相关论文
2012
Symmetry-Growing for Skewed Rotational Symmetry Detection

This paper introduces a robust and accurate algorithm for detecting skewed rotationally symmetric patterns from realworld images. Previous local feature-based rotational symmetry detection methods have several problems such as unreliable performance depending on the number of features and weakness in coping with deformation and background clutters. Dealing with skew is another important issue because perspective skew maligns the detection performance. However, no straightforward solution has been reached yet. Our method overcomes the limitations of the existing local feature-based methods via symmetrygrowing scheme that efficiently explores image space and grows rotational symmetry from a few symmetrically matched initial feature pairs. Furthermore, a powerful method for handling skewed rotational symmetry is presented. Unlike previous iterative methods that consider center points and orientations only, the proposed method exploits the shape of affine covariant features, thus giving a closed form solution for the rotational axis of a feature pair on an affine plane. The experimental evaluation demonstrates that the proposed method successfully detects and segments multiple rotational symmetric patterns from realworld images, and clearly outperforms the state-of-the-art methods in terms of accuracy and robustness.

2018
Modelling Visual Properties and Visual Context in Multimodal Semantics

Multimodal semantic models that extend linguistic representations with additional perceptual input have proved successful in a range of natural language processing (NLP) tasks. However, existing research has extracted visual features from complete images, and has not examined how different kinds of visual information impact performance. We construct multimodal models that differentiate between internal visual properties of the objects and their external visual context. We evaluate the models on the task of decoding brain activity associated with the meanings of nouns, demonstrating their advantage over those based on complete images.

2020 - Proceedings of the 2020 International Conference on Computers, Information Processing and Advanced Education
Underwater Object Detection using Transfer Learning with Deep Learning

In recent years, Deep learning based methods have proven their excellent performance in generic object detection. However, underwater object detection is still a challenge, especially from underwater optical images. In contrast to generic datasets, underwater images usually have color shift and low contrast, underwater detection datasets are scarce and the objects in the available underwater datasets and real applications are usually small. To address these issues, we propose a underwater object detection algorithm which choose Yolov3-tiny network as backbone and pre-train on Pascal VOC datasets. Experiments show that our proposed method improves the performance of region-based object detectors on CHINAMM2019 datasets, compared with SSD algorithm and Yolov3.

2019 - ArXiv
ROBO: Robust, Fully Neural Object Detection for Robot Soccer

Deep Learning has become exceptionally popular in the last few years due to its success in computer vision and other fields of AI. However, deep neural networks are computationally expensive, which limits their application in low power embedded systems, such as mobile robots. In this paper, an efficient neural network architecture is proposed for the problem of detecting relevant objects in robot soccer environments. The ROBO model's increase in efficiency is achieved by exploiting the peculiarities of the environment. Compared to the state-of-the-art Tiny YOLO model, the proposed network provides approximately 35 times decrease in run time, while achieving superior average precision, although at the cost of slightly worse localization accuracy.

2018 - ICINCO
3D Pose Estimation of Bin Picking Object using Deep Learning and 3D Matching

In this paper, we propose a method to estimate 3D pose information of an object in a randomly piled-up environment by using image data obtained from an RGB-D camera. The proposed method consists of two modules: object detection by deep learning, and pose estimation by Iterative Closest Point (ICP) algorithm. In the first module, we propose an image encoding method to generate three channel images by integrating depth and infrared images captured by the camera. We use these encoded images as both the input data and training data set in a deep learning-based object detection step. Also, we propose a depth-based filtering method to improve the precision of object detection and to reduce the number of false positives by preprocessing input data. ICP-based 3D pose estimation is done in the second module, where we applied a plane-fitting method to increase the accuracy of the estimated pose.

2021 - Cryptogr.
Why Is Deep Learning Challenging for Printed Circuit Board (PCB) Component Recognition and How Can We Address It?

