A Novel Lane Detection System With Efficient Ground Truth Generation

2012 ◽  
Vol 13 (1) ◽  
pp. 365-374 ◽  
Author(s):  
Amol Borkar ◽  
Monson Hayes ◽  
Mark T. Smith
Keyword(s):  
Detection System ◽  
Ground Truth ◽  
Lane Detection ◽  
Ground Truth Generation

scholarly journals Critical Aspects of Person Counting and Density Estimation

2021 ◽  
Vol 7 (2) ◽  
pp. 21
Author(s):  
Roland Perko ◽  
Manfred Klopschitz ◽  
Alexander Almer ◽  
Peter M. Roth
Keyword(s):  
Density Estimation ◽  
Network Architecture ◽  
Reference Data ◽  
State Of The Art ◽  
Limit State ◽  
Ground Truth ◽  
Data Sets ◽  
Ground Truth Generation ◽  
Baseline Approach ◽  
Critical Aspects

Many scientific studies deal with person counting and density estimation from single images. Recently, convolutional neural networks (CNNs) have been applied for these tasks. Even though often better results are reported, it is often not clear where the improvements are resulting from, and if the proposed approaches would generalize. Thus, the main goal of this paper was to identify the critical aspects of these tasks and to show how these limit state-of-the-art approaches. Based on these findings, we show how to mitigate these limitations. To this end, we implemented a CNN-based baseline approach, which we extended to deal with identified problems. These include the discovery of bias in the reference data sets, ambiguity in ground truth generation, and mismatching of evaluation metrics w.r.t. the training loss function. The experimental results show that our modifications allow for significantly outperforming the baseline in terms of the accuracy of person counts and density estimation. In this way, we get a deeper understanding of CNN-based person density estimation beyond the network architecture. Furthermore, our insights would allow to advance the field of person density estimation in general by highlighting current limitations in the evaluation protocols.

Analysing political events on Twitter

2019 ◽  
Vol 53 (1) ◽  
pp. 38-39
Author(s):  
Anjie Fang
Keyword(s):  
Topic Model ◽  
Ground Truth ◽  
Topic Modelling ◽  
Time Dimension ◽  
Social Scientists ◽  
Political Events ◽  
Community Classification ◽  
Ground Truth Generation ◽  
Twitter Users ◽  
Modelling Approach

Recently, political events, such as elections, have raised a lot of discussions on social media networks, in particular, Twitter. This brings new opportunities for social scientists to address social science tasks, such as understanding what communities said or identifying whether a community has an influence on another. However, identifying these communities and extracting what they said from social media data are challenging and non-trivial tasks. We aim to make progress towards understanding 'who' (i.e. communities) said 'what' (i.e. discussed topics) and 'when' (i.e. time) during political events on Twitter. While identifying the 'who' can benefit from Twitter user community classification approaches, 'what' they said and 'when' can be effectively addressed on Twitter by extracting their discussed topics using topic modelling approaches that also account for the importance of time on Twitter. To evaluate the quality of these topics, it is necessary to investigate how coherent these topics are to humans. Accordingly, we propose a series of approaches in this thesis. First, we investigate how to effectively evaluate the coherence of the topics generated using a topic modelling approach. The topic coherence metric evaluates the topical coherence by examining the semantic similarity among words in a topic. We argue that the semantic similarity of words in tweets can be effectively captured by using word embeddings trained using a Twitter background dataset. Through a user study, we demonstrate that our proposed word embedding-based topic coherence metric can assess the coherence of topics like humans [1, 2]. In addition, inspired by the precision at k metric, we propose to evaluate the coherence of a topic model (containing many topics) by averaging the top-ranked topics within the topic model [3]. Our proposed metrics can not only evaluate the coherence of topics and topic models, but also can help users to choose the most coherent topics. Second, we aim to extract topics with a high coherence from Twitter data. Such topics can be easily interpreted by humans and they can assist to examine 'what' has been discussed and 'when'. Indeed, we argue that topics can be discussed in different time periods (see [4]) and therefore can be effectively identified and distinguished by considering their time periods. Hence, we propose an effective time-sensitive topic modelling approach by integrating the time dimension of tweets (i.e. 'when') [5]. We show that the time dimension helps to generate topics with a high coherence. Hence, we argue that 'what' has been discussed and 'when' can be effectively addressed by our proposed time-sensitive topic modelling approach. Next, to identify 'who' participated in the topic discussions, we propose approaches to identify the community affiliations of Twitter users, including automatic ground-truth generation approaches and a user community classification approach. We show that the mentioned hashtags and entities in the users' tweets can indicate which community a Twitter user belongs to. Hence, we argue that they can be used to generate the ground-truth data for classifying users into communities. On the other hand, we argue that different communities favour different topic discussions and their community affiliations can be identified by leveraging the discussed topics. Accordingly, we propose a Topic-Based Naive Bayes (TBNB) classification approach to classify Twitter users based on their words and discussed topics [6]. We demonstrate that our TBNB classifier together with the ground-truth generation approaches can effectively identify the community affiliations of Twitter users. Finally, to show the generalisation of our approaches, we apply our approaches to analyse 3.6 million tweets related to US Election 2016 on Twitter [7]. We show that our TBNB approach can effectively identify the 'who', i.e. classify Twitter users into communities. To investigate 'what' these communities have discussed, we apply our time-sensitive topic modelling approach to extract coherent topics. We finally analyse the community-related topics evaluated and selected using our proposed topic coherence metrics. Overall, we contribute to provide effective approaches to assist social scientists towards analysing political events on Twitter. These approaches include topic coherence metrics, a time-sensitive topic modelling approach and approaches for classifying the community affiliations of Twitter users. Together they make progress to study and understand the connections and dynamics among communities on Twitter. Supervisors : Iadh Ounis, Craig Macdonald, Philip Habel The thesis is available at http://theses.gla.ac.uk/41135/

