A robust and efficient pre processing techniques for stereo images

Deepa, .; Jyothi, K.

doi:10.1109/iceeccot.2017.8284645

Cited by 4 publications

(5 citation statements)

References 16 publications

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“…The results of structure from motion (SFM) and multi-view stereo (MVS) were shown to evaluate the applicability of our method. Moreover, four other state-of-the-art approaches, including HES [ 43 ], LFA [ 22 ], LIME [ 47 ] and NPEA [ 50 ], were used for comparisons.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Based on the two datasets, the results obtained with the proposed methods were compared with those of the other image-optimization methods. The other methods, including HES [ 43 ], LFA [ 22 ], LIME [ 47 ] and NPEA [ 50 ], which can be classified into two types, namely, spatial and frequency domain analysis. HES is an important family among the spatial domain analysis-based image-optimization approaches and has been proposed for illumination-robust image matching.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…On the basis of our review, spatial domain analysis has been demonstrated to be effective to reduce illumination variation and enhance image contrast [ 42 , 43 , 47 ]. In addition, the spatial domain analysis-based image optimization focus on illumination equalization rather than removing all illumination components, which can preserve more image information than other methods such as phase congruency [ 22 , 49 ].…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…In addition, the HES approaches have been widely employed to correct the brightness difference. A histogram equalization-based algorithm [ 43 ] was particularly designed to improve the quality of stereo image matching with illumination differences. The adaptive histogram equalization technique in [ 44 ] was proposed to improve weak illumination and low contrast contained in matched images.…”

Section: Related Workmentioning

confidence: 99%

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A Spatial-Frequency Domain Associated Image-Optimization Method for Illumination-Robust Image Matching

Liu

Jia

et al. 2020

Sensors

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Image matching forms an essential means of data association for computer vision, photogrammetry and remote sensing. The quality of image matching is heavily dependent on image details and naturalness. However, complex illuminations, denoting extreme and changing illuminations, are inevitable in real scenarios, and seriously deteriorate image matching performance due to their significant influence on the image naturalness and details. In this paper, a spatial-frequency domain associated image-optimization method, comprising two main models, is specially designed for improving image matching with complex illuminations. First, an adaptive luminance equalization is implemented in the spatial domain to reduce radiometric variations, instead of removing all illumination components. Second, a frequency domain analysis-based feature-enhancement model is proposed to enhance image features while preserving image naturalness and restraining over-enhancement. The proposed method associates the advantages of the spatial and frequency domain analyses to complete illumination equalization, feature enhancement and naturalness preservation, and thus acquiring the optimized images that are robust to the complex illuminations. More importantly, our method is generic and can be embedded in most image-matching schemes to improve image matching. The proposed method was evaluated on two different datasets and compared with four other state-of-the-art methods. The experimental results indicate that the proposed method outperforms other methods under complex illuminations, in both matching performances and practical applications such as structure from motion and multi-view stereo.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Experimental Results and Analysismentioning

confidence: 99%

Section: The Proposed Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A Spatial-Frequency Domain Associated Image-Optimization Method for Illumination-Robust Image Matching

Liu

Jia

et al. 2020

Sensors

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“…Image pre-processing is very crucial due to camera orientation, illumination difference and noise. Wiener filter is one of the most powerful noise removal approach [9], which has been used in several applications. Dilation and erosion are morphological operations [2] that are dual to each other i.e.…”

Section: Related Workmentioning

confidence: 99%

Image Training, Corner and FAST Features based Algorithm for Face Tracking in Low Resolution Different Background Challenging Video Sequences

Ranganatha¹,

Gowramma²

2018

IJIGSP

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We are proposing a novel algorithm for tracking human face(s) in different background video sequences. We have trained both face and non-face images which help in face(s) detection process. At first, FAST features and corner points are extracted from the detected face(s). Further, mid points are calculated from corner points. FAST features, corner points and mid points are combined together. Using the combined points, point tracker tracks face(s) in the frames of the video sequence. Standard metrics were adopted for measuring the performance of the proposed algorithm. Low resolution video sequences with challenges such as partial occlusion, changes in expression, variations in illumination and pose took part while testing the proposed algorithm. Test results clearly indicate the robustness of the proposed algorithm on all different background challenging video sequences.

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Image preprocessing phase with artificial intelligence methods on medical images

Dimililer,

Erdem,

Kayali

et al. 2024

Artificial Intelligence and Image Processing in Medical Imaging

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A robust and efficient pre processing techniques for stereo images

Cited by 4 publications

References 16 publications

A Spatial-Frequency Domain Associated Image-Optimization Method for Illumination-Robust Image Matching

A Spatial-Frequency Domain Associated Image-Optimization Method for Illumination-Robust Image Matching

Image Training, Corner and FAST Features based Algorithm for Face Tracking in Low Resolution Different Background Challenging Video Sequences

Image preprocessing phase with artificial intelligence methods on medical images

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