Pedestrian Detection in Visual Images Using Combination of HOG and HOM Features

Chee, Kok Wei; Teoh, Soo Siang

doi:10.1007/978-981-13-6447-1_75

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…(C) T20-HOG HOG features are used for different types of object detection tasks including pedestrian [11,14], human [49], crop pest [17], and palm tree [56] detection.…”

Section: Feature Extractionmentioning

confidence: 99%

Paddy seed variety identification using T20-HOG and Haralick textural features

Uddin

Islam

Shajalal

et al. 2021

Complex Intell. Syst.

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The seed is an inevitable element for agricultural and industrial production. The non-destructive paddy seed variety identification is essential to assure paddy purity and quality. This research is aimed at developing a computer vision-based system to identify paddy varieties using multiple heterogeneous features, exploiting textural, external, and physical properties. We captured the paddy seed images without any fixed setup to make the system user friendly at both industry and farmer levels, which can lead to illumination problems in the images. To overcome this problem, we introduced a modified histogram oriented gradient (T20-HOG) feature that can describe the illumination, scale, and rotational variations of a paddy image. We also utilized the existing Haralick and traditional features and the dimensionality of the features is reduced by the Lasso feature selection technique. The selected features are used to train the feed-forward neural network (FNN) to predict the paddy variety. The experiments conducted on two different datasets: BDRICE, and VNRICE. Results of our method are shown in terms of four standard evaluation metrics, namely, accuracy, precision, recall, and F_1 score, and achieved 99.28%, 98.64%, 98.48%, and 98.56% score, respectively. We also compared our system efficiency with existing studies. The experimental results demonstrate that our proposed features are effective to identify paddy variety and achieved a new state-of-the-art performance. And we also observed that our newly proposed T20-HOG features have a major impact on overall system performance.

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“…(C) T20-HOG HOG features are used for different types of object detection tasks including pedestrian [11,14], human [49], crop pest [17], and palm tree [56] detection.…”

Section: Feature Extractionmentioning

confidence: 99%

Paddy seed variety identification using T20-HOG and Haralick textural features

Uddin

Islam

Shajalal

et al. 2021

Complex Intell. Syst.

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“…Image vision knowledge is reflected by image features. The image feature acquisition can be divided into two categories: hand-crafted features extraction method [15]- [17] and deep learning extraction method [18]- [20]. The method based on hand-crafted features usually need professional knowledge and use the surface properties of the image to extract image features, so the learning ability of the model suffers from great limitation and can not fully reflect the essential attributes of the object.…”

Section: A Acquisition Of Image Vision Knowledgementioning

confidence: 99%

From Vision to Content: Construction of Domain-Specific Multi-Modal Knowledge Graph

et al. 2019

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Knowledge graphs are usually constructed to describe the various concepts that exist in real world as well as the relationships between them. There are many knowledge graphs in specific fields, but they usually pay more attention on text or structured data, ignoring the image vision information, and cannot play an adequate role in the emerging visualization applications. Aiming at this issue, we design a method that integrates image vision information and text information derived from Wikimedia Commons to construct a domain-specific multi-modal knowledge graph, taking the metallic materials domain as an example to illustrate the method. The text description of each image is regarded as its context semantic to acquire the image's context semantic labels based on the DBpedia resource. Furthermore, we adopt deep neural network model instead of simple visual descriptors to acquire the image's visual semantic labels using the concepts from WordNet. In order to fuse the visual semantic labels and context semantic labels, a path-based concept extension and fusion strategy is proposed based on the conceptual hierarchies of WordNet and DBpedia to obtain the effective extension concepts as well as the links between them, increasing the scale of the knowledge graph and enhancing the correlation between images. The experimental results show that the maximum extension level has a significant impact on the quality of the generated domain knowledge graph, and the best extension level number is respectively determined for both DBpedia and WordNet. In addition, the results of this paper are compared with IMGpedia to further show the effectiveness of the proposed method. INDEX TERMS Knowledge graph, information fusion, multi-modal knowledge, metallic materials knowledge. IV. CONSTRUCTION METHOD OF DOMAIN-SPECIFIC MULTI-MODAL KNOWLEDGE GRAPH A. APPROACH OVERVIEW

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Cell Segmentation of Histopathological Images of Glioma Using Voronoi Tessellation and Quadtree Representation

Brindha

Jayashree

2023

Lecture Notes in Electrical Engineering

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Pedestrian Detection in Visual Images Using Combination of HOG and HOM Features

Cited by 5 publications

References 8 publications

Paddy seed variety identification using T20-HOG and Haralick textural features

Paddy seed variety identification using T20-HOG and Haralick textural features

From Vision to Content: Construction of Domain-Specific Multi-Modal Knowledge Graph

Cell Segmentation of Histopathological Images of Glioma Using Voronoi Tessellation and Quadtree Representation

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