13,848 research outputs found
Hierarchy-based Image Embeddings for Semantic Image Retrieval
Deep neural networks trained for classification have been found to learn
powerful image representations, which are also often used for other tasks such
as comparing images w.r.t. their visual similarity. However, visual similarity
does not imply semantic similarity. In order to learn semantically
discriminative features, we propose to map images onto class embeddings whose
pair-wise dot products correspond to a measure of semantic similarity between
classes. Such an embedding does not only improve image retrieval results, but
could also facilitate integrating semantics for other tasks, e.g., novelty
detection or few-shot learning. We introduce a deterministic algorithm for
computing the class centroids directly based on prior world-knowledge encoded
in a hierarchy of classes such as WordNet. Experiments on CIFAR-100, NABirds,
and ImageNet show that our learned semantic image embeddings improve the
semantic consistency of image retrieval results by a large margin.Comment: Accepted at WACV 2019. Source code:
https://github.com/cvjena/semantic-embedding
Identification and analysis of seven effector protein families with different adaptive and evolutionary histories in plant-associated members of the Xanthomonadaceae.
The Xanthomonadaceae family consists of species of non-pathogenic and pathogenic γ-proteobacteria that infect different hosts, including humans and plants. In this study, we performed a comparative analysis using 69 fully sequenced genomes belonging to this family, with a focus on identifying proteins enriched in phytopathogens that could explain the lifestyle and the ability to infect plants. Using a computational approach, we identified seven phytopathogen-enriched protein families putatively secreted by type II secretory system: PheA (CM-sec), LipA/LesA, VirK, and four families involved in N-glycan degradation, NixE, NixF, NixL, and FucA1. In silico and phylogenetic analyses of these protein families revealed they all have orthologs in other phytopathogenic or symbiotic bacteria, and are involved in the modulation and evasion of the immune system. As a proof of concept, we performed a biochemical characterization of LipA from Xac306 and verified that the mutant strain lost most of its lipase and esterase activities and displayed reduced virulence in citrus. Since this study includes closely related organisms with distinct lifestyles and highlights proteins directly related to adaptation inside plant tissues, novel approaches might use these proteins as biotechnological targets for disease control, and contribute to our understanding of the coevolution of plant-associated bacteria
Multiscale Discriminant Saliency for Visual Attention
The bottom-up saliency, an early stage of humans' visual attention, can be
considered as a binary classification problem between center and surround
classes. Discriminant power of features for the classification is measured as
mutual information between features and two classes distribution. The estimated
discrepancy of two feature classes very much depends on considered scale
levels; then, multi-scale structure and discriminant power are integrated by
employing discrete wavelet features and Hidden markov tree (HMT). With wavelet
coefficients and Hidden Markov Tree parameters, quad-tree like label structures
are constructed and utilized in maximum a posterior probability (MAP) of hidden
class variables at corresponding dyadic sub-squares. Then, saliency value for
each dyadic square at each scale level is computed with discriminant power
principle and the MAP. Finally, across multiple scales is integrated the final
saliency map by an information maximization rule. Both standard quantitative
tools such as NSS, LCC, AUC and qualitative assessments are used for evaluating
the proposed multiscale discriminant saliency method (MDIS) against the
well-know information-based saliency method AIM on its Bruce Database wity
eye-tracking data. Simulation results are presented and analyzed to verify the
validity of MDIS as well as point out its disadvantages for further research
direction.Comment: 16 pages, ICCSA 2013 - BIOCA sessio
Machine Vision-Based Crop-Load Estimation Using YOLOv8
Labor shortages in fruit crop production have prompted the development of
mechanized and automated machines as alternatives to labor-intensive orchard
operations such as harvesting, pruning, and thinning. Agricultural robots
capable of identifying tree canopy parts and estimating geometric and
topological parameters, such as branch diameter, length, and angles, can
optimize crop yields through automated pruning and thinning platforms. In this
study, we proposed a machine vision system to estimate canopy parameters in
apple orchards and determine an optimal number of fruit for individual
branches, providing a foundation for robotic pruning, flower thinning, and
fruitlet thinning to achieve desired yield and quality.Using color and depth
information from an RGB-D sensor (Microsoft Azure Kinect DK), a YOLOv8-based
instance segmentation technique was developed to identify trunks and branches
of apple trees during the dormant season. Principal Component Analysis was
applied to estimate branch diameter (used to calculate limb cross-sectional
area, or LCSA) and orientation. The estimated branch diameter was utilized to
calculate LCSA, which served as an input for crop-load estimation, with larger
LCSA values indicating a higher potential fruit-bearing capacity.RMSE for
branch diameter estimation was 2.08 mm, and for crop-load estimation, 3.95.
