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Conference on Optical Metrology and Inspection for Industrial Applications X

作者： Semenishchev, Evgenii Urunov, Slavat Ilyukhin, Yuriy Siryakov, Aleksey Voronin, Viacheslav Moscow State Tech Univ STANKIN 1a Vadkovsky Moscow 127055 Russia

ISBN: (纸本)9781510667877;9781510667884

The article proposes an approach to the determination of small-form objects against a complex background. The proposed approach uses a parallel data processing algorithm that includes the following main modules: a multi-criteria image filtering block built on an objective function that minimizes the weighted average sum of the average square of the first-order finite difference, as well as the average square of the distance difference between the input implementation and the generated data;parallel separation of objects by analyzing local features, statistical analysis of histogram changes, building a mask of object detailing and frequency analysis;the formation of a feature mask and the search for similarity elements by analyzing the generated features. On the test data set, an example of determining small-sized objects on a complex background with their subsequent classification into class objects is presented. The data were obtained by a machine vision system installed on a robotic complex. Data on the required parameters of the formed machine vision systems are given, recommendations on the required parameters of the algorithms are presented.

关键词： image/signal smoothing edge detection preprocessing multicriteria method objects detection

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Vision-Based System for Automatic detection of Suspicious objects on ATM 16th

Vision-Based System for Automatic Detection of Suspicious Ob...

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16th International Conference on Computer Analysis of Images and Patterns (CAIP)

作者： Rattanapitak, Wirat Wangsiripitak, Somkiat King Mongkuts Inst Technol Ladkrabang Fac Informat Technol Ladkrabang 10520 Bankok Thailand

ISBN: (纸本)9783319231921;9783319231914

Most skimming devices attached to an automatic teller machine (ATM) are similar in color and shape to the host machine, vision-based detection of such things is therefore difficult. A background subtraction method may be used to detect changes in a normal situation. However, without human detection, its background model is sometimes polluted by the ATM user, and the method cannot detect suspicious objects left in the scene. This paper proposes a real-time system which integrates (i) a simple image subtraction for detection of user arrival and departure, and (ii) an automatic detection of suspicious objects left on the ATM. The background model is updated only when no user is found, and used to detect suspicious objects based on a guided adaptive threshold. To avoid a detection miss, nonlinear enhancement is applied to amplify the intensity differences between foreign objects and host machine. Experimental results show that the proposed system increases correctly detected area by 13.21% compared with the fixed threshold method. It has no detection miss and false alarm either.

关键词： Skimming Automated teller machine Suspicious objects objects detection Video surveillance

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METHODS FOR detection OF objects IN AGRICULTURE

METHODS FOR DETECTION OF OBJECTS IN AGRICULTURE

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5th International Conference on Trends in Agricultural Engineering

作者： Skerik, Filip Volf, Jaromir Linda, Miloslav Martinek, Tomas Czech Univ Life Sci Prague Prague 16521 6 Suchdol Czech Republic

ISBN: (纸本)9788021323889

Recognizing the size and type of the object in agriculture is important in moving the machinery and the material and their classification. Any material that is moving along a vibrating conveyor belt, or only after the conveyor can be detected and evaluated using a program created in a development environment Visual basic. net. The sensing element is Kinect sensor which senses the space in the direction of its rotation and transmits the data to a computer for further processing. After detecting the individual elements of the system evaluates this information and transmit signal actuators.

关键词： Pattern recognition objects detection edge detection Kinect sensor laser measure system actuator

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GL-YOLO: Pedestrian detection Algorithm for Small objects in Long Distances 20th

GL-YOLO: Pedestrian Detection Algorithm for Small Objects in...

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20th Chinese Intelligent Systems Conference

作者： Abdulatef, Mohammed Shaker Zhai, Junyong Southeast Univ Sch Automat Nanjing 210096 Peoples R China

ISBN: (纸本)9789819786527;9789819786503;9789819786497

A new algorithm named GL-YOLO based on YOLOv8 is proposed to address the issues of low accuracy and high computational complexity in pedestrian detection. Firstly, spatial and channel reconfiguration convolutions are introduced to reduce redundant computations and promote the learning of representative features, thereby reducing the computational load of the model. Secondly, a multi-scale attention module is incorporated to preserve information on each channel while reducing the computational burden, significantly improving the detection accuracy of the model. Finally, an additional small objects detection layer is added, transforming the three detection layers of YOLOv8 into four layers, effectively enhancing the model's performance in detecting small objects. To validate the effectiveness of GL-YOLO, experiments were conducted on the VOC 2007 and VC2012 datasets. The experimental results demonstrate that compared to YOLOv8, the improved algorithm reduces parameters by 15.3%, increases mAP@0.5 by 4.72%, and mAP@0.5:0.95 by 5.09%. The enhanced algorithm exhibits stronger generalization performance.

