Multispecies Fruit Flower Detection Using a Refined Semantic Segmentation Network

In fruit production, critical crop management decisions are guided by bloom intensity, i.e., the number of flowers present in an orchard. Despite its importance, bloom intensity is still typically estimated by means of human visual inspection. Existing automated computer vision systems for flower identification are based on hand-engineered techniques that work only under specific conditions and with limited performance. This letter proposes an automated technique for flower identification that is robust to uncontrolled environments and applicable to different flower species. Our method relies on an end-to-end residual convolutional neural network (CNN) that represents the state-of-the-art in semantic segmentation. To enhance its sensitivity to flowers, we fine-tune this network using a single dataset of apple flower images. Since CNNs tend to produce coarse segmentations, we employ a refinement method to better distinguish between individual flower instances. Without any preprocessing or dataset-specific training, experimental results on images of apple, peach, and pear flowers, acquired under different conditions demonstrate the robustness and broad applicability of our method

Automatic Segmentation of Trees in Dynamic Outdoor Environments

Segmentation in dynamic outdoor environments can be difficult when the illumination levels and other aspects of the scene cannot be controlled. Specifically in orchard and vineyard automation contexts, a background material is often used to shield a camera\u27s field of view from other rows of crops. In this paper, we describe a method that uses superpixels to determine low texture regions of the image that correspond to the background material, and then show how this information can be integrated with the color distribution of the image to compute optimal segmentation parameters to segment objects of interest. Quantitative and qualitative experiments demonstrate the suitability of this approach for dynamic outdoor environments, specifically for tree reconstruction and apple flower detection application

Multi-View Face Recognition From Single RGBD Models of the Faces

This work takes important steps towards solving the following problem of current interest: Assuming that each individual in a population can be modeled by a single frontal RGBD face image, is it possible to carry out face recognition for such a population using multiple 2D images captured from arbitrary viewpoints? Although the general problem as stated above is extremely challenging, it encompasses subproblems that can be addressed today. The subproblems addressed in this work relate to: (1) Generating a large set of viewpoint dependent face images from a single RGBD frontal image for each individual; (2) using hierarchical approaches based on view-partitioned subspaces to represent the training data; and (3) based on these hierarchical approaches, using a weighted voting algorithm to integrate the evidence collected from multiple images of the same face as recorded from different viewpoints. We evaluate our methods on three datasets: a dataset of 10 people that we created and two publicly available datasets which include a total of 48 people. In addition to providing important insights into the nature of this problem, our results show that we are able to successfully recognize faces with accuracies of 95% or higher, outperforming existing state-of-the-art face recognition approaches based on deep convolutional neural networks

Wireless Medical Sensor Networks: Design Requirements and Enabling Technologies

This article analyzes wireless communication protocols that could be used in healthcare environments (e.g., hospitals and small clinics) to transfer real-time medical information obtained from noninvasive sensors. For this purpose the features of the three currently most widely used protocols—namely, Bluetooth® (IEEE 802.15.1), ZigBee (IEEE 802.15.4), and Wi-Fi (IEEE 802.11)—are evaluated and compared. The important features under consideration include data bandwidth, frequency band, maximum transmission distance, encryption and authentication methods, power consumption, and current applications. In addition, an overview of network requirements with respect to medical sensor features, patient safety and patient data privacy, quality of service, and interoperability between other sensors is briefly presented. Sensor power consumption is also discussed because it is considered one of the main obstacles for wider adoption of wireless networks in medical applications. The outcome of this assessment will be a useful tool in the hands of biomedical engineering researchers. It will provide parameters to select the most effective combination of protocols to implement a specific wireless network of noninvasive medical sensors to monitor patients remotely in the hospital or at home

Apple Flower Detection Using Deep Convolutional Networks

To optimize fruit production, a portion of the flowers and fruitlets of apple trees must be removed early in the growing season. The proportion to be removed is determined by the bloom intensity, i.e., the number of flowers present in the orchard. Several automated computer vision systems have been proposed to estimate bloom intensity, but their overall performance is still far from satisfactory even in relatively controlled environments. With the goal of devising a technique for flower identification which is robust to clutter and to changes in illumination, this paper presents a method in which a pre-trained convolutional neural network is fine-tuned to become specially sensitive to flowers. Experimental results on a challenging dataset demonstrate that our method significantly outperforms three approaches that represent the state of the art in flower detection, with recall and precision rates higher than 90%. Moreover, a performance assessment on three additional datasets previously unseen by the network, which consist of different flower species and were acquired under different conditions, reveals that the proposed method highly surpasses baseline approaches in terms of generalization capability