Based on commercial apple orchard management practices, the target crop-load
(number of fruit) for each segmented branch was estimated with a mean absolute
error (MAE) of 2.99 (ground truth crop-load was 6 apples per LCSA). This study
demonstrated a promising workflow with high performance in identifying trunks
and branches of apple trees in dynamic commercial orchard environments and
integrating farm management practices into automated decision-making
Fruit sizing using AI: A review of methods and challenges
Fruit size at harvest is an economically important variable for high-quality table fruit production in orchards and vineyards. In addition, knowing the number and size of the fruit on the tree is essential in the framework of precise production, harvest, and postharvest management. A prerequisite for analysis of fruit in a real-world environment is the detection and segmentation from background signal. In the last five years, deep learning convolutional neural network have become the standard method for automatic fruit detection, achieving F1-scores higher than 90 %, as well as real-time processing speeds. At the same time, different methods have been developed for, mainly, fruit size and, more rarely, fruit maturity estimation from 2D images and 3D point clouds. These sizing methods are focused on a few species like grape, apple, citrus, and mango, resulting in mean absolute error values of less than 4 mm in apple fruit. This review provides an overview of the most recent methodologies developed for in-field fruit detection/counting and sizing as well as few upcoming examples of maturity estimation. Challenges, such as sensor fusion, highly varying lighting conditions, occlusions in the canopy, shortage of public fruit datasets, and opportunities for research transfer, are discussed.This work was partly funded by the Department of Research and Universities of the Generalitat de Catalunya (grants 2017 SGR 646 and 2021 LLAV 00088) and by the Spanish Ministry of Science and Innovation / AEI/10.13039/501100011033 / FEDER (grants RTI2018-094222-B-I00 [PAgFRUIT project] and PID2021-126648OB-I00 [PAgPROTECT project]). The Secretariat of Universities and Research of the Department of Business and Knowledge of the Generalitat de Catalunya and European Social Fund (ESF) are also thanked for financing Juan Carlos Miranda’s pre-doctoral fellowship (2020 FI_B 00586). The work of Jordi Gené-Mola was supported by the Spanish Ministry of Universities through a Margarita Salas postdoctoral grant funded by the European Union - NextGenerationEU.info:eu-repo/semantics/publishedVersio
Rapid tree model reconstruction for fruit harvesting robot system based on binocular stereo vision
In this paper, the method of spatial information extraction of tree branch was studied. The region matching method was used to get the disparity map of stereo image, extracted feature points combining with branch skeleton image by multi-segment approximation method, and calculated the spatial coordinates and the radius of branch feature points by using binocular stereo vision. Real-time model reconstruction for fruit tree has been researched on. Test proposed that each branch module was constructed by 12-prism in the coordinate origin, and then rotated twice and translated once to get correct posture, finally combined with other modules for the fruit tree model. Test has optimized extraction algorithm and matching algorithm of the branch region, improved matching rate, reduced matching errors, avoided matching confusion, accurately extracted branch spatial information and improved the success rate of robot path planning for obstacle avoidance
Deep learning in food category recognition
Integrating artificial intelligence with food category recognition has been a field of interest for research for the
past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The
modern advent of big data and the development of data-oriented fields like deep learning have provided advancements
in food category recognition. With increasing computational power and ever-larger food datasets,
the approach’s potential has yet to be realized. This survey provides an overview of methods that can be applied
to various food category recognition tasks, including detecting type, ingredients, quality, and quantity. We