关键词： objects detection Attention mechanism Lightweight network

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AUTOMATIC detection AND RECOGNITION OF MAN-MADE objects IN HIGH RESOLUTION REMOTE SENSING IMAGES USING HIERARCHICAL SEMANTIC GRAPH MODEL

AUTOMATIC DETECTION AND RECOGNITION OF MAN-MADE OBJECTS IN H...

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International-Society-for-Photogrammetry-and-Remote-Sensing Hannover Workshop

作者： Sun, X. Thiele, A. Hinz, S. Fu, K. Karlsruhe Inst Technol Inst Photogrammetry & Remote Sensing IPF D-76021 Karlsruhe Germany Chinese Acad Sci Inst Elect Beijing Peoples R China Chinese Acad Sci Key Lab Spatial Informat Proc & Applicat Syst Tec Beijing Peoples R China

ISBN: (纸本)9781629935201

In this paper, we propose a hierarchical semantic graph model to detect and recognize man-made objects in high resolution remote sensing images automatically. Following the idea of part-based methods, our model builds a hierarchical possibility framework to explore both the appearance information and semantic relationships between objects and background. This multi-levels structure is promising to enable a more comprehensive understanding of natural scenes. After training local classifiers to calculate parts properties, we use belief propagation to transmit messages quantitatively, which could enhance the utilization of spatial constrains existed in images. Besides, discriminative learning and generative learning are combined interleavely in the inference procedure, to improve the training error and recognition efficiency. The experimental results demonstrate that this method is able to detect man-made objects in complicated surroundings with satisfactory precision and robustness.

关键词： objects detection objects recognition High resolution remote sensing images Semantic graph model

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Haar descriptors preliminary sampling

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第1期82卷 819-837页

作者： Dashkov, Andrii Khaleiev, Oleksii P Prod Inc Kharkiv Ukraine

The selection of meaningful features for training computer vision solutions is the most important part of a robust framework construction. A huge amount of possible features with different parameters makes this task chalenging. We have developed a preliminary procedure of sampling of Haar descriptors for more effective learning of the binary classifier. A dataset and boosting weights are used to sample a data driven subset of descriptors from the vast space of all potential candidates, using the Kullback-Leibler divergence as a metric. Our aim was to create a simple technique which makes Haar features mining more reasonable and helps to reach higher learning rates, and further to investigate the practical usage of this data-driven approach. The proposed technique can be used for improvement of learning rates during the training process of cascade Haar classifiers with the usage of the AdaBoost framework. The developed data-driven sampling procedure can help to improve learning rates up to 34% depending on the dataset type. Additionally, the research clarifies the influence of the 'complexity' of dataset samples on learning effectiveness and provides practical recommendations for a reasonable choice of the initial size of descriptors' list.

关键词： objects detection Haar descriptors Face detection Pedestrian detection Cascade classifier Binary classifier Features mining

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A dual approach for machine-awareness in indoor environment combining pseudo-3D imaging and soft-computing techniques

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2017年第6期8卷 1795-1814页

作者： Madani, Kurosh Hassan, Dayana Sabourin, Christophe UPEC Images Signals & Intelligence Syst Lab LISSI EA 3956 Univ Paris EstSenart Fontainebleau Inst Technol 36-37 Rue Charpak F-77127 Lieusaint France

3D spatial characterization of objects is a key-step for autonomous knowledge extraction leading to machine-awareness. In this paper we describe a dual-images-based approach for objects' 3D characterization and localization for Machine-Awareness in indoor environment. The so-called dual images are provided by color and depth cameras of Kinect system, which presents an appealing potential for 3D objects modeling and localization. Placing the human-machine (including human-robot) interaction as a primary outcome of the intended visual Machine-Awareness in investigated system, we aspire proffering the machine spatial awareness about its surrounding environment with identification and semantically description of relative positions of detected items in that environment. In this research work, the aforementioned pseudo-3D imaging and Soft-computing techniques are combined in order to extract, to recognize and to spatially characterize objects and the distance between objects in 3D environment. In other words, on the one hand, a pseudo-3D object modeling and localization method based on the dual-images of Kinect system is proposed, in which the computation of object's spatial characterization is presented. On the other hand, a computational semantically description algorithm by using Fuzzy Inference System is proposed, in which the numerical-to-semantic conversion for machine awareness is accomplished. Experimental results validating the investigated approach are reported and discussed.

