Identifying vehicles across a network of cameras with non-overlapping fields of view remains a challenging research problem due to scene occlusions, significant inter-class similarity and intra-class variability. In t...
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ISBN:
(纸本)9781728188089
Identifying vehicles across a network of cameras with non-overlapping fields of view remains a challenging research problem due to scene occlusions, significant inter-class similarity and intra-class variability. In this paper, we propose an end-to-end multi-level re-identification network that is capable of successfully projecting same identity vehicles closer to one another in the embedding space, compared to vehicles of different identities. Robust feature representations are obtained by combining features at multiple levels of the network. As for the learning process, we employ a recent state-of-the-art structured metric learning loss function previously applied to other retrieval problems and adjust it to the vehicle re-identification task. Furthermore, we explore the cases of image-to-image, image-tovideo and video-to-video similarity metric. Finally, we evaluate our system and achieve great performance on two large-scale publicly available datasets, CityFlow-ReID and VeRi-776. Compared to most existing state-of-art approaches, our approach is simpler and more straightforward, utilizing only identity-level annotations, while avoiding post-processing the ranking results (re-ranking) at the testing phase.
In this article a Vector Symbolic Architectures is purposed to implement a hierarchical Graph Neuron for memorizing patterns of Persian/Arabic isolated characters. The main challenge in this topic is using Vector Symb...
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Understanding images in terms of logical and hierarchical structures is crucial for many semantic tasks, including image retrieval, scene understanding and robotic vision. This paper combines robust feature extraction...
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Understanding images in terms of logical and hierarchical structures is crucial for many semantic tasks, including image retrieval, scene understanding and robotic vision. This paper combines robust feature extraction, qualitative spatial relations, relational instance-based learning and compositional hierarchies in one framework. For each layer in the hierarchy, qualitative spatial structures in images are detected, classified and then employed one layer up the hierarchy to obtain higher-level semantic structures. We apply a four-layer hierarchy to street view images and subsequently detect corners, windows, doors, and individual houses. (C) 2013 Elsevier B.V. All rights reserved.
The proceedings contain 17 papers. The special focus in this conference is on Pan-African Intelligence and Smart Systems. The topics include: Hybridised Loss Functions for Improved Neural Network Generalisation;d...
ISBN:
(纸本)9783030933135
The proceedings contain 17 papers. The special focus in this conference is on Pan-African Intelligence and Smart Systems. The topics include: Hybridised Loss Functions for Improved Neural Network Generalisation;diverging Hybrid and Deep learning Models into Predicting students’ Performance in Smart learning Environments – A Review;combining Multi-Layer Perceptron and Local Binary patterns for Thermite Weld Defects Classification;an Elliptic Curve Biometric Based User Authentication Protocol for Smart Homes Using Smartphone;Efficient Subchannel and Power Allocation in Multi-cell Indoor VLC Systems;autonomic IoT: Towards Smart System Components with Cognitive IoT;study of Customer Sentiment Towards Smart Lockers;A Patch-Based Convolutional Neural Network for Localized MRI Brain Segmentation;facial recognition Through Localized Siamese Convolutional Neural Networks;face recognition in Databases of images with Hidden Markov’s Models;Brain MRI Segmentation Using Autoencoders;effective Feature Selection for Improved Prediction of Heart Disease;Convolutional Neural Network Feature Extraction for EEG Signal Classification;race recognition Using Enhanced Local Binary pattern;detection and Classification of Coffee Plant Diseases by imageprocessing and machinelearning.
image enhancement is a fundamental step and plays an important role in imageprocessing, patternrecognition, and computer vision. image enhancement can sharpen the edges of objects in an image, making it easier to ex...
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image enhancement is a fundamental step and plays an important role in imageprocessing, patternrecognition, and computer vision. image enhancement can sharpen the edges of objects in an image, making it easier to extract objects and attain more information from the enhanced image. In this paper, a few popular image enhancement methods based Fusion Method was studied. This paper presents an exhaustive review of these studies and suggests a direction for future developments of image enhancement methods. Each method shows the owned advantages and drawbacks. In future, this work will give the direction to other researchers in order to propose new advanced enhancement techniques.
