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JUVDsi v1: developing and benchmarking a new still image database in Indian scenario for automatic vehicle detection

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第21期82卷 32883-32915页

作者： Bhattacharyya, Avirup Bhattacharya, Avigyan Maity, Sourajit Singh, Pawan Kumar Sarkar, Ram Jadavpur Univ Dept Comp Sci & Engn 188 Raja SC Mallick Rd Kolkata 700032 West Bengal India Jadavpur Univ Dept Informat Technol Jadavpur Univ Campus 2Plot 8LB BlockSect 3 Kolkata 700106 West Bengal India

Designing an automatic vehicle detection (AVD) system from still images or videos would be a useful tool to cater to the requirements of the traffic management system. Over the past few years, numerous databases have been developed for the use of researchers in this field of AVD. However, most of them are not acceptable in the Indian scenarios due to certain practical constraints like the road infrastructure, nature of congestion, and vehicle types commonly found in India. The aim of this research is to develop a still image database, named as JUVDsi v1, which includes nine different types of vehicle classes collected through mobile phone cameras in various ways for designing an automated traffic management system. Identifying and analyzing the shortcomings of existing databases, the developed database presents an improvement to address such bottle-necks. Furthermore, the efficiency of this database is evaluated using an ensemble of three state-of-the-art deep learning architectures. At first, each vehicle in the scene images is localized and categorized. Five base object detection models, namely, YOLOv3, Faster-RCNN, RFCN, SSDv1 and SSDLitev2 are used. Finally, the Weighted Boxes Fusion technique is used as the ensemble method (ensemble of best three out of the five base learners), thereby enhancing the performance obtained by the individual object detection models. The database can be found at: https://***/JUVDsi/***.

关键词： Automatic vehicle detection JUVD image database Object detection Deep learning Ensemble

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Small Tool image database and Object Detection Approach for Indoor Construction Site Safety

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KSCE JOURNAL OF CIVIL ENGINEERING 2023年第3期27卷 930-939页

作者： Lee, Kanghyeok Jeon, Chanwoong Shin, Do Hyoung Korea Expressway Corp Ctr Smart Construct Technol Hwaseong South Korea Inha Univ Dept Civil Engn Incheon 22212 South Korea

Object detection refers to computer vision technology that identifies the class and location of objects in an image. With the development of deep-learning techniques, object detection has been widely studied in the construction industry and has mainly focused on detecting heavy equipment and workers at outdoor sites. Therefore, an object detection model for small tools in indoor sites, where various accidents frequently occur owing to them, is required to enhance safety. In particular, many high-quality image databases are important for developing object detection approaches for small tools. In this study, 12 small tools commonly used in indoor construction were selected, and a database of 34,738 images of the selected tools was created and used to train the object detection models. YOLOv5x and YOLOv5s are selected as object detection models, for which the mean average precision with the 0.5 intersection-over-union value were 69.1% and 64.4%, respectively, and their frame rates were 58.82 and 142.86 frame per second, respectively. The results demonstrate that the established database is appropriate for the development of object detection for small tools and that object detection can be used for real-time safety management of construction indoor sites.

关键词： image database Object detection Small construction tool Indoor construction site Safety management

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ISIEA: An image database of social inclusion and exclusion in young Asian adults

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BEHAVIOR RESEARCH METHODS 2022年第5期54卷 2409-2421页

作者： Zheng, Zixin Li, Sijin Mo, Licheng Chen, Weimao Zhang, Dandan Shenzhen Univ Sch Psychol Shenzhen 518060 Peoples R China Shenzhen Univ Magnet Resonance Imaging MRI Ctr Shenzhen 518060 Peoples R China Shenzhen Hong Kong Inst Brain Sci Shenzhen 518055 Peoples R China

Human beings have a fundamental need to belong. Evaluating and dealing with social exclusion and social inclusion events, which represent negative and positive social interactions, respectively, are closely linked to our physical and mental health. In addition to traditional paradigms that simulate scenarios of social interaction, images are utilized as effective visual stimuli for research on socio-emotional processing and regulation. Since the current mainstream emotional image database lacks social stimuli based on a specific social context, we introduced an open-access image database of social inclusion/exclusion in young Asian adults (ISIEA). This database contains a set of 164 images depicting social interaction scenarios under three categories of social contexts (social exclusion, social neutral, and social inclusion). All images were normatively rated on valence, arousal, inclusion score, and vicarious feeling by 150 participants in Study 1. We additionally examined the relationships between image ratings and the potential factors influencing ratings. The importance of facial expression and social context in the image rating of ISIEA was examined in Study 2. We believe that this database allows researchers to select appropriate materials for socially related studies and to flexibly conduct experimental control.

