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A quantum bi-directional self-organizing neural network (QBDSONN) architecture for binary object extraction from a noisy perspective

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APPLIED SOFT COMPUTING 2016年 46卷 731-752页

作者： Konar, Debanjan Bhattacharyya, Siddhartha Panigrahi, Bijaya Ketan Nakamatsu, Kazumi Sikkim Manipal Inst Technol Dept Comp Sci & Engn Sikkim India RCC Inst Informat Technol Detp Informat Technol Canal South Rd Kolkata 700015 India Indian Inst Technol Delhi Dept Elect Engn Delhi India Univ Hyogo Sch Human Sci & Environm 1-1-12 Shinzaike Hon Cho Himeji Hyogo 6700092 Japan

This article proposes an efficient technique for binary object extraction in real time from noisy background using quantum bi-directional self-organizing neural network ( QBDSONN) architecture. QBDSONN exploits the power of quantum computation. It is composed of three second order neighborhood topology based inter-connected layers of neurons (represented by qubits) arranged as input, intermediate and output layers. In the suggested network architecture, the inter-connection weights and activation values are represented by rotation gates. A self-supervised learning algorithm, suggested in this proposed architecture, relies on the steepest descent algorithm. The quantum neurons enjoy full-connectivity in each layer of the network architecture. The image pixels in terms of qubits are self-organized in between the intermediate or hidden and output layers of the QBDSONN architecture using counter-propagation of the quantum states to obviate time consuming quantum back propagation algorithm. In the final phase, quantum measurement is carried out at the output layer to eliminate superposition of the quantum states of the outputs. In order to establish the result, the proposed QBDSONN architecture is applied on an artificial synthetic and on a real life spanner image with different degrees of uniform and Gaussian noises. Experimental results show that QBDSONN outperforms both its classical counterpart and the supervised auto-associative Hopfield network as far as extraction time is concerned and it retains the shapes of the extracted images with great precision. Experiments are also carried out using a linear method named local statistics (Wiener filter) and a nonlinear technique named median filter with adaptive discrete wavelet transformations (DWT) for binary object extraction to show the dominance of the proposed QBDSONN with respect to the quality of extracted images. Finally, a statistical significance of the proposed QBDSONN is reported by applying 2 sample one sided

关键词： object extraction BDSONN Quantum computing QBDSONN Kolmogorov-Smirnov test

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object extraction Using an Edge-Based Feature for Query-by-Sketch Image Retrieval

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2015年第1期E98D卷 214-217页

作者： Takasu, Takuya Kumagai, Yoshiki Ohashi, Gosuke Shizuoka Univ Dept Elect & Elect Engn Hamamatsu Shizuoka 4328561 Japan

We previously proposed a query-by-sketch image retrieval system that uses an edge relation histogram (ERH). However, it is difficult for this method to retrieve partial objects from an image, because the ERH is a feature of the entire image, not of each object. Therefore, we propose an object-extraction method that uses edge-based features in order to enable the query-by-sketch system to retrieve partial images. This method is applied to 20,000 images from the Corel Photo Gallery. We confirm that retrieval accuracy is improved by using the edge-based features for extracting objects, enabling the query-by-sketch system to retrieve partial images.

关键词： query-by-sketch image retrieval object extraction partial image retrieval edge-based feature

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A video codec based on background extraction and moving object detection

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第21期83卷 59675-59698页

作者： Hadi, Soheib Shahbahrami, Asadollah Azgomi, Hossien Islamic Azad Univ Dept Comp Engn Rasht Branch Rasht Iran Univ Guilan Fac Engn Dept Comp Engn Rasht Iran

Cameras are the primary data sources in video surveillance systems and produce massive data every second. Video surveillance is an extremely beneficial functionality brought to us by modern technology. An essential application of video surveillance in public security is facilitating the observation and analysis of events. Video surveillance systems require high-bandwidth media to transfer, high-capacity media to store, and high-performance hardware to process data. Consequently, these systems impose many costs on organizations. Video compression techniques can reduce the amount of data transferred or stored by surveillance systems and, as a result, lower the costs. Fixed CCTV cameras are the largest category of surveillance cameras. Backgrounds in these videos are typically constant and saving them for every frame is redundant. Therefore, a background-aware approach can achieve a higher compression rate in compressing these cameras' videos than conventional approaches. This paper proposes a video codec for fixed cameras based on background extraction and moving-object detection algorithms. By background extraction, the pure backgrounds of the video are generated and stored in JPEG format for consecutive time intervals. By moving-object detection, the objects, and their coordinates are extracted in each frame using YOLOv7 and stored in JPEG format separately from the backgrounds. At the decoder side, each frame is built up using the generated background and detected objects which have been stored and transmitted as JPEG files. The evaluation of the proposed method on an appropriate set of videos from CDnet2014 and EWAP datasets shows that the proposed method can compress the videos effectively by significant compression ratios up to 46.76x, while the worst quality loss resulting from the compression is 0.99 according to the SSIM measure.