关键词： Machine-awareness Pseudo-3D imaging Soft-computing objects detection objects characterization Implementation

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A novel multiple moving objects recognition and segmentation based on dense optical flow and K-means clustering

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第12期18卷 9275-9284页

作者： Ding, Jing Zhang, Zhen Wang, Yangyang Anhui Univ Sci & Technol Sch Geomat Huainan 232001 Peoples R China Anhui Univ Sci & Technol Coal Ind Engn Res Ctr Min Area Environm & Disaster Huainan 232001 Peoples R China Shandong Prov 4 Inst Geol & Mineral Survey Weifang 261021 Peoples R China

Moving objects detection has always been a prominent research topic for the computer vision and image processing field. In recent years, many methods of moving objects detection in optical imaging have been proposed. However, the high-precision detection and recognition of multiple moving objects is still quite challenging. Many key problems that restrict the performance of multiple moving objects detection, such as dynamic background, complex environment, different objects classification, and detection accuracy, have not been effectively solved. This study proposes a dense optical flow multiple moving objects recognition and extraction method to overcome these problems. Unlike most moving objects detection methods based on sparse optical flow, we propose a multiple moving objects detection and recognition method which combines an optical flow histogram and K-means clustering analysis based on dense optical flow. First, we utilize an enhanced optical flow calculation and feature threshold processing to segment the moving objects foreground region. Subsequently, according to the histogram of optical flow amplitude and directions, K-means iterative clustering is utilized to cluster the foreground optical flow. Different moving objects can be recognized and segmented using different optical flow amplitudes and direction. The implementation results indicate that the proposed method exhibits more optimal results for both rigid and non-rigid multiple targets with small internal optical flow difference in static and dynamic environments.

关键词： Multiple moving objects objects detection Clustering analysis K-means HS optical flow

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detection algorithm of targets for infrared search system based on area infrared focal plane array under complicated background

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OPTIK 2012年第3期123卷 235-239页

作者： Sui, Xiubao Chen, Qian Bai, Lianfa Nanjing Univ Sci & Technol Natl Def Key Lab Optoelect Engn Nanjing 210094 Jiangsu Peoples R China

Infrared Search and Track (IRST) system is a wide field of view surveillance system, meant for autonomous search, detection, acquisition, and cue of potential targets. The first and second generation IRSTs utilized detectors with multiple elements followed by discrete preamplifiers for signal read-out. They have many performance limitations. With the advent of infrared focal plane array (IRFPA) sensors, the present trend is to build IRSTs based on line FPA sensors to achieve higher sensitivity and resolution. This article describes a new IRST system, different from other systems with area infrared focal plane array (IRFPA), using area infrared focal plane array (AFPA). This system has the simple and reliable structure. First this paper described the surveillance system based on AFPA. And the Point Scattering Function (PSF) of degradation model images blurred by AFPAs' rotations and the imaging feature of the targets is presented. And the target detection algorithm is described and analyzed, the maximum filter and center filter is advanced. Finally the experiment results have proved that the IRST system using AFPA has high detection ratio and low false alarm ratio. (C) 2011 Elsevier GmbH. All rights reserved.

关键词： Infrared technique Infrared search system objects detection Maximum value detection algorithm

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Stereovision-based object segmentation for automotive applications

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EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING 2005年第14期2005卷 2322-2329页

作者： Huang, YP Fu, S Thompson, C Univ Warwick Int Automot Res Ctr Warwick Mfg Grp Coventry CV4 7AL W Midlands England Cranfield Univ Appl Math & Comp Grp Sch Engn Cranfield MK43 0AL Beds England

Obstacle detection and classification in a complex urban area are highly demanding, but desirable for pedestrian protection, stop & go, and enhanced parking aids. The most difficult task for the system is to segment objects from varied and complicated background. In this paper, a novel position-based object segmentation method has been proposed to solve this problem. According to the method proposed, object segmentation is performed in two steps: in depth map (X-Z plane) and in layered images (X-Y planes). The stereovision technique is used to reconstruct image points and generate the depth map. objects are detected in the depth map. Afterwards, the original edge image is separated into different layers based on the distance of detected objects. Segmentation performed in these layered images can be easier and more reliable. It has been proved that the proposed method offers robust detection of potential obstacles and accurate measurement of their location and size.

关键词： stereovision segmentation objects detection

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