In this paper, we present the need for specialized artificial intelligence (AI) for counterfeit and defect detection of PCB components. Popular computer vision object detection techniques are not sufficient for such dense, low inter-class/high intra-class variation, and limited-data hardware assurance scenarios in which accuracy is paramount. Hence, we explored the limitations of existing object detection methodologies, such as region based convolutional neural networks (RCNNs) and single shot detectors (SSDs), and compared them with our proposed method, the electronic component localization and detection network (ECLAD-Net). The results indicate that, of the compared methods, ECLAD-Net demonstrated the highest performance, with a precision of 87.2% and a recall of 98.9%. Though ECLAD-Net demonstrated decent performance, there is still much progress and collaboration needed from the hardware assurance, computer vision, and deep learning communities for automated, accurate, and scalable PCB assurance.

2019 - 2019 Chinese Control Conference (CCC)
Object Detection for Low-resolution Infrared Image in Land Battlefield Based on Deep Learning

This paper proposes a network for low-resolution infrared image target detection in the land battlefield based on YOLO v3 network. By reducing the number of pooling layers, the risk of infrared small target miss detection is reduced. In addition, when the number of infrared training samples is limited, a strategy of using visible light samples to assist the infrared image training model is proposed, which effectively reduces over-fitting. Experiments show that the proposed network has better performance in detecting small targets in infrared low-resolution images, and can effectively reduce over-fitting during model training through visible-light image-assisted training strategies.

2020 - Pattern Recognit. Lett.
Comparative analysis of image classification algorithms based on traditional machine learning and deep learning

Abstract Image classification is a hot research topic in today's society and an important direction in the field of image processing research. SVM is a very powerful classification model in machine learning. CNN is a type of feedforward neural network that includes convolution calculation and has a deep structure. It is one of the representative algorithms of deep learning. Taking SVM and CNN as examples, this paper compares and analyzes the traditional machine learning and deep learning image classification algorithms. This study found that when using a large sample mnist dataset, the accuracy of SVM is 0.88 and the accuracy of CNN is 0.98; when using a small sample COREL1000 dataset, the accuracy of SVM is 0.86 and the accuracy of CNN is 0.83. The experimental results in this paper show that traditional machine learning has a better solution effect on small sample data sets, and deep learning framework has higher recognition accuracy on large sample data sets.

2019 - 2019 24th International Conference on Methods and Models in Automation and Robotics (MMAR)
Performance of LiDAR object detection deep learning architectures based on artificially generated point cloud data from CARLA simulator

Training deep neural network algorithms for LiDAR based object detection for autonomous cars requires huge amount of labeled data. Both data collection and labeling requires a lot of effort, money and time. Therefore, the use of simulation software for virtual data generation environments is gaining wide interest from both researchers and engineers. The big question remains how well artificially generated data resembles the data gathered by real sensors and how the differences affects the final algorithms performance. The article is trying to make a quantitative answer to the above question. Selected state-of-the-art algorithms for LiDAR point cloud object detection were trained on both real and artificially generated data sets. Their performance on different test sets were evaluated. The main focus was to determinate how well artificially trained networks perform on real data and if combined train sets can achieve better results overall.

2015 - Neurocomputing
Deep learning driven blockwise moving object detection with binary scene modeling

As an important and challenging topic in computer vision, moving object detection is typically posed as a problem of scene analysis, which usually requires both robust feature description and effec...

2019 - 2020 IEEE European Symposium on Security and Privacy (EuroS&P)
Evaluating Explanation Methods for Deep Learning in Security

Deep learning is increasingly used as a building block of security systems. Unfortunately, neural networks are hard to interpret and typically opaque to the practitioner. The machine learning community has started to address this problem by developing methods for explaining the predictions of neural networks. While several of these approaches have been successfully applied in the area of computer vision, their application in security has received little attention so far. It is an open question which explanation methods are appropriate for computer security and what requirements they need to satisfy. In this paper, we introduce criteria for comparing and evaluating explanation methods in the context of computer security. These cover general properties, such as the accuracy of explanations, as well as security-focused aspects, such as the completeness, efficiency, and robustness. Based on our criteria, we investigate six popular explanation methods and assess their utility in security systems for malware detection and vulnerability discovery. We observe significant differences between the methods and build on these to derive general recommendations for selecting and applying explanation methods in computer security.