关键词： Social exclusion Social inclusion Ostracism image database Social cognition Social reward Social punishment

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An image database of handwritten Bangla words with automatic benchmarking facilities for character segmentation algorithms

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NEURAL COMPUTING & APPLICATIONS 2021年第1期33卷 449-468页

作者： Malakar, Samir Sarkar, Ram Basu, Subhadip Kundu, Mahantapas Nasipuri, Mita Asutosh Coll Dept Comp Sci Kolkata India Jadavpur Univ Dept Comp Sci & Engn Kolkata India

Recognition of unconstrained handwritten word images is an interesting research problem which gets more challenging when lexicon-free words are considered. Prerequisite for developing a lexicon-free handwritten word recognition technique is the segmentation of a word image into its constituent character set. Therefore, a competent character segmentation technique is required to design a comprehensive word recognition module. However, the literature study reveals that there is no standard word image database with ground truth information. As a result, most character segmentation algorithms found in the literature rely on self-made databases with manual evaluation. To fill the research need, in the present scope of the work, a comprehensive database consisting of handwritten Bangla word images is prepared primarily for evaluating any character segmentation algorithms. Additionally, the present work also provides two types of ground truth images related to segmented character shapes of the word images. Besides, an evaluation tool is developed for assessing the performance of any character segmentation algorithm on the developed benchmark database. The benchmark result, as found here, is 0.9212 (F-score) which outperforms some state-of-the-art methods.

关键词： Character segmentation Handwritten word Bangla script image database Word recognition

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HIGH RESOLUTION image QUALITY database 49

HIGH RESOLUTION IMAGE QUALITY DATABASE

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Huang, Huang Wan, Qiang Korhonen, Jari Shenzhen Univ Sch Comp Sci & Software Engn Shenzhen Peoples R China Univ Aberdeen Sch Nat & Comp Sci Aberdeen Scotland

ISBN: (纸本)9798350344868;9798350344851

With technology for digital photography and high resolution displays rapidly evolving and gaining popularity, there is a growing demand for blind image quality assessment (BIQA) models for high resolution images. Unfortunately, the publicly available large scale image quality databases used for training BIQA models contain mostly low or general resolution images. Since image resizing affects image quality, we assume that the accuracy of BIQA models trained on low resolution images would not be optimal for high resolution images. Therefore, we created a new high resolution image quality database (HRIQ), consisting of 1120 images with resolution of 2880 x 2160 pixels. We conducted a subjective study to collect the subjective quality ratings for HRIQ in a controlled laboratory setting, resulting in accurate MOS at high resolution. To demonstrate the importance of a high resolution image quality database for training BIQA models to predict mean opinion scores (MOS) of high resolution images accurately, we trained and tested several traditional and deep learning based BIQA methods on different resolution versions of our database. The database is publicly available in https://***/jarikorhonen/hriq.

关键词： image database high resolution subjective image quality assessment

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The Foveal Avascular Zone image database (FAZID) 43

The Foveal Avascular Zone Image Database (FAZID)

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Conference on Applications of Digital image Processing XLIII

作者： Agarwal, Arpit Balaji, J. Jothi Raman, Rajiv Lakshminarayanan, Vasudevan Indian Inst Technol Dept Chem Engn Kanpur 208016 Uttar Pradesh India Med Res Fdn Dept Optometry Chennai 600006 Tamil Nadu India Med Res Fdn Shri Bhagwan Mahavir Vitreoretinal Serv Chennai 600006 Tamil Nadu India Univ Waterloo Sch Optometry & Vis Sci Theoret & Expt Epistemol Lab Waterloo ON N2L 3G1 Canada

ISBN: (数字)9781510638273

ISBN: (纸本)9781510638273

The Foveal Avascular Zone (FAZ) is of clinical importance since the vascular arrangement around the fovea changes with disease and refractive state of the eye. Therefore, it is important to segment and quantify the FAZ accurately. Studies done to date have achieved reasonable segmentation but there is a need for considerable improvement. In order to test and validate newly developed automated segmentation algorithms, we have created a public dataset of these retinal fundus images. The 304 images in the dataset are classified into: diabetic (107), myopic (109) and normal (88) eyes. The images were classified by a clinical expert and include clinical grading of diabetic retinopathy and myopia. The images are of dimensions 420 x 420 pixels (6mm x 6mm of retina). Both clear and manually segmented by a clinical expert (ground truth) images are available (608 total images). In these images, the FAZ is the green region marked in manually segmented image. The images can be used to test newly developed techniques and the manual segmentation images can be used as a ground truth for making performance comparisons and validation. It should also be noted there are only a few studies using supervised learning to segment the FAZ and this dataset will potentially be useful for machine learning training and validation. The image database, The Foveal Avascular Zone image database (FAZID) dataset can be accessed from the ICPSR website at the University of Michigan (https://***/10.3886/E117543V2).

