Object detection in the images in industrial process control systems based on salient points of wavelet transform analysis

Shleymovich, Mikhail P.; Medvedev, Mikhail V.; Lyasheva, Stella A.

doi:10.1109/icieam.2016.7911633

Cited by 21 publications

(7 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The described approach to detecting and analysing the characteristic features of images can serve as a basis for constructing systems of objects detection and recognition in various systems based on methods and means of computer vision, including onboard systems of pilotless aerial vehicles -to detect and recognise the objects on images, process control systems, intelligent transport systems, etc. [20][21][22].…”

Section: Resultsmentioning

confidence: 99%

The analysis of image characteristics on the base of energy features of the wavelet transform

Lyasheva

Medvedev²,

Shleymovich

et al. 2018

Collection of Selected Papers of the IV International Conference on Information Technology and Nanotechnology

View full text Add to dashboard Cite

The article shows the relevance of creating models and methods that provide effective solutions to image processing and analyzing problems in computer vision systems. We consider models of an average level of image representation. They are constructed on the basis of their characteristic features (contours, regions and points of interest). To construct such models, we suggest using the procedure of forming energy features based on the wavelet transform. As a result, the original image will be transformed to a view where different points will have different weights. That characterize their contribution to the overall energy of the image. It is also possible to provide a selection of tuning coefficients. It allows to take into account the interrelations between the wavelet coefficients of various scales. After receiving the weight images, they can be processed to form the required characteristics. For example, to obtain contours, you can perform binarization of a weight image with a certain threshold. To get singular points, you can define a specified number of the most significant weights in different areas of the weight image. For texture analysis, you can use statistical characteristics calculated by the histogram of the scale.

show abstract

Section: Resultsmentioning

confidence: 99%

The analysis of image characteristics on the base of energy features of the wavelet transform

Lyasheva

Medvedev²,

Shleymovich

et al. 2018

Collection of Selected Papers of the IV International Conference on Information Technology and Nanotechnology

View full text Add to dashboard Cite

show abstract

“…As emphasized by Table 1 , parts segmentation already has an important role in the manufacturing industry. According to Shleymovich, Madvedev, and Lyasheva [ 5 ], those methods are usually based on four different types of approaches: contour detection, morphological operations, threshold segmentation, or clustering.…”

Section: Related Workmentioning

confidence: 99%

Efficient and Consumer-Centered Item Detection and Classification with a Multicamera Network at High Ranges

Mandischer

Huhn

Hüsing

et al. 2021

Sensors

View full text Add to dashboard Cite

In the EU project SHAREWORK, methods are developed that allow humans and robots to collaborate in an industrial environment. One of the major contributions is a framework for task planning coupled with automated item detection and localization. In this work, we present the methods used for detecting and classifying items on the shop floor. Important in the context of SHAREWORK is the user-friendliness of the methodology. Thus, we renounce heavy-learning-based methods in favor of unsupervised segmentation coupled with lenient machine learning methods for classification. Our algorithm is a combination of established methods adjusted for fast and reliable item detection at high ranges of up to eight meters. In this work, we present the full pipeline from calibration, over segmentation to item classification in the industrial context. The pipeline is validated on a shop floor of 40 sqm and with up to nine different items and assemblies, reaching a mean accuracy of 84% at 0.85 Hz.

show abstract

“…Here we are focusing on object detection using the template as well as HOG (Histogram of Oriented Gradient) feature-based techniques. The most widely used computer vision-based technologies are needed to resolve the problems of object matching and recognition in the field of image processing and analysis [2]. For any vision-based image processing application, object detection is the most integrated part [3].…”

Section: Introductionmentioning

confidence: 99%

Object Detection using Template and HOG Feature Matching

Sultana¹,

Ahmed²,

Chakraborty³

et al. 2020

IJACSA

View full text Add to dashboard Cite

In the present era, the applications of computer vision is increasing day by day. Computer vision is related to the automatic recognition, exploration and extraction of the necessary information from a particular image or a group of image sets. This paper addresses the method to detect the desired object from an image. Usually, a template of the desired object is used in detection through a matching technique named Template Matching. But it works well when the template image is cropped from the original one, which is not always invariant due to various transformations in the test images. To cope with this difficulty and to develop a generalized approach, we investigate in detail another technique which is known as HOG (Histogram of Oriented Gradient) approach. In HOG, the image is divided into overlapping blocks of template size and then compare each block's normalized HOG with the normalized HOG of the template to find the best match of the object. We perform experiments with a large number of images and have found satisfactory performance.

show abstract

Object detection in the images in industrial process control systems based on salient points of wavelet transform analysis

Cited by 21 publications

References 12 publications

The analysis of image characteristics on the base of energy features of the wavelet transform

The analysis of image characteristics on the base of energy features of the wavelet transform

Efficient and Consumer-Centered Item Detection and Classification with a Multicamera Network at High Ranges

Object Detection using Template and HOG Feature Matching

Contact Info

Product

Resources

About