BIND: Binary Integrated Net Descriptors for Texture-Less Object Recognition

Chan, Jacob; Lee, Jimmy Addison; Kemao, Qian

doi:10.1109/cvpr.2017.322

Cited by 14 publications

(8 citation statements)

References 28 publications

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“…This method shows high potential in multimodal retinal image registration applications. Chan et al [35] proposed a multi-layered net-based binary descriptor for texture-less object recognition which is called BIND (Binary Integrated Net Descriptor).It provides precise regional object description through a triple-layered net design to encode edges and internal homogeneous spaces into compact rotation-invariant binary strings.…”

Section: Related Workmentioning

confidence: 99%

Learning Robust Feature Descriptor for Image Registration With Genetic Programming

et al. 2020

IEEE Access

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The robustness and accuracy of feature descriptor are two essential factors in the process of image registration. Existing feature descriptors can extract important image features, but it may be difficult to find enough correct correspondences for sophisticated images. And these feature descriptors often require domain expertise and human intervention. The aim of this paper is to utilise Genetic Programming (GP) to automatically evolve feature descriptors which are adaptive to various images including remote sensing images and optical images. In this paper, a novel GP-based method (GPFD) is proposed to extract feature vectors and evolve image descriptors for image registration without supervision. The proposed method designs a set of simple arithmetic operators and first-order statistics to construct feature descriptors in order to reduce noise interference. The performance of the proposed method is evaluated and compared against five methods including SIFT, SURF, RIFT, GLPM and GP. These results demonstrate that the feature descriptors evolved by GPFD are robust to complex geometric transformation, the illumination difference and noise. INDEX TERMS Image registration, genetic programming, feature descriptor, scale-invariant feature transform (SIFT).

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Section: Related Workmentioning

confidence: 99%

Learning Robust Feature Descriptor for Image Registration With Genetic Programming

et al. 2020

IEEE Access

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“…The method of LineMod [10,11,12] put forward by Hinterstoisser is regarded as one of the most advanced method employed for template matching. Combining the depth information with the gradient information of the color image, this method is usually employed to generate a template by quantizing the gradient direction to a fixed direction, and then several schemes of optimization are adopted for quick measures of windowing similarity between input images [13]. In this paper, the Line2d module in the LineMod method is slightly modified, and thus it is made suitable for grayscale images and improving the recognition rate of the experimental product.…”

Section: Template Matchingmentioning

confidence: 99%

Visual Positioning and Grasping Application of Industrial Robot for Casting Parts

Wan¹

2019

ACIS

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In order to guide an industrial robot to pick a rough casting object, a visual servoing system have been designed to perform the task. This solution present an efficient method to perform robot and vision system's hand eye calibration. Shape matching method is used to find a rough casting object, by combining Line2d algorithm and ROI selection. This vision system can find rough objects with high computational speed and it is robust to environmental noise. The experiment shows that the system repeatability is within 2mm and verify the feasibility of the method and robustness of the algorithm.

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“…Xu et al [62] had proposed a model using the ImageNet database to conduct the few-shot object recognition task which works on machine labeled annotated images with some novel categorical object databases. Chan et al [7] had built a binary integrated descriptor that had various invariant properties such as rotation, scale and polarity of edges through the unique binary logical operated encoding and matching techniques for texture-less object databases. The integrated part based representations had been introduced into convolutional neural networks that result in rotational and translational invariant features for recognizing the different birds [51].…”

Section: Introductionmentioning

confidence: 99%

Machine learning method for cosmetic product recognition: a visual searching approach

Umer

Mohanta

Rout

et al. 2020

Multimed Tools Appl

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A cosmetic product recognition system is proposed in this paper. For this recognition system, we have proposed a cosmetic product database that contains image samples of forty different cosmetic items. The purpose of this recognition system is to recognize Cosmetic products with there types, brands and retailers such that to analyze a customer experience what kind of products and brands they need. This system has various applications in such as brand recognition, product recognition and also the availability of the products to the vendors. The implementation of the proposed system is divided into three components: preprocessing, feature extraction and classification. During preprocessing we have scaled and transformed the color images into gray-scaled images to speed up the process. During feature extraction, several different feature representation schemes: transformed, structural and statistical texture analysis approaches have been employed and investigated by employing the global and local feature representation schemes. Various machine learning supervised classification methods such as Logistic Regression, Linear Support Vector Machine, Adaptive k-Nearest Neighbor, Artificial Neural Network and Decision Tree classifiers have been employed to perform the classification tasks. Apart from this, we have also performed some data analytic tasks for Brand Recognition as well as Retailer Recognition and for these experimentation, we have employed some datasets from the 'Kaggle' website and have obtained the performance due to the above-mentioned classifiers. Finally, the performance of the cosmetic product recognition system, Brand Recognition and Retailer Recognition have been aggregated for the customer decision process in the form of the state-of-the-art for the proposed system.

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BIND: Binary Integrated Net Descriptors for Texture-Less Object Recognition

Cited by 14 publications

References 28 publications

Learning Robust Feature Descriptor for Image Registration With Genetic Programming

Learning Robust Feature Descriptor for Image Registration With Genetic Programming

Visual Positioning and Grasping Application of Industrial Robot for Casting Parts

Machine learning method for cosmetic product recognition: a visual searching approach

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