Pub Date : 2019-12-01DOI: 10.1109/DICTA47822.2019.8946085
Nosheen Munir, M. Awrangjeb, Bela Stantic
The speedy development in electricity infrastructure due to urge in domestic and business usage requires a safe and secure maintenance of power line corridor (PLC) for the delivery of uninterrupted power supply to consumers. Inspection of PLC using light detection and ranging (LiDAR) has gain importance in the recent decade. In this paper, we have presented a new method for automatic extraction of wires from PLC using LiDAR point cloud data. Firstly, the pylons and vegetations around the PLC is removed by using their shape and area properties. Then, pylon locations are used to extract the wire points in the form of span between two consecutive pylons. These spans are divided into several segments such that each segment has slice of wires from span. A clustering algorithm based on the Euclidean distance is further used to separate wires which are at different height levels in each segment and the wire-count on each cluster is generated by defining the distance thresholds. Each clustered points are projected into a plane perpendicular to the direction of wires. Finally, wires are separated by fitting a 2D line and by using point and line distance formula. Results are presented on different spans from complex and semi-complex datasets with low point density. Experimental results show that the proposed method is able to extract the single or bundle wires with completeness and correctness of 99% and 100% respectively.
{"title":"An Automated Method for Individual Wire Extraction from Power Line Corridor using LiDAR Data","authors":"Nosheen Munir, M. Awrangjeb, Bela Stantic","doi":"10.1109/DICTA47822.2019.8946085","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8946085","url":null,"abstract":"The speedy development in electricity infrastructure due to urge in domestic and business usage requires a safe and secure maintenance of power line corridor (PLC) for the delivery of uninterrupted power supply to consumers. Inspection of PLC using light detection and ranging (LiDAR) has gain importance in the recent decade. In this paper, we have presented a new method for automatic extraction of wires from PLC using LiDAR point cloud data. Firstly, the pylons and vegetations around the PLC is removed by using their shape and area properties. Then, pylon locations are used to extract the wire points in the form of span between two consecutive pylons. These spans are divided into several segments such that each segment has slice of wires from span. A clustering algorithm based on the Euclidean distance is further used to separate wires which are at different height levels in each segment and the wire-count on each cluster is generated by defining the distance thresholds. Each clustered points are projected into a plane perpendicular to the direction of wires. Finally, wires are separated by fitting a 2D line and by using point and line distance formula. Results are presented on different spans from complex and semi-complex datasets with low point density. Experimental results show that the proposed method is able to extract the single or bundle wires with completeness and correctness of 99% and 100% respectively.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"103 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87907610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/DICTA47822.2019.8946046
Annus Zulfiqar, A. Ul-Hasan, F. Shafait
Logical layout analysis plays an important part in document understanding. It can become a challenging task due to varying formats and layouts. Researchers have proposed different ways to solve this problem, mostly using visual information in some way and a complex pipeline. In this paper, we present a simple technique for labelling the logical structures in document images. We use visual and textual features from the document images to label zones. We utilize Recurrent Neural Networks, specifically 2 layers of LSTM, which input the text from the zone that we want to classify as sequences of words and the normalized position of each word with respect to the page width and height. Comparisons are made by comparing the image under test with the known layouts and labels are assigned to zones accordingly. The labels are abstract, title, author names, and affiliation; however, the text also contains very important information for the task at hand. The presented approach achieved an overall accuracy of 96.21% on publicly available MARG dataset.
{"title":"Logical Layout Analysis using Deep Learning","authors":"Annus Zulfiqar, A. Ul-Hasan, F. Shafait","doi":"10.1109/DICTA47822.2019.8946046","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8946046","url":null,"abstract":"Logical layout analysis plays an important part in document understanding. It can become a challenging task due to varying formats and layouts. Researchers have proposed different ways to solve this problem, mostly using visual information in some way and a complex pipeline. In this paper, we present a simple technique for labelling the logical structures in document images. We use visual and textual features from the document images to label zones. We utilize Recurrent Neural Networks, specifically 2 layers of LSTM, which input the text from the zone that we want to classify as sequences of words and the normalized position of each word with respect to the page width and height. Comparisons are made by comparing the image under test with the known layouts and labels are assigned to zones accordingly. The labels are abstract, title, author names, and affiliation; however, the text also contains very important information for the task at hand. The presented approach achieved an overall accuracy of 96.21% on publicly available MARG dataset.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"34 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75491786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/DICTA47822.2019.8945870
Giang Truong, S. Z. Gilani, S. Islam, D. Suter
Deep learning has recently delivered relatively high quality semantic segmentation of visual and point-cloud data. This paper is primarily concerned with the use of such semantic segmentation for point cloud registration. In particular, we are motivated by the need to speed up, for large scale data sets, algorithms for registration that guarantee optimality (in terms of maximising consensus). That semantic information can help prune bad hypotheses for point matches is rather obvious, and we demonstrate one such relatively simple approach by modifying a recent optimal registration algorithm [6] to take advantage of semantic information. However, we also make another contribution in proposing a novel variation of deep learning approaches to point cloud registration. Again, our motivation is handling large data sets and in this case we are able to provide an algorithm that achieves on par with state-of-the-art performance on the semantic segmentation task. In short, we have shown how to speed up both the generation of the semantic information, and how to use that semantic information to speed up point cloud registration, in the context of large scale point cloud data-sets.