关键词： Video compression Video surveillance Background generation Background extraction object detection object extraction

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Multi-object extraction from topview group-housed pig images based on adaptive partitioning and multilevel thresholding segmentation

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BIOSYSTEMS ENGINEERING 2015年 135卷 54-60页

作者： Guo, Yi-zheng Zhu, Wei-xing Jiao, Peng-peng Ma, Chang-hua Yang, Jian-jun Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Jiangsu Peoples R China Nanjing Normal Univ Taizhou Coll Taizhou 225300 Jiangsu Peoples R China

The aim of this study is to provide a feasible method that can accurately extract individual pigs from a drinker and feeder zone;therefore, an object extraction method based on adaptive partitioning and multilevel thresholding segmentation is proposed. First, a single frame image is enhanced using histogram equalisation, and then it is segmented with a maximum entropy global threshold. The initial segmentation objects are obtained by extracting a "valid area" and morphological processing. Then, each object centroid is calculated from the initially segmented objects, and the original image is adaptively divided into multiple circular sub-blocks whose origin is the centroid and radius is the maximum distance from the centroid to the edge point. Finally, an accurate secondary segmentation result is obtained using multilevel thresholding segmentation in each sub-block. The test data included thirty random videos collected in AVI format, and 9000 frames from 5 days x 6 videos x 120 s x 25 frames s(-1) were selected. Results show that the average detection rate is 92.5%. This paper also analyses the possible applications of the proposed method to pig behaviour analysis, individual recognition, and weight estimation. (C) 2015 IAgrE. Published by Elsevier Ltd. All rights reserved.

关键词： Group-housed pigs object extraction Adaptive partitioning Multilevel thresholding Maximum Shannon entropy

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Automated object extraction from MLS Data: a Survey 10

Automated Object Extraction from MLS Data: a Survey

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10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)

作者： Chen Kunyuan Cheng Ming Zhou Menglan Chen Xinqu Chen Yifei Jonathan, Li Nie Hongshan Xiamen Univ Sch Informat Sci & Engn Fujian Key Lab Sensing & Comp Smart City Xiamen 361005 Fujian Peoples R China Univ Waterloo Dept Geog & Environm Management GeoSTARS Lab Waterloo ON N2L 3G1 Canada Hunan Intelligent Things Technol Co Ltd Changsha 410000 Hunan Peoples R China

ISBN: (纸本)9781467393232

Realistic 3D city modeling using Mobile Laser Scanning (MLS) technique experienced a remarkable revolution in aiding urban planning, regulation design, city management, navigation, and emergency responses. This paper focuses on thoroughly examining the advance of automated MLS object extraction techniques over the last five years. Categorized as either on-road or off-road, mainly six objects are included in this paper (road curbs, road markings, pavement cracks, building facades, pole-like objects and trees). MLS extraction techniques applied on typical objects is evaluated according to their method design, degree of automation, precision, and computational efficiency. Recent researches mostly focus on developing accurate object extraction algorithms and most of the reviewed methods can achieve high precision;however, optimizing the trade-off between computational cost and accuracy remains a big challenge. In addition, there is still no general standardized approach to deal with MLS objects extractions to date;most algorithms reviewed in this paper still need some artificial interference to ensure accuracy and efficiency.

关键词： MLS technique automatic algorithm object extraction literature review

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Foreground object extraction using variation of blurs based on camera focusing

Foreground object extraction using variation of blurs based ...

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International Conference on Cyberworlds (CW)

作者： Takayama, Natsuki Takahashi, Hiroki Univ Electrocommun Grad Sch Informat & Engn 1-5-1 Chofugaoka Chofu Tokyo Japan

ISBN: (纸本)9781467394031

Image foreground object extraction is still challenging yet interesting topic in visual computing area. This paper proposes foreground extraction method based on one of the basic camera functions, focusing. A focus point and variation of blurs are extracted based on focusing, and these can be essential information to extract the foreground image. A focus point shows the rough position of the foreground, and variation of blurs becomes a distinctive image feature to segment foreground and background. The major contributions of this paper are developing a framework to extract a foreground object based on a camera function, and estimating the blur with the matching position between images using SIFT (Scale Invariant Feature Transform). SIFT is able to detect and describe image features which are invariant to scale, rotation and luminance changing. Moreover, scale-space extrema in SIFT can be used to estimate the variation of blurs. This is shown experimentally. Proposed foreground extraction is conducted in three steps. First, sparse variation of blurs between images is computed using SIFT. Next, the sparse variation of blurs is propagated using EAI (Edge Aware Interpolation) and a full variation of blur map is generated. Finally, foreground object extraction is obtained by GraphCut algorithm using a focus point as a constraint of the object. Experimental evaluation result shows improvement in performance of foreground object extraction using variation of blurs.

关键词： object extraction blur estimation SIFT EAI GraphCut

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An Efficient object extraction with Graph-Based Image Segmentation 4

An Efficient Object Extraction with Graph-Based Image Segmen...

