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

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

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