Moving object extraction using background difference and region growing with a spatiotemporal watershed algorithm

Sakaida, Shinichi; Naemura, Masahide; Kanatsugu, Yasuaki

doi:10.1002/scj.10237

Cited by 2 publications

(2 citation statements)

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“…Step 1 (pre-process): Background Subtraction A moving hand can be recognized from a set of still images by background subtraction algorithms accurately and efficiently [7], [8]. However, if the image background contains a color close to the target hand skin color, the resulting hand obtained may not be satisfactory.…”

Section: Amentioning

confidence: 99%

Partitioning Method in Noise Reduction to Improve Hand Detection

Woun¹,

Tan²,

Won³

et al. 2013

IJFCC

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Abstract-This paper presents a multi-step algorithm for noise reduction in hand detection by using partitioning method. The main idea of the partitioning method is to divide an image into equal-size blocks, and obtain the percentage of number of pixels with skin color (skin pixels) in each block. Each of the blocks is labelled as either noise or part of the hand based on the percentage of skin pixels in that block. The image is then refined by repeating the processes to work on smaller blocks in the image with different comparison percentage values. The result of this paper shows partitioning method not only can filter the noise, but it also preserves the shape of the hand better, as compared with the Mathematical Morphology algorithm.Index Terms-Hand detection, noise reduction, human computer interaction, partitioning method. I. INTRODUCTIONImage noise is a major issue yet unavoidable in image processing. Noisy images are produced mainly during image sampling or transmission. Various types of noise such as photo electronic noise, impulse and structured noise present challenging problems in image restoration, particularly in object detection. Several noise reduction techniques have been developed to overcome different types of noise and for different aspects of image restoration.Adaptive noise reduction algorithms such as median filtering, K-nearest neighbor averaging, gradient inverse weighted smoothing, etc. have been introduced in [1]-[3]. Median filtering is particularly effective for reducing impulse noise. It is performed by running through the entire image from one region to another, and replaces each pixel in question with the median of its neighboring entries. However the median based filter often cannot differentiate the thin lines and impulse noises and thus mistakenly removes the thin lines [4].Fuzzy image filtering is a nonlinear processing technique used widely as an alternative to linear adaptive filtering. Mathematical morphology uses fuzzy image filtering to extract image components such as boundaries and skeletons; Manuscript received December 19, 2012; revised February 21, 2013 and also to filter the image noise using erosion processes [5], [6]. However, by using morphology in hand detection, the end effect tends to distort the size of the hand.In our research, we propose to use partitioning method in image noise reduction to improve hand detection for an image with background color close to skin color. Our proposed methodology divides an image equally into blocks. The percentage of number of pixels with skin color (skin pixels) in each block is calculated and compared against a threshold value (skin threshold) to decide if the block is noise or part of the hand. A comparison between partitioning method and morphology algorithm is made, particularly on image noise reduction in hand detection. The result produced by partitioning method displays a cleaner hand image with better shape preservation. II. METHODOLOGYOur project uses partitioning method, which adopts divide-and-conquer strategy to re...

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Section: Amentioning

confidence: 99%

Partitioning Method in Noise Reduction to Improve Hand Detection

Woun¹,

Tan²,

Won³

et al. 2013

IJFCC

View full text Add to dashboard Cite

show abstract

“…Robust extraction and tracking of semantic moving objects over extended periods of time is an essential technique for video-object linked data broadcasting. Despite a considerable amount of research effort [e.g., [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19], extracting moving objects from a general image sequence is still a challenging task. Available algorithms for moving object extraction are either designed for constrained image sequences or nonrobust.…”

Section: Introductionmentioning

confidence: 99%

Moving object extraction with cooperative cameras

Zheng

Misu²,

Naemura³

et al. 2003

Systems & Computers in Japan

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SUMMARYWe present a novel approach to robust extraction of moving objects with cooperative static and active cameras. The active camera, for usual broadcasts, is fixed in location but free to rotate and zoom. The static camera is a completely fixed panoramic camera viewing a whole scene. Precalibration and on-the-ground calibration are carried out to determine the intrinsic and extrinsic camera parameters. The two cameras work in synchronism, and the motion parameters (e.g., pan, tilt, zoom, focus) of the active camera are measured for each image frame. Background subtraction is applied to the image sequence of the static camera to extract moving objects, and the obtained moving objects are then transformed to the image sequence of the active camera using both calibrated camera parameters and camera motion parameters. The approach is robust and fast and has a number of other desirable features.

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Moving object extraction using background difference and region growing with a spatiotemporal watershed algorithm

Cited by 2 publications

References 6 publications

Partitioning Method in Noise Reduction to Improve Hand Detection

Partitioning Method in Noise Reduction to Improve Hand Detection

Moving object extraction with cooperative cameras

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