The study of illumination compensation correction algorithm

Huang, Xiaowei; Jiang, Zhaohui; Lu, Lili; Tan, Chunjie; Jiao, Jun

doi:10.1109/icecc.2011.6066457

Cited by 4 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The structural similarity (SSIM) is an indicator used to measure the similarity of images, consisting of a comparison of brightness l, contrast c, and structure s [40,41]. As the image size must be the same during the calculation, the image needs to be converted to grayscale first, and then the SSIM value of the corresponding sub-image is obtained by moving along the image pixel-by-pixel with a window of a certain size.…”

Section: Ssimmentioning

confidence: 99%

Improved Mask R-CNN Combined with Otsu Preprocessing for Rice Panicle Detection and Segmentation

et al. 2022

Self Cite

View full text Add to dashboard Cite

Rice yield is closely related to the number and proportional area of rice panicles. Currently, rice panicle information is acquired with manual observation, which is inefficient and subjective. To solve this problem, we propose an improved Mask R-CNN combined with Otsu preprocessing for rice detection and segmentation. This method first constructs a rice dataset for rice images in a large field environment, expands the dataset using data augmentation, and then uses LabelMe to label the rice panicles. The optimized Mask R-CNN is used as a rice detection and segmentation model. Actual rice panicle images are preprocessed by the Otsu algorithm and input into the model, which yields accurate rice panicle detection and segmentation results using the structural similarity and perceptual hash value as the measurement criteria. The results show that the proposed method has the highest detection and segmentation accuracy for rice panicles among the compared algorithms. When further calculating the number and relative proportional area of the rice panicles, the average error of the number of rice panicles is 16.73% with a minimum error of 5.39%, and the error of the relative proportional of rice panicles does not exceed 5%, with a minimum error of 1.97% and an average error of 3.90%. The improved Mask R-CNN combined with Otsu preprocessing for rice panicle detection and segmentation proposed in this paper can operate well in a large field environment, making it highly suitable for rice growth monitoring and yield estimation.

show abstract

Section: Ssimmentioning

confidence: 99%

Improved Mask R-CNN Combined with Otsu Preprocessing for Rice Panicle Detection and Segmentation

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…As already mentioned, illumination is one of the most important factors in diminution of detection rate. However, recently a couple of plenary review papers have been published discussing illumination correction techniques in general [46][47][48][49][50]. Words are saved here and interesting readers are encouraged to recourse those papers.…”

Section: E Other Factorsmentioning

confidence: 99%

A Comprehensive Survey on Human Skin Detection

Mahmoodi

Sayedi

2016

IJIGSP

View full text Add to dashboard Cite

Human Skin detection is one of the most widely used algorithms in vision literature which has been numerously exploited both directly and indirectly in multifarious applications. This scope has received a great deal of attention specifically in face analysis and human detection/tracking/recognition systems. As regards, there are several challenges mainly emanating from nonlinear illumination, camera characteristics, imaging conditions, and intra-personal features. During last twenty years, researchers have been struggling to overcome these challenges resulting in publishing hundreds of papers. The aim of this paper is to survey applications, color spaces, methods and their performances, compensation techniques and benchmarking datasets on human skin detection topic, covering the related researches within more than last two decades. In this paper, different difficulties and challenges involved in the task of finding skin pixels are discussed. Skin segmentation algorithms are mainly based on color information; an in-depth discussion on effectiveness of disparate color spaces is elucidated. In addition, using standard evaluation metrics and datasets make the comparison of methods both possible and reasonable. These databases and metrics are investigated and suggested for future studies. Reviewing most existing techniques not only will ease future studies, but it will also result in developing better methods. These methods are classified and illustrated in detail. Variety of applications in which skin detection has been either fully or partially used is also provided.

show abstract

“…The literature has often dealt with the problem of compensating for illumination problems. State-of-the-art image enhancement typically implies algorithms aimed at improving image 'quality' (for example see [2]); such an approach is effective, but any processing technique will always provide better results if the original data have better 'quality'. In imaging techniques, original data means the acquired image.…”

Section: Measurement Science and Technologymentioning

confidence: 99%

Optimization of spatial light distribution through genetic algorithms for vision systems applied to quality control

Castellini¹,

Cecchini²,

Stroppa³

et al. 2015

Meas. Sci. Technol.

View full text Add to dashboard Cite

The paper presents an adaptive illumination system for image quality enhancement in vision-based quality control systems. In particular, a spatial modulation of illumination intensity is proposed in order to improve image quality, thus compensating for different target scattering properties, local reflections and fluctuations of ambient light. The desired spatial modulation of illumination is obtained by a digital light projector, used to illuminate the scene with an arbitrary spatial distribution of light intensity, designed to improve feature extraction in the region of interest. The spatial distribution of illumination is optimized by running a genetic algorithm. An image quality estimator is used to close the feedback loop and to stop iterations once the desired image quality is reached. The technique proves particularly valuable for optimizing the spatial illumination distribution in the region of interest, with the remarkable capability of the genetic algorithm to adapt the light distribution to very different target reflectivity and ambient conditions. The final objective of the proposed technique is the improvement of the matching score in the recognition of parts through matching algorithms, hence of the diagnosis of machine vision-based quality inspections. The procedure has been validated both by a numerical model and by an experimental test, referring to a significant problem of quality control for the washing machine manufacturing industry: the recognition of a metallic clamp. Its applicability to other domains is also presented, specifically for the visual inspection of shoes with retro-reflective tape and T-shirts with paillettes.

show abstract

The study of illumination compensation correction algorithm

Cited by 4 publications

References 4 publications

Improved Mask R-CNN Combined with Otsu Preprocessing for Rice Panicle Detection and Segmentation

Improved Mask R-CNN Combined with Otsu Preprocessing for Rice Panicle Detection and Segmentation

A Comprehensive Survey on Human Skin Detection

Optimization of spatial light distribution through genetic algorithms for vision systems applied to quality control

Contact Info

Product

Resources

About