic decussation in PWA corresponds to VEP measured cortical asymmetry and is consistent with chiasmatic misrouting in albinism. We also demonstrate a significant relationship between retinal pigment epithelium and visual cortex thickness indicating that retinal pigmentation defects in albinism lead to downstream structural reorganisation of the visual cortex
Shape Matching and Object Recognition
We approach recognition in the framework of deformable shape matching, relying on a new algorithm for finding correspondences between feature points. This algorithm sets up correspondence as an integer quadratic programming problem, where the cost function has terms based on similarity of corresponding geometric blur point descriptors as well as the geometric distortion between pairs of corresponding feature points. The algorithm handles outliers, and thus enables matching of exemplars to query images in the presence of occlusion and clutter. Given the correspondences, we estimate an aligning transform, typically a regularized thin plate spline, resulting in a dense correspondence between the two shapes. Object recognition is handled in a nearest neighbor framework where the distance between exemplar and query is the matching cost between corresponding points. We show results on two datasets. One is the Caltech 101 dataset (Li, Fergus and Perona), a challenging dataset with large intraclass variation. Our approach yields a 45 % correct classification rate in addition to localization. We also show results for localizing frontal and profile faces that are comparable to special purpose approaches tuned to faces
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