{"title":"Fast Point Cloud Registration using Semantic Segmentation","authors":"Giang Truong, S. Z. Gilani, S. Islam, D. Suter","doi":"10.1109/DICTA47822.2019.8945870","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8945870","url":null,"abstract":"Deep learning has recently delivered relatively high quality semantic segmentation of visual and point-cloud data. This paper is primarily concerned with the use of such semantic segmentation for point cloud registration. In particular, we are motivated by the need to speed up, for large scale data sets, algorithms for registration that guarantee optimality (in terms of maximising consensus). That semantic information can help prune bad hypotheses for point matches is rather obvious, and we demonstrate one such relatively simple approach by modifying a recent optimal registration algorithm [6] to take advantage of semantic information. However, we also make another contribution in proposing a novel variation of deep learning approaches to point cloud registration. Again, our motivation is handling large data sets and in this case we are able to provide an algorithm that achieves on par with state-of-the-art performance on the semantic segmentation task. In short, we have shown how to speed up both the generation of the semantic information, and how to use that semantic information to speed up point cloud registration, in the context of large scale point cloud data-sets.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"59 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80850311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/DICTA47822.2019.8946023
M. Ghaffari, A. Sowmya, R. Oliver, Len Hamey
Reliable brain tumour segmentation methods from brain scans are essential for accurate diagnosis and treatment planning. In this paper, we propose a semantic segmentation method based on convolutional neural networks for brain tumour segmentation using multimodal brain scans. The proposed model is a modified version of the well-known U-net architecture. It gains from DenseNet blocks between the encoder and decoder parts of the U-net to transfer more semantic information from the input to the output. In addition, to speed up the training process, we employed deep supervision by adding segmentation blocks at the end of the decoder layers and summing up their outputs to generate the final output of the network. We trained and evaluated our model using the BraTS 2018 dataset. Comparing the results from the proposed model and a generic U-net, our model achieved higher segmentation accuracy in terms of the Dice score.
{"title":"Multimodal Brain Tumour Segmentation using Densely Connected 3D Convolutional Neural Network","authors":"M. Ghaffari, A. Sowmya, R. Oliver, Len Hamey","doi":"10.1109/DICTA47822.2019.8946023","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8946023","url":null,"abstract":"Reliable brain tumour segmentation methods from brain scans are essential for accurate diagnosis and treatment planning. In this paper, we propose a semantic segmentation method based on convolutional neural networks for brain tumour segmentation using multimodal brain scans. The proposed model is a modified version of the well-known U-net architecture. It gains from DenseNet blocks between the encoder and decoder parts of the U-net to transfer more semantic information from the input to the output. In addition, to speed up the training process, we employed deep supervision by adding segmentation blocks at the end of the decoder layers and summing up their outputs to generate the final output of the network. We trained and evaluated our model using the BraTS 2018 dataset. Comparing the results from the proposed model and a generic U-net, our model achieved higher segmentation accuracy in terms of the Dice score.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"39 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90159836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/DICTA47822.2019.8945905
Teng Zhang, Liangchen Liu, A. Wiliem, Stephen Connor, Zelkjo Ilich, Eddie Van Der Draai, B. Lovell
Galvanised steel transmission towers in electrical power grids suffer from corrosion to different levels depending on age and environment. To ensure the power grid can operate safely, significant resources are spent to monitor the corrosion level of towers. Photographs from helicopters, drones, and a variety of staffs are often used to capture condition, however, these images still need manual inspection to determine the corrosion level before carrying out maintenance works. In this paper, we describe a framework employing multiple deep neural networks based classifiers and detectors to perform automatic image-based condition monitoring for steel transmission towers. Given a random variety of images of a tower, our proposed framework will first determine the location of the image on the structure via a trained zone classifier. Then, fine-grain corrosion inspection will be performed on both fasteners and structural members, respectively. In addition, an automatic zoomin functionality will be applied to images which have high resolution but are a long distance away. This step will ensure the detection performance on small objects on the tower. Finally, the overall corrosion status report for this tower will be calculated and generated automatically. Additionally, we released a subset of our data to contribute to this novel direction. Experiments show that our framework can assess the tower efficiently.