Evaluation of two-view geometry methods with automatic ground-truth generation

2013 ◽  
Vol 31 (12) ◽  
pp. 921-934 ◽  
Author(s):  
Ruan Lakemond ◽  
Clinton Fookes ◽  
Sridha Sridharan
Keyword(s):  
Ground Truth ◽  
Ground Truth Generation

Deep learning for automated detection and numbering of permanent teeth on panoramic images

2021 ◽  
Author(s):  
Mohamed Estai ◽  
Marc Tennant ◽  
Dieter Gebauer ◽  
Andrew Brostek ◽  
Janardhan Vignarajan ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Detection System ◽  
Ground Truth ◽  
Automated Detection ◽  
Permanent Teeth ◽  
Automated Method ◽  
Step Procedure ◽  
Panoramic Images ◽  
Tooth Number

Objective: This study aimed to evaluate an automated detection system to detect and classify permanent teeth on orthopantomogram (OPG) images using convolutional neural networks (CNNs). Methods: In total, 591 digital OPGs were collected from patients older than 18 years. Three qualified dentists performed individual teeth labelling on images to generate the ground truth annotations. A three-step procedure, relying upon CNNs, was proposed for automated detection and classification of teeth. Firstly, U-Net, a type of CNN, performed preliminary segmentation of tooth regions or detecting regions of interest (ROIs) on panoramic images. Secondly, the Faster R-CNN, an advanced object detection architecture, identified each tooth within the ROI determined by the U-Net. Thirdly, VGG-16 architecture classified each tooth into 32 categories, and a tooth number was assigned. A total of 17,135 teeth cropped from 591 radiographs were used to train and validate the tooth detection and tooth numbering modules. 90% of OPG images were used for training, and the remaining 10% were used for validation. 10-folds cross-validation was performed for measuring the performance. The intersection over union (IoU), F1 score, precision, and recall (i.e. sensitivity) were used as metrics to evaluate the performance of resultant CNNs. Results: The ROI detection module had an IoU of 0.70. The tooth detection module achieved a recall of 0.99 and a precision of 0.99. The tooth numbering module had a recall, precision and F1 score of 0.98. Conclusion: The resultant automated method achieved high performance for automated tooth detection and numbering from OPG images. Deep learning can be helpful in the automatic filing of dental charts in general dentistry and forensic medicine.

scholarly journals Lane detection system for day vision using altera DE2

2019 ◽  
Vol 17 (1) ◽  
pp. 349 ◽  
Author(s):  
Amjad J. Humaidi ◽  
Mohammed Abdulraheem Fadhel ◽  
Ahmed R. Ajel
Keyword(s):  
Detection System ◽  
Lane Detection

BYANJON: A Ground Truth Preparation System for Online Handwritten Bangla Documents

2021 ◽  
Vol 20 (6) ◽  
pp. 1-16
Author(s):  
Shibaprasad Sen ◽  
Ankan Bhattacharyya ◽  
Ram Sarkar ◽  
Kaushik Roy
Keyword(s):  
Extraction Procedure ◽  
Ground Truth ◽  
Word Segmentation ◽  
Text Line ◽  
Line Extraction ◽  
Manual Intervention ◽  
Ground Truth Generation ◽  
Class Labels ◽  
Text Line Extraction ◽  
Ground Truth Information

The work reported in this article deals with the ground truth generation scheme for online handwritten Bangla documents at text-line, word, and stroke levels. The aim of the proposed scheme is twofold: firstly, to build a document level database so that future researchers can use the database to do research in this field. Secondly, the ground truth information will help other researchers to evaluate the performance of their algorithms developed for text-line extraction, word extraction, word segmentation, stroke recognition, and word recognition. The reported ground truth generation scheme starts with text-line extraction from the online handwritten Bangla documents, then words extraction from the text-lines, and finally segmentation of those words into basic strokes. After word segmentation, the basic strokes are assigned appropriate class labels by using modified distance-based feature extraction procedure and the MLP ( Multi-layer Perceptron ) classifier. The Unicode for the words are then generated from the sequence of stroke labels. XML files are used to store the stroke, word, and text-line levels ground truth information for the corresponding documents. The proposed system is semi-automatic and each step such as text-line extraction, word extraction, word segmentation, and stroke recognition has been implemented by using different algorithms. Thus, the proposed ground truth generation procedure minimizes huge manual intervention by reducing the number of mouse clicks required to extract text-lines, words from the document, and segment the words into basic strokes. The integrated stroke recognition module also helps to minimize the manual labor needed to assign appropriate stroke labels. The freely available and can be accessed at https://byanjon.herokuapp.com/ .

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