Automated Mobile System for Accurate Outdoor Tree Crop Enumeration Using an Uncalibrated Camera.

This paper demonstrates an automated computer vision system for outdoor tree crop enumeration in a seedling nursery. The complete system incorporates both hardware components (including an embedded microcontroller, an odometry encoder, and an uncalibrated digital color camera) and software algorithms (including microcontroller algorithms and the proposed algorithm for tree crop enumeration) required to obtain robust performance in a natural outdoor environment. The enumeration system uses a three-step image analysis process based upon: (1) an orthographic plant projection method integrating a perspective transform with automatic parameter estimation; (2) a plant counting method based on projection histograms; and (3) a double-counting avoidance method based on a homography transform. Experimental results demonstrate the ability to count large numbers of plants automatically with no human effort. Results show that, for tree seedlings having a height up to 40 cm and a within-row tree spacing of approximately 10 cm, the algorithms successfully estimated the number of plants with an average accuracy of 95.2% for trees within a single image and 98% for counting of the whole plant population in a large sequence of images

Human Pose Estimation with Implicit Shape Models

This work presents a new approach for estimating 3D human poses based on monocular camera information only. For this, the Implicit Shape Model is augmented by new voting strategies that allow to localize 2D anatomical landmarks in the image. The actual 3D pose estimation is then formulated as a Particle Swarm Optimization (PSO) where projected 3D pose hypotheses are compared with the generated landmark vote distributions

Local-HDP:Interactive Open-Ended 3D Object Categorization

We introduce a non-parametric hierarchical Bayesian approach for open-ended 3D object categorization, named the Local Hierarchical Dirichlet Process (Local-HDP). This method allows an agent to learn independent topics for each category incrementally and to adapt to the environment in time. Hierarchical Bayesian approaches like Latent Dirichlet Allocation (LDA) can transform low-level features to high-level conceptual topics for 3D object categorization. However, the efficiency and accuracy of LDA-based approaches depend on the number of topics that is chosen manually. Moreover, fixing the number of topics for all categories can lead to overfitting or underfitting of the model. In contrast, the proposed Local-HDP can autonomously determine the number of topics for each category. Furthermore, an inference method is proposed that results in a fast posterior approximation. Experiments show that Local-HDP outperforms other state-of-the-art approaches in terms of accuracy, scalability, and memory efficiency with a large margin

Dense Wide-Baseline Stereo with Varying Illumination and its Application to Face Recognition

We study the problem of dense wide baseline stereo with varying illumination. We are motivated by the problem of face recognition across pose. Stereo matching allows us to compare face images based on physically valid, dense correspondences. We show that the stereo matching cost provides a very robust measure of the similarity of faces that is insensitive to pose variations. We build on the observation that most illumination insensitive local comparisons require the use of relatively large windows. The size of these windows is affected by foreshortening. If we do not account for this effect, we incur misalignments that are systematic and significant and are exacerbated by wide baseline conditions. We present a general formulation of dense wide baseline stereo with varying illumination and provide two methods to solve them. The first method is based on dynamic programming (DP) and fully accounts for the effect of slant. The second method is based on graph cuts (GC) and fully accounts for the effect of both slant and tilt. The GC method finds a global solution using the unary function from the general formulation and a novel smoothness term that encodes surface orientation. Our experiments show that DP dense wide baseline stereo achieves superior performance compared to existing methods in face recognition across pose. The experiments with the GC method show that accounting for both slant and tilt can improve performance in situations with wide baselines and lighting variation. Our formulation can be applied to other more sophisticated window based image comparison methods for stereo