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Underwater object detection, which is crucial to the exploration and exploitation of marine resources, remains a challenge because noisy, weak contrast, and color distorted images are provided as sources of supervision. To address the issues of low detection accuracy caused by imprecise images, and inefficiency due to huge amount of parameters in m...
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... To classify signals into distinct categories based on their temporal dependency as one-dimensional functions, dependence on image properties as two-dimensional functions, or three-dimensional functions, mathematical models are commonly employed for signal representation. Specifically, vector functions find application in the representation of color images with three constituent colors, whereas scalar functions are employed for the depiction of monochromatic images [21]. ...
In the recent past, the global prevalence of autism spectrum disorder (ASD) has witnessed a remarkable surge, underscoring its significance as a widespread neurodevelopmental disorder affecting children, with an incidence rate of 0.62%. Individuals diagnosed with ASD often grapple with challenges in language acquisition and comprehending verbal communication, compounded by difficulties in nonverbal communication aspects such as gestures and eye contact. Eye movement analysis, a multifaceted field spanning industrial engineering to psychology, offers invaluable insights into human attention and behavior patterns. The present study proposes an economical eye movement analysis system that adroitly integrates Neuro Spectrum Net (NSN) techniques with Kalman filtering, enabling precise eye position estimation. The overarching objective is to enhance deep learning models for early autism detection by leveraging eye-tracking data, a critical consideration given the pivotal role of early intervention in mitigating the disorder’s impact. Through the synergistic incorporation of NSN and contrast-limited adaptive histogram equalization for feature extraction, the proposed model exhibits superior scalability and accuracy when compared to existing methodologies, thereby holding promising potential for clinical applications. A comprehensive series of experiments and rigorous evaluations underscore the system’s efficacy in eye movement classification and pupil position identification, outperforming traditional Recurrent Neural Network approaches. The dataset utilized in the aforementioned scholarly article is accessible through the Zenodo repository and can be retrieved via the following link: [https://zenodo.org/records/10935303?preview=1].
... While vector functions can be used to represent color images with three component colors, scalar functions can be used to represent monochrome images, (Patil et al. 2020). Applications for vision might make use of discrete, continuous, or digital functions. ...
Digital cameras have made it possible for people to capture many different images in their daily lives. Some pictures turn out well, while others don’t. Additionally, noise causes variations in image quality. It is possible that these noises are caused by low light levels or other challenges with intensity. The primary goal of this proposal is to discuss ways to enhance image attributes. Using image enhancement methods, the main goal is to reduce image noise and improve image quality. HE (Histogram Equalization) is used to improve the image quality, but the results are underwhelming. Image properties are enhanced using Contrast-Limited Adaptive Histogram Equalization (CLAHE). Bilinear interpolation and the CLAHE algorithm will be used to enhance the images in the initial stages of the proposal, with the main goal being noise reduction. Second, approaches for expanding the contrast of an image have been suggested as a strategy to enhance image quality. The findings demonstrate that the suggested methodology creates great noise-free images by lowering the amount of noise in the images and boosting visual contrast.
