Recent advances in convolutional neural networks (CNN) have achieved remarkable results in locating objects in images. In these networks, the training procedure usually requires providing bounding boxes or the maximum number of expected objects. In this paper, we address the task of estimating object locations without annotated bounding boxes which are typically hand-drawn and time consuming to label. We propose a loss function that can be used in any fully convolutional network (FCN) to estimate object locations. This loss function is a modification of the average Hausdorff distance between two unordered sets of points. The proposed method has no notion of bounding boxes, region proposals, or sliding windows. We evaluate our method with three datasets designed to locate people's heads, pupil centers and plant centers. We outperform state-of-the-art generic object detectors and methods fine-tuned for pupil tracking.
Methods for pupil tracking and precision agriculture are usually domain-specific. In pupil tracking, the center of the pupil must be resolved in images obtained in real-world illumination conditions @cite_19 . A wide range of applications, from commercial applications such as video games @cite_32 , driving @cite_48 @cite_24 or microsurgery @cite_44 rely on accurate pupil tracking. In remote precision agriculture, it is critical to locate the center of plants in a crop field. Agronomists use plant traits such as plant spacing to predict future crop yield @cite_20 @cite_35 @cite_54 @cite_47 @cite_51 , and plant scientists to breed new plant varieties @cite_10 @cite_11 . @cite_36 , Aich al count wheat plants by first segmenting plant regions and then counting the number of plants in each segmented patch.
@cite_35 @cite_36 @cite_48 @cite_54 @cite_10 @cite_32 @cite_24 @cite_19 @cite_44 @cite_47 @cite_51 @cite_20 @cite_11
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To reach the goals laid out by the U.S. Government for displacing fossil fuels with biofuels, high-biomass sorghum is well-suited to achieving this goal because it requires less water per unit dry biomass and can produce very high biomass yields. In order to make biofuels economically competitive with fossil fuels it is essential to maximize production efficiency throughout the system. The goal of this study was to use remote sensing technologies to optimize the yield and harvest logistics of high-biomass sorghum with respect to production costs based on spatial variability within and among fields. Specific objectives were to compare yield to aerial multispectral imagery and develop predictive relationships. A 19.2-ha high- biomasssorghumfieldwasselectedasastudysiteandaerialmultispectralimageswereacquired with a four-camera imaging system on July 17, 2009. Sorghum plant samples were collected at predeterminedgeographiccoordinatestodeterminebiomassyield.Aerialimageswereprocessed to find relationships between image reflectance and yield of the biomass sorghum. Results showed that sorghum biomass yield in early August was closely related (R 2 = 0.76) to spectral reflectance. However, in the late season the correlations between the biomass yield and spectral reflectance were not as positive as in the early season. The eventual outcome of this work could lead to predicted-yield maps based on remotely sensed images, which could be used in developing field management practices to optimize yield and harvest logistics. C 2011 Society of In this paper, we investigate estimating emergence and biomass traits from color images and elevation maps of wheat field plots. We employ a state-of-the-art deconvolutional network for segmentation and convolutional architectures, with residual and Inception-like layers, to estimate traits via high dimensional nonlinear regression. Evaluation was performed on two different species of wheat, grown in field plots for an experimental plant breeding study. Our framework achieves satisfactory performance with mean and standard deviation of absolute difference of 1.05 and 1.40 counts for emergence and 1.45 and 2.05 for biomass estimation. Our results for counting wheat plants from field images are better than the accuracy reported for the similar, but arguably less difficult, task of counting leaves from indoor images of rosette plants. Our results for biomass estimation, even with a very small dataset, improve upon all previously proposed approaches in the literature. With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations. However, learning from synthetic images may not achieve the desired performance due to a gap between synthetic and real image distributions. To reduce this gap, we propose Simulated+Unsupervised (S+U) learning, where the task is to learn a model to improve the realism of a simulators output using unlabeled real data, while preserving the annotation information from the simulator. We develop a method for S+U learning that uses an adversarial network similar to Generative Adversarial Networks (GANs), but with synthetic images as inputs instead of random vectors. We make several key modifications to the standard GAN algorithm to preserve annotations, avoid artifacts, and stabilize training: (i) a self-regularization term, (ii) a local adversarial loss, and (iii) updating the discriminator using a history of refined images. We show that this enables generation of highly realistic images, which we demonstrate both qualitatively and with a user study. We quantitatively evaluate the generated images by training models for gaze estimation and hand pose estimation. We show a significant improvement over using synthetic images, and achieve state-of-the-art results on the MPIIGaze dataset without any labeled real data. Abstract The improved grain yield per unit area of modern maize ( Zea mays L.) hybrids is due to the increased optimum plant population rather than the improved grain yield per plant. Traits associated with tolerance to various stresses, including high