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Crowded pedestrian counting at bus stops from perspective transformations of foreground areas

IET computer vision 2012年第4期6卷 296-305页

作者： Garcia-Bunster, G. Torres-Torriti, M. Oberli, C. Pontificia Univ Catolica Chile Dept Elect Engn Santiago 30622 Chile

Automated bus fleet scheduling and dispatch require an accurate measurements of current passenger demand. This study presents an effective holistic approach for estimating the number of people waiting at regular open bus stops by means of image processing. This is a non-trivial problem because of several varying conditions that complicate the detection process, such as illumination, crowdedness and different people poses, to name a few. The proposed method estimates the pedestrian count using measurements of foreground areas corrected by perspective. Four approaches are evaluated to find the best mapping between the area measurements and the people count. These mappings include two parametric (standard linear regression model, linear discriminant analysis) and two non-parametric (probabilistic neural network, k-nearest neighbours) approaches. This study also evaluates the performance of the algorithm when thermal and panoramic catadioptric cameras are used instead of standard perspective colour cameras. The proposed method is shown to yield better pedestrian count estimates than those obtained using milestone detectors, and requires model fitting procedures than can be easily implemented without requiring very large datasets for proper classifier training. The approach can also be employed to count people in other public spaces, such as buildings and crosswalks.

关键词： image processing crowded pedestrian counting milestone detectors Traffic engineering computing standard perspective colour cameras cameras nonparametric approaches area measurement panoramic catadioptric cameras automated bus fleet dispatch model fitting procedures scheduling automated bus fleet scheduling Systems theory applications in transportation classifier training current passenger demand measurements perspective transformations object detection pedestrians Optical, image and video signal processing image classification computer vision and image processing techniques Spatial variables measurement detection process image colour analysis image sensors very large datasets foreground areas measurements pedestrian count estimation public spaces thermal cameras dispatching regular open bus stops

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SAR image despeckling using deep CNN

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IET image processing 2021年第6期15卷 1285-1297页

作者： Passah, Alicia Amitab, Khwairakpam Kandar, Debdatta North Eastern Hill Univ Dept Informat Technol Shillong Meghalaya India

Synthetic aperture radar (SAR) images are contaminated with noise called speckle that is multiplicative in nature. The presence of speckles in SAR images makes it impossible to understand and interpret for extensive range of applications. However, certain characteristics of SAR and the ability to function irrespective of weather conditions, are making such images worth processing in order to be able to extract relevant information. A despeckling model is proposed that uses deeper convolutional neural networks, which was never used before, as far as authors are concerned, for diminishing speckle in noisy SAR images. Multiple skip connections from the ResNet model are also employed in authors' proposed architecture. In order to maintain uniformity, a formula to be followed while applying skip connections is also derived. A hybrid loss function is developed to train the network more consistently to achieve the desired output. Experiments on simulated SAR images using the NWPU-RESISC benchmark are conducted and tested on real TerraSAR-X images and compared with the state-of-the-art techniques. Results show that the proposed method achieved considerable improvements compared to state-of-the-art methods with PSNRs 27.02, 24.60, and 22.01 for looks 10, 4, and 1, respectively.

关键词： Optical, image and video signal processing Radar equipment, systems and applications computer vision and image processing techniques

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Multifocus image fusion using modified pulse coupled neural network for improved image quality

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IET image processing 2010年第6期4卷 443-451页

作者： Agrawal, D. Singhai, J. MANIT Dept Elect & Commun Engn Bhopal MP India

The present day camera systems have the limitation of acquiring the clearer image of a scene having objects at different distances. This limitation can be overcome by fusion of multiple images of the scene taken with different camera settings. The fusion of these images comes under the category of multifocus image fusion. In the existing method of image, fusion partitioned source image blocks are fused by pulse coupled neural network (PCNN) based on their clarity measure. PCNN plays an important role in the image fusion process in choosing the best-quality image block for fused image. Fusion method becomes tedious and time consuming because of the inherent complexity of PCNN. In this study, a modified approach of PCNN suitable for application in image fusion technique is proposed by reducing the processing time and computational complexity. The modifications proposed are in linking and feeding field of PCNN. This study presents a method for multifocus image fusion by using modified PCNN (MPCNN) with spatial frequency (SF) and energy of Laplacian (EOL) as clarity measures. The proposed method of image fusion using MPCNN results in better quality of fused image with reduced root mean square error (RMSE) and computational time requirements as compared to conventional PCNN.

