Mean Shift Parallel Tracking on GPU

Li, Peihua; Xiao, Lijuan

doi:10.1007/978-3-642-02172-5_17

Cited by 16 publications

(2 citation statements)

References 4 publications

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“…It has been proved that GPU's performance in floating-point operations, and some other parallel computing is even hundreds times better than that of CPU. Moreover, with the appearance of high resolution images and real time requirement in image retrieval, more and more scholars start to research the parallelization of accelerated image segmentation algorithms [11][12][13]. We also accelerated our algorithm on GPU.…”

Section: Parallel Acceleration On Gpumentioning

confidence: 99%

An Improved Graph-Based Image Segmentation Algorithm and Its GPU Acceleration

Feng¹,

Xiang²,

Zhu³

2011

2011 Workshop on Digital Media and Digital Content Management

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The paper proposes an improved graph-based image segmentation algorithm. We introduce a new merging mechanism based on minimum region size. The mechanism not only limits the minimum region size but also avoids very large regions which may cause over-merging. We can obtain better segmentation results utilizing our method which highlights the characters of single object and is more applicable for image feature extraction. In addition, we accelerate the algorithm on GPU (Graphics Processing Unit), which achieves large speedup (about 3.52 times) and comparable performance. Moreover, the advantage in speed is more obvious with the image resolution increasing. It can meet the real-time requirements to a degree.

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Section: Parallel Acceleration On Gpumentioning

confidence: 99%

An Improved Graph-Based Image Segmentation Algorithm and Its GPU Acceleration

Feng¹,

Xiang²,

Zhu³

2011

2011 Workshop on Digital Media and Digital Content Management

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“…Li et.al. [14] speed it up on the GPU platform. Affinity propagation clustering algorithm (APC) [9] also needs not to know the number of clustering centers previously.…”

Section: Related Workmentioning

confidence: 99%

A Replacement Algorithm of Non-Maximum Suppression Base on Graph Clustering

Zhao

Dou

2017

ITM Web Conf.

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Non-maximum suppression is an important step in many object detection and object counting algorithms. In contrast with the extensive studies of object detection, NMS method has not caused too much attention. Although traditional NMS method has demonstrated promising performance in detection tasks, we observe that it is a hard decision approach, which only uses the confidential scores and Intersection-over-Unions (IoUs) to discard proposals. By this way, NMS method would keep many false proposals whose IoU with the ground truth proposal is smaller than the threshold, which indicates that NMS may not suitable for counting the object number in images. To eliminate the limitation on object counting task, we propose a novel algorithm base on graph clustering to replace the NMS method in this paper. Experiments on faster-rcnn and SSD show that our algorithm achieves better performance than that of NMS on the object counting task.

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Parallel Mean Shift for Interactive Volume Segmentation

Zhou

Zhao

2010

Machine Learning in Medical Imaging

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Mean Shift Parallel Tracking on GPU

Cited by 16 publications

References 4 publications

An Improved Graph-Based Image Segmentation Algorithm and Its GPU Acceleration

An Improved Graph-Based Image Segmentation Algorithm and Its GPU Acceleration

A Replacement Algorithm of Non-Maximum Suppression Base on Graph Clustering

Parallel Mean Shift for Interactive Volume Segmentation

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