Object Recognition

Bennamoun, Mohammed; Mamic, George

doi:10.1007/978-1-4471-3722-1

Cited by 32 publications

(6 citation statements)

References 200 publications

(389 reference statements)

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“…In the late 1950s, computer vision was understood as a research area and in the 1980s, the underwater object recognition became of interest [6]. Since that time until the last few years, the underwater object recognition researchers were working on sonar images and they used template matching in many of the algorithms such as data-adaptive algorithms [7] [8].…”

Section: A Underwater Object Recognition Perspectivementioning

confidence: 99%

Recognition of stonefish from underwater video

Ashour

Sasi

2015

2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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There is thousands of organisms under the water and every group of organisms have many types or species. Some are dangerous and will attack when touched and some others will attack directly without any reason. In this research, a method that can recognize a stonefish, which is the most venomous fish in the world from a video to help divers or swimmers in open water to avoid danger, is presented. When a stonefish hides, it keeps its head visible until it can attack the small fish quickly. This property is used to formulate the algorithm. The video is enhanced using Gaussian filter, Median filter, and Wiener filter. The method uses a database of images that contain the images of the head of stonefish. The features of the stonefish head is detected from the video by comparing the features of head images in the database by using a part of the Speeded Up Robust Features (SURF) method. The features in the video are compared with the features of the images of the database by using the k-Nearest Neighbor algorithm and the Histogram. The results of the comparison will decide if there is a stonefish in the video or not. Finally, if there is a stonefish in the video, the system will generate a warning signal to help the divers or swimmers to move away from it immediately.

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Section: A Underwater Object Recognition Perspectivementioning

confidence: 99%

Recognition of stonefish from underwater video

Ashour

Sasi

2015

2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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“…A good variety of approaches have been proposed to solve object pose detection and their categorization. Literature differentiates between model-and view-based approaches (Bennamoun and Mamic, 2002;Bicego et al, 2005), feature-and appearance-based approaches or introduces several classes (Belongie et al, 2002). Among all other methods, the model of the object and the image data are represented by local geometric features.…”

Section: Related Workmentioning

confidence: 99%

Work area monitoring in dynamic environments using multiple auto-aligning 3-D sensors

Wang

Ewert

Meisen

et al. 2014

J. Sens. Sens. Syst.

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Abstract. Compared to current industry standards future production systems will be more flexible and robust and will adapt to unforeseen states and events. Industrial robots will interact with each other as well as with human coworkers. To be able to act in such a dynamic environment, each acting entity ideally needs complete knowledge of its surroundings, concerning working materials as well as other working entities. Therefore new monitoring methods providing complete coverage for complex and changing working areas are needed. While single 3-D sensors already provide detailed information within their field of view, complete coverage of a complete work area can only be achieved by relying on a multitude of these sensors.However, to provide useful information all data of each sensor must be aligned to each other and fused into an overall world picture. To be able to align the data correctly, the position and orientation of each sensor must be known with sufficient exactness. In a quickly changing dynamic environment, the positions of sensors are not fixed, but must be adjusted to maintain optimal coverage. Therefore, the sensors need to autonomously align themselves in real time. This can be achieved by adding defined markers with given geometrical patterns to the environment which can be used for calibration and localization of each sensor. As soon as two sensors detect the same markers, their relative position to each other can be calculated. Additional anchor markers at fixed positions serve as global reference points for the base coordinate system.In this paper we present a prototype for a self-aligning monitoring system based on a robot operating system (ROS) and Microsoft Kinect. This system is capable of autonomous real-time calibration relative to and with respect to a global coordinate system as well as to detect and track defined objects within the working area.

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“…For example, if an object is known (or assumed) to belong to a certain class of objects, one might be interested in its dimensions, or some parameters of its underlying shape, given an image of it. These sorts of problems are handled are known as Geometric Estimation, which plays a vital role in computer vision and medical imaging Bennamoun and Mamic (2002); Kanatani and Sugaya (2016); Zhao and Xiong (2011), petroleum engineering M. Heidari (1993); Kilambi et al (2011), astronomy Ambartsoumian and Xie (2010), bioinformatics Kumar et al (2018), object classifications S. D. Connell (2001), and many other fields.…”

Section: Introductionmentioning

confidence: 99%

Fitting Concentric Elliptical Shapes Under General Model

Al-Sharadqah

Piga

2022

Preprint

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The problem of fitting concentric ellipses is a vital problem in image processing, pattern recognition, and astronomy. Several methods have been developed but all address very special cases. In this paper, this problem has been investigated under a more general setting and two estimators for estimating the parameters have been proposed. Since both estimators are obtained iteratively fashion, several numerical schemes are investigated and the best initial guess is determined. Furthermore, the constraint Crame Rao lower bound for this problem is derived and it is compared with the variance of each estimator. Finally, our theory is assessed and validated by a series of numerical experiments.

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Object Recognition

Cited by 32 publications

References 200 publications

Recognition of stonefish from underwater video

Recognition of stonefish from underwater video

Work area monitoring in dynamic environments using multiple auto-aligning 3-D sensors

Fitting Concentric Elliptical Shapes Under General Model

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