Automated License Plate recognition (ALPR) has many applications in intelligent transport system. The ALPR has three main steps, License Plate (LP) localization, segmentation and Optical Character recognition (OCR). E...
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In this paper, we explore the possibility of generating artificial biomedical images that can be used as a substitute for real image datasets in applied machinelearning tasks. We are focusing on generation of realist...
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ISBN:
(纸本)9783030354305;9783030354299
In this paper, we explore the possibility of generating artificial biomedical images that can be used as a substitute for real image datasets in applied machinelearning tasks. We are focusing on generation of realistic chest X-ray images as well as on the lymph node histology images using the two recent GAN architectures including DCGAN and PGGAN. The possibility of the use of artificial images instead of real ones for training machinelearning models was examined by benchmark classification tasks being solved using conventional and deep learning methods. In particular, a comparison was made by replacing real images with synthetic ones at the model training stage and comparing the prediction results with the ones obtained while training on the real image data. It was found that the drop of classification accuracy caused by such training data substitution ranged between 2.2% and 3.5% for deep learning models and between 5.5% and 13.25% for conventional methods such as LBP + Random Forests.
Partial discharge(PD) gray intensity image is regarded as the research object in this paper, a new principle and method based on genetic programming is proposed to extract PD features aiming at PRPD mode, This article...
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ISBN:
(纸本)9780769536156
Partial discharge(PD) gray intensity image is regarded as the research object in this paper, a new principle and method based on genetic programming is proposed to extract PD features aiming at PRPD mode, This article firstly introduces the PRPD mode and the method to get the three-dimensional spectrum and two-dimensional gray intensity image, then gray intensity images identification technology based on genetic programming is introduced, Furthermore, the paper presents software flow chart of search algorithm of gray intensity imagerecognition. Finally, the results of experiment are given. Compared with the original imagerecognition technology, this method provides an effective pathway for the better recognition in image information, Using this method, the storage requirement and the calculation involved may be reduced, the results show that it is effective.
This paper presents a new method for facial expression modelling and recognition based on diffeomorphic image registration parameterised via stationary velocity fields in Log-Euclidean framework. The validation and co...
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ISBN:
(纸本)9789898425980
This paper presents a new method for facial expression modelling and recognition based on diffeomorphic image registration parameterised via stationary velocity fields in Log-Euclidean framework. The validation and comparison are done using different statistical shape models (SSM) built using the Point Distribution Model (PDM), velocity fields, and deformation fields. The obtained results show that the facial expression representation based on stationary velocity field can be successfully utilised in facial expression recognition, and this parameterisation produces higher recognition rate than the facial expression representation based on deformation fields.
An analog implementation of a deep machine-learning system for efficient feature extraction is presented in this work. It features online unsupervised trainability and non-volatile floating-gate analog storage. It uti...
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ISBN:
(纸本)9781479909186
An analog implementation of a deep machine-learning system for efficient feature extraction is presented in this work. It features online unsupervised trainability and non-volatile floating-gate analog storage. It utilizes a massively parallel reconfigurable current-mode analog architecture to realize efficient computation, and leverages algorithm-level feedback to provide robustness to circuit imperfections in analog signal processing. A 3-layer, 7-node analog deep machine-learning engine was fabricated in a 0.13 mu m standard CMOS process, occupying 0.36 mm(2) active area. At a processing speed of 8300 input vectors per second, it consumes 11.4 mu W from the 3 V supply, achieving 1x10(12) operation per second per Watt of peak energy efficiency. Measurement demonstrates real-time cluster analysis, and feature extraction for patternrecognition with 8-fold dimension reduction with an accuracy comparable to the floating-point software simulation baseline.
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