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Deep Visible and Thermal Image Fusion for Enhancement Visibility for Surveillance Application 19

Deep Visible and Thermal Image Fusion for Enhancement Visibi...

引用

conference on Electro-Optical and Infrared Systems - Technology and Applications xix

作者： Voronin, V. Zhdanova, M. Gapon, N. Alepko, A. Zelensky, A. Semenishchev, E. Moscow State Univ Technol STANKIN Ctr Cognit Technol & Machine Vis Moscow Russia Don State Tech Univ Rostov Na Donu Russia

ISBN: (纸本)9781510655461

The additional sources of information (such as depth sensors, thermal sensors) allow to get more informative features and thus increase the reliability and stability of recognition. In this research, we focus on how to combine the multi-level deep fusion for visible and thermal information. We present the algorithm, combining information from visible cameras and thermal sensors based on the deep learning and parameterized model of logarithmic image processing (PLIP). The proposed neural network based on the principle of an autoencoder. We use an encoder to extract the features of images, and the fused image is obtained by a decoding network. The encoder consists of a convolutional layer and a dense block, which also consists of convolutional layers. Fusing images are in the decoder and the fusion layer operating to the principle of PLIP which close to the human visual system's perception. This fusion approach applied for surveillance application. Experimental results showed the effectiveness of the proposed algorithm.

关键词： deep image fusion thermal fusion enhancement surveillance logarithmic image processing

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Biologically-inspired approach to automatic processing of fly eye radar antenna array patterns with convolutional neural networks 19

Biologically-inspired approach to automatic processing of fl...

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Unmanned Systems Technology xix conference

作者： Dzieciuch, Iryna SPAWAR Syst Ctr Pacific San Diego CA 92152 USA

ISBN: (纸本)9781510608924

Autonomous Air Vehicles (AUV) are used for survey, patrol and exploration purposes. Command and control of such vehicles is usually done by a human operator through use of joystick and visual camera. Most of the perception, target detection, and maneuvering is also done by a human. This approach does not allow for autonomy to evolve much further. Additionally, path planning algorithms are limited in scope and do not account for real-time events. A proposed fly eye antenna array system, gives an opportunity to scan its surroundings and detect multiple targets. It consists of angularly spaced directional and overlapping antennas with wide area coverage. Each directional antenna is coupled with front end circuit, and a digital processor. Signals coming from such an antenna array records information about position, direction, target proximity with good penetration through scattered media [1]. Object detection using radio frequency signals from such sensors is challenging. The object reflects electromagnetic signals, coming from multitude of the fly-eye antennas. Based on the correlative values obtained from the sensors, a determination is made of the location, shape, and size of the object. Such patterns are recorded and used for convolutional neural network training purposes. The reflected and positional information of the radar sensors provides valuable information to the pattern system: 9 sensor covering 360 C view, with a correlative signal strength matrix. The objective of using signals from antenna array of signals with together with artificially labeled data makes a system intelligent. This approach is much simpler and we think it will have better generalization and performance then other RF signal processing approaches.

关键词： AUV RF imaging signal processing holograms AI learning antennas navigation

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visual representaion of special numbers for complex structure system analysis

Visual representaion of special numbers for complex structur...

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International conference on Soft Computing and Measurements (SCM)

作者： A. M. Kistanov S. P. Orlov Computer Technology Department Samara State Technical University Samara Russia

ISBN: (纸本)9781467389204

The aim of the article is to solve problems of researching of complex system structure. The methods of visual combinatorial analysis are used for representation variable structure complex systems. We consider the set of structures generated by special numbers - Gaussian binomial coefficients. An example of the construction of structure lattices using new recurrence formula is shown. The special “mathematical microscope” suggested by P. Florensky is a procedure revealing the consistent complication of the structure based on special numbers. The proposed method can be applied in the analysis of the numbering components complex structure, evaluating the effectiveness of the structure transformation with changing sets of data processing tasks.

关键词： Lattices visualization Microscopy information systems Computational modeling System analysis and design Complex systems

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Smoothing parameter estimation framework for Markov random field by using contextual and spectral information

Smoothing parameter estimation framework for Markov random f...

