@article {233, title = {Grapevine buds detection and localization in 3D space based on Structure from Motion and 2D image classification}, journal = {Computers in Industry}, volume = {99C }, year = {2018}, month = {04/2018}, pages = {303-312}, chapter = {303}, abstract = {

In viticulture, there are several applications where 3D bud detection and localization in vineyards is a necessary task susceptible to automation: measurement of sunlight exposure, autonomous pruning, bud counting, type-of-bud classification, bud geometric characterization, internode length, and bud development stage.\ This paper presents a workflow to achieve quality 3D localizations of grapevine buds based on well-known computer vision and machine learning algorithms when provided with images captured in natural field conditions (i.e., natural sunlight and the addition of no artificial elements), during the winter season and using a mobile phone RGB camera. Our pipeline combines the Oriented FAST and Rotated BRIEF (ORB) for keypoint detection, a Fast Local Descriptor for Dense Matching (DAISY) for describing the keypoint, and the Fast Approximate Nearest Neighbor (FLANN) technique for matching keypoints, with the Structure from Motion multi-view scheme for generating consistent 3D point clouds. Next, it uses a 2D scanning window classifier based on Bag of Features and Support Vectors Machine for classification of 3D points in the cloud. Finally, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for 3D bud localization is applied. Our approach resulted in a maximum precision\ of 1.0\ (i.e., no false detections), a maximum recall\ of 0.45\ (i.e. 45\%\ of the buds detected), and a localization error within the range of 259-554\ pixels (corresponding to approximately 3\ bud diameters, or 1.5cm) when evaluated over the whole range of user-given parameters of workflow components.

}, keywords = {computer vision, Grapevine bud detection, Precision viticulture}, issn = {0166-3615}, doi = {10.1016/j.compind.2018.03.033}, url = {https://www.sciencedirect.com/science/article/pii/S0166361517304815}, author = {Diaz, Carlos Ariel and P{\'e}rez, Diego Sebasti{\'a}n and Miatello, Humberto and Bromberg, Facundo} } @article {210, title = {Image classification for detection of winter grapevine buds in natural conditions using scale-invariant features transform, bag of features and support vector machines}, journal = {Computers and Electronics in Agriculture}, volume = {135}, year = {2017}, month = {04/2017}, pages = {81-95}, chapter = {81}, abstract = {

In viticulture, there are several applications where bud detection in vineyard images is a necessary task, susceptible of being automated through the use of computer vision methods. A common and effective family of visual detection algorithms are the scanning-window type, that slide a (usually) fixed size window along the original image, classifying each resulting windowed-patch as containing or not containing the target object. The simplicity of these algorithms finds its most challenging aspect in the classification stage. Interested in grapevine buds detection in natural field conditions, this paper presents a classification method for images of grapevine buds ranging 100 to 1600 pixels in diameter, captured in outdoor, under natural field conditions, in winter (i.e., no grape bunches, very few leaves, and dormant buds), without artificial background, and with minimum equipment requirements. The proposed method uses well-known computer vision technologies: Scale-Invariant Feature Transform for calculating low-level features, Bag of Features for building an image descriptor, and Support Vector Machines for training a classifier. When evaluated over images containing buds of at least 100 pixels in diameter, the approach achieves a recall higher than 0.9 and a precision of 0.86 over all windowed-patches covering the whole bud and down to 60\% of it, and scaled up to window patches containing a proportion of 20\%-80\% of bud versus background pixels. This robustness on the position and size of the window demonstrates its viability for use as the classification stage in a scanning-window detection algorithms.

}, keywords = {computer vision, Grapevine bud, Image classification, Precision viticulture, Scanning-window detection}, issn = {0168-1699}, doi = {10.1016/j.compag.2017.01.020}, url = {http://www.sciencedirect.com/science/article/pii/S0168169916301818}, author = {P{\'e}rez, Diego Sebasti{\'a}n and Bromberg, Facundo and Diaz, Carlos Ariel} } @article {256, title = {Towards practical 2D grapevine bud detection with fully convolutional networks}, journal = {Computers and Electronics in Agriculture}, volume = {182}, year = {2021}, month = {03/2021}, pages = {105947}, abstract = {