关键词： Neural computing techniques pulse coupled neural network image quality scene image acquisition spatial frequency neural nets best-quality image block EOL image processing computer vision and image processing techniques image fusion Optical, image and video signal processing multifocus image fusion energy of Laplacian day camera system root mean square error method Interpolation and function approximation (numerical analysis) Computational complexity computational complexity mean square error methods cameras modified PCNN

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A discriminative self-attention cycle GAN for face super-resolution and recognition

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IET image processing 2021年第11期15卷 2614-2628页

作者： Li, Xiaoguang Dong, Ning Huang, Jianglu Zhuo, Li Li, Jiafeng Beijing Univ Technol Fac Informat Technol Beijing Peoples R China

Face image captured via surveillance videos in an open environment is usually of low quality, which seriously affects the visual quality and recognition accuracy. Most image super-resolution methods adopt paired high-quality and its interpolated low-resolution version to train the super-resolution network. It is difficult to achieve contented visual quality and restoring discriminative features in real scenarios. A discriminative self-attention cycle generative adversarial network is proposed for real-world face image super-resolution. Based on the cycle GAN framework, unpaired samples are adopted to train a degradation network and a reconstruction network simultaneously. A self-attention mechanism is employed to capture the contextual information for details restoring. A Siamese face recognition network is introduced to provide a constraint on identify consistency. In addition, an asymmetric perceptual loss is introduced to handle the imbalance between the degradation model and the reconstruction model. Experimental results show that the observation model achieved more realistic low-quality face images, and the super-resolved face images have shown better subjective quality and higher face recognition performance.

关键词： image resolution low resolution version open environment image super resolution methods image reconstruction Siamese face recognition network image recognition computer vision and image processing techniques degradation network real-world face image super resolution self attention mechanism face images discriminative self-attention cycle generative adversarial network reconstruction network visual quality discriminative self-attention cycle GAN cycle GAN framework super resolved face images face super resolution video surveillance higher face recognition performance recognition accuracy feature extraction Video signal processing face recognition subjective quality super resolution network Television and video equipment, systems and applications restoring discriminative features

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image restoration by regularisation in uncorrelated transform domain

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IEE PROCEEDINGS-vision image AND SIGNAL processing 2000年第6期147卷 587-594页

作者： Choy, SO Chan, YH Siu, WC Hong Kong Polytech Univ Dept Elect & Informat Engn Ctr Multimedia Signal Proc Hong Kong Hong Kong Peoples R China

Conventional spatially adaptive regularised image restoration schemes weight the amount of regularisation according to the spatial content of an image. The authors first separately decorrelate the signals under analysis into uncorrelated components and then weight the amount of regularisation performed to these components accordingly. The proposed approach works better than conventional schemes, especially in edge regions.

