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2nd International Conference on Advanced Intelligent Technologies (ICAIT)

作者： Yao, YaJuan Tan, Han Yao, Juan Zhang, Cheng Tian, Fang Huazhong Agr Univ Coll Informat Wuhan Hubei Peoples R China Wuhan Vocat Coll Software & Engn Coll Mech Engn Wuhan Hubei Peoples R China

ISBN: (纸本)9789811697357;9789811697340

The evaluation of body size parameters and morphology based on body size parameters can reflect the growth and development characteristics, production performance and genetic characteristics of sheep. Therefore, studying the measurement of body size parameters and morphological evaluation is an effective way to determine the reasonable breeding of sheep farms and improve the breeding efficiency. In the measurement of human body size based on machine vision, the change of posture has a great influence on the human body size parameters. In this paper, a segmentation algorithm of sheep head, body and legs based on the sheep contour is proposed. Through this algorithm, five posture features are extracted, which are the angle between the hip point and the lower right corner coordinate of the head, the angle between the hip point and the lower left corner coordinate of the head, the angle between the lower right corner coordinate of the body and the lower right corner coordinate of the head, the angle between the lower right corner coordinate of the body and the upper left corner coordinate of the head and the length-width ratio of the circumscribed rectangle. The test results show that the accuracy of the program compiled by the hierarchical vector machine algorithm reaches 95.68%.

关键词： Hierarchical vector machine algorithm segmentation algorithm Sheep posture recognition

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An Intelligent Approach for Robust Detection and Recognition of Multiple Color and Font Styles Automobiles License Plates: A Feature-Based algorithm 4

An Intelligent Approach for Robust Detection and Recognition...

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International Conference on Audio, Language and Image Processing

作者： Bhutta, M. Usman Maqbool Mahmood, Hasan Malik, Hafiz Quaid I Azam Univ Dept Elect Islamabad Pakistan Univ Michigan Dept Elect & Comp Engn Dearborn MI 48128 USA

ISBN: (纸本)9781479939039

This paper presents a robust real time feature based algorithm for localization and recognition of multiple color and font style license plates. The algorithm uses digitized license plate images for robust detection and recognition. The resolution of the captured image is adjusted and the brightness and contrast are set to appropriate levels. An optimal threshold is applied to detect white regions of the enhanced image. The algorithm uses segmentation and component labeling to create candidate regions intelligently. For localization of the license plate, features of each region are extracted and analyzed by rectangularity percentage of the automobile's license plate. The use of color filter renders the algorithm more robust on license plate localization. Moreover, we use HSV color space to find the specific color regions. After localization of license plate in small sized image, the proposed algorithm uses rapid intelligent method for localization of license plate form the original image. The localized license plate image is cropped and processed by the recognition algorithm. Each character's region is identified by using segmentation algorithm based on the profile which includes character dilation and resizing for subsequent operations. In this paper, correlation optical character recognition and multi-layer perception neural networks techniques are used for recognizing the characters. The performance of the proposed algorithm is acceptable even in low-quality or bad light images.

关键词： correlation methods image colour analysis image enhancement image resolution image segmentation multilayer perceptrons optical character recognition traffic engineering computing HSV color space character dilation color detection color filter color localization color recognition component labeling correlation optical character recognition digitized license plate images feature-based algorithm font style license plate localization font styles automobile license plate recognition intelligent approach multilayer perception neural networks optimal threshold region extraction segmentation algorithm white region detection Correlation Feature extraction Image color analysis Image segmentation Licenses Neural networks Vehicles License plates image colour analysis image resolution image enhancement image segmentation COLOR FILTER optical character recognition Licence licensing Multilayer perceptrons correlation methods color spaces Neural network Feature extraction vehicle

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A new approach to diagnosis of pulmonary embolism using multi-slice CT

A new approach to diagnosis of pulmonary embolism using mult...

