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TherISuRNet - A Computationally Efficient Thermal Image Super-Resolution Network

TherISuRNet - A Computationally Efficient Thermal Image Supe...

引用

ieee computer Society Conference on computer vision and Pattern Recognition workshops (CVPRW)

作者： Vishal Chudasama Heena Patel Kalpesh Prajapati Kishor Upla Raghavendra Ramachandra Kiran Raja Christoph Busch Sardar Vallabhbhai National Institute of Technology (SVNIT) Surat India Norwegian University of Science and Technology (NTNU) Gjøvik Norway

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Human perception is limited to perceive the objects beyond the range of visible wavelengths in the Electromagnetic (EM) spectrum. This prevents them to recognize objects in different conditions such as poor illumination or severe weather (e.g., under fog or smoke). The technological advancement in thermographic imaging enables the visualization of objects beyond visible range which enables it's use in many applications such as military, medical, agriculture, etc. However, due to the hardware constraints, the thermal cameras are limited with poor spatial resolution when compared to similar visible range RGB cameras. In this paper, we propose a Super-Resolution (SR) of thermal images using a deep neural network architecture which we refer to as TherISuRNet. We use progressive upscaling strategy with asymmetrical residual learning in the network which is computationally efficient for different upscaling factors such as ×2, ×3 and ×4. The proposed architecture consists of different modules for low and high-frequency feature extraction along with upsampling blocks. The effectiveness of the proposed architecture in TherISuRNet is verified by evaluating it with different datasets. The obtained results indicate superior results as compared to other state-of-the-art SR methods.

关键词： Cameras Spatial resolution Feature extraction Training computer architecture

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Internet of Things (IoT) Discovery Using Deep Neural Networks

Internet of Things (IoT) Discovery Using Deep Neural Network...

引用

ieee workshop on applications of computer vision (WACV)

作者： Ephraim Lo JoHannah Kohl North Point Defense

ISBN: (数字)9781728165530

ISBN: (纸本)9781728165547

We present a novel approach to Internet of Things (IoT) discovery using Deep Neural Network (DNN) based object detection. Traditional methods of IoT discovery are based on either manual or automated monitoring of predetermined channel frequencies. Our method takes the spectrogram images that a human analyst visually scans for manual spectrum exploration and applies the state-of-the-art You Only Look Once (YOLO) object detection algorithm to detect and localize signal objects in time and frequency. We focus specifically on the class of signals that employ the Long Range (LoRa) modulation scheme, which uses chirp spread spectrum technology to provide high network efficiency and robustness against both in- and out-of-band interference. Our detection system is designed with scalability for real or near real-time processing capabilities and achieves 81.82% mAP in real-time on a fourth generation mobile Intel CPU without GPU support. Lastly, we present preliminary detection results for other IoT signals including Zigbee, Bluetooth, and Wi-Fi.

关键词： Object detection Training computer architecture Internet of Things Modulation Spectrogram Microprocessors

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Online Lens Motion Smoothing for Video Autofocus

Online Lens Motion Smoothing for Video Autofocus

引用

ieee workshop on applications of computer vision (WACV)

作者： Abdullah Abuolaim Michael S. Brown York University Toronto

ISBN: (数字)9781728165530

ISBN: (纸本)9781728165547

Autofocus (AF) is the process of moving the camera's lens such that desired scene content is in focus. AF for single image capture is a well-studied research topic and most modern cameras have hardware support that allows quick lens movements to optimize image sharpness. How to best perform AF for video is less clear. Conventional wisdom would suggest that each temporal frame should be as sharp as possible. However, unlike single image capture, the effects of the lens movement is visible in the captured video. As a result, there are two parameters to consider in AF for video: sharpness and lens movement. In this paper, we show that users preferred videos with smooth lens movement, even if it results in less overall sharpness. Based on this observation, we propose two novel AF algorithms for video that strive for both smooth lens movement and sharp scene content. Specifically, we introduce (1) a bidirectional long short-term memory (BLSTM) module trained on smooth lens trajectories and (2) a simple weighted moving average (WMA) method that factors in prior lens motion. Both of these methods have demonstrated excellent results in terms of reducing lens movements (up to 64% reduction) without greatly affecting the sharpness (less than 5.2% change in sharpness). Moreover, videos produced using our methods are more preferred by users over conventional AF that aims only for maximizing sharpness.

