Classifying smoke in laparoscopic videos using SVM

AbstractSmoke in laparoscopic videos usually appears due to the use of electrocautery when cutting or coagulating tissues. Therefore, detecting smoke can be used for event-based annotation in laparoscopic surgeries by retrieving the events associated with the electrocauterization. Furthermore, smoke detection can also be used for automatic smoke removal. However, detecting smoke in laparoscopic video is a challenge because of the changeability of smoke patterns, the moving camera and the different lighting conditions. In this paper, we present a video-based smoke detection algorithm to detect smoke of different densities such as fog, low and high density in laparoscopic videos. The proposed method depends on extracting various visual features from the laparoscopic images and providing them to support vector machine (SVM) classifier. Features are based on motion, colour and texture patterns of the smoke. We validated our algorithm using experimental evaluation on four laparoscopic cholecystectomy videos. These four videos were manually annotated by defining every frame as smoke or non-smoke frame. The algorithm was applied to the videos by using different feature combinations for classification. Experimental results show that the combination of all proposed features gives the best classification performance. The overall accuracy (i.e. correctly classified frames) is around 84%, with the sensitivity (i.e. correctly detected smoke frames) and the specificity (i.e. correctly detected non-smoke frames) are 89% and 80%, respectively.

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Binary Spectrum Feature for Improved Classiﬁer Performance

10.36227/techrxiv.12993122 ◽

2020 ◽

Author(s):

Nalika Ulapane ◽

Karthick Thiyagarajan ◽

sarath kodagoda

Keyword(s):

Machine Learning ◽

Classification Performance ◽

Feature Reduction ◽

Sensor Data ◽

Machine Learning Techniques ◽

Support Vector ◽

Svm Classifier ◽

Monitoring Task ◽

Classifier Performance ◽

Spectrum Feature

<div>Classiﬁcation has become a vital task in modern machine learning and Artiﬁcial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classiﬁcation. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classiﬁer performance. In this paper, we consider the case of a given supervised learning classiﬁcation task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classiﬁcation performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classiﬁcation accuracy of a Support Vector Machine (SVM) classiﬁer increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

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Shape-restricted support vector machine (SR-SVM): a SVM classifier taking supplementary shape information of input

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202155 ◽

2021 ◽

Vol 40 (1) ◽

pp. 1481-1494

Author(s):

Geng Deng ◽

Yaoguo Xie ◽

Xindong Wang ◽

Qiang Fu

Keyword(s):

Support Vector Machine ◽

Classification Performance ◽

Research Literature ◽

Support Vector ◽

Svm Classifier ◽

Classification Problems ◽

Active Set ◽

Shape Information ◽

Convex Optimization Problem ◽

Shape Restrictions

Many classification problems contain shape information from input features, such as monotonic, convex, and concave. In this research, we propose a new classifier, called Shape-Restricted Support Vector Machine (SR-SVM), which takes the component-wise shape information to enhance classification accuracy. There exists vast research literature on monotonic classification covering monotonic or ordinal shapes. Our proposed classifier extends to handle convex and concave types of features, and combinations of these types. While standard SVM uses linear separating hyperplanes, our novel SR-SVM essentially constructs non-parametric and nonlinear separating planes subject to component-wise shape restrictions. We formulate SR-SVM classifier as a convex optimization problem and solve it using an active-set algorithm. The approach applies basis function expansions on the input and effectively utilizes the standard SVM solver. We illustrate our methodology using simulation and real world examples, and show that SR-SVM improves the classification performance with additional shape information of input.

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Early smoke detection of forest fires based on SVM image segmentation

Journal of Forest Science ◽

10.17221/82/2018-jfs ◽

2019 ◽

Vol 65 (No. 4) ◽

pp. 150-159

Author(s):

Ding Xiong ◽

Lu Yan

Keyword(s):

Image Segmentation ◽

Forest Fire ◽

Forest Fires ◽

Fire Detection ◽

Detection Algorithm ◽

Support Vector ◽

Smoke Detection ◽

Large Space ◽

Merging Algorithm ◽

Detection Technology

A smoke detection method is proposed in single-frame video sequence images for forest fire detection in large space and complex scenes. A new superpixel merging algorithm is further studied to improve the existing horizon detection algorithm. This method performs Simple Linear Iterative Clustering (SLIC) superpixel segmentation on the image, and the over-segmentation problem is solved with a new superpixel merging algorithm. The improved sky horizon line segmentation algorithm is used to eliminate the interference of clouds in the sky for smoke detection. According to the spectral features, the superpixel blocks are classified by support vector machine (SVM). The experimental results show that the superpixel merging algorithm is efficient and simple, and easy to program. The smoke detection technology based on image segmentation can eliminate the interference of noise such as clouds and fog on smoke detection. The accuracy of smoke detection is 77% in a forest scene, it can be used as an auxiliary means of monitoring forest fires. A new attempt is given for forest fire warning and automatic detection.