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Medical Imaging 2001 Conference

作者： Niethammer, MU Schoepf, UJ Wildberger, JE Klotz, E Fichte, H Schaller, S Siemens Med Solut Comp Tomog D-91301 Forchheim Germany

ISBN: (纸本)0819440078

Suspected pulmonary embolism (PE) is a common indication for CT scanning of the thorax. Usually, intravenous contrast agent is administered utilizing a power-injector and the vascular structures are examined for the presence of pulmonary emboli. Current Multi-Slice CT-technology allows extending this morphological analysis by adding a more functional visualization of the actual parenchymal perfusion disturbance. We have developed a new image processing technique which allows selective color encoded display of parenchymal enhancement of the lung, which will be reduced in the presence of PE. Based on thin slice reconstructions an automatic 3D segmentation of the lung is performed followed by threshold based extraction of the major airways and vascular structures. This allows applying an adaptive 3D low-pass filter to the parenchymal volume only. The filtered volume data are then color encoded and overlaid onto the original CT-images. This combination of low-resolution perfusion-weighted color maps and high-resolution gray scale structural data from the same data set greatly enhances visualization of spatial relationships. The resulting images can be displayed in axial, sagittal and coronal orientation. Initial experience indicates that this new technique provides relevant additional information for the clinical management of patients with proven PE. A larger controlled patient study is under way.

关键词： lung perfusion parenchymography segmentation algorithm pulmonary embolism computed tomography (CT)

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Within-field zoning using a region growing algorithm guided by geostatistical analysis

Within-field zoning using a region growing algorithm guided ...

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9th European Conference on Precision Agriculture

作者： Zane, L. Tisseyre, B. Guillaume, S. Charnomordic, B. INRA UMR MISTEA F-34060 Montpellier France UMR ITAP Montpellier SupAgro Cemagref F-34060 Montpellier France

ISBN: (纸本)9789086862245

Region growing methods are of potential interest to define within-field zones and resulting site-specific management. These methods are unsupervised and based on regions which grow from the initial seeds according to homogeneity criteria. However, the determination of seed number and seed locations has strong repercussions on the zoning output. This paper proposes an approach to allow knowledge inherited from geostatistical analysis to guide the seed initialization (seed number and location) of a region growing-based segmentation method. In this study, the segmentation method is a general division/merging procedure which, in this study, is used for merging and region growing. An original point is the possibility to use it either for irregularly located data or data arranged on a regular grid. The initialization of the segmentation method is guided by a prior analysis where a few parameters of the semi-variogram model are used to set: (1) the number of seeds required to initialize the region growing procedure;(2) to decide their relative locations (i e minimal distance between seeds);and (3) to identify potential outliers as seeds that may flaw the growing procedure. Both methods were tested on two data sets: (1) three hypothetical fields of known distribution and known spatial organization;(2) a real field where yield monitor data were obtained. A qualitative analysis of the results is presented, as well as the evolution of the variance explained by the model.

关键词： zoning segmentation algorithm growing region geostatistics

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Investigating the Correlation between Swallow Accelerometry Signal Parameters and Anthropometric and Demographic Characteristics of Healthy Adults

Investigating the Correlation between Swallow Accelerometry ...

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作者： Hanna, Fady University of Toronto

学位级别：master

Thesis studied correlations between swallowing accelerometry parameters and anthropometrics in 50 healthy participants. Anthropometrics include: age, gender, weight, height, body fat percent, neck circumference and mandibular length. Dual-axis swallowing signals, from a biaxial accelerometer were obtained for 5-saliva and 10-water (5-wet and 5-wet chin-tuck) swallows per participant. Two patient-independent automatic segmentation algorithms using discrete wavelet transforms of swallowing sequences segmented: 1) saliva/wet swallows and 2) wet chin-tuck swallows. Extraction of swallows hinged on dynamic thresholding based on signal *** correlation analysis was performed on sets of anthropometric and swallowing signal variables including: variance, skewness, kurtosis, autocorrelation decay time, energy, scale and peak-amplitude. For wet swallows, significant linear relationships were found between signal and anthropometric variables. In superior-inferior directions, correlations linked weight, age and gender to skewness and signal-memory. In anterior-posterior directions, age was correlated with kurtosis and signal-memory. No significant relationship was observed for dry and wet chin-tuck swallowing