关键词： Lenses Smoothing methods Clocks Face Cameras Trajectory Image capture

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Towards Open-Set Semantic Segmentation Of Aerial Images

Towards Open-Set Semantic Segmentation Of Aerial Images

引用

Remote Sensing Conference (LAGIRS), ieee Latin American GRSS & ISPRS

作者： Caio C. V. da Silva Keiller Nogueira Hugo N. Oliveira Jefersson A. dos Santos Department of Computer Science Universidade Federal de Minas Gerais Belo Horizonte Brazil

ISBN: (数字)9781728143507

Classical and more recently deep computer vision methods are optimized for visible spectrum images, commonly encoded in grayscale or RGB colorspaces acquired from smartphones or cameras. A more uncommon source of images exploited in the remote sensing field are satellite and aerial images. However the development of pattern recognition approaches for these data is relatively recent, mainly due to the limited availability of this type of images, as until recently they were used exclusively for military purposes. Access to aerial imagery, including spectral information, has been increasing mainly due to the low cost of drones, cheapening of imaging satellite launch costs, and novel public datasets. Usually remote sensing applications employ computer vision techniques strictly modeled for classification tasks in closed set scenarios. However, real-world tasks rarely fit into closed set contexts, frequently presenting previously unknown classes, characterizing them as open set scenarios. Focusing on this problem, this is the first paper to study and develop semantic segmentation techniques for open set scenarios applied to remote sensing images. The main contributions of this paper are: 1) a discussion of related works in open set semantic segmentation, showing evidence that these techniques can be adapted for open set remote sensing tasks; 2) the development and evaluation of a novel approach for open set semantic segmentation. Our method yielded competitive results when compared to closed set methods for the same dataset.

关键词： Semantics Image segmentation Remote sensing computer vision Task analysis Two dimensional displays Satellites

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Generating visible spectrum Images from Thermal Infrared 31

Generating Visible Spectrum Images from Thermal Infrared

引用

ieee/CVF Conference on computer vision and Pattern Recognition (CVPR)

作者： Berg, Amanda Ahlberg, Jorgen Felsberg, Michael Term Syst Tekn AB Diskettgatan 11 B S-58335 Linkoping Sweden Linkoping Univ Comp Vis Lab Dept EE S-58183 Linkoping Sweden

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Transformation of thermal infrared (TIR) images into visual, i.e. perceptually realistic color (RGB) images, is a challenging problem. TIR cameras have the ability to see in scenarios where vision is severely impaired, for example in total darkness or fog, and they are commonly used, e.g., for surveillance and automotive applications. However, interpretation of TIR images is difficult, especially for untrained operators. Enhancing the TIR image display by transforming it into a plausible, visual, perceptually realistic RGB image presumably facilitates interpretation. Existing grayscale to RGB, so called, colorization methods cannot be applied to TIR images directly since those methods only estimate the chrominance and not the luminance. In the absence of conventional colorization methods, we propose two fully automatic TIR to visual color image transformation methods, a two-step and an integrated approach, based on Convolutional Neural Networks. The methods require neither pre- nor postprocessing, do not require any user input, and are robust to image pair misalignments. We show that the methods do indeed produce perceptually realistic results on publicly available data, which is assessed both qualitatively and quantitatively.

关键词： Visualization Image color analysis Gray-scale Cameras Convolution Channel estimation Semantics

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Matching Disparate Image Pairs Using Shape-Aware ConvNets

Matching Disparate Image Pairs Using Shape-Aware ConvNets

引用

ieee workshop on applications of computer vision (WACV)

作者： Shefali Srivastava Abhimanyu Chopra Arun Cs Kumar Suchendra M. Bhandarkar Deepak Sharma The University of Georgia Athens GA USA Netaji Subhas Institute of Technology New Delhi India Univ. of Georgia Athens GA USA Netaji Subhas Inst. of Technol. New Delhi India

An end-to-end trainable ConvNet architecture, that learns to harness the power of shape representation for matching disparate image pairs, is proposed. Disparate image pairs are deemed those that exhibit strong affine variations in scale, viewpoint and projection parameters accompanied by the presence of partial or complete occlusion of objects and extreme variations in ambient illumination. Under these challenging conditions, neither local nor global feature-based image matching methods, when used in isolation, have been observed to be effective. The proposed correspondence determination scheme for matching disparate images exploits high-level shape cues that are derived from low-level local feature descriptors, thus combining the best of both worlds. A graph-based representation for the disparate image pair is generated by constructing an affnity matrix that embeds the distances between feature points in two images, thus modeling the correspondence determination problem as one of graph matching. The eigen-spectrum of the affnity matrix, i.e., the learned global shape representation, is then used to further regress the transformation or homography that defnes the correspondence between the source image and target image. The proposed scheme is shown to yield state-of-the-art results for both, coarse-level shape matching as well as fine point-wise correspondence determination.