survey the core components for constructing a machine learning system for food category recognition, including
datasets, data augmentation, hand-crafted feature extraction, and machine learning algorithms. We place a
particular focus on the field of deep learning, including the utilization of convolutional neural networks, transfer
learning, and semi-supervised learning. We provide an overview of relevant studies to promote further developments
in food category recognition for research and industrial applicationsMRC (MC_PC_17171)Royal Society (RP202G0230)BHF (AA/18/3/34220)Hope Foundation for Cancer Research (RM60G0680)GCRF (P202PF11)Sino-UK Industrial
Fund (RP202G0289)LIAS (P202ED10Data Science
Enhancement Fund (P202RE237)Fight for Sight (24NN201);Sino-UK
Education Fund (OP202006)BBSRC (RM32G0178B8
Fruit detection and 3D location using instance segmentation neural networks and structure-from-motion photogrammetry
The development of remote fruit detection systems able to identify and 3D locate fruits provides opportunities to improve the efficiency of agriculture management. Most of the current fruit detection systems are based on 2D image analysis. Although the use of 3D sensors is emerging, precise 3D fruit location is still a pending issue. This work presents a new methodology for fruit detection and 3D location consisting of: (1) 2D fruit detection and segmentation using Mask R-CNN instance segmentation neural network; (2) 3D point cloud generation of detected apples using structure-from-motion (SfM) photogrammetry; (3) projection of 2D image detections onto 3D space; (4) false positives removal using a trained support vector machine. This methodology was tested on 11 Fuji apple trees containing a total of 1455 apples. Results showed that, by combining instance segmentation with SfM the system performance increased from an F1-score of 0.816 (2D fruit detection) to 0.881 (3D fruit detection and location) with respect to the total amount of fruits. The main advantages of this methodology are the reduced number of false positives and the higher detection rate, while the main disadvantage is the high processing time required for SfM, which makes it presently unsuitable for real-time work. From these results, it can be concluded that the combination of instance segmentation and SfM provides high performance fruit detection with high 3D data precision. The dataset has been made publicly available and an interactive visualization of fruit detection results is accessible at http://www.grap.udl.cat/documents/photogrammetry_fruit_detection.html. Dades primà ries associades a l'article http://hdl.handle.net/10459.1/68505This work was partly funded by the Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya (grant 2017 SGR646), the Spanish Ministry of Economy and Competitiveness (project AGL2013-48297-C2-2-R) and the Spanish Ministry of Science, Innovation and Universities (project RTI2018-094222-B-I00). Part of the work was also developed within the framework of the project TEC2016-75976-R, financed by the Spanish Ministry of Economy, Industry and Competitiveness and the European Regional Development Fund (ERDF). The Spanish Ministry of Educationis thanked for Mr. J.Gené’s pre-doctoral fellowships (FPU15/03355). We would also like to thank Nufri (especially Santiago Salamero and Oriol Morreres) and Vicens Maquinà ria AgrÃcola S.A. for their support during data acquisition, and Ernesto Membrillo and Roberto Maturino for their support in dataset labelling
Detection and localization of cotton based on deep neural networks
Cotton detection is the localization and identification of the cotton in an image. It has a wide application in robot harvesting. Various modern algorithms use deep learning techniques for detection of fruits/flowers. As per the survey, the topics travelled include numerous algorithms used, and accuracy obtained on using those algorithms on their data set. The limitations and the advantages in each paper, are also discussed. This paper focuses on various fruit detection algorithms- the Faster RCNN, the RCNN, YOLO. Ultimately, a rigorous survey of many papers related to the detection of objects like fruits/flowers, analysis of the assets and faintness of each paper leads us to understanding the techniques and purpose of algorithms.
 
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