Very large‐scale integration architecture for video stabilisation and implementation on a field programmable gate array‐based autonomous vehicle

Nou-Shene, Tahiyah; Pudi, Vikramkumar; Sridharan, K.; Thomas, Vineetha; Arthi, J.

doi:10.1049/iet-cvi.2014.0120

Cited by 7 publications

(2 citation statements)

References 36 publications

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“…The direction and the magnitude of the gradient are encoded as vector information. The vector length determines magnitude while directional flow determines gradient direction [19]. Partial derivative of x & y are combined to form gradient vector as in Eq.…”

Section: Image Gradientsmentioning

confidence: 99%

An Automatic Region Based Optimal Segmentation and Detection of Features on Dermoscopy Images Using V-Shaped Waterfall and Water Ridges

Gopal,

Alagarsamy,

Kalivaradhan

et al. 2023

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Among all cancer, skin cancer is the devastating cancerous growth. This disease initially starts from the epidermis of human body. To obtain accurate evaluation of skin cancer, the computerized analysis on the image, produces efficient influence. All around the world, skin cancer affects many people in different parts of the body. To make a perfect diagnosis of skin cancer, the dermatologist should examine the pigment on the skin image using computational method. This could be a pre-screening system for the dermatologist for an early diagnosis. The proposed work reports about the segmentation of lesion from the dermoscopy images with the fundamentals steps such as pre-processing, segmentation and post processing. In this work, a set of patterns are extracted from uneven borders using watershed segmentation. The levelset and active contour detection makes a perfect curve as boundary to segment affected region. The proposed work initiates with pre-processing followed by segmentation and ends with post processing and this is explained perfectly. The proposed simulation measures the accurate diagnosis of Ground Truth image and Segmented Image and confirms the best-offered values of accuracy up to 94.79% for PH2dataset and 90.658% for DermQuest Dataset.

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Section: Image Gradientsmentioning

confidence: 99%

An Automatic Region Based Optimal Segmentation and Detection of Features on Dermoscopy Images Using V-Shaped Waterfall and Water Ridges

Gopal,

Alagarsamy,

Kalivaradhan

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Perform tests with different parallel computing or hardware acceleration technologies. The synchronization required by HSA could be provided by another architecture such as the FPGA [65], which would allow designing a parallel computing architecture with custom capabilities.…”

mentioning

confidence: 99%

Parallelization of the Honeybee Search Algorithm for Object Tracking

Perez-Cham

Puente

Soubervielle‐Montalvo

et al. 2020

Applied Sciences

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Object tracking refers to the relocation of specific objects in consecutive frames of a video sequence. Presently, this visual task is still considered an open research issue, and the computer science community attempted solutions from the standpoint of methodologies, algorithms, criteria, benchmarks, and so on. This article introduces a GPU-parallelized swarm algorithm, called the Honeybee Search Algorithm (HSA), which is a hybrid algorithm combining swarm intelligence and evolutionary algorithm principles, and was previously designed for three-dimensional reconstruction. This heuristic inspired by the search for food of honeybees, and here adapted to the problem of object tracking using GPU parallel computing, is extended from the original proposal of HSA towards video processing. In this work, the normalized cross-correlation (NCC) criteria is used as the fitness function. Experiments using 314 video sequences of the ALOV benchmark provides evidence about the quality regarding tracking accuracy and processing time. Also, according to these experiments, the proposed methodology is robust to high levels of Gaussian noise added to the image frames, and this confirms that the accuracy of the original NCC is preserved with the advantage of acceleration, offering the possibility of accelerating latest trackers using this methodology.

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Anatomical region segmentation method from dermoscopic images of pigmented skin lesions

Chakkaravarthy

Chandrasekar²

2020

Int J Imaging Syst Tech

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Melanoma tumor can cause a serious life threatening problem in humans, if left untreated for a long time without early diagnosis. For early diagnosis of melanoma, it is more significant to develop novel methods based on biophysics analyses, molecular targets recognitions, and new image analysis criteria. In this article, anatomical region segmentation and diameter identification is proposed to detect melanoma from dermoscopic images. Four main steps of the proposed system are as follows: In the first step, the preprocessing is performed to smooth the melanoma extraction process. The region segmentation is done in the second step using watershed segmentation and Sobel operator. In the third step, the postprocessing procedures like as morphological open, canny edge detection also applied to improve the region of interest. Finally, the melanoma region is identified using color symmetry features. The proposed method is tested with two data sets to prove the performance proposed method. The proposed method achieved an accuracy of 95.31% and specificity of 98.3%, which is better than other methods. Experimental results show that the effectiveness of the proposed method and illustrate viability of real‐time clinical applications.

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Very large‐scale integration architecture for video stabilisation and implementation on a field programmable gate array‐based autonomous vehicle

Cited by 7 publications

References 36 publications

An Automatic Region Based Optimal Segmentation and Detection of Features on Dermoscopy Images Using V-Shaped Waterfall and Water Ridges

An Automatic Region Based Optimal Segmentation and Detection of Features on Dermoscopy Images Using V-Shaped Waterfall and Water Ridges

Parallelization of the Honeybee Search Algorithm for Object Tracking

Anatomical region segmentation method from dermoscopic images of pigmented skin lesions

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