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4th International Conference on Advanced Computer Science Applications and Technologies (ACSAT)

作者： Saglam, Ali Baykan, Nurdan Akhan Selcuk Univ Fac Engn Dept Comp Engn Konya Turkey

ISBN: (纸本)9781509004249

object extraction process is a closely related issue with image segmentation process. To separate an image to several segments formed similar pixels, many methods are proposed in the area of image processing. Graph-based image segmentation is also one of the segmentation methods. Because of their representation convenience and ease of use, graphs are used as important tools in many image processing applications. While an image segmentation process runs, the processes splitting a graph to sub graphs and merging sub graphs are carried out in the meanwhile. To fulfill these processes, the method uses some local features such as differences between vertices in the graph, which represent pixels, or global features of the image and its segments. To extract an object from an image, we first segmented the entire image, because of to save global features, or to obtain more accurate segmentation. Finally, we extract the intended object from the image by merging the segments that are inside the area drawn before by us. We test the method on some images in the Segmentation Evaluating Database from Weizmann Institute of Science and evaluate the segmentation results. Our F-measure score values show that it seems noticeable good segmentation.

关键词： Segmentation object extraction Graph Minimum Spanning Tree

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Fusion of infrared and visible light images based on the grey theory to object extraction

Fusion of infrared and visible light images based on the gre...

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12th IEEE International Conference on Electronic Measurement and Instruments (ICEMI)

作者： Wang Chunhua Fu Yuchen Suzhou Ind Pk Inst Serv Outsourcing Informat Technol Dept Suzhou 215123 Peoples R China

ISBN: (纸本)9781479970711

For visualization and contrast of lR images are weak, this paper proposed a novel image fusion algorithm for infrared and visible images based on object extraction via grey theory. Firstly, the grey correlation theory is applied to the infrared image for target extract;and then acquire the final object directly from the infrared source image. Finally, the image fusion quality is evaluated. Compared with four commonly used methods, the results show that the fusion image has a good visual effect, and some objective evaluation indexes are improved obviously. Its fused image can effectively preserve the detail of visible image and object information of infrared image.

关键词： grey system theory object extraction infrared image fusion

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Nonreference object-based pansharpening quality assessment

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EGYPTIAN JOURNAL OF REMOTE SENSING AND SPACE SCIENCES 2024年第2期27卷 227-241页

作者： Maleki, Shiva Aghapour Ghassemian, Hassan Imani, Maryam Tarbiat Modares Univ Fac Elect & Comp Engn Image Proc & Informat Anal Lab Tehran Iran

Pansharpening involves the fusion of panchromatic (PAN) and multispectral (MS) images to obtain a highresolution image with enhanced spectral and spatial information. Assessing the quality of the resulting fused image poses a challenge due to the absence of a high -resolution reference image. Numerous methods have been proposed to address this, from assessing quality at reduced resolution to full -resolution evaluations. Many existing approaches are pixel -based, where quality metrics are applied and averaged on individual pixels. In this article, we introduce a novel object -based method for assessing the quality of pansharpened images at full resolution. In object -based quality assessment methods, the reaction of different areas of the fused image to the fusion process is reflected. Our approach revolves around extracting objects from the given image and evaluating extracted objects. By doing so, the distinct responses of different objects within the fused image to the fusion process are captured. The proposed method leverages a unique object extraction technique known as segmentation by nearest neighbor (SNN) to extract objects of the MS image. This method extracts the objects based on the image ' s characteristics without any requirement for parameter tuning. These extracted objects are then mapped onto both PAN and fused images. The proposed spectral index measures the spectral homogeneity of the fused image ' s objects and the proposed spatial index measures the injected spatial content from the PAN image to the fused image ' s objects. Experimental results underscore the robustness and reliability of the proposed method. Additionally, by visualizing distortion values on object -maps, we gain insights into fusion quality across diverse areas within the scene.

关键词： object -based quality assessment object extraction Pansharpening Spatial index Spectral index

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Discriminative appearance model with template spatial adjustment for visual object tracking

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SOFT COMPUTING 2023年第14期27卷 9787-9800页

作者： Vadamala, Purandhar Reddy Aklak, Annis Fathima Vellore Inst Technol Sch Elect Engn Chennai India

In computer vision applications, visual object tracking is a complex task in which object appearances change in the presence of illumination variation, occlusion, in-plane rotation, and fast motion. In the state-of-the-art approaches, trackers deal with the common model to address appearance variations with coexisting challenges. However, this approach is ineffective when dealing with simultaneous challenges because the object's features differ due to the appearance variations. To alleviate these limitations, in this paper, a visual object tracking framework that relies on an object appearance feature update is proposed. The appearance tracking model was developed using templated spatial information and object features. The tracked object template is identified by comparing the tracked template from the previous frame with the directional templates in the current frame. To adapt to appearance variations, the proposed tracking model updates the tracked template feature vector, the motion parameters, and the spatial information as it tracks the target object in successive frames. The experimental results on challenging video sequences in object tracking benchmarks demonstrate that the proposed tracking model can track objects with a precision of 84.5% at a 10.1 fps tracking speed. The qualitative analysis shows that the proposed tracking model outperforms the related conventional trackers.

关键词： Visual tracking object extraction Template extraction Template matching Feature extraction Feature update Motion estimation

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