{"title":"Deep Corrosion Assessment for Electrical Transmission Towers","authors":"Teng Zhang, Liangchen Liu, A. Wiliem, Stephen Connor, Zelkjo Ilich, Eddie Van Der Draai, B. Lovell","doi":"10.1109/DICTA47822.2019.8945905","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8945905","url":null,"abstract":"Galvanised steel transmission towers in electrical power grids suffer from corrosion to different levels depending on age and environment. To ensure the power grid can operate safely, significant resources are spent to monitor the corrosion level of towers. Photographs from helicopters, drones, and a variety of staffs are often used to capture condition, however, these images still need manual inspection to determine the corrosion level before carrying out maintenance works. In this paper, we describe a framework employing multiple deep neural networks based classifiers and detectors to perform automatic image-based condition monitoring for steel transmission towers. Given a random variety of images of a tower, our proposed framework will first determine the location of the image on the structure via a trained zone classifier. Then, fine-grain corrosion inspection will be performed on both fasteners and structural members, respectively. In addition, an automatic zoomin functionality will be applied to images which have high resolution but are a long distance away. This step will ensure the detection performance on small objects on the tower. Finally, the overall corrosion status report for this tower will be calculated and generated automatically. Additionally, we released a subset of our data to contribute to this novel direction. Experiments show that our framework can assess the tower efficiently.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"71 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88857474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the development of computer vision research, the architecture of convolutional neural network becomes more and more complex to reach the state-of-the-art performance. Is the complexity of the model necessarily proportional to its accuracy? To answer this, the compression of the network has attracted much attention in the academy and industry. Existing network pruning methods mostly rely on the scoring mechanism of complexity or diversity of kernels to compress the network, and then build the network model after removing the kernels by tuning or training on the input data. These methods are cumbersome and depend on a well-trained pre-trained model. In this paper, we propose an end-to-end block pruning method based on kernel and feature stability by pruning blocks efficiently. To accomplish this, we firstly introduce a mask to scale the output of the blocks, and the L1 regularization term to monitor the mask update. Second, we introduce the Center Loss to guarantee that the feature does not deviate greatly during learning. To converge fast, we introduce fast iterative shrinkage-thresholding algorithm (FISTA) to optimize the mask, by which a more fast and reliable pruning process is achieved. We implement experiments on different datasets, including CIFAR-10 and ImageNet ILSVRC2012. All the experiments have achieved the state-of-the-art accuracy.
{"title":"Efficient Block Pruning Based on Kernel and Feature Stablization","authors":"Sheng Xu, Hanlin Chen, Kexin Liu, Jinhu Lii, Baochang Zhang","doi":"10.1109/DICTA47822.2019.8946001","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8946001","url":null,"abstract":"With the development of computer vision research, the architecture of convolutional neural network becomes more and more complex to reach the state-of-the-art performance. Is the complexity of the model necessarily proportional to its accuracy? To answer this, the compression of the network has attracted much attention in the academy and industry. Existing network pruning methods mostly rely on the scoring mechanism of complexity or diversity of kernels to compress the network, and then build the network model after removing the kernels by tuning or training on the input data. These methods are cumbersome and depend on a well-trained pre-trained model. In this paper, we propose an end-to-end block pruning method based on kernel and feature stability by pruning blocks efficiently. To accomplish this, we firstly introduce a mask to scale the output of the blocks, and the L1 regularization term to monitor the mask update. Second, we introduce the Center Loss to guarantee that the feature does not deviate greatly during learning. To converge fast, we introduce fast iterative shrinkage-thresholding algorithm (FISTA) to optimize the mask, by which a more fast and reliable pruning process is achieved. We implement experiments on different datasets, including CIFAR-10 and ImageNet ILSVRC2012. All the experiments have achieved the state-of-the-art accuracy.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"1 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80003359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/DICTA47822.2019.8945893
Mohammad Al-Naser, Shoaib Ahmed Siddiqui, Hiroki Ohashi, Sheraz Ahmed, Nakamura Katsuyki, Takuto Sato, A. Dengel
This paper proposes a novel gaze-estimation model for attentional object selection tasks. The key features of our model are two-fold: (i) usage of the deformable convolutional layers to better incorporate spatial dependencies of different shapes of objects and background, (ii) formulation of the gaze-estimation problem in two different ways, i.e. as a classification as well as a regression problem. We combine the two different formulations using a joint loss that incorporates both the cross-entropy as well as the mean-squared error in order to train our model. The experimental results on two publicly available datasets indicates that our model not only achieved real-time performance (13–18 FPS), but also outperformed the state-of-the-art models on the OSdataset along with comparable performance on GTEA-plus dataset.
