Region of Interest Detection  Based on Histogram Segmentation for Satellite Image

Kiadtikornthaweeyot, Warinthorn; Tatnall, A.R.L.

doi:10.5194/isprs-archives-xli-b7-249-2016

Cited by 9 publications

(4 citation statements)

References 12 publications

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“…Tatnall are proposed for the Region of interest detection using histogram segmentation, and the method can be applied for the forest, agriculture, urban areas. In this process, they have taken satellite images to train and test histogram segmentation, morphology dilation, and image ROI on the given datasets [38]. In [39], the authors attempted to classify a few apparels classes using convolution neural networks based on the GoogleNet Architecture.…”

Section: Related Workmentioning

confidence: 99%

Single Stage Deep Transfer Learning Model for Apparel Detection and Classification for E-Commerce

Addagarla

Amalanathan

2021

IJECS

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Although many computer vision-based object detection techniques have evolved in the past decade, it suffers from inconsistent detection accuracy, especially for multiclass classification problems. This paper proposed an approach using the Single Stage Deep Transfer Learning model (SS-DTLM) for multiclass apparel detection using a customized YoloV3 algorithm by adapting 3-level Spatial pyramid pooling (SPP), a multi-scale image feature extractor for faster and reasonable apparel detection and classification. This approach produced a reasonable Mean Average Precision (mAP), reliable object detection, and classification. Our model trained and tested on Open Images Dataset (OIDV4) with six object classes and Custom built Apparel Dataset with five object classes of apparels. Finally, experimental results compared with baseline Yolov3 and Yolov3-Tiny algorithms. Further, this paper also emphasized the detected image's various color spaces using SS-DTLM by applying the K-Means clustering algorithm for further analysis.

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Section: Related Workmentioning

confidence: 99%

Single Stage Deep Transfer Learning Model for Apparel Detection and Classification for E-Commerce

Addagarla

Amalanathan

2021

IJECS

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“…Kiadtikornthaweeyot et al [26] used a profile of intensity values along the ROIL for image enhancements of satellite images. Jeong et al [27] applied this methodology in maritime images.…”

Section: Candidate Selectionmentioning

confidence: 99%

Nondestructive inspection of aerospace composite laminate using thermal image processing

2020

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In this work, a novel image segmentation method in thermographic images using the computer vision algorithm, active contours, is presented. These studies were done on thermal images obtained from pulse thermography. Many aerospace components are made of composite laminates. De-bonds/de-laminations set in during the manufacturing or in-service stages of these materials. Identification of de-bonds in composite laminates is a difficult task and faces many challenges when it is done through the processing of thermographic images. The first challenge is to get an accurate seed point to be used in active contours. In this study, the seed points for active contours were obtained from the moving averages of the pixel values taken along the region of interest lines. It is shown that the active contours with the help of seed points identified through this method are able to distinguish the de-bonds. The experimental results, supported by segmentation metrics, suggest that the segmentation method used here outperforms some earlier results which were based on the combination of Gabor filters, watershed models and divide-and-conquer methods such as subdivision of images.

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“…Image Segmentation is a profoundly critical tool in Image examination. To recognize the object in the picture, the region of interest (ROI) detection has been applied in numerous areas, for example [22] observed from a satellite image, that is contains an assortment of features, surfaces and shadows and it can in this way be very complex to detect the ROI. The fascinating part of a satellite picture relies upon the application field for Earth perception.…”

Section: Image Segmentationmentioning

confidence: 99%

Streetlight Objects Recognition by Region and Histogram Features in an Autonomous Vehicle System

Irhebhude¹,

Shabi²,

Kolawole³

2019

IJIVP

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In this paper Streetlight object identification is addressed using the notion of image processing. An approach based on Image Processing Techniques is proposed for selection and processing of features from the images. Histogram and Region was applied on the extracted images. Histogram and Region features were then extracted and employed to train the Support Vector Machine (SVM) classifier for streetlight recognition. Experimental results shows 99.1%, 84% and 100% for histogram, region features and combination of both respectively. Experimental results have proved that the proposed method is robust, accurate, and powerful in object recognition.

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Region of Interest Detection Based on Histogram Segmentation for Satellite Image

Cited by 9 publications

References 12 publications

Single Stage Deep Transfer Learning Model for Apparel Detection and Classification for E-Commerce

Single Stage Deep Transfer Learning Model for Apparel Detection and Classification for E-Commerce

Nondestructive inspection of aerospace composite laminate using thermal image processing

Streetlight Objects Recognition by Region and Histogram Features in an Autonomous Vehicle System

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