Spatial color histogram based center voting method for subsequent object tracking and segmentation

Suryanto, Suryanto; Kim, Dae-Hwan; Kim, Hyo-Kak; Ko, Sung-Jea

doi:10.1016/j.imavis.2011.09.008

Cited by 22 publications

(7 citation statements)

References 9 publications

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“…In this model, hue and shape are used as complementary features. One of the main parts of particle filter is dissemination algorithm of conditional probability density function [21][22][23][24][25][26][27][28][29][30][31][32][33]. An improved factored sampling algorithm is also realized to resample and weight particles.…”

Section: A Review Of Most Important Applied Methodsmentioning

confidence: 99%

A powerful approach to real-time mobile objects tracking in crowded environments utilizing 3D video analysis of fixed camera, particle filter and neural network

Mazinan

2014

Optik

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Section: A Review Of Most Important Applied Methodsmentioning

confidence: 99%

A powerful approach to real-time mobile objects tracking in crowded environments utilizing 3D video analysis of fixed camera, particle filter and neural network

Mazinan

2014

Optik

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“…The comparisons showed that the accuracy of the semantic segmentation is improved thanks to the included polarization features. Suryanto et al [159] introduced an algorithm for object tracking in video sequences. In order to represent the object to be tracked, a new spatial color histogram model was proposed, which encodes both the color distribution and spatial distribution.…”

Section: Multi-feature Fusionmentioning

confidence: 99%

Segmentation of images by color features: A survey

et al. 2018

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Image segmentation is an important stage for object recognition. Many methods have been proposed in the last few years for grayscale and color images. In this paper, we present a deep review of the state of the art on color image segmentation methods; through this paper, we explain the techniques based on edge detection, thresholding, histogram-thresholding, region, feature clustering and neural networks. Because color spaces play a key role in the methods reviewed, we also explain in detail the most commonly color spaces to represent and process colors. In addition, we present some important applications that use the methods of image segmentation reviewed. Finally, a set of metrics frequently used to evaluate quantitatively the segmented images is shown.

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“…Suryanto presents a color histogram with spatial information, which needs to estimate the location of the target object and then vote to determine the final results [11]. A weighted color histogram is proposed by Yang.…”

Section: Introductionmentioning

confidence: 99%

A Novel Image Retrieval Method with Saliency Feature Vector

Wu¹

2018

IJPE

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In the past few years, image retrieval has been one of the research focuses in the field of computer vision. For most retrieval methods, the accuracy of the retrieval results mainly depends on the extracted feature vectors. But, the foreground and the background in the images are not distinguished for most methods. It is obvious that these methods are not in accordance with the visual characteristics of the human eye. In this paper, salient objects are extracted from images in order to improve the pertinence of feature vector extraction. The paper utilizes a spatial pyramid model to divide the image into different parts with different scales. The feature vectors extracted in different scale are connected. Then, the saliency map and saliency score are used to rebuild the joint vector. Each feature vector is assigned different weighted values according to its different location in the image and scale. Finally, the newly constructed feature vectors are used to measure the similarity between images. In order to test the effectiveness of the algorithm, we evaluate our method on the SIMPLIcity dataset and Stanford dataset. Experimental results show that the proposed method has a great improvement in both accuracy and efficiency.

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Spatial color histogram based center voting method for subsequent object tracking and segmentation

Cited by 22 publications

References 9 publications

A powerful approach to real-time mobile objects tracking in crowded environments utilizing 3D video analysis of fixed camera, particle filter and neural network

A powerful approach to real-time mobile objects tracking in crowded environments utilizing 3D video analysis of fixed camera, particle filter and neural network

Segmentation of images by color features: A survey

A Novel Image Retrieval Method with Saliency Feature Vector

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