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Deep Transfer Learning For Plant Center Localization

Deep Transfer Learning For Plant Center Localization

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Enyu Cai Sriram Baireddy Changye Yang Melba Crawford Edward J. Delp Video and Image Processing Laboratory (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA School of Civil Engineering Purdue University West Lafayette Indiana USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Plant phenotyping focuses on the measurement of plant characteristics throughout the growing season, typically with the goal of evaluating genotypes for plant breeding. Estimating plant location is important for identifying genotypes which have low emergence, which is also related to the environment and management practices such as fertilizer applications. The goal of this paper is to investigate methods that estimate plant locations for afield-based crop using RGB aerial images captured using Unmanned Aerial Vehicles (UAVs). Deep learning approaches provide promising capability for locating plants observed in RGB images, but they require large quantities of labeled data (ground truth) for training. Using a deep learning architecture fine-tuned on a single field or a single type of crop on fields in other geographic areas or with other crops may not have good results. The problem of generating ground truth for each new field is labor-intensive and tedious. In this paper, we propose a method for estimating plant centers by transferring an existing model to a new scenario using limited ground truth data. We describe the use of transfer learning using a model fine-tuned for a single field or a single type of plant on a varied set of similar crops and fields. We show that transfer learning provides promising results for detecting plant locations.

关键词： Training Machine learning Task analysis Agriculture Data models Training data Shape

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A Novel Technique Combining Image Processing, Plant Development Properties, and the Hungarian Algorithm, to Improve Leaf Detection in Maize

A Novel Technique Combining Image Processing, Plant Developm...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Nazifa Azam Khan Oliver A. S. Lyon Mark Eramian Ian McQuillan Department of Computer Science University of Saskatchewan Saskatoon SK Canada School of Computing Queen’s University Kingston ON Canada

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Manual determination of plant phenotypic properties such as plant architecture, growth, and health is very time consuming and sometimes destructive. Automatic image analysis has become a popular approach. This research aims to identify the position (and number) of leaves from a temporal sequence of high-quality indoor images consisting of multiple views, focussing in particular of images of maize. The procedure used a segmentation on the images, using the convex hull to pick the best view at each time step, followed by a skeletonization of the corresponding image. To remove skeleton spurs, a discrete skeleton evolution pruning process was applied. Pre-existing statistics regarding maize development was incorporated to help differentiate between true leaves and false leaves. Furthermore, for each time step, leaves were matched to those of the previous and next three days using the graph-theoretic Hungarian algorithm. This matching algorithm can be used to both remove false positives, and also to predict true leaves, even if they were completely occluded from the image itself. The algorithm was evaluated using an open dataset consisting of 13 maize plants across 27 days from two different views. The total number of true leaves from the dataset was 1843, and our proposed techniques detects a total of 1690 leaves including 1674 true leaves, and only 16 false leaves, giving a recall of 90.8%, and a precision of 99.0%.

关键词： Skeleton Image segmentation Topology Prediction algorithms Agriculture computer vision

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computer-aided diagnosis system of lung carcinoma using Convolutional Neural Networks

Computer-aided diagnosis system of lung carcinoma using Conv...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Fangjian Han Li Yu Yi Jiang Lensee Bio-Technology Co.Ltd Ningbo China 2nd Xiangya Hospital of Central South University Changsha China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

With the number of lung cancers' morbidity and mortality is showing a trend of increasing year by year, the demand for pathologists is increasing rapidly, so in this study, we aimed to design a medical pathologically assistant diagnostic system to help pathologists complete diagnostic analysis tasks. A Deep Convolutional Neural Network(DCNN) is adopted to automatically distinguish tumor tissues from normal tissues in digitized hematoxylin and eosin (H&E) stained lung cell pathological slides that collected from The Cancer Genome Atlas (TCGA) and collaborate hospitals, we trained and evaluate WSIs(the whole slide images) captured at 10x magnification and other higher magnification, results show the difference are negligible. Moreover, we also compared the training effect of different models on same level magnification WSIs, the results show that performance of Resnet-18 network model and Resnet-50 network model is nearly consistent. Actually processing time based on Resnet-18 model is shorter than Resnet-50 model, so we don't need deeper network for study. Our system was shown to enormous advantages in accuracy, sensitivity and efficiency, could reduce the burden on pathologists, enable them to spend more time on advanced decision-making tasks, would be widely applied to pathological diagnosis, clinical practice, scientific research and so on.