2018 - 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)
RGBD Salient Object Detection using Spatially Coherent Deep Learning Framework

In this paper, a learning based salient object detection method for RGBD images is introduced. With the assistance of depth information, the silhouette features of an object can be retrieved primarily, and it can lead to better detection of salient objects. In addition, many recent works still rely on some image post-processing methods to improve their performance. We develop a more efficient end-to-end model with a modified design of loss function used in our training network. The design of the new loss function is to increase the spatial coherence of detected salient objects. From the evaluation results, the proposed approach shows good performance compared with the methods that are considered to be state-of-the-art.

2018 - HEALTHINF
Object Detection Featuring 3D Audio Localization for Microsoft HoloLens - A Deep Learning based Sensor Substitution Approach for the Blind

Finding basic objects on a daily basis is a difficult but common task for blind people. This paper demonstrates the implementation of a wearable, deep learning backed, object detection approach in the context of visual impairment or blindness. The prototype aims to substitute the impaired eye of the user and replace it with technical sensors. By scanning its surroundings, the prototype provides a situational overview of objects around the device. Object detection has been implemented using a near real-time, deep learning model named YOLOv2. The model supports the detection of 9000 objects. The prototype can display and read out the name of augmented objects which can be selected by voice commands and used as directional guides for the user, using 3D audio feedback. A distance announcement of a selected object is derived from the HoloLens’s spatial model. The wearable solution offers the opportunity to efficiently locate objects to support orientation without extensive training of the user. Preliminary evaluation covered the detection rate of speech recognition and the response times of the server.

2020 - Image Vis. Comput.
Deep learning-based object detection in low-altitude UAV datasets: A survey

Abstract Deep learning-based object detection solutions emerged from computer vision has captivated full attention in recent years. The growing UAV market trends and interest in potential applications such as surveillance, visual navigation, object detection, and sensors-based obstacle avoidance planning have been holding good promises in the area of deep learning. Object detection algorithms implemented in deep learning framework have rapidly became a method for processing of moving images captured from drones. The primary objective of the paper is to provide a comprehensive review of the state of the art deep learning based object detection algorithms and analyze recent contributions of these algorithms to low altitude UAV datasets. The core focus of the studies is low-altitude UAV datasets because relatively less contribution was seen in the literature when compared with standard or remote-sensing based datasets. The paper discusses the following algorithms: Faster RCNN, Cascade RCNN, R-FCN etc. into two-stage, YOLO and its variants, SSD, RetinaNet into one-stage and CornerNet, Objects as Point etc. under advanced stages in deep learning based detectors. Further, one-two and advanced stages of detectors are studied in detail focusing on low-altitude UAV datasets. The paper provides a broad summary of low altitude datasets along with their respective literature in detection algorithms for the potential use of researchers. Various research gaps and challenges for object detection and classification in UAV datasets that need to deal with for improving the performance are also listed.

2020 - Electronics
Deep Learning Framework for Vehicle and Pedestrian Detection in Rural Roads on an Embedded GPU

Object detection, one of the most fundamental and challenging problems in computer vision. Nowadays some dedicated embedded systems have emerged as a powerful strategy for deliver high processing capabilities including the NVIDIA Jetson family. The aim of the present work is the recognition of objects in complex rural areas through an embedded system, as well as the verification of accuracy and processing time. For this purpose, a low power embedded Graphics Processing Unit (Jetson Nano) has been selected, which allows multiple neural networks to be run in simultaneous and a computer vision algorithm to be applied for image recognition. As well, the performance of these deep learning neural networks such as ssd-mobilenet v1 and v2, pednet, multiped and ssd-inception v2 has been tested. Moreover, it was found that the accuracy and processing time were in some cases improved when all the models suggested in the research were applied. The pednet network model provides a high performance in pedestrian recognition, however, the sdd-mobilenet v2 and ssd-inception v2 models are better at detecting other objects such as vehicles in complex scenarios.