{"title":"OGaze: Gaze Prediction in Egocentric Videos for Attentional Object Selection","authors":"Mohammad Al-Naser, Shoaib Ahmed Siddiqui, Hiroki Ohashi, Sheraz Ahmed, Nakamura Katsuyki, Takuto Sato, A. Dengel","doi":"10.1109/DICTA47822.2019.8945893","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8945893","url":null,"abstract":"This paper proposes a novel gaze-estimation model for attentional object selection tasks. The key features of our model are two-fold: (i) usage of the deformable convolutional layers to better incorporate spatial dependencies of different shapes of objects and background, (ii) formulation of the gaze-estimation problem in two different ways, i.e. as a classification as well as a regression problem. We combine the two different formulations using a joint loss that incorporates both the cross-entropy as well as the mean-squared error in order to train our model. The experimental results on two publicly available datasets indicates that our model not only achieved real-time performance (13–18 FPS), but also outperformed the state-of-the-art models on the OSdataset along with comparable performance on GTEA-plus dataset.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"14 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87873428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/DICTA47822.2019.8945850
N. Bhagat, Y. Vishnusai, G. Rathna
Speech impaired people use hand based gestures to communicate. Unfortunately, the vast majority of the people are not aware of the semantics of these gestures. In a attempt to bridge the same, we propose a real time hand gesture recognition system based on the data captured by the Microsoft Kinect RGB-D camera. Given that there is no one to one mapping between the pixels of the depth and the RGB camera, we used computer vision techniques like 3D contruction and affine transformation. After achieving one to one mapping, segmentation of the hand gestures was done from the background noise. Convolutional Neural Networks (CNNs) were utilised for training 36 static gestures relating to Indian Sign Language (ISL) alphabets and numbers. The model achieved an accuracy of 98.81% on training using 45,000 RGB images and 45,000 depth images. Further Convolutional LSTMs were used for training 10 ISL dynamic word gestures and an accuracy of 99.08% was obtained by training 1080 videos. The model showed accurate real time performance on prediction of ISL static gestures, leaving a scope for further research on sentence formation through gestures. The model also showed competitive adaptability to American Sign Language (ASL) gestures when the ISL models weights were transfer learned to ASL and it resulted in giving 97.71% accuracy.
{"title":"Indian Sign Language Gesture Recognition using Image Processing and Deep Learning","authors":"N. Bhagat, Y. Vishnusai, G. Rathna","doi":"10.1109/DICTA47822.2019.8945850","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8945850","url":null,"abstract":"Speech impaired people use hand based gestures to communicate. Unfortunately, the vast majority of the people are not aware of the semantics of these gestures. In a attempt to bridge the same, we propose a real time hand gesture recognition system based on the data captured by the Microsoft Kinect RGB-D camera. Given that there is no one to one mapping between the pixels of the depth and the RGB camera, we used computer vision techniques like 3D contruction and affine transformation. After achieving one to one mapping, segmentation of the hand gestures was done from the background noise. Convolutional Neural Networks (CNNs) were utilised for training 36 static gestures relating to Indian Sign Language (ISL) alphabets and numbers. The model achieved an accuracy of 98.81% on training using 45,000 RGB images and 45,000 depth images. Further Convolutional LSTMs were used for training 10 ISL dynamic word gestures and an accuracy of 99.08% was obtained by training 1080 videos. The model showed accurate real time performance on prediction of ISL static gestures, leaving a scope for further research on sentence formation through gestures. The model also showed competitive adaptability to American Sign Language (ASL) gestures when the ISL models weights were transfer learned to ASL and it resulted in giving 97.71% accuracy.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"1 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86439365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/DICTA47822.2019.8946114
P. Ambalathankandy, Yafei Ou, Jyotsna Kochiyil, Shinya Takamaeda-Yamazaki, M. Motomura, T. Asai, M. Ikebe
In this paper, we analyze and propose the usefulness of smoothed LHE (Local Histogram Equalization) filters for processing images with low contrast like digital radiographic images. Digital X-rays are known to have optical illusions like Mach bands and background contrast effects, which are caused by lateral inhibition phenomena. We observe that using multilayer (ML) methods with latest edge preserving filter for contrast enhancement in medical images can be problematic and could lead to faulty diagnosis from detail exaggeration which are caused by uncontrolled texture boosting from user defined gain settings. ML filters are designed with few subjectively selected filter kernel sizes, which can result in unnaturalness in output images. We propose a smoothed LHE-like filter with an adaptive gain control, that is more robust and can enhance fine details in digital X-rays while maintaining their intrinsic naturalness. Preserving naturalness in X-ray images are an essential feature for radiographic diagnostics. Our proposed filter has 0(1) complexity and can easily be controlled and operated with a continuously varying kernel size, which functions like an active high pass filter, amplifying all frequencies within the kernel.