关键词： Shape computer architecture Image matching computer vision Lighting Feature extraction Matrix decomposition

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Multi-Adapter RGBT Tracking

Multi-Adapter RGBT Tracking

引用

International Conference on computer vision workshops (ICCV workshops)

作者： Cheng Long Li Andong Lu Ai Hua Zheng Zhengzheng Tu Jin Tang Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Key Laboratory of Industrial Image Processing and Analysis of Anhui Province Hefei China

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

The task of RGBT tracking aims to take the complementary advantages from visible spectrum and thermal infrared data to achieve robust visual tracking, and receives more and more attention in recent years. Existing works focus on modality-specific information integration by introducing modality weights to achieve adaptive fusion or learning robust feature representations of different modalities. Although these methods could effectively deploy the modality-specific properties, they ignore the potential values of modality-shared cues as well as instance-aware information, which are crucial for effective fusion of different modalities in RGBT tracking. In this paper, we propose a novel Multi-Adapter convolutional Network (MANet) to jointly perform modality-shared, modality-specific and instance-aware feature learning in an end-to-end trained deep framework for RGBT tracking. We design three kinds of adapters within our network. In a specific, the generality adapter is to extract shared object representations, the modality adapter aims at encoding modality-specific information to deploy their complementary advantages, and the instance adapter is to model the appearance properties and temporal variations of a certain object. Moreover, to reduce computational complexity for real-time demand of visual tracking, we design a parallel structure of generic adapter and modality adapter. Extensive experiments on two RGBT tracking benchmark datasets demonstrate the outstanding performance of the proposed tracker against other state-of-the-art RGB and RGBT tracking algorithms.

关键词： Adaptation models Robustness Target tracking Feature extraction Visualization Task analysis

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Mutual Foreground Segmentation with Multispectral Stereo Pairs

Mutual Foreground Segmentation with Multispectral Stereo Pai...

引用

International Conference on computer vision workshops (ICCV workshops)

作者： Robert Bergevin Pierre-Luc St-Charles Guillaume-Alexandre Bilodeau Dept. of Computer & Software Eng. LITIV lab. Montreal QC Canada LVSN - REPARTI Universite Laval Quebec City QC Canada

The foreground-background segmentation of video sequences is a low-level process commonly used in machine vision, and highly valued in video content analysis and smart surveillance applications. Its efficacy directly relies on the contrast between objects observed by the sensor. In this work, we study how the combination of sensors operating in the long-wavelength infrared (LWIR) and visible spectra can improve the performance of foreground-background segmentation methods. As opposed to a classic visible spectrum stereo pair, this multispectral pair is more adequate for foreground object segmentation since it reduces the odds of observing low-contrast regions simultaneously in both images. We show that by alternately minimizing a stereo disparity energy and a binary segmentation energy with dynamic priors, we can drastically improve the results of a traditional video segmentation approach applied to each sensor individually. Our implementation is freely available online for anyone wishing to recreate our results.

关键词： Image segmentation Shape Object segmentation Motion segmentation Imaging Labeling

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Visual Saliency Models Based on spectrum Processing

Visual Saliency Models Based on Spectrum Processing

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ieee workshop on applications of computer vision (WACV)

作者： Bin Zhao Edward J. Delp Video and Image Processing Laboratory (VIPER) Purdue University West Lafayette Indiana USA

ISBN: (纸本)9781479966844

Some visual saliency models have been proposed to describe how the human visual system perceives and processes visual information. In this paper we describe four frequency domain visual saliency models based on new spectrum processing methods. The four saliency models are the Gamma Corrected spectrum (GCS) model, the Gamma Corrected Log spectrum (GCLS) model, the Gaussian Filtered spectrum (GFS) model, and the Gaussian Filtered Log spectrum (GFLS) model. A set of saliency map candidates are generated by inverse transform of a set of modified spectrums. An output saliency map is selected by minimizing the entropy among the set of saliency map candidates. Extension of these models are also described using various color spaces. Experimental results show that four extensions of our GCS, GCLS, GFS, and GFLS models are more accurate and efficient than some state-of-the-art saliency models in predicting eye fixation on standard image datasets.

关键词： Visualization Image color analysis Mathematical model Computational modeling Frequency-domain analysis Standards Equations

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A Global-to-Local Framework for Infrared and visible Image Sequence Registration

A Global-to-Local Framework for Infrared and Visible Image S...

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ieee workshop on applications of computer vision (WACV)

作者： Michael Ying Yang Yu Qiang Bodo Rosenhahn Institute for Information Processing (TNT) Leibniz University Hannover Hannover Germany

Based on the development of image registration, sequence registration can be done by computing the transformations between consecutive frames. To take into account the accumulated error, global registration method is usually employed as a global error minimizing approach. However, in real surveillance applications, the visible sequence and infrared sequence may be taken at different times, or from different viewpoints, and may have different dynamic contents. Therefore, global registration is only an approximate estimation for two sequences, resulting in inferior local contents. In this paper we present a novel integrated global-to-local framework that addresses the problems of dynamic infrared and visible image sequence registration. We propose to maximize the sum of the mutual information of two sequences for the global homography estimation. Then, frame-to-frame registration is performed to estimate the per-frame local homography. Finally, a smoothing strategy is adopted to smooth the local homographies in the temporal domain to enforce temporal consistency. We evaluate our proposed framework by comparing it to the state-of-the art sequence registration algorithm. Our method achieves improved performance on the public benchmark dataset.

关键词： Image sequences Estimation Smoothing methods Cameras Robustness Image registration Sensors

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