Journal Menu
By: Kazi Kutubuddin Sayyad Liyakat and Heena T Shaikh.
1 Professor, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India
2 Assistant Professor, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India
The intricate tapestry of image mosaicing, while a cornerstone of computer vision, has long been constrained by the rigidity of classical, geometry-driven pipelines. Hand-crafted feature detectors and homography estimators, while effective in controlled environments, prove brittle when confronted with the challenges of real-world scenes: significant parallax, dynamic moving objects, repetitive textures, and illumination variance. This work introduces a fundamental paradigm shift, proposing a deep convolutional neural network (CNN) architecture designed for end-to-end, perceptually-driven image mosaic generation. Our model eschews the multi-stage, error-prone traditional workflow, instead learning a latent, correspondence-aware representation directly from image data. This network is trained not just to align pixels, but to synthesize a visually coherent scene, implicitly handling occlusions, warping artifacts, and exposure inconsistencies. Through extensive evaluation on benchmark and challenging real-world datasets, our CNN-based approach demonstrates a marked improvement in quantitative metrics and qualitative visual fidelity, particularly in high-parallax and dynamic scenarios. It successfully produces seamless, ghost-free mosaics where classical methods fail. This research establishes a new state-of-the-art, proving that a holistic, data-driven learning approach can master the complex art of scene assembly, paving the way for more robust and intelligent visual stitching applications in robotics, aerial imaging, and virtual reality.
Citation:
Refrences:
- Mulani AO, Patil RM, Liyakat KK. Discriminative appearance model for robust online multiple target tracking. Telematique. 2023;22(1):24–43.
- Kumar MS, Ganesh D, Turukmane AV, Batta U, Sayyadliyakat KK. Deep convolution neural network based solution for detecting plant diseases. J Pharm Negat Results. 2022;13(1).
- Liyakat KK. Significance and usage of face recognition system. Sch Res J Humanit Sci Engl Lang. 2017;4(20):4764–72.
- Dixit AJ, Kazi MK. Iris recognition by Daugman’s method. Int J Latest Technol Eng Manag Appl Sci. 2015;4(6):90–3.
- Liyakat KK. Significance of projection and rotation of image in color matching for high-quality panoramic images used for aquatic study. Int J Aquat Sci. 2018;9(2):130–45.
- Kazi K. Reverse engineering’s neural network approach to human brain. J Commun Eng Syst. 2022;12(2):17–24.
- Panwar V. A review on iris recognition system using machine and deep learning. In: Proc Int Conf Comput Commun Intell Syst (ICCCIS); 2022 Nov 4; India. IEEE; 2022. p. 857–66.
- Shahdi SO, Abu-Bakar SA. Neural network-based approach for face recognition across varying pose. Int J Pattern Recognit Artif Intell. 2015;29(8):1556015.
- Abd-Elhamied MR, Hashima WA, ElKateb S, Elhawary I, El-Geiheini A. Prediction of cotton yarn’s characteristics by image processing and ANN. Alexandria Eng J. 2022;61(4):3335–40.
- Maniruzzaman M, Rahman MJ, Ahammed B, Abedin MM. Classification and prediction of diabetes disease using machine learning paradigm. Health Inf Sci Syst. 2020;8(1):7.
- Mulani DA. Effect of rotation and projection on real time hand gesture recognition system for human computer interaction. J Gujrat Res Soc. 2019;21(16):3710–8.
- Sreenivasulu MD, Devi JS, Arulprakash P, Venkataramana S, Kazi KS. Implementation of latest machine learning approaches for students grade prediction. Int J Early Child. 2022;14(3):3027–57.
- Pereira F, Carvalho V, Soares F, Vasconcelos R, Machado J. Computer vision techniques for detecting yarn defects. In: Applications of Computer Vision in Fashion and Textiles. Woodhead Publishing; 2018. p. 123–45.
- Hemamalini V, Rajarajeswari S, Nachiyappan S, Sambath M, Devi T, Singh BK, et al. Food quality inspection and grading using efficient image segmentation and machine learning-based system. J Food Qual. 2022;2022:5262294.
- Meng Y, Wang R, Wang J, Yang J, Gui G. IRIS: Smart phone aided intelligent reimbursement system using deep learning. IEEE Access. 2019;7:165635–45.