关键词： aspiration Accelerometry Anthropometric Canonical Correlation segmentation algorithm

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White Spot Syndrome Detection in Shrimp using Neural Network Model 18

White Spot Syndrome Detection in Shrimp using Neural Network...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Vembarasi, K. Thotakura, Vishnu Priya Senthilkumar, S. Ramachandran, L. Lakshmi Praba, V. Vetriselvi, S. Chinnadurai, M. E.G.S. Pillay Engineering College Department of Electronics and Communication Engineering Nagapattinam India Srk Institute of Technology Department of Electronics and Communication Engineering Vijayawada India E.G.S. Pillay Engineering College Department of Computer Science and Engineering Nagapattinam India

ISBN: (纸本)9789380544519

White Spot Syndrome Virus (WSSV) epidemics have seriously harmed penaeid shrimp aquaculture all over the world. There remains an absence of information concerning these complicated viral-host interactions, despite significant attempts to describe the virus, the circumstances that cause infection, and the processes of infection. This understanding is required to develop reliable and efficient treatment strategies for WSSV. Mechanisms for segmenting and categorizing images offer a method for extracting features from images based on their objects. Those certain objects are produced using an image segmentation technique in which segments are formed by grouping together pixels with similar spectral properties that are close to one another. The area of interest on any underlying image is protected by image segmentation, a crucial step before actual analysis is recommended in any image processing methodology. In fact, the effectiveness of the segmentation algorithm used will have a big impact on how accurate any image processing performs. This study proposes a typical segmentation technique for segmenting shrimp variability by using essential Canny-GLCM (Gray Level Co-occurrence Matrix) features with a simple Artificial Neural Network (ANN) model. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Artificial Neural Network segmentation algorithm shrimp White spot syndrome virus

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A BST-based Approach to Dictionary Structure for Chinese Word segmentation

A BST-based Approach to Dictionary Structure for Chinese Wor...

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2011 IEEE International Conference on Computer Science and Automation Engineering(CSAE 2011)

作者： Chang Ge,Ningjing Ma and Xudong Chen National Pilot School of Software Engineering Beijing Jiaotong University Beijing,P.R.China

This paper firstly analyzes current Chinese word segmentation methods,and then bases on word dictionary segmentation and binary search tree(BST) proposing a way to organize the dictionary which focuses on reducing comparison times in order to increase the segmentation *** that,a practical demo is used to illustrate the feasibility and effectiveness of the proposed algorithm.

关键词： word segmentation dictionary structure binary serach tree segmentation algorithm

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A RECOGNITION SYSTEM FOR JAPANESE ZIP CODE USING ARC FEATURES

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 1994年第7期E77D卷 810-816页

作者： YOSHIMURA, M SHIMIZU, T YOSHIMURA, I Chubu Univ Kasugai-shi Japan

An automatic zip code recognition system for Japanese mail is proposed in this paper. It is assumed that a zip code is composed of three numerals and required to be written in a specified frame. In actual images, however, the three numerals sometimes extend outside the specified frame and are not clearly separated. Considering this situation, the authors devised a system with two stages, the segmentation stage and the recognition stage. The segmentation stage consists of five steps: setting and adjusting of initial areas for numeral images (figures), calculation of the center of gravity of each figure, search for the horizontal and vertical boundaries of each figure, determination of the final area for each figure, and normalization of the figure in each final area. In the recognition stage, the Localized Arc Pattern Method (Arc method) proposed by Yoshimura et al. (1991) is implemented hierarchically;that is, a simpler Arc method is applied first to each figure and a more complex one is applied subsequently unless the figure is identified in the first step. In the recognition process, every figure is judged as a numeral or otherwise rejected. The proposed system was applied to a database provided by the Institute for Post and Telecommunications Policy (IPTP). The segmentation algorithm yielded an adequate result. The recognition algorithm yielded scores as high as 90.6% in correct recognition rate and 0.7% in error rate. The best score of the precision index (P-index) specified by the IPTP was as low as 15.7 for the above mentioned IPTP database, while the score for another IPTP database was 16.9.

