Part-based object detection into a hierarchy of image segmentations combining color and topology

Antúnez, E.; Marfil, Rebeca; Bandera, Juan Pedro; Bandera, Antonio

doi:10.1016/j.patrec.2013.01.012

Cited by 10 publications

(2 citation statements)

References 23 publications

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“…Overview of the object detection method method is significantly better than similar full-body detectors. It is possible to find several other studies using the component-based approach like [6][7] [8][9] [10]. Although this approach is not a clear winner, most of these studies have proved to be more successful compared to other approaches processing objects as a whole.…”

Section: Introductionmentioning

confidence: 99%

A component-based object detection method extended with a fuzzy inference engine

Koyuncu

Cetinkaya

2015

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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In this paper, we propose a component-based object detection method extended with the fuzzy inference technique. The proposed method detects constituent components of a complex object instead of a whole object in images. For component detection, multiple multi-class support vector machines (SVM) are used in parallel. Each SVM classifies the candidate component using a different low-level image feature. The obtained results are fused to reach a decision about the component. Then, a fuzzy object extractor determines the whole object considering the detected components and their geometric configurations. The fuzzy object extractor is a fuzzy inference engine which tests various combinations of detected components and their fuzzified directions and distances. The initial tests yield promising results and encourage further studies to extend proposed method.

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Section: Introductionmentioning

confidence: 99%

A component-based object detection method extended with a fuzzy inference engine

Koyuncu

Cetinkaya

2015

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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“…Pedestrian detection based on computer vision algorithms acts an important role in many applications such as surveillance system and driver assistance system [1–5]. Since pedestrian detection is one of the important component in these systems, it has attracted much attention in recent years [6–9].…”

Section: Introductionmentioning

confidence: 99%

Partially occluded pedestrian classification using histogram of oriented gradients and local weighted linear kernel support vector machine

Aly

2014

IET Computer Vision

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One of the main challenges in pedestrian classification is partial occlusion. This study presents a new method for pedestrian classification with partial occlusion handling. The proposed method involves a set of part-based classifiers trained on histogram of oriented gradients features derived from non-occluded pedestrian data set. The score of each part classifier is then employed to weight features used to train a second stage full-body classifier. The full-body classifier based on local weighted linear kernel support vector machine is trained using both non-occluded and artificially generated partial occlusion pedestrian dataset. The new kernel allows to significantly focus on the non-occluded parts and reduce the impact of the occluded ones. Experimental results on real-world dataset, with both partially occluded and non-occluded data, show high performance of the proposed method compared with other state-of-the-art methods.

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A New Modification of Fuzzy C-Means via Particle Swarm Optimization for Noisy Image Segmentation

Mirghasemi

Rayudu

Zhang

2016

Lecture Notes in Computer Science

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Part-based object detection into a hierarchy of image segmentations combining color and topology

Cited by 10 publications

References 23 publications

A component-based object detection method extended with a fuzzy inference engine

A component-based object detection method extended with a fuzzy inference engine

Partially occluded pedestrian classification using histogram of oriented gradients and local weighted linear kernel support vector machine

A New Modification of Fuzzy C-Means via Particle Swarm Optimization for Noisy Image Segmentation

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