2017 - ACM Trans. Asian Low Resour. Lang. Inf. Process.
AROMA

While research on English opinion mining has already achieved significant progress and success, work on Arabic opinion mining is still lagging. This is mainly due to the relative recency of research efforts in developing natural language processing (NLP) methods for Arabic, handling its morphological complexity, and the lack of large-scale opinion resources for Arabic. To close this gap, we examine the class of models used for English and that do not require extensive use of NLP or opinion resources. In particular, we consider the Recursive Auto Encoder (RAE). However, RAE models are not as successful in Arabic as they are in English, due to their limitations in handling the morphological complexity of Arabic, providing a more complete and comprehensive input features for the auto encoder, and performing semantic composition following the natural way constituents are combined to express the overall meaning. In this article, we propose A Recursive Deep Learning Model for Opinion Mining in Arabic (AROMA) that addresses these limitations. AROMA was evaluated on three Arabic corpora representing different genres and writing styles. Results show that AROMA achieved significant performance improvements compared to the baseline RAE. It also outperformed several well-known approaches in the literature.

2017 - ICIAP
Object Detection for Crime Scene Evidence Analysis Using Deep Learning

Object detection is the key module in most visual-based surveillance applications and security systems. In crime scene analysis, the images and videos play a significant role in providing visual documentation of a scene. It allows police officers to recreate a scene for later analysis by detecting objects related to a specific crime. However, due to the presence of a large volume of data, the task of detecting objects of interest is very tedious for law enforcement agencies. In this work, we present a Faster R-CNN (Region-based Convolutional Neural Network) based real-time system, which automatically detects objects which might be found in an indoor environment. To test the effectiveness of the proposed system, we applied it to a subset of ImageNet containing 12 object classes and Karina dataset. We achieved an average accuracy of 74.33%, and the mean time taken to detect objects per image was 0.12 s in Nvidia-TitanX GPU.

2019 - Comput. Electr. Eng.
A real-time object detection algorithm for video

Abstract Deep learning technology has been widely used in object detection. Although the deep learning technology greatly improves the accuracy of object detection, we also have the challenge of a high computational time. You Only Look Once (YOLO) is a network for object detection in images. In this paper, we propose a real-time object detection algorithm for videos based on the YOLO network. We eliminate the influence of the image background by image preprocessing, and then we train the Fast YOLO model for object detection to obtain the object information. Based on the Google Inception Net (GoogLeNet) architecture, we improve the YOLO network by using a small convolution operation to replace the original convolution operation, which can reduce the number of parameters and greatly shorten the time for object detection. Our Fast YOLO algorithm can be applied to real-time object detection in video.

2019 - Progress in Artificial Intelligence
Convolutional neural network: a review of models, methodologies and applications to object detection

Deep learning has developed as an effective machine learning method that takes in numerous layers of features or representation of the data and provides state-of-the-art results. The application of deep learning has shown impressive performance in various application areas, particularly in image classification, segmentation and object detection. Recent advances of deep learning techniques bring encouraging performance to fine-grained image classification which aims to distinguish subordinate-level categories. This task is extremely challenging due to high intra-class and low inter-class variance. In this paper, we provide a detailed review of various deep architectures and model highlighting characteristics of particular model. Firstly, we described the functioning of CNN architectures and its components followed by detailed description of various CNN models starting with classical LeNet model to AlexNet, ZFNet, GoogleNet, VGGNet, ResNet, ResNeXt, SENet, DenseNet, Xception, PNAS/ENAS. We mainly focus on the application of deep learning architectures to three major applications, namely (i) wild animal detection, (ii) small arm detection and (iii) human being detection. A detailed review summary including the systems, database, application and accuracy claimed is also provided for each model to serve as guidelines for future work in the above application areas.

