Support Vector Machines (SVM) for Color Image Segmentation with Applications to Mobile Robot Localization Problems

Zou, An‐Min; Hou, Zeng-Guang; Tan, Min

doi:10.1007/11538356_46

Cited by 9 publications

(3 citation statements)

References 12 publications

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“…Other approaches proposed in [19] and [20] are based on a discriminative classification algorithm using multiple cues, specifically, using an SVM and combined up to four different histogram-based features with the kernel averaging method. The output of the classifier for each frame is a label and its associated margin, which it took as a measure of the confidence of the decision.…”

Section: Robot Localization and Place Recognitionmentioning

confidence: 99%

Multi-stage localization given topological map for autonomous robots

Salem

2012

2012 Seventh International Conference on Computer Engineering &Amp; Systems (ICCES)

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Vision-based place recognition is of a particular importance for autonomous systems that aim to navigate intelligently in a human-inhabited environment. Given a topological map of an indoor environment, the autonomous system shall localize itself invariantly with different illumination and imaging conditions. To address these challenges, we propose to use global-local feature extraction and classification in multiple stages. Scale Invariant Feature Transform (SIFT) is used as a local feature detector and descriptor which has been proven to be a robust local invariant feature descriptor. Fourier Transform, Hue Saturation Value (HSV), and Hough Transform are used as global features. The Support Vector Machines (SVM) is used to localize the system by classifying the global features. However the K-nearest neighbors matching technique (K-NN) is used to support SVM's classification in ambiguous decisions by classifying the local features.

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Section: Robot Localization and Place Recognitionmentioning

confidence: 99%

Multi-stage localization given topological map for autonomous robots

Salem

2012

2012 Seventh International Conference on Computer Engineering &Amp; Systems (ICCES)

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“…10, for our developed mobile robot using the proposed neural units with higher-order synaptic operations. We are also developing dynamic neural units for wider applications in the field of information processing and control for robotics [10,25,26].…”

Section: Edge Detection Using Neural Units With Higher-order Synapticmentioning

confidence: 99%

Neural Units with Higher-Order Synaptic Operations for Robotic Image Processing Applications

et al. 2006

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Neural units with higher-order synaptic operations have good computational properties in information processing and control applications. This paper presents neural units with higher-order synaptic operations for visual image processing applications. We use the neural units with higherorder synaptic operations for edge detection and employ the Hough transform to process the edge detection results. The edge detection method based on the neural unit with higherorder synaptic operations has been applied to solve routing problems of mobile robots. Simulation results show that the proposed neural units with higher-order synaptic operations are efficient for image processing and routing applications of mobile robots.

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“…The decision surface is constructed in the high-dimension space by using support vectors. Earlier reports (Li, yan Shi, & Xu, 2013;Wang, Wang, & Bu, 2011;Zou, Hou, & Tan, 2005) demonstrated the potential of SVM method in separating the pixels according to their color characteristic. Therefore, SVM method was chosen as representative of a conventional method by separating pixels into TB (M. tuberculosis) or NTB (non M. tuberculosis) class at the stage of candidate detection.…”

Section: Introductionmentioning

confidence: 99%

An effective and accurate identification system of Mycobacterium tuberculosis using convolution neural networks

Kuok

Horng

Liao

et al. 2019

Microscopy Res & Technique

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Tuberculosis (TB) remains the leading cause of morbidity and mortality from infectious disease in developing countries. The sputum smear microscopy remains the primary diagnostic laboratory test. However, microscopic examination is always time‐consuming and tedious. Therefore, an effective computer‐aided image identification system is needed to provide timely assistance in diagnosis. The current identification system usually suffers from complex color variations of the images, resulting in plentiful of false object detection. To overcome the dilemma, we propose a two‐stage Mycobacterium tuberculosis identification system, consisting of candidate detection and classification using convolution neural networks (CNNs). The refined Faster region‐based CNN was used to distinguish candidates of M. tuberculosis and the actual ones were classified by utilizing CNN‐based classifier. We first compared three different CNNs, including ensemble CNN, single‐member CNN, and deep CNN. The experimental results showed that both ensemble and deep CNNs were on par with similar identification performance when analyzing more than 19,000 images. A much better recall value was achieved by using our proposed system in comparison with conventional pixel‐based support vector machine method for M. tuberculosis bacilli detection.

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Support Vector Machines (SVM) for Color Image Segmentation with Applications to Mobile Robot Localization Problems

Cited by 9 publications

References 12 publications

Multi-stage localization given topological map for autonomous robots

Multi-stage localization given topological map for autonomous robots

Neural Units with Higher-Order Synaptic Operations for Robotic Image Processing Applications

An effective and accurate identification system of Mycobacterium tuberculosis using convolution neural networks

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