关键词： decorrelation discrete cosine transforms edge regions regularisation uncorrelated transform domain Integral transforms in numerical analysis spatial content uncorrelated components image restoration Optical, image and video signal processing Karhunen-Loeve transforms discrete Fourier transforms DFT computer vision and image processing techniques DCT KLT spatially adaptive weighting

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Optimized deep learning model for mango grading: Hybridizing lion plus firefly algorithm

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IET image processing 2021年第9期15卷 1940-1956页

作者： Tripathi, Mukesh Kumar Maktedar, Dhananjay D. Guru Nanak Dev Engn Coll Comp Sci & Engn Bidar India Visvesvaraya Technol Univ Belagavi India

This paper intends to present an automated mango grading system under four stages (1) pre-processing, (2) feature extraction, (3) optimal feature selection and (4) classification. Initially, the input image is subjected to the pre-processing phase, where the reading, sizing, noise removal and segmentation process happens. Subsequently, the features are extracted from the pre-processed image. To make the system more effective, from the extracted features, the optimal features are selected using a new hybrid optimization algorithm termed the lion assisted firefly algorithm (LA-FF), which is the combination of LA and FF, respectively. Then, the optimal features are given for the classification process, where the optimized deep convolutional neural network (CNN) is deployed. As a major contribution, the configuration of CNN is fine-tuned via selecting the optimal count of convolutional layers. This obviously enhances the classification accuracy in grading system. For fine-tuning the convolutional layers in the deep CNN, the LA-FF algorithm is used so that the classifier is optimized. The grading is evaluated on the basis of healthydiseased, ripeunripe and bigmediumvery big cases with respect to type I and type II measures and the performance of the proposed grading model is compared over the other state-of-the-art models.

关键词： Optical, image and video signal processing Optimisation techniques computer vision and image processing techniques Data handling techniques Optimisation techniques Neural nets

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Residual dense U-Net for abnormal exposure restoration from single images

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IET image processing 2021年第1期15卷 115-126页

作者： Que, Yue Lee, Hyo Jong Jeonbuk Natl Univ Div Comp Sci & Engn Jeonju 54896 South Korea Jeonbuk Natl Univ Ctr Adv Image & Informat Technol Jeonju South Korea

Digital imaging devices sometimes capture images with abnormal exposure because of the complex lighting conditions and limited dynamic range of luminance. In this work, a new residual dense U-Net is proposed to predict the information that has been lost in saturated image areas, to enable abnormal exposure restoration from a single image. Full advantage of the multi-level features is taken from all the convolution layers in the restoration process. Specifically, the densely connected convolutional layers are used in a contracting encoder net to extract abundant local features. The transition layer and local residual learning after each dense block is then applied to adaptively learn more effectively from prior with present local features. Further, an expanding decoder net with dense layers is used and added with skip connections to preserve low-level information and existing details. Finally, multiple global residual learning is used to adaptively extract hierarchical features and help train the network. It is shown that such a network can be trained end-to-end from abnormal exposure images and outperform the prior best method on image enhancement. Experimental results show that the proposed model can greatly enhance the dynamic range of an abnormal exposure image.

关键词： residual dense U-Net dense block saturated image areas multiple global residual learning multilevel features image enhancement image capture abundant local features extraction densely connected convolutional layers abnormal exposure image computer vision and image processing techniques local residual learning convolutional neural nets hierarchical features extraction Optical, image and video signal processing single image low-level information complex lighting conditions feature extraction decoder net transition layer image sensors encoder net learning (artificial intelligence) image sensors abnormal exposure restoration

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Deep spectral unmixing framework via 3D denoising convolutional autoencoder

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IET image processing 2021年第7期15卷 1399-1409页

作者： Jia, Peiyuan Zhang, Miao Shen, Yi Harbin Inst Technol Dept Control Sci & Engn Harbin Peoples R China

Hyperspectral unmixing is an important technique which attempts to acquire pure spectra of distinct substances (endmembers) and estimate fractional abundances from highly mixed pixels. This paper proposed a novel deep network-based framework for unmixing problem. It contains two parts: a three-dimensional convolutional autoencoder for hyperspectral denoising (denoising 3D CAE) which aims to recover data from highly noised input imagery through an unsupervised manner, and a restrictive non-negative sparse autoencoder which extracts endmembers and abundances from the scene simultaneously. The proposed denoising 3D CAE network integrates 3D operations in each layer, which allows manipulating volumetric representation of the image data directly and facilitates hierarchical exploration for latent information. Being trained with corrupted hyperspectral image data, the denoising 3D CAE network has strong capacity of capturing the principle and robust local features in spatial and spectral domains efficiently, and it shows superior performance for image recovery with high noise disturbance. Moreover, a part-based non-negative autoencoder is concatenated, and the l2,1-norm penalty is imposed for sparsity enhancement of the solution. Comparative experiments are conducted both on synthetic and real-world hyperspectral data, which demonstrate the applicability and effectiveness of the proposed unmixing framework.