plant populations, and the efficiency of capture and use of resources rendered modern hybrids more productive. As a consequence of the improved tolerance to high plant populations and the low yield potential per plant, modern hybrids are strongly population-dependent as their maximum grain yield is achieved only under a narrow spectrum of high plant populations, followed by adverse effects on hybrids’ productivity and stability. Missing plants and unevenness in the plant emergence constitute common problems in the field. Grain yield loss due to missing plants is poorly compensated by the increased yield of the surrounding plants, so grain yield per unit area decreases. Unevenness in the plant emergence leads to increased plant-to-plant variability, the impact being stronger under higher plant populations. Plant-to-plant variability is associated with decreased resource use efficiency, and therefore with reduced grain yield. At higher plant populations reduced grain yield also results from the increased pollen-to-silking interval and the following barrenness. Consequently, maize breeding should seek hybrids that combine population-tolerance and improved yield potential per plant, so that a wider range of optimum plant populations insure both high productivity and stable performance. Constraints in field phenotyping capability limit our ability to dissect the genetics of quantitative traits, particularly those related to yield and stress tolerance (e.g., yield potential as well as increased drought, heat tolerance, and nutrient efficiency, etc.). The development of effective field-based high-throughput phenotyping platforms (HTPPs) remains a bottleneck for future breeding advances. However, progress in sensors, aeronautics, and high-performance computing are paving the way. Here, we review recent advances in field HTPPs, which should combine at an affordable cost, high capacity for data recording, scoring and processing, and non-invasive remote sensing methods, together with automated environmental data collection. Laboratory analyses of key plant parts may complement direct phenotyping under field conditions. Improvements in user-friendly data management together with a more powerful interpretation of results should increase the use of field HTPPs, therefore increasing the efficiency of crop genetic improvement to meet the needs of future generations. Facial analysis in videos, including head pose estimation and facial landmark localization, is key for many applications such as facial animation capture, human activity recognition, and human-computer interaction. In this paper, we propose to use a recurrent neural network (RNN) for joint estimation and tracking of facial features in videos. We are inspired by the fact that the computation performed in an RNN bears resemblance to Bayesian filters, which have been used for tracking in many previous methods for facial analysis from videos. Bayesian filters used in these methods, however, require complicated, problem-specific design and tuning. In contrast, our proposed RNN-based method avoids such tracker-engineering by learning from training data, similar to how a convolutional neural network (CNN) avoids feature-engineering for image classification. As an end-to-end network, the proposed RNN-based method provides a generic and holistic solution for joint estimation and tracking of various types of facial features from consecutive video frames. Extensive experimental results on head pose estimation and facial landmark localization from videos demonstrate that the proposed RNN-based method outperforms frame-wise models and Bayesian filtering. In addition, we create a large-scale synthetic dataset for head pose estimation, with which we achieve state-of-the-art performance on a benchmark dataset. Modern microsurgery is a long and complex task requiring the surgeon to handle multiple microscope controls while performing the surgery. Eye tracking provides an additional means of interaction for the surgeon that could be used to alleviate this situation, diminishing surgeon fatigue and surgery time, thus decreasing risks of infection and human error. In this paper, we introduce a novel algorithm for pupil detection tailored for eye images acquired through an unmodified microscope ocular. The proposed approach, the Hough transform, and six state-of-the-art pupil detection algorithms were evaluated on over 4000 hand-labeled images acquired from a digital operating microscope with a non-intrusive monitoring system for the surgeon eyes integrated. Our results show that the proposed method reaches detection rates up to 71 for an error of 3 w.r.t the input image diagonal; none of the state-of-the-art pupil detection algorithms performed satisfactorily. The algorithm and hand-labeled data set can be downloaded at:: www.ti.uni-tuebingen.de perception. New data set of high resolution pupil recordings.Novel algorithm for pupil detection improving over the state-of-the art.Novel challenges rising through to the high resolution recordings of the pupil.A novel non-invasive pupil monitoring system. Producing corn (Zea mays L.) in narrow row spacings has been attempted over the last several decades with varying degrees of success. The objectives of this study, are to determine: (i) if there is a different optimum plant density or (ii) if hybrids of varying relative maturity respond differently for corn grown in narrow row spacings (38 cm) compared with conventional row widths (76 cm). These studies were conducted at six locations across Iowa over 3 yr. Averaged across years, locations, and plant densities, corn grown in 76-cm row spacings produced higher yields than that grown in 38-cm rows (10.5 vs. 10.3 Mg ha 1 , respectively). Harvest moisture content of corn grown in 38-cm row spacings was significantly less than that of corn grown in 76-cm row spacings (160 vs. 161 g kg -1 , respectively). Averaged across years and locations, there was no statistically