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Classification of Hyperspectral In Vivo Brain Tissue Based on Linear Unmixing

Applied Sciences ◽

10.3390/app10165686 ◽

2020 ◽

Vol 10 (16) ◽

pp. 5686

Author(s):

Ines A. Cruz-Guerrero ◽

Raquel Leon ◽

Daniel U. Campos-Delgado ◽

Samuel Ortega ◽

Himar Fabelo ◽

...

Keyword(s):

Brain Tissue ◽

Classification Performance ◽

Training Dataset ◽

Support Vector ◽

Svm Classifier ◽

Tissue Classification ◽

Processing Times ◽

Main Challenge ◽

Linear Unmixing

Hyperspectral imaging is a multidimensional optical technique with the potential of providing fast and accurate tissue classification. The main challenge is the adequate processing of the multidimensional information usually linked to long processing times and significant computational costs, which require expensive hardware. In this study, we address the problem of tissue classification for intraoperative hyperspectral images of in vivo brain tissue. For this goal, two methodologies are introduced that rely on a blind linear unmixing (BLU) scheme for practical tissue classification. Both methodologies identify the characteristic end-members related to the studied tissue classes by BLU from a training dataset and classify the pixels by a minimum distance approach. The proposed methodologies are compared with a machine learning method based on a supervised support vector machine (SVM) classifier. The methodologies based on BLU achieve speedup factors of ~459× and ~429× compared to the SVM scheme, while keeping constant and even slightly improving the classification performance.

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Structuring of tactile sensory information for category formation in robotics palpation

Autonomous Robots ◽

10.1007/s10514-020-09931-y ◽

2020 ◽

Vol 44 (8) ◽

pp. 1377-1393

Author(s):

Luca Scimeca ◽

Perla Maiolino ◽

Ed Bray ◽

Fumiya Iida

Keyword(s):

Sensory Information ◽

Tactile Sensor ◽

Classification Performance ◽

Support Vector ◽

Svm Classifier ◽

Category Formation ◽

Physical Interactions ◽

Robotic Palpation ◽

Motion Strategy

Abstract This paper proposes a framework to investigate the influence of physical interactions to sensory information, during robotic palpation. We embed a capacitive tactile sensor on a robotic arm to probe a soft phantom and detect and classify hard inclusions within it. A combination of PCA and K-Means clustering is used to: first, reduce the dimensionality of the spatiotemporal data obtained through the probing of each area in the phantom; second categorize the re-encoded data into a given number of categories. Results show that appropriate probing interactions can be useful in compensating for the quality of the data, or lack thereof. Finally, we test the proposed framework on a palpation scenario where a Support Vector Machine classifier is trained to discriminate amongst different types of hard inclusions. We show the proposed framework is capable of predicting the best-performing motion strategy, as well as the relative classification performance of the SVM classifier, solely based on unsupervised cluster analysis methods.

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A Mature-Tomato Detection Algorithm Using Machine Learning and Color Analysis

Sensors ◽

10.3390/s19092023 ◽

2019 ◽

Vol 19 (9) ◽

pp. 2023 ◽

Cited By ~ 7

Author(s):

Guoxu Liu ◽

Shuyi Mao ◽

Jae Ho Kim

Keyword(s):

Detection Algorithm ◽

Substantial Improvement ◽

Support Vector ◽

Svm Classifier ◽

Color Analysis ◽

Scanning Method ◽

False Color ◽

Histograms Of Oriented Gradients ◽

Coarse To Fine ◽

Tomato Detection

An algorithm was proposed for automatic tomato detection in regular color images to reduce the influence of illumination and occlusion. In this method, the Histograms of Oriented Gradients (HOG) descriptor was used to train a Support Vector Machine (SVM) classifier. A coarse-to-fine scanning method was developed to detect tomatoes, followed by a proposed False Color Removal (FCR) method to remove the false-positive detections. Non-Maximum Suppression (NMS) was used to merge the overlapped results. Compared with other methods, the proposed algorithm showed substantial improvement in tomato detection. The results of tomato detection in the test images showed that the recall, precision, and F1 score of the proposed method were 90.00%, 94.41 and 92.15%, respectively.

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Research on a Fast Human-Detection Algorithm for Unmanned Surveillance Area in Bulk Ports

Mathematical Problems in Engineering ◽

10.1155/2014/386764 ◽

2014 ◽

Vol 2014 ◽

pp. 1-17 ◽

Cited By ~ 2

Author(s):

Chao Mi ◽

Xin He ◽

Haiwei Liu ◽

Youfang Huang ◽

Weijian Mi

Keyword(s):