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conference on Image and Signal processing for Remote Sensing xix

作者： Aghighi, Hossein Trinder, John Univ New S Wales Sch Civil & Environm Engn Sydney NSW 2052 Australia

ISBN: (纸本)9780819497611

Markov random field (MRF) is currently the most common method to find the optimal solution for the classification of image data incorporating contextual visual information. The labeling for a site in MRF is dependent on smoothing parameters. Therefore, this paper deals with the development of a new robust two-step method to determine the smoothing parameter which balances spatial and spectral energies for the purpose of maximizing the classification accuracy. Multispectral images obtained by WorldView-2 satellite were employed in this research. In the first step, a support vector machine (SVM) was used to provide a vector of multi-class probability and a class label for each pixel. Then, the summation of the maximum probability of each pixel and its 8 neighbors is calculated for a dynamic block and this value is assigned to the central pixels of each block. The blocks of each class are sorted and an equal proportion of blocks of each class with the highest probability are selected. Then, the class codes and spectral information of the selected blocks are extracted from the classified map and multispectral image, respectively. This information is used to calculate class label co-occurrence matrices of the blocks (CLCMB), class label co-occurrence matrix (CLCM) and class separability indices. Finally, different smoothing parameters are calculated and the results show that estimated smoothing parameter can produce a more accurate map.

关键词： Markov Random field smoothing parameter CLCM class separability dynamic block

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Detection and tracking of gas plumes in LWIR hyperspectral video sequence data

Detection and tracking of gas plumes in LWIR hyperspectral v...

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conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery xix

作者： Gerhart, Torin Sunu, Justin Lieu, Lauren Merkurjev, Ekaterina Chang, Jen-Mei Gilles, Jerome Bertozzi, Andrea L. Calif State Univ Long Beach Dept Math & Stat Long Beach CA 90840 USA Univ Calif Los Angeles Dept Math Los Angeles CA 90095 USA Harvey Mudd Coll Dept Engn Claremont CA 91711 USA

ISBN: (纸本)9780819495341

Automated detection of chemical plumes presents a segmentation challenge. The segmentation problem for gas plumes is difficult due to the diffusive nature of the cloud. The advantage of considering hyperspectral images in the gas plume detection problem over the conventional RGB imagery is the presence of non-visual data, allowing for a richer representation of information. In this paper we present an effective method of visualizing hyperspectral video sequences containing chemical plumes and investigate the effectiveness of segmentation techniques on these post-processed videos. Our approach uses a combination of dimension reduction and histogram equalization to prepare the hyperspectral videos for segmentation. First, Principal Components Analysis (PCA) is used to reduce the dimension of the entire video sequence. This is done by projecting each pixel onto the first few Principal Components resulting in a type of spectral filter. Next, a Midway method for histogram equalization is used. These methods redistribute the intensity values in order to reduce flicker between frames. This properly prepares these high-dimensional video sequences for more traditional segmentation techniques. We compare the ability of various clustering techniques to properly segment the chemical plume. These include K-means, spectral clustering, and the Ginzburg-Landau functional.

关键词： Hyperspectral data analysis midway equalization Ginzburg-Landau MBO image processing video processing

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Enhancing Low-Contrast Image by Separately processing Illuminance and Reflectance

Enhancing Low-Contrast Image by Separately Processing Illumi...

引用

conference on visual information processing xix

作者： Huang, Rui Sang, Nong Luo, Dapeng Tong, Xiaojun Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence Wuhan 430074 Peoples R China Wuhan Polytech Univ Dept Math & Phys Wuhan 430023 Peoples R China

ISBN: (纸本)9780819481658

A simple method for enhancing low-contrast gray image is proposed. The method decomposes an image into the layers of illuminance and reflectance, by using an edge-preserving low-pass filter, and separately enhances each layer. Finally, the enhanced results of these two layers are recomposed as the output. Experiment shows the good results on real images.

关键词： Low-contrast enhancement illuminace reflectance Retinex Theory

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A special algorithm based on structure for ship classification

A special algorithm based on structure for ship classificati...

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conference on visual information processing xix

作者： Sun, Bo Guo, Wei He, Jun Zhu, Xiaoming Beijing Normal Univ Coll Informat Sci & Technol Beijing 100875 Peoples R China

ISBN: (纸本)9780819481658

In this paper, we use the aspect ratio of a vessel for its recognition and classification with overhead images. For aspect ratio extraction, a morphology-based local adaptive threshold method of detection has been applied for a more accurate outline. With Radon transforms on the minimum bounding rectangle regions of those extracted outlines, central axis of each vessel can be got. Thus, the aspect ratio of a vessel could be accurately calculated through scanning the boundary contours of every target by lines along and perpendicular to the direction of central axis. If remote sensing information is also considered, such as the height and pitching angle of shooting, the real values of a vessel can also be calculated.

