Uneven illumination correction of digital images: A survey of the state-of-the-art

Dey, Nilanjan

doi:10.1016/j.ijleo.2019.02.118

Cited by 52 publications

(32 citation statements)

References 111 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Vectorbased representation performed well in color featurebased visual tracking. Color features are robust to object deformation, insensitive to shape variation [38], but suffer from small sample size problem and uneven illumination changes [39].…”

Section: Visual Representationmentioning

confidence: 99%

Vision Tracking: A Survey of the State-of-the-Art

et al. 2020

Self Cite

View full text Add to dashboard Cite

Vision tracking is a well-studied framework in vision computing. Developing a robust visual tracking system is challenging because of the sudden change in object motion, cluttered background, partial occlusion and camera motion. In this study, the state-of-the art visual tracking methods are reviewed and different categories are discussed. The overall visual tracking process is divided into four stages-object initialization, appearance modeling, motion estimation, and object localization. Each of these stages is briefly elaborated and related researches are discussed. A rapid growth of visual tracking algorithms is observed in last few decades. A comprehensive review is reported on different performance metrics to evaluate the efficiency of visual tracking algorithms which might help researchers to identify new avenues in this area. Various application areas of the visual tracking are also discussed at the end of the study.

show abstract

Section: Visual Representationmentioning

confidence: 99%

Vision Tracking: A Survey of the State-of-the-Art

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…In literature several approaches exist addressing the area of correcting inconsistent illumination. In digital image processing, Histogram Equalization (HE) [10]- [20] has been a simple prevalent method for contrast improvement in images. It uses cumulative density function to obtain the result.…”

Section: Literature Surveymentioning

confidence: 99%

“…Illumination change can occur due to climate changes, sudden alterations of light etc. Inconsistent illumination can affect several applications in the areas of medical diagnosis using imaging, remote sensed imaging, aerial imaging and underground imaging [4]- [10]. There are occurrences of sudden on-off variations in light or sometimes those can be even gradual.…”

Section: Introductionmentioning

confidence: 99%

Video Frame Illumination Inconsistency Reduction using CLAHE with Kekre’s LUV Color Space

Abin¹,

Thepade²

2020

IJEAT

View full text Add to dashboard Cite

 Abstract: Visual frame quality is of utmost significance and is relevant in numerous computer vision applications such as object detection, video surveillance, optical motion capture, multimedia and human computer interface. Under controlled or uncontrolled environment, the video visual frame quality gets affected due to illumination variations. This may further hamper the interpretability and may lead to significant loss of information for background modeling. An excellent background model can enhance good visual perception. In this work, local enhancement technique with improved background modeling, Clipped Adaptive Histogram Equalization (CLAHE) is explored with Kekre's LUV color space to reduce the illumination inconsistency especially with darker set of video frames and a significant improvedaverage entropy of 7.7225 has been obtained, which is higher than the existing explored variations of CLAHE. .

show abstract

“…c) Beneficial impacts on the excellence of feature extraction and the outcomes of image analysis. [14] Although, images are fetched and predicted from the satellites. When taking an image from the digital camera, there was a shortage in brightness as well as contrast due to the boundaries of illumination constraints and imaging subsystems.…”

Section: B Pre-processingmentioning

confidence: 99%

Defect Detection in Printed Board Circuit using Image Processing

Munisankar*¹,

Nagarajan²,

Rao³

2019

IJITEE

View full text Add to dashboard Cite

A printed circuit board without connecting with any components called as a bare PCB. Consider a PCB as a basic part which has been settled with more electronic units. In order to display the manufacturing process, the drawbacks have been taken by PCB individually. The reflection of this separation process impacts the performance of the circuits. Also, we have examined about classification methodologies as well as referential based PCB detection. From the input images, the needed and related information has been pulled out using image processing methodologies by the referential based PCB detection. Comparing with the un-defected PCB images, this was used to find out the defects. To meet the goal of the PCB defect detection, several feature extraction and pre-processing methods are derived in this article. The PCB defects have been classified by those features using the machine learning algorithms. Moreover, several types of machine learning algorithms are derived in this article. This paper helps the researchers for achieving a better solution for image processing and machine learning-based printed circuit board the defect classification

show abstract

Uneven illumination correction of digital images: A survey of the state-of-the-art

Cited by 52 publications

References 111 publications

Vision Tracking: A Survey of the State-of-the-Art

Vision Tracking: A Survey of the State-of-the-Art

Video Frame Illumination Inconsistency Reduction using CLAHE with Kekre’s LUV Color Space

Defect Detection in Printed Board Circuit using Image Processing

Contact Info

Product

Resources

About