Real object recognition using moment invariants

Mercimek, Muharrem; Gülez, Kayhan; Mumcu, Tarık Veli

doi:10.1007/bf02716709

Cited by 86 publications

(39 citation statements)

References 10 publications

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“…An image-processing algorithm developed by Zion et al (1999) and Shutler and Nixon (2001) has been used for discrimination between images of three fish species for use on freshwater fish farms. Zernike velocity moments were developed by Dudani et al (1977), to describe an object using not only its shape, but also its motion throughout an image as claimed by Mercimekm et al (2005). Classification is the final stage of any image-processing system where each unknown pattern is assigned to a category.…”

Section: Problem Statementmentioning

confidence: 99%

“…The degree of difficulty of the classification problem depends on the variability in feature values for objects in the same category, relative to the difference between feature values for objects in different categories. Mercimekm et al (2005) and Lee et al (2008) have proposed shape analysis of images of fish to deal with the fish classification problem. A new shape analysis algorithm was developed for removing edge noise and redundant data point such as short straight line.…”

Section: Problem Statementmentioning

confidence: 99%

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Fish Recognition Based on Robust Features Extraction from Size and Shape Measurements Using Neural Network

Alsmadi

Omar

Noah

et al. 2010

Journal of Computer Science

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Problem statement: Image recognition is a challenging problem researchers had been research into this area for so long especially in the recent years, due to distortion, noise, segmentation errors, overlap and occlusion of objects in digital images. In our study, there are many fields concern with pattern recognition, for example, fingerprint verification, face recognition, iris discrimination, chromosome shape discrimination, optical character recognition, texture discrimination and speech recognition, the subject of pattern recognition appears. A system for recognizing isolated pattern of interest may be as an approach for dealing with such application. Scientists and engineers with interests in image processing and pattern recognition have developed various approaches to deal with digital image recognition problems such as, neural network, contour matching and statistics. Approach: In this study, our aim was to recognize an isolated pattern of interest in the image based on the combination between robust features extraction. Where depend on size and shape measurements, that were extracted by measuring the distance and geometrical measurements. Results: We presented a system prototype for dealing with such problem. The system started by acquiring an image containing pattern of fish, then the image features extraction is performed relying on size and shape measurements. Our system has been applied on 20 different fish families, each family has a different number of fish types and our sample consists of distinct 350 of fish images. These images were divided into two datasets: 257 training images and 93 testing images. An overall accuracy was obtained using the neural network associated with the back-propagation algorithm was 86% on the test dataset used. Conclusion: We developed a classifier for fish images recognition. We efficiently have chosen a features extraction method to fit our demands. Our classifier successfully design and implement a decision which performed efficiently without any problems. Eventually, the classifier is able to categorize the given fish into its cluster and categorize the clustered fish into its poison or non-poison fish and categorizes the poison and non-poison fish into its family.

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Section: Problem Statementmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

Fish Recognition Based on Robust Features Extraction from Size and Shape Measurements Using Neural Network

Alsmadi

Omar

Noah

et al. 2010

Journal of Computer Science

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“…Invariance with respect to translation, rotation and scaling is required in almost all practical applications, because the object should be correctly recognized, regardless of its position and orientation in the scene and of the object-to-camera distance [26,27].…”

Section: Introductionmentioning

confidence: 99%

A New Combined Strategy for Discrimination between Types of Weed

Herrera

Dorado

Ribeiro

2014

ROBOT2013: First Iberian Robotics Conference

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Abstract. Specific weed management consists on adjusting herbicide treatments depending on the zone infested and the type of weed. In this context, the discrimination between grasses (monocots) and broad-leaved weeds (dicots) is an important objective mainly because the two weed groups can be appropriately controlled by different specific herbicides. This work proposes a method of discrimination between these types of weeds based on a combined strategy, the Sugeno Fuzzy Integral, where the final decision is taken by combining seven attributes, the Hu moments. The main challenge in terms of image analysis is to achieve an appropriate discrimination between both groups in outdoor field images under varying conditions of lighting as well as of soil background texture.

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“…A pattern may be represented by a density distribution function, moments can be obtained for a pattern representing an object, and they can be used to discriminate between objects (or classes) [6]. This technique has been previously used in pattern and object recognition as far as the early 60s [7,6,8,9]. Nevertheless, they were usually applied on measures from an RGB image and the classifiers used were simple.…”

Section: Introductionmentioning

confidence: 99%

3D Representation for Object Detection and Verification

Villavicencio

López-Franco

Arana‐Daniel

et al. 2013

Lecture Notes in Computer Science

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Abstract. In this paper we introduce a representation for object verification and a system for object recognition based on local features, invariant moments, silhouette creation and a 'net' reduction for depth information. The results are then compared with some of the most recent approaches for object detection such as local features and orientation histograms. Additionally, we used depth information to create descriptors that can be used for 3D verification of detected objects. Moments are computed from a 3D set of points which are arranged to create a descriptive object model. This information showed to be of matter in the decision whether the object is present within the analyzed image segment, or not.

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Real object recognition using moment invariants

Cited by 86 publications

References 10 publications

Fish Recognition Based on Robust Features Extraction from Size and Shape Measurements Using Neural Network

Fish Recognition Based on Robust Features Extraction from Size and Shape Measurements Using Neural Network

A New Combined Strategy for Discrimination between Types of Weed

3D Representation for Object Detection and Verification

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