significant (P < 0.05) yield difference between the two row spacings for four of the six hybrids tested. Hybrid MAX23 yielded significantly more grain in 76-cm row spacings (9.4 vs. 8.9 Mg ha -1 ) while MAX454' yielded more grain in 38-cm row spacings (10.0 vs. 9.8 Mg ha -1 ). It is thus concluded that optimum plant densities for narrow-row corn production are similar to those required to produce maximum yields for conventional wide-row corn production. In addition, a strong hybrid x row spadng interaction among the six hybrids tested here suggests that certain hybrids may perform better at prescribed row spacings. Field experiments were conducted during the wet season of 2009 and dry season of 2010 to determine the effects of row spacing and timing of weed control on weed growth and yield of aerobic rice. Ten weed management treatments were used to identify critical periods of weed competition with aerobic rice grown in three different row spacings (15-cm, 30-cm, and as paired rows 10-20-10-cm). Dominant weed species during both growing seasons were Rottboellia cochinchinensis, Digitaria ciliaris, Echinochloa colona, and Eleusine indica. Rice grown in 30-cm rows had greater weed biomass and less grain yield than in 15-cm and 10-20-10-cm rows; weed growth and grain yields were similar between 15-cm and 10-20-10-cm rows. Rice yields in the wet season ranged from 170kgha-1 where weeds were not controlled throughout the crop duration to 2940kgha-1 in weed-free treatment, indicating a 94 yield loss with uncontrolled weed growth. Similarly in the dry season, plots with no weed control (140kgha-1) compared to weed-free plots (3640kgha-1) indicate a 96 yield loss with no weed control. Gompertz and logistic equations were fitted to yield data resulting from increasing durations of weed control and weed interference, respectively. Critical periods for weed control in the wet season, to obtain 95 of a weed-free yield, were estimated as between 18 and 52 days after sowing (DAS) for crops in rows at 15-cm, 20-51DAS at 10-20-10-cm, and 15-58DAS at 30-cm. These intervals in the dry season were 17-56DAS for crops in rows at 15-cm and 17-60DAS at 10-20-10-cm and 15-64DAS at 30-cm. Durations of the critical periods in the wet season were 31 days at 10-20-10-cm, 34 days at 15-cm and 43 days at 30-cm, while in the dry season, these were 43 days at 10-20-10-cm, 39 days at 15-cm and 49 days at 30-cm. In both seasons, crops in the wider spacing (30-cm) were vulnerable to weed competition for the longest period. The information gained from this study suggests that the aerobic rice yields better in 15-cm rows and 10-20-10-cm arrangements than in 30-cm rows and there is very little benefit of weed control beyond 8 weeks after sowing. From the established responses of plants to photosynthetic activity and to the availability of nutrients in the soil, a crop yield : planting density response equation is derived with parameters that are of biological significance. The relationship between crop yield, Y (kg m 2) and planting density, p (number of plants m 2), is of considerable agronomic interest (Holliday, 1960 a, b , c ). Many different equations have been applied to the problem (Willey and Heath, 1 969, give a comprehensive review), and considerable success has been achieved in finding empirical expressions that fit data from a variety of experimental situations. Efforts have been made to provide a biological or mechanistic basis for some of the relationships used (Willey and Heath, 1969; Pant, 1979), although these are largely unconvincing. The purpose of this note is to derive, from the established responses of plants to photosynthetic activity and to nutrients from the soil, a crop yield : planting density response equation with parameters that are of biological significance. Let c (kg carbon per plant) be the quantity of carbon substrate available to each plant; to be specific, it is assumed that the soil nutrient limiting growth is nitrogen, and n (kg nitrogen per plant) is defined as the quantity of nitrogen substrate available to each plant. The dependence of plant dry matter w (kg) upon the carbon and nitrogen substrates c and n is, by assumption, The use of high-throughput phenotyping systems and non-destructive imaging is widely regarded as a key technology allowing scientists and breeders to develop crops with the ability to perform well under diverse environmental conditions. However, many of these phenotyping studies have been optimized using the model plant Arabidopsis thaliana. In this study, The Plant Accelerator® at The University of Adelaide, Australia, was used to investigate the growth and phenotypic response of the important cereal crop, Sorghum bicolor L. Moench and related hybrids to water-limited conditions and different levels of fertilizer. Imaging in different spectral ranges was used to monitor plant composition, chlorophyll, and moisture content. Phenotypic image analysis accurately measured plant biomass. The data set obtained enabled the responses of the different sorghum varieties to the experimental treatments to be differentiated and modelled. Plant architectural instead of architecture elements were determined using imaging and found to correlate with an improved tolerance to stress, for example diurnal leaf curling and leaf area index. Analysis of colour images revealed that leaf ‘greenness’ correlated with foliar nitrogen and chlorophyll, while near infrared reflectance (NIR) analysis was a good predictor of water content and leaf thickness, and correlated with plant moisture content. It is shown that imaging sorghum using a high-throughput system can accurately identify and differentiate between growth and specific phenotypic traits. R scripts for robust, parsimonious models are provided to allow other users of phenomic imaging systems to extract useful data readily, and thus relieve a bottleneck in phenotypic screening of multiple genotypes of key crop plants.