{"title":"Radiography Contrast Enhancement: Smoothed LHE Filter a Practical Solution for Digital X-Rays with Mach Band","authors":"P. Ambalathankandy, Yafei Ou, Jyotsna Kochiyil, Shinya Takamaeda-Yamazaki, M. Motomura, T. Asai, M. Ikebe","doi":"10.1109/DICTA47822.2019.8946114","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8946114","url":null,"abstract":"In this paper, we analyze and propose the usefulness of smoothed LHE (Local Histogram Equalization) filters for processing images with low contrast like digital radiographic images. Digital X-rays are known to have optical illusions like Mach bands and background contrast effects, which are caused by lateral inhibition phenomena. We observe that using multilayer (ML) methods with latest edge preserving filter for contrast enhancement in medical images can be problematic and could lead to faulty diagnosis from detail exaggeration which are caused by uncontrolled texture boosting from user defined gain settings. ML filters are designed with few subjectively selected filter kernel sizes, which can result in unnaturalness in output images. We propose a smoothed LHE-like filter with an adaptive gain control, that is more robust and can enhance fine details in digital X-rays while maintaining their intrinsic naturalness. Preserving naturalness in X-ray images are an essential feature for radiographic diagnostics. Our proposed filter has 0(1) complexity and can easily be controlled and operated with a continuously varying kernel size, which functions like an active high pass filter, amplifying all frequencies within the kernel.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"7 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86201287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/DICTA47822.2019.8945929
M. Shahzad, Rabeya Noor, Sheraz Ahmad, A. Mian, F. Shafait
Traditional computer vision approaches heavily relied on hand-crafted features for tasks such as visual object detection and recognition. The recent success of deep learning in automatically extracting representative and powerful features from images has brought a paradigm shift in this area. As a side effect, decades of research into hand-crafted features is considered outdated. In this paper, we present an approach for table detection in which we leverage a deep learning based table detection model with hand-crafted features from a classical table detection method. We demonstrate that by using a suitable encoding of hand-crafted features, the deep learning model is able to perform better at the detection task. Experiments on publicly available UNLV dataset show that the presented method achieves an accuracy comparable with the state-of-the-art deep learning methods without the need of extensive hyper-parameter tuning.
{"title":"Feature Engineering Meets Deep Learning: A Case Study on Table Detection in Documents","authors":"M. Shahzad, Rabeya Noor, Sheraz Ahmad, A. Mian, F. Shafait","doi":"10.1109/DICTA47822.2019.8945929","DOIUrl":"https://doi.org/10.1109/DICTA47822.2019.8945929","url":null,"abstract":"Traditional computer vision approaches heavily relied on hand-crafted features for tasks such as visual object detection and recognition. The recent success of deep learning in automatically extracting representative and powerful features from images has brought a paradigm shift in this area. As a side effect, decades of research into hand-crafted features is considered outdated. In this paper, we present an approach for table detection in which we leverage a deep learning based table detection model with hand-crafted features from a classical table detection method. We demonstrate that by using a suitable encoding of hand-crafted features, the deep learning model is able to perform better at the detection task. Experiments on publicly available UNLV dataset show that the presented method achieves an accuracy comparable with the state-of-the-art deep learning methods without the need of extensive hyper-parameter tuning.","PeriodicalId":6696,"journal":{"name":"2019 Digital Image Computing: Techniques and Applications (DICTA)","volume":"1 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81733879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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