关键词： Overhead image vessel recognition Radon transformation aspect ratio remote sensing information top view adaptive threshold central axis

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A Differential Interpolation Image Correction Approach for Bidirectional Resonant Scanners

A Differential Interpolation Image Correction Approach for B...

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conference on visual information processing xix

作者： Haji-saeed, Bahareh Woods, Charles L. Kierstead, John Khoury, Jed US Air Force Res Lab Sensors Directorate Hanscom AFB MA 01731 USA Solid State Sci Corp Hollis NH 03049 USA

ISBN: (纸本)9780819481658

We have developed a differential interpolation method for correcting sinusoidally scanned distorted images. In our approach, the scanned image is processed by a line-by-line interpolation technique based on differentiation. As a natural consequence of the method, the image can be divided into four domains/zones perpendicular to the scan direction. The domain boundaries are set by our interpolation algorithm. Each domain is corrected using its specific algorithm;corrected domains are reassembled to construct the corrected image. The implementation of this algorithm shows that, for our 100 pixel wide test image, it is possible to retrieve at least 97.45% of the original image, as measured by the recovered energy, which is superior to the established methods we have applied to this problem.

关键词： interpolation scanning image processing

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Super-resolution reconstruction of images captured from airborne unmanned vehicles

Super-resolution reconstruction of images captured from airb...

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conference on visual information processing xix

作者： Yousef, Amr Hussein Rahman, Zia-ur Old Dominion Univ Dept Elect & Comp Engn Norfolk VA USA

ISBN: (纸本)9780819481658

Super-resolution (SR) reconstruction refers to the process of combining a sequence of under-sampled and degraded low-resolution (LR) images in order to produce a single high-resolution (HR) image. The LR input images are assumed to have slightly different views of the same scene. In the broad sense, super-resolution techniques attempt to improve spatial resolution by incorporating into the final HR result the additional new details that are revealed in each LR image. This can be the case of the images captured from unmanned aerial vehicles (UAVs). These images must have sufficient overlap to produce an HR image. Additionally, information about the UAV altitude and attitude-rotational parameters yaw, pitch, and roll-that allows us to relate the different images to a common coordinate system is also needed. This extra information can be used to get an SR image of the overlapping area common to all these images. In this paper, we define a metric to determine if there is enough overlap between a set of frames that would allow SR reconstruction. When this overlap exists, we use the set of registered data to reconstruct an SR image.

关键词： Lawrencium Unmanned aerial vehicles Image registration Super resolution Cameras Image processing Distortion Unmanned vehicles Spatial resolution Computer engineering

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The Spatial Vision Tree: A Generic Pattern Recognition Engine-Scientific Foundations, Design Principles, and Preliminary Tree Design

The Spatial Vision Tree: A Generic Pattern Recognition Engin...

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conference on visual information processing xix

作者： Rahman, Zia-ur Jobson, Daniel J. Woodell, Glenn A. Old Dominion Univ Dept Elect & Comp Engn Norfolk VA 23529 USA Old Dominion Univ Norfolk VA USA NASA Langley Res Ctr Hampton VA 23681 USA

ISBN: (纸本)9780819481658

New foundational ideas are used to define a novel approach to generic visual pattern recognition. These ideas proceed from the starting point of the intrinsic equivalence of noise reduction and pattern recognition when noise reduction is taken to its theoretical limit of explicit matched filtering. This led us to think of the logical extension of sparse coding using basis function transforms for both de-noising and pattern recognition to the full pattern specificity of a lexicon of matched filter pattern templates. A key hypothesis is that such a lexicon can be constructed and is, in fact, a generic visual alphabet of spatial vision. Hence it provides a tractable solution for the design of a generic pattern recognition engine. Here we present the key scientific ideas, the basic design principles which emerge from these ideas, and a preliminary design of the Spatial Vision Tree (SVT). The latter is based upon a cryptographic approach whereby we measure a large aggregate estimate of the frequency of occurrence (FOO) for each pattern. These distributions are employed together with Hamming distance criteria to design a two-tier tree. Then using information theory, these same FOO distributions are used to define a precise method for pattern representation. Finally the experimental performance of the preliminary SVT on computer generated test images and complex natural images is assessed.

关键词： Pattern recognition visualization Denoising Image enhancement information theory Visibility Convolution Image processing information visualization Sensors

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