关键词： Foveal Avascular Zone optical coherence tomography angiography diabetic retinopathy ametropia and emmetropia image segmentation image database ophthalmology retina

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OCTID: Optical coherence tomography image database

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COMPUTERS & ELECTRICAL ENGINEERING 2020年第0期81卷 106532-000页

作者： Gholami, Peyman Roy, Priyanka Parthasarathy, Mohana Kuppuswamy Lakshminarayanan, Vasudevan Univ Waterloo Sch Optometry & Vis Sci TEEL 200 Univ Ave West Waterloo ON N2L 3G1 Canada

Optical coherence tomography (OCT) is a non-invasive imaging modality that is widely used in clinical ophthalmology. OCT images are capable of visualizing deep retinal layers which is crucial for early diagnosis of retinal diseases. In this paper, we describe a comprehensive open-access database containing more than 500 high-resolution images categorized into different pathological conditions. The image classes include Normal (NO), Macular Hole (MH), Age-related Macular Degeneration (AMD), Central Serous Retinopathy (CSR), and Diabetic Retinopathy (DR). The images were obtained from a raster scan protocol with a 2 mm scan length and 512 x 1024 pixel resolution. We have also included 25 normal OCT images with their corresponding ground truth delineations which can be used for an accurate evaluation of OCT image segmentation. In addition, we have provided a user friendly GUI which can be used by clinicians for manual (and semi-automated) segmentation. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Optical coherence tomography image database image processing Retinal disease Retina Macular hole Age-related macular degeneration Cenrtal serous retinopathy Diabetic retinopathy Segmentation

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Tencent ML-images: A Large-Scale Multi-Label image database for Visual Representation Learning

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IEEE ACCESS 2019年 7卷 172683-172693页

作者： Wu, Baoyuan Chen, Weidong Fan, Yanbo Zhang, Yong Hou, Jinlong Liu, Jie Zhang, Tong Tencent AI Lab Shenzhen 518057 Guangdong Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Math Hong Kong Peoples R China

In existing visual representation learning tasks, deep convolutional neural networks (CNNs) are often trained on images annotated with single tag, such as imageNet. However, single tag annotation cannot describe all important contents of one image, and some useful visual information may be wasted during training. In this work, we propose to train CNNs from images annotated with multiple tags, to enhance the quality of visual representation of the trained CNN model. To this end, we build a large-scale multi-label image database with 18M images and 11K categories, dubbed Tencent ML-images. We efficiently train the ResNet-101 model with multi-label outputs on Tencent ML-images, taking 90 hours for 60 epochs, based on a large-scale distributed deep learning framework, i.e., TFplus. The good quality of the visual representation of the Tencent ML-images checkpoint is verified through three transfer learning tasks, including single-label image classification on imageNet and Caltech-256, object detection on PASCAL VOC 2007, and semantic segmentation on PASCAL VOC 2012. The Tencent ML-images database, the checkpoints of ResNet-101, and all the training codes have been released at https://***/Tencent/tencent-ml-images . It is expected to promote other vision tasks in the research and industry community.

关键词： Visual representation learning multi-label image database

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Rapid Feature Retrieval Method in Large-Scale image database

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2018年第7期22卷 1088-1092页

作者： Gao, Fei Tibet Univ Sch Informat Sci & Technol 36 Jiangsu Rd Lhasa 850000 Tibet Peoples R China

The retrieval of features in a large-scale image database can improve the degree of visualization of images. The conventional method of feature-retrieval is a time-consuming process because it retrieves by searching the keywords. In this paper, a rapid feature retrieval method based on granular computing is proposed for use in a large-scale image database. In this method, we first collect and process the images from the database. Next, we construct a binary tree to realize the multi-class classification of the image features and complete the feature retrieval using support vector machines. The experimental results demonstrate that the proposed method can effectively retrieve the features in the large-scale image database. The effectiveness of retrieval can reach more than 95%.

关键词： image database feature classification rapid retrieval

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VOIDGA: A View-Approximation Oriented image database Generation Approach 8

VOIDGA: A View-Approximation Oriented Image Database Generat...

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8th IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV)

作者： Lukasczyk, Jonas Kinner, Eric Ahrens, James Leitte, Heike Garth, Christoph TU Kaiserslautern Kaiserslautern Germany Los Alamos Natl Lab Los Alamos NM USA

ISBN: (纸本)9781538668733

In this work, we propose a novel view-approximation oriented image database generation approach (VOIDGA) that enables the adequate generation of arbitrary views. Our approach utilizes Depth image Based Rendering (DIBR) techniques to derive novel views based on a set of depth images. In contrast to approaches that store a huge amount of images to cover a wide range of possible view directions, VOIDGA identifies and stores only those images that significantly contribute to the overall view-approximation quality while bounding the resulting approximation error. This further reduces the size of image databases and the number of images that need to be processed by DIBR algorithms. We demonstrate VOIDGA on several challenging real-world examples, and compare our approximations against ground truth renderings using two image-based metrics.

关键词： image database Depth image Based Rendering Geometry Reconstruction View Approximation

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