Threshold Evaluation Techniques.

Weszka, Joan S.

doi:10.21236/ada043410

Cited by 37 publications

(48 citation statements)

References 3 publications

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“…[21] To apply the busyness measure they assume that the images are composed of objects and background of compact shapes and not strongly textured. Under these assumptions, the thresholded images should look smooth rather than busy.…”

Section: Goodness Based On Intra-region Uniformitymentioning

confidence: 99%

A survey on evaluation methods for image segmentation

Zhang

1996

Pattern Recognition

916

501

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This paper studies different methods proposed so far for segmentation evaluation. Most methods can be classified into three groups: the analytical, the empirical goodness and the empirical discrepancy groups. Each group has its own characteristics. After a brief description of each method in every group, some comparative discussions about different method groups are first carried out. An experimental comparison for some empirical (goodness and discrepancy) methods commonly used is then performed to provide a rank of their evaluation abilities. In addition, some special methods are also discussed. This study is helpful for an appropriate use of existing evaluation methods and for improving their performance as well as for systematically designing new evaluation methods.

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Section: Goodness Based On Intra-region Uniformitymentioning

confidence: 99%

A survey on evaluation methods for image segmentation

Zhang

1996

Pattern Recognition

916

501

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“…In this step, we run a skin color detection for the second time but with some variations from the detection process in step 2. Let p be the pixel coordinate for the hand (white pixels) in Image HAND 3 . The hue values of neighboring pixels for p in the HSV version of Image IN 2 in step 2 is compared against a wider range of hue values, 0-100 and 270-360.…”

Section: Step 4: Skin Color Detection -Passmentioning

confidence: 99%

“…have been introduced in [1]- [3]. Median filtering is particularly effective for reducing impulse noise.…”

Section: Introductionmentioning

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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“…Due to its simplicity thresholding [3] is more practical for realtime implementation. The purpose of bilevel thresholding or multilevel thresholding operation is that objects and background are separated into non-overlapping sets.…”

Section: Introductionmentioning

confidence: 99%

“…Although the thresholding [3] appears simple, it is very important and fundamental with the wide applicability, as it is relevant not only for segmenting the original image data but also for segmenting its linear and non-linear image to image transforms.…”

Section: Introductionmentioning

confidence: 99%

Multilevel Thresholding using PSO Clustering

Dash¹,

Nayak²

2014

IJCA

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Thresholding algorithms are quite easy and effective for bilevel thresholding but in case of multilevel thresholding, the performance becomes unreliable due to complexity in computation because the complexity will exponentially increase. In this approach, multilevel thresholding is done for comparison by taking help of Otsu's clustering method and PSO clustering method. A dendogram of gray levels is created based on histogram of an image. The bottom-up generation of clusters employing a dendogram by the proposed method yields good separation of the clusters and obtains a robust estimate of the threshold. Such cluster organization will yield a clear separation between object and background even for the case of nearly unimodal or multimodal histogram. Since the hierarchical clustering method performs an iterative merging operation, it is extended to multi-level thresholding problem by eliminating grouping of clusters when the pixel values are obtained from the expected number of clusters.

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Threshold Evaluation Techniques.

Cited by 37 publications

References 3 publications

A survey on evaluation methods for image segmentation

A survey on evaluation methods for image segmentation

Partitioning Method in Noise Reduction to Improve Hand Detection

Multilevel Thresholding using PSO Clustering

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