关键词： Training Lung Cancer Pathology Hospitals Medical diagnostic imaging

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End-to-End Lane Marker Detection via Row-wise Classification

End-to-End Lane Marker Detection via Row-wise Classification

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Seungwoo Yoo Hee Seok Lee Heesoo Myeong Sungrack Yun Hyoungwoo Park Janghoon Cho Duck Hoon Kim Qualcomm Korea YH

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

In autonomous driving, detecting reliable and accurate lane marker positions is a crucial yet challenging task. The conventional approaches for the lane marker detection problem perform a pixel-level dense prediction task followed by sophisticated post-processing that is inevitable since lane markers are typically represented by a collection of line segments without thickness. In this paper, we propose a method performing direct lane marker vertex prediction in an end-to-end manner, i.e., without any post-processing step that is required in the pixel-level dense prediction task. Specifically, we translate the lane marker detection problem into a row-wise classification task, which takes advantage of the innate shape of lane markers but, surprisingly, has not been explored well. In order to compactly extract sufficient information about lane markers which spread from the left to the right in an image, we devise a novel layer, inspired by [8], which is utilized to successively compress horizontal components so enables an end-to-end lane marker detection system where the final lane marker positions are simply obtained via argmax operations in testing time. Experimental results demonstrate the effectiveness of the proposed method, which is on par or outperforms the state-of-the-art methods on two popular lane marker detection benchmarks, i.e., TuSimple and CULane.

关键词： Task analysis Image segmentation computer architecture Semantics Visualization Spatial resolution Computational complexity

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Anomaly candidate identification and starting time estimation of vehicles from traffic videos 32

Anomaly candidate identification and starting time estimatio...

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32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Wang, Gaoang Yuan, Xinyu Zhang, Aotian Hsu, Hung-Min Hwang, Jenq-Neng Department of Electrical and Computer Engineering University of Washington United States Research Center for Information Technology Innovation Academia Sinica Taiwan

ISBN: (纸本)9781728125060

Anomaly event detection on road traffic has been a challenging field mainly due to lack of training data and a wide variety of anomaly cases. In this paper, we propose a novel two-stage framework for anomaly event detection in road traffic based on anomaly candidate identification and starting time estimation of vehicles. First, we use Gaussian mixture models (GMMs) to generate the foreground mask and background image to identify the anomaly candidates. Foreground mask is used to produce the region of interest (ROI) to filter out the noise from the object detector, YOLOv3, in the background image. Then, we apply the TrackletNet Tracker (TNT) to extract the trajectory of anomaly candidate to estimate the anomaly starting time. Experimental results, with achieved S3 score performance of 93.62%, on the Track 3 testing set of CVPR AI City Challenge 2019 City Flow dataset, show the effectiveness of the proposed framework and its robustness in different real scenes. © 2019 ieee computer Society. All rights reserved.

关键词： Object detection

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Generative Feature Replay For Class-Incremental Learning

Generative Feature Replay For Class-Incremental Learning

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Xialei Liu Chenshen Wu Mikel Menta Luis Herranz Bogdan Raducanu Andrew D. Bagdanov Shangling Jui Joost van de Weijer Computer Vision Center Universitat Autonoma de Barcelona Barcelona Spain Media Integration and Communication Center University of Florence Florence Italy Huawei Kirin Solution China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Humans are capable of learning new tasks without forgetting previous ones, while neural networks fail due to catastrophic forgetting between new and previously-learned tasks. We consider a class-incremental setting which means that the task-ID is unknown at inference time. The imbalance between old and new classes typically results in a bias of the network towards the newest ones. This imbalance problem can either be addressed by storing exemplars from previous tasks, or by using image replay methods. However, the latter can only be applied to toy datasets since image generation for complex datasets is a hard *** propose a solution to the imbalance problem based on generative feature replay which does not require any exemplars. To do this, we split the network into two parts: a feature extractor and a classifier. To prevent forgetting, we combine generative feature replay in the classifier with feature distillation in the feature extractor. Through feature generation, our method reduces the complexity of generative replay and prevents the imbalance problem. Our approach is computationally efficient and scalable to large datasets. Experiments confirm that our approach achieves state-of-the-art results on CIFAR-100 and ImageNet, while requiring only a fraction of the storage needed for exemplar-based continual learning. Code available at https://***/xialeiliu/GFR-IL.

关键词： Task analysis Feature extraction Gallium nitride Image generation Correlation Training Generators

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C3Net: Demoiréing Network Attentive in Channel, Color and Concatenation

C3Net: Demoiréing Network Attentive in Channel, Color and C...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Sangmin Kim Hyungjoon Nam Jisu Kim Jechang Jeong Department of Electronics and Computer Engineering Hanyang University Seoul Korea Department of Electronic Engineering Hanyang University Seoul Korea

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Attentive neural networks for image restoration are in the spotlight because they got remarkable results both qualitatively and quantitatively. Networks attentive in RGB channels were effective in fields such as single image super-resolution and RAW to RGB mapping. In addition, networks attentive in positions of pixels were used in image denoising. However, networks attentive in positions of pixels, so called spatial attention or pixel attention algorithm, were not as effective in image restoration because the number of pixels in patches of an image is so many that the weights by sigmoid function are insignificant. Also, networks attentive in positions of pixels were mainly used in high-level vision such as image classification and image captioning where there is no need to restore an image itself. In this paper, we propose a demoiréing network attentive in channel, color, and concatenation, named C3Net. The proposed algorithm uses residual blocks attentive in RGB channels to take advantage of channel attention algorithm. In addition, we introduce a L1 color loss for demoiréing to solve moiré patterns caused by color-striped patterns. Also, we transferred multi-scale information by concatenation, not multiplying with the insignificant weights by sigmoid function. As a result, our proposed C3Net showed state-of-the-art results in the benchmark dataset on NTIRE 2020 demoiréing challenge.

