JSEG Algorithm and Statistical ANN Image Segmentation Techniques for Natural Scenes

Does context or color improve object recognition in patients with low vision?

Visual Neuroscience ◽

10.1017/s0952523808080826 ◽

2008 ◽

Vol 25 (5-6) ◽

pp. 685-691 ◽

Cited By ~ 22

Author(s):

MURIEL BOUCART ◽

PASCAL DESPRETZ ◽

KATRINE HLADIUK ◽

THOMAS DESMETTRE

Keyword(s):

Image Segmentation ◽

Object Recognition ◽

Low Vision ◽

Object Perception ◽

Age Related Macular Degeneration ◽

Natural Scenes ◽

Gray Level ◽

Spatial Properties ◽

Age Related ◽

Complex Stimuli

AbstractMost studies on people with age-related macular degeneration (AMD) have been focused on investigations of low-level processes with simple stimuli like gratings, letters, and in perception of isolated faces or objects. We investigated the ability of people with low vision to analyze more complex stimuli like photographs of natural scenes. Fifteen participants with AMD and low vision (acuity on the better eye <20/200) and 11 normally sighted age-matched controls took part in the study. They were presented with photographs of either colored or achromatic gray level scenes in one condition and with photographs of natural scenes versus isolated objects extracted from these scenes in another condition. The photographs were centrally displayed for 300 ms. In both conditions, observers were instructed to press a key when they saw a predefined target (a face or an animal). The target was present in half of the trials. Color facilitated performance in people with low vision, while equivalent performance was found for colored and achromatic pictures in normally sighted participants. Isolated objects were categorized more accurately than objects in scenes in people with low vision. No difference was found for normally sighted observers. The results suggest that spatial properties that facilitate image segmentation (e.g., color and reduced crowding) help object perception in people with low vision.

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A study in finding effectiveness of Gaussian blur filter over bilateral filter in natural scenes for graph based image segmentation

2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS) ◽

10.1109/icaccs.2017.8014612 ◽

2017 ◽

Author(s):

Pyushi Singhal ◽

Akhilesh Verma ◽

Akash Garg

Keyword(s):

Image Segmentation ◽

Bilateral Filter ◽

Natural Scenes ◽

Gaussian Blur

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Image segmentation for complex natural scenes

Image and Vision Computing ◽

10.1016/0262-8856(90)90031-y ◽

1990 ◽

Vol 8 (2) ◽

pp. 155-163 ◽

Cited By ~ 5

Author(s):

Albert P Choo ◽

Anthony J Maeder ◽

Binh Pham

Keyword(s):

Image Segmentation ◽

Natural Scenes

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Robust Image Segmentation Method for Cotton Leaf Under Natural Conditions Based on Immune Algorithm and PCNN Algorithm

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001418540113 ◽

2018 ◽

Vol 32 (05) ◽

pp. 1854011 ◽

Cited By ~ 5

Author(s):

Jianhua Zhang ◽

Fantao Kong ◽

Zhifen Zhai ◽

Jianzhai Wu ◽

Shuqing Han

Keyword(s):

Image Segmentation ◽

Weather Conditions ◽

Bare Soil ◽

Immune Algorithm ◽

Cotton Leaf ◽

Natural Scenes ◽

Segmentation Method ◽

Natural Conditions ◽

Mulch Film ◽

Color Components

In the actual cotton planting environment, rapid change of light within a day, reflection from different backgrounds and different weather conditions can affect the imaging of cotton. Therefore, the crop object segmentation is difficult. Images which were captured in 12 natural scenes during cotton planting, including three weather conditions, such as sunny, cloudy and rainy and four soil cover conditions, such as white mulch film, black mulch film, straw and bare soil were regarded as the research objects. This paper presents the cotton leaf segmentation method based on Immune algorithm and pulse coupled neural networks (PCNN). First, 17 color components of white mulch film, black mulch film, straw, bare soil and cotton under the conditions of sunny, cloudy and rainy days were analyzed by using statistical method. Three high feasible and anti-light color components were selected by histogram statistical with mean gray value. Second, the optimal parameters of PCNN model and the optimal number of iterations were determined by using immune algorithm optimization theory, and the method in this paper was tested by using 1200 cotton images which were captured under 12 natural scenes. Finally, the test results showed that this method can distinguish cotton target region from soil and other background regions. Meanwhile, for reflection of mulch film, crop shadow, dark light, complex background, noise, etc. which are often appeared in natural scene, four image segmentation methods of Otsu algorithm, [Formula: see text]-Means algorithm, FCM algorithm and PCNN were compared with the proposed method in this paper. The segmentation result showed that the proposed method has good resistance to change of light and complex background. The average [Formula: see text] of the proposed method is 6.5%, significantly lower than that of other four methods and the performance is better than other four methods. This method can segment cotton images in different weather conditions and different backgrounds accurately under complex natural conditions. It will contribute to the subsequent growth status determination and pest diagnosis of cotton.

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