In Viticulture, visual inspection of the plant is a necessary task for measuring relevant variables. In many cases, these visual inspections are susceptible to automation through computer vision methods. Bud detection is one such visual task, central for the measurement of important variables such as: measurement of bud sunlight exposure, autonomous pruning, bud counting, type-of-bud classification, bud geometric characterization, internode length, bud area, and bud development stage, among others. This paper presents a computer method for grapevine bud detection based on a Fully Convolutional Networks MobileNet architecture (FCN-MN). To validate its performance, this architecture was compared in the detection task with a strong method for bud detection, Scanning Windows (SW) based on a patch classifier, showing improvements over three aspects of detection: segmentation, correspondence identification and localization. The best version of FCN-MN showed a detection F1-measure of 88.6\% (for true positives defined as detected components whose intersection-over-union with the true bud is above 0.5), and false positives that are small and near the true bud. Splits {\textendash}false positives overlapping the true bud{\textendash} showed a mean segmentation precision of 89.3\%(21.7), while false alarms {\textendash}false positives not overlapping the true bud{\textendash} showed a mean pixel area of only 8\% the area of a true bud, and a distance (between mass centers) of 1.1 true bud diameters. The paper concludes by discussing how these results for FCN-MN would produce sufficiently accurate measurements of bud variables such as bud number, bud area, and internode length, suggesting a good performance in a practical setup.

}, keywords = {computer vision, Fully convolutional network, Grapevine bud detection, Precision viticulture}, issn = {0168-1699}, doi = {https://doi.org/10.1016/j.compag.2020.105947}, url = {https://www.sciencedirect.com/science/article/pii/S0168169920331525}, author = {Wenceslao Villegas Marset and Diego Sebastian Perez and Carlos Ariel D{\'\i}az and Facundo Bromberg} } @article {78, title = {Computer Vision: Summary and Discussion}, year = {2011}, abstract = {
Review of important concepts, Some important open problems, Feedback and course evaluation
}, keywords = {computer vision, discussion, lecture, summary}, url = {http://courses.engr.illinois.edu/cs543/sp2011/lectures/Lecture\%2029\%20-\%20Summary\%20and\%20Discussion\%20-\%20Vision_Spring2011.pdf}, author = {Derek Hoiem} } @article {209, title = {Computer Vision Approach for Low Cost, High Precision Measurement of Grapevine Trunk Diameter in Outdoor Conditions}, year = {2016}, institution = {arXiv.org}, abstract = {

Trunk diameter is a variable of agricultural interest, used mainly in the prediction of fruit trees production. It is correlated with leaf area and biomass of trees, and consequently gives a good estimate of the potential production of the plants. This work presents a low cost, high precision method for the measurement of trunk diameter of grapevines based on Computer Vision techniques. Several methods based on Computer Vision and other techniques are introduced in the literature. These methods present different advantages for crop management: they are amenable to be operated by unknowledgeable personnel, with lower operational costs; they result in lower stress levels to knowledgeable personnel, avoiding the deterioration of the measurement quality over time; and they make the measurement process amenable to be embedded in larger autonomous systems, allowing more measurements to be taken with equivalent costs. To date, all existing autonomous methods are either of low precision, or have a prohibitive cost for massive agricultural adoption, leaving the manual Vernier caliper or tape measure as the only choice in most situations. In this work we present a semi-autonomous measurement method that is susceptible to be fully automated, cost effective for mass adoption, and its precision is competitive (with slight improvements) over the caliper manual method.

}, keywords = {computer vision, Gaussian mixture model, Grapevine, Measurements, Trunk Diameter}, issn = {arXiv:1406.4845v2}, url = {http://arxiv.org/abs/1406.4845}, author = {P{\'e}rez, Diego Sebasti{\'a}n and Bromberg, Facundo and Gonzalez-Antivilo, Francisco} } @article {76, title = {Frontiers in Computer Vision: NSF White Paper}, year = {2010}, institution = {Frontiers in Computer Vision}, address = {Cambridge, EEUU}, abstract = {
Computer vision started with the goal of building machines that can see like humans and perform perception for robots, but it has become much broader than that. Applications such as image database search in the world wide web, computational photography, biological imaging, vision for graphics, GIS, biometrics, vision for nanotechnology, were unanticipated and other applications keep arising as computer vision technology develops. Areas such as document analysis and medical image analysis have developed rapidly and have their own conferences. As our computers achieve even a crude understanding of video imagery, computer vision will profoundly change our lives as visual sensors becomes increasingly ubiquitous and enable us to transcend current human limitations. Rapid developments in supportive technologies -- such as digital cameras and computers -- ensure that computer vision systems will become increasingly more capable and affordable. Moreover, the field of robotics itself has enormous potential to revolutionize manufacturing, to provide service by assistive robots, to perform medical surgery -- applications which all require perceptual in put from computer vision systems. In addition, there are many applications to defense, homeland security, and the intelligence community.
}, keywords = {Challenges, computer vision, Frontiers, Historical Perspective, Impact}, url = {http://www.frontiersincomputervision.com/WhitePaperInvite.pdf}, author = {Alan Yuille and Aude Oliva} } @article {39, title = {CVPapers}, year = {2014}, keywords = {computer vision, link, online, papers, resources}, url = {http://www.cvpapers.com/} }