Real Time ◽

Computing Time ◽

Training Sample ◽

Detection Algorithm ◽

Human Detection ◽

Security Requirements ◽

Support Vector ◽

Svm Classifier ◽

Histograms Of Oriented Gradients ◽

Heavy Equipment

With the development of port automation, most operational fields utilizing heavy equipment have gradually become unmanned. It is therefore imperative to monitor these fields in an effective and real-time manner. In this paper, a fast human-detection algorithm is proposed based on image processing. To speed up the detection process, the optimized histograms of oriented gradients (HOG) algorithm that can avoid the large number of double calculations of the original HOG and ignore insignificant features is used to describe the contour of the human body in real time. Based on the HOG features, using a training sample set consisting of scene images of a bulk port, a support vector machine (SVM) classifier combined with the AdaBoost classifier is trained to detect human. Finally, the results of the human detection experiments on Tianjin Port show that the accuracy of the proposed optimized algorithm has roughly the same accuracy as a traditional algorithm, while the proposed algorithm only takes 1/7 the amount of time. The accuracy and computing time of the proposed fast human-detection algorithm were verified to meet the security requirements of unmanned port areas.

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DETERMINATION OF OPTIMUM CLASSIFICATION SYSTEM FOR HYPERSPECTRAL IMAGERY AND LIDAR DATA BASED ON BEES ALGORITHM

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-1-w5-651-2015 ◽

2015 ◽

Vol XL-1-W5 ◽

pp. 651-656

Author(s):

F. Samadzadega ◽

H. Hasani

Keyword(s):

Urban Area ◽

Hyperspectral Imagery ◽

Feature Space ◽

Classification Performance ◽

Feature Subset Selection ◽

Bees Algorithm ◽

Support Vector ◽

Svm Classifier ◽

Lidar Data ◽

Feature Subset

Hyperspectral imagery is a rich source of spectral information and plays very important role in discrimination of similar land-cover classes. In the past, several efforts have been investigated for improvement of hyperspectral imagery classification. Recently the interest in the joint use of LiDAR data and hyperspectral imagery has been remarkably increased. Because LiDAR can provide structural information of scene while hyperspectral imagery provide spectral and spatial information. The complementary information of LiDAR and hyperspectral data may greatly improve the classification performance especially in the complex urban area. In this paper feature level fusion of hyperspectral and LiDAR data is proposed where spectral and structural features are extract from both dataset, then hybrid feature space is generated by feature stacking. Support Vector Machine (SVM) classifier is applied on hybrid feature space to classify the urban area. In order to optimize the classification performance, two issues should be considered: SVM parameters values determination and feature subset selection. Bees Algorithm (BA) is powerful meta-heuristic optimization algorithm which is applied to determine the optimum SVM parameters and select the optimum feature subset simultaneously. The obtained results show the proposed method can improve the classification accuracy in addition to reducing significantly the dimension of feature space.

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Research on athlete training behavior based on improved support vector algorithm and target image detection

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189050 ◽

2020 ◽

Vol 39 (4) ◽

pp. 5725-5736

Author(s):

Jiang Min

Keyword(s):

Target Detection ◽

Training Model ◽

Detection Algorithm ◽

Support Vector ◽

Svm Classifier ◽

Target Image ◽

Classification Result ◽

Detection And Tracking ◽

Neural Network Algorithm ◽

Fully Connected

In view of the defects and shortcomings of the traditional target detection and tracking algorithm in accurately detecting targets and targets in different scenarios, based on the current research status and technical level of target detection and tracking at home and abroad, this paper proposes a target detection algorithm and tracking method using neural network algorithm, and applies it to the athlete training model. Based on the Alex-Net network structure, this paper designs a three-layer convolutional layer and two layers of fully connected layers. The last layer is used as the input of the SVM classifier, and the target classification result is obtained by the SVM classifier. In addition, this article adds SPP-Layer between the convolutional layer and the fully connected layer, enabling the same dimension of the Feature Map to be obtained before the fully connected layer for different sized input images. The research results show that the proposed method has certain recognition effect and can be applied to athlete training.

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Study on XML Retrieval Results Classification

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.263-266.1773 ◽

2012 ◽

Vol 263-266 ◽

pp. 1773-1777

Author(s):

Hong Yu ◽

Xiao Lei Huang ◽

Zhi Ling Wei ◽

Chen Xia Yang

Keyword(s):

Nearest Neighbor ◽

Classification Performance ◽

Feedback Mechanism ◽

Support Vector ◽

Svm Classifier ◽

K Nearest Neighbor ◽

Xml Retrieval ◽

Text Documents ◽

Plain Text ◽

Xml Documents

Mining (classify or clustering) retrieval results to serve relevance feedback mechanism of search engine is an important solution to improve effectiveness of retrieval. Unlike plain text documents, since the XML documents are semi-structured data, for XML retrieval results classification, consider exploiting structure features of XML documents, such as tag paths and edges etc. We propose to use Support Vector Machine (SVM) classifier to classify XML retrieval results exploiting both their content and structure features. We implemented the classification method on XML retrieval results based on the IEEE SC corpus. Compared with k-nearest neighbor classification (KNN) on the same dataset in our application, SVM perform better. The experiment results have also shown that the use of structure features, especially tag paths and edges, can improve the classification performance significantly.

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