关键词： Data and information acquisition, processing, storage and dissemination in geophysics Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research image and video coding image recognition Geophysical techniques and equipment computer vision and image processing techniques Geography and cartography computing Machine learning (artificial intelligence)

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Mean shift tracking through scale and occlusion

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IET SIGNAL processing 2012年第5期6卷 534-540页

作者： Dulai, A. Stathaki, T. Univ London Imperial Coll Sci Technol & Med Dept Elect & Elect Engn London SW7 2AZ England

This study describes a method for tracking objects through scale and occlusion. The technique presented is based on the mean shift algorithm, which provides an efficient way to track objects based on their colour characteristics. A novel and efficient method is derived for tracking through changes in the target scale, where an object of interest moves away or towards the camera and therefore appears to change size in the image plane. The method works by interleaving spatial mean shift iterations with scale iterations. It is shown that this method is considerably more efficient than other methods and possesses other advantages too. It is also demonstrated that the Bhattacharyya coefficient, a histogram similarity metric that is used in the mean shift framework, can be used to reliably detect when target occlusion occurs. In such situations, the motion of an object can be extrapolated to give an accurate estimate of its position. This is used as the basis of a technique for tracking through occlusion. Experimental results are presented on data from various scenarios.

关键词： computer vision and image processing techniques extrapolation position estimate image motion analysis colour characteristics Optical, image and video signal processing object motion occlusion spatial mean shift iteration Bhattacharyya coefficient scale iteration mean shift algorithm histogram similarity metric image colour analysis mean shift tracking Interpolation and function approximation (numerical analysis) image plane object tracking cameras camera

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MFNet-LE: Multilevel fusion network with, Laplacian embedding for face presentation attacks detection

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IET image processing 2021年第14期15卷 3608-3622页

作者： Niu, Sijie Qu, Xiaofeng Chen, Junting Gao, Xizhan Wang, Tingwei Dong, Jiwen Jinan Univ Sch Informat Sci & Engn Shandong Prov Key Lab Network Based Intelligent C Jinan Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan Peoples R China

Face detection is playing a pivotal role for crowd counting and abnormal events detection. However, it is vulnerable to face presentation attacks by printed photos, videos, and 3D masks of real human faces. Although numerous detection techniques based on deep learning have been employed to address the problem of face presentation attacks, there are still several weaknesses in these approaches, such as high algorithm complexity and a lack of detection ability. To overcome these weaknesses, a method based on a multilevel fusion network with Laplacian embedding (MFNet-LE) for the detection of face presentation attacks is proposed. First, a shallow network that contains just three layers was developed, which makes the model faster. Then, an optimised multilevel fusion strategy was developed to combine the input with the output of all previous layers to improve the detection ability of the method. Finally, a Laplacian embedding algorithm is introduced to maintain the inter-class discrimination and penalise the intra-class distance. Under the joint supervision of Laplacian loss and softmax loss, the proposed approach can obtain more discriminative features, which enhance the accuracy of attack detection. Experiments were conducted with three public databases for face presentation attacks: CASIA FASD, Idiap Replay Attack database and MSU USSA. The results demonstrate that the MFNet-LE model can outperform the state-of-the-art methods.

关键词： Algebra Integral transforms Optical, image and video signal processing image recognition image sensors Integral transforms computer vision and image processing techniques Video signal processing Data handling techniques Spatial and pictorial databases

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