关键词： HANDWRITTEN NUMERALS JAPANESE ZIP CODE LOCALIZED ARC PATTERN METHOD NUMERIC CHARACTER RECOGNITION segmentation algorithm

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Gotta Trace 'em All: A Mini-Review on Tools and Procedures for Segmenting Single Neurons Toward Deciphering the Structural Connectome

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FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2019年第AUG期7卷 202-202页

作者： Magliaro, Chiara Callara, Alejandro L. Vanello, Nicola Ahluwalia, Arti Univ Pisa Res Ctr E Piaggio Pisa Italy Univ Pisa Dipartimento Ingn Informaz Pisa Italy

Decoding the morphology and physical connections of all the neurons populating a brain is necessary for predicting and studying the relationships between its form and function, as well as for documenting structural abnormalities in neuropathies. Digitizing a complete and high-fidelity map of the mammalian brain at the micro-scale will allow neuroscientists to understand disease, consciousness, and ultimately what it is that makes us humans. The critical obstacle for reaching this goal is the lack of robust and accurate tools able to deal with 3D datasets representing dense-packed cells in their native arrangement within the brain. This obliges neuroscientist to manually identify the neurons populating an acquired digital image stack, a notably time-consuming procedure prone to human bias. Here we review the automatic and semi-automatic algorithms and software for neuron segmentation available in the literature, as well as the metrics purposely designed for their validation, highlighting their strengths and limitations. In this direction, we also briefly introduce the recent advances in tissue clarification that enable significant improvements in both optical access of neural tissue and image stack quality, and which could enable more efficient segmentation approaches. Finally, we discuss new methods and tools for processing tissues and acquiring images at sub-cellular scales, which will require new robust algorithms for identifying neurons and their sub-structures (e.g., spines, thin neurites). This will lead to a more detailed structural map of the brain, taking twenty-first century cellular neuroscience to the next level, i.e., the Structural Connectome.

关键词： structural connectome segmentation algorithm 3D neuron segmentation single-cell segmentation CLARITY

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A Method of Segmenting Apples Based on Gray-Centered RGB Color Space

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REMOTE SENSING 2021年第6期13卷 1211-1211页

作者： Fan, Pan Lang, Guodong Yan, Bin Lei, Xiaoyan Guo, Pengju Liu, Zhijie Yang, Fuzeng Northwest A&F Univ Coll Mech & Elect Engn Yangling 712100 Shaanxi Peoples R China Baoji Univ Arts & Sci Sch Comp Sci & Technol Baoji 721016 Peoples R China Shannxi Key Lab Apple Yangling 712100 Shaanxi Peoples R China Apple Mech Res Base Yangling 712100 Shaanxi Peoples R China State Key Lab Soil Eros & Dryland Farming Loess P Yangling 712100 Shaanxi Peoples R China

In recent years, many agriculture-related problems have been evaluated with the integration of artificial intelligence techniques and remote sensing systems. The rapid and accurate identification of apple targets in an illuminated and unstructured natural orchard is still a key challenge for the picking robot's vision system. In this paper, by combining local image features and color information, we propose a pixel patch segmentation method based on gray-centered red-green-blue (RGB) color space to address this issue. Different from the existing methods, this method presents a novel color feature selection method that accounts for the influence of illumination and shadow in apple images. By exploring both color features and local variation in apple images, the proposed method could effectively distinguish the apple fruit pixels from other pixels. Compared with the classical segmentation methods and conventional clustering algorithms as well as the popular deep-learning segmentation algorithms, the proposed method can segment apple images more accurately and effectively. The proposed method was tested on 180 apple images. It offered an average accuracy rate of 99.26%, recall rate of 98.69%, false positive rate of 0.06%, and false negative rate of 1.44%. Experimental results demonstrate the outstanding performance of the proposed method.

关键词： fruit detection fruit segmentation color space segmentation algorithm

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