关键词： Image color analysis Image restoration Neural networks Feature extraction Benchmark testing Natural language processing Sensors

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Categorical timeline allocation and alignment for diagnostic head movement tracking feature analysis 32

Categorical timeline allocation and alignment for diagnostic...

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32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Ogihara, Mitsunori Hammal, Zakia Martin, Katherine B. Cohn, Jeffrey F. Cassell, Justine Ren, Gang Messinger, Daniel S. Dept. Computer Science University of Miami United States Robotics Institute Carnegie Mellon University United States Tobii Pro Dept. Psychology University of Pittsburgh United States Human-Computer Interaction Institute Carnegie Mellon University United States Ctr. Comp. Science University of Miami United States Dept. Psychology University of Miami United States

ISBN: (纸本)9781728125060

Atypical head movement pattern characterization is a potentially important cue for identifying children with autism spectrum disorder. In this paper, we implemented a computational framework for extracting the temporal patterns of head movement and utilizing the imbalance of temporal pattern distribution between diagnostic categories (e.g., children with or without autism spectrum disorder) as potential diagnostic cues. The timeline analysis results show a large number of temporal patterns with significant imbalances between diagnostic categories. The temporal patterns show strong classification power on discriminative and predictive analysis metrics. The long time-span temporal patterns (e.g., patterns spanning 15-30 sec.) exhibit stronger discriminative capabilities compared with the temporal patterns with relatively shorter time spans. Temporal patterns with high coverage ratios (existing in a large portion of the video durations) also show high discriminative capacity. © 2019 ieee computer Society. All rights reserved.

关键词： Diseases

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Cross-Domain Knowledge Transfer for Prediction of Chemosensitivity in Ovarian Cancer Patients

Cross-Domain Knowledge Transfer for Prediction of Chemosensi...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Asfand Yaar Amina Asif Shan E Ahmed Raza Nasir Rajpoot Fayyaz Minhas Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences Pakistan Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan Tissue Image Analytics Lab & PathLAKE CoE University of Warwick UK

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

In this paper, we report a novel deep neural network framework for prediction of chemo-sensitivity in ovarian cancer patients. The proposed model is based on Multiple Instance Learning (MIL) and a novel variant of Learning using Privileged Information (LUPI). LUPI allows knowledge transfer from highly informative privileged features that are available only at training time to give improved generalization performance on input space features which are available in both training and inference. The proposed model is trained on image patches from Hematoxylin and Eosin (H&E) stained multi-gigapixel whole-slide images (WSIs, the input space) of ovarian cancer tissue sections and their associated gene expression profiles, the privileged feature space. Through cross-domain knowledge transfer with a novel combination of MIL and LUPI, we achieve improved generalization with a limited number of labeled examples in the input space. Informed by the privileged space model output based on relatively expensive and time-consuming gene expression profiles in its training, the proposed LUPI model can generate accurate predictions using routine WSI data alone at the time of inference. The proposed method paves the way for further applications of LUPI in computational pathology and medical image analysis by cross-domain learning especially in cases with a limited number of labeled examples in training.

关键词： Training Predictive models Gene expression Cancer Machine learning Data models Chemotherapy

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Vulnerability of Person Re-Identification Models to Metric Adversarial Attacks

Vulnerability of Person Re-Identification Models to Metric A...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Quentin Bouniot Romaric Audigier Angélique Loesch CEA LIST Vision and Learning Lab for Scene Analysis Gif-sur-Yvette France

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Person re-identification (re-ID) is a key problem in smart supervision of camera networks. Over the past years, models using deep learning have become state of the art. However, it has been shown that deep neural networks are flawed with adversarial examples, i.e. human-imperceptible perturbations. Extensively studied for the task of image closed- set classification, this problem can also appear in the case of open-set retrieval tasks. Indeed, recent work has shown that we can also generate adversarial examples for metric learning systems such as re-ID ones. These models remain vulnerable: when faced with adversarial examples, they fail to correctly recognize a person, which represents a security breach. These attacks are all the more dangerous as they are impossible to detect for a human operator. Attacking a metric consists in altering the distances between the feature of an attacked image and those of reference images, i.e. guides. In this article, we investigate different possible attacks depending on the number and type of guides available. From this metric attack family, two particularly effective attacks stand out. The first one, called Self Metric Attack, is a strong attack that does not need any image apart from the attacked image. The second one, called FurthestNegative Attack, makes full use of a set of images. Attacks are evaluated on commonly used datasets: Market1501 and DukeMTMC. Finally, we propose an efficient extension of adversarial training protocol adapted to metric learning as a defense that increases the robustness of re-ID models. 1

关键词： Measurement Training Task analysis Protocols Testing Robustness Perturbation methods

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