2018 - International Journal of Computer Vision
Deep Learning for Generic Object Detection: A Survey

Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. More than 300 research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. We finish the survey by identifying promising directions for future research.

Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Context

Lucian Popa
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D. Burdick
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N. Wang
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Xinyi Zheng

Abstract:Documents are often used for knowledge sharing and preservation in business and science, within which are tables that capture most of the critical data. Unfortunately, most documents are stored and distributed as PDF or scanned images, which fail to preserve logical table structure. Recent vision-based deep learning approaches have been proposed to address this gap, but most still cannot achieve state-of-the-art results. We present Global Table Extractor (GTE), a vision-guided systematic framework for joint table detection and cell structured recognition, which could be built on top of any object detection model. With GTE-Table, we invent a new penalty based on the natural cell containment constraint of tables to train our table network aided by cell location predictions. GTE-Cell is a new hierarchical cell detection network that leverages table styles. Further, we design a method to automatically label table and cell structure in existing documents to cheaply create a large corpus of training and test data. We use this to enhance PubTabNet with cell labels and create FinTabNet, real-world and complex scientific and financial datasets with detailed table structure annotations to help train and test structure recognition. Our framework surpasses previous state-of-the-art results on the ICDAR 2013 and ICDAR 2019 table competition in both table detection and cell structure recognition. Further experiments demonstrate a greater than 45% improvement in cell structure recognition when compared to a vanilla RetinaNet object detection model in our new out-of-domain FinTabNet.

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引用
LakeBench: Benchmarks for Data Discovery over Data Lakes
ArXiv
2023
Robust Table Structure Recognition with Dynamic Queries Enhanced Detection Transformer
Pattern Recognit.
2023
DocTr: Document Transformer for Structured Information Extraction in Documents
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
2023
SciA11y: Converting Scientific Papers to Accessible HTML
ASSETS
2021
KAAPA: Knowledge Aware Answers from PDF Analysis
AAAI
2021
Show, Read and Reason: Table Structure Recognition with Flexible Context Aggregator
ACM Multimedia
2021
Parsing Table Structures in the Wild
2021 IEEE/CVF International Conference on Computer Vision (ICCV)
2021
An Accuracy-Maximization Approach for Claims Classifiers in Document Content Analytics for Cybersecurity
J. Cybersecur. Priv.
2022
Beyond Document Page Classification: Design, Datasets, and Challenges
ArXiv
2023
AttesTable at SemEval-2021 Task 9: Extending Statement Verification with Tables for Unknown Class, and Semantic Evidence Finding
SEMEVAL
2021
Design of a Novel Information System for Semi-automated Management of Cybersecurity in Industrial Control Systems
ACM Trans. Manag. Inf. Syst.
2022
Retrieving Complex Tables with Multi-Granular Graph Representation Learning
SIGIR
2021
FinQA: A Dataset of Numerical Reasoning over Financial Data
EMNLP
2021
Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks
EMNLP
2023
DocILE 2023 Teaser: Document Information Localization and Extraction
ECIR
2023
Current Status and Performance Analysis of Table Recognition in Document Images With Deep Neural Networks
IEEE Access
2021
TDeLTA: A Light-weight and Robust Table Detection Method based on Learning Text Arrangement
AAAI
2023
TC-OCR: TableCraft OCR for Efficient Detection & Recognition of Table Structure & Content
MMIR@MM
2023
CORD-19: The Covid-19 Open Research Dataset
NLPCOVID19
2020
WEATHERGOV+: A Table Recognition and Summarization Dataset to Bridge the Gap Between Document Image Analysis and Natural Language Generation
DocEng
2023