Deep Convolutional Particle Filter with Adaptive Correlation Maps for Visual Tracking

The robustness of the visual trackers based on the correlation maps generated from convolutional neural networks can be substantially improved if these maps are used to employed in conjunction with a particle filter. In this article, we present a particle filter that estimates the target size as well as the target position and that utilizes a new adaptive correlation filter to account for potential errors in the model generation. Thus, instead of generating one model which is highly dependent on the estimated target position and size, we generate a variable number of target models based on high likelihood particles, which increases in challenging situations and decreases in less complex scenarios. Experimental results on the Visual Tracker Benchmark vl.0 demonstrate that our proposed framework significantly outperforms state-of-the-art methods

Stochastic Search Methods for Mobile Manipulators

Mobile manipulators are a potential solution to the increasing need for additional flexibility and mobility in industrial applications. However, they tend to lack the accuracy and precision achieved by fixed manipulators, especially in scenarios where both the manipulator and the autonomous vehicle move simultaneously. This paper analyzes the problem of dynamically evaluating the positioning error of mobile manipulators. In particular, it investigates the use of Bayesian methods to predict the position of the end-effector in the presence of uncertainty propagated from the mobile platform. The precision of the mobile manipulator is evaluated through its ability to intercept retroreflective markers using a photoelectric sensor attached to the end-effector. Compared to a deterministic search approach, we observed improved robustness with comparable search times, thereby enabling effective calibration of the mobile manipulator

A note on spin-s duality

Duality is investigated for higher spin ($s \geq 2$), free, massless, bosonic gauge fields. We show how the dual formulations can be derived from a common "parent", first-order action. This goes beyond most of the previous treatments where higher-spin duality was investigated at the level of the equations of motion only. In D=4 spacetime dimensions, the dual theories turn out to be described by the same Pauli-Fierz (s=2) or Fronsdal ($s \geq 3$) action (as it is the case for spin 1). In the particular s=2 D=5 case, the Pauli-Fierz action and the Curtright action are shown to be related through duality. A crucial ingredient of the analysis is given by the first-order, gauge-like, reformulation of higher spin theories due to Vasiliev.Comment: Minor corrections, reference adde

Discovery of a weak magnetic field in the photosphere of the single giant Pollux

Aims: We observe the nearby, weakly-active single giant, Pollux, in order to directly study and infer the nature of its magnetic field. Methods: We used the new generation spectropolarimeters ESPaDOnS and NARVAL to observe and detect circular polarization within the photospheric absorption lines of Pollux. Our observations span 18 months from 2007-2009. We treated the spectropolarimetric data using the Least-Squares Deconvolution method to create high signal-to-noise ratio mean Stokes V profiles. We also measured the classical activity indicator S-index for the Ca H&K lines, and the stellar radial velocity (RV). Results: We have unambiguously detected a weak Stokes V signal in the spectral lines of Pollux, and measured the related surface-averaged longitudinal magnetic field Bl. The longitudinal field averaged over the span of the observations is below one gauss. Our data suggest variations of the longitudinal magnetic field, but no significant variation of the S-index. We observe variations of RV which are qualitatively consistent with the published ephemeris for a proposed exoplanet orbiting Pollux. The observed variations of Bl appear to mimic those of RV, but additional data for this relationship to be established. Using evolutionary models including the effects of rotation, we derive the mass of Pollux and we discuss its evolutionary status and the origin of its magnetic field. Conclusions: This work presents the first direct detection of the magnetic field of Pollux, and demonstrates that ESPaDOnS and NARVAL are capable of obtaining sub-G measurements of the surface-averaged longitudinal magnetic field of giant stars, and of directly studying the relationships between magnetic activity, stellar evolution and planet hosting of these stars.Comment: 8 pages, 6 figures, accepted for publication in Astronomy and Astrophysic