Informatics Publishing Limited

Harjeet Kaur ¹, Silky Narang ¹

Affiliations
1 Department of Computer Science and Engineering Department, Chandigarh University,National Highway 95, Mohali – 140413, Punjab, IN

Abstract

Objectives: Visual object tracking allude the process for tracking the path of the object in the video sequence. Tracking of the object under the foggy environment is the challenging task in the video sequence. There is a great need to implement spectral analysis for the visual attention tracking model. The proposed methodology deals with object tracking using Dubbed Density of Fog Assessment based Defogger (DEFADE) filter to resist environment based detail restoration. Methods/Statistical analysis: DEFADE guided filter is used to improve the visual detail of the frame. Then frames are moved to learning network where particle region is detected and then is registered using Binary Pixel Distance Mapping (BPDM). Further Calculation of color values for the given object or selected object in the entire three channels RGB (Red, Green, Blue). The spectral analysis of visual attention tracking model is compared with the proposed approach using BPDM_CMF model. In this system an existing filter for binary conversion and color filtering was used to evaluate and estimate the trajectory of the tracked objects in foggy weather affected videos. In case of visual tracking and object detection, multi-scale detection methods constitute to combine the effort of object detection in spatial scale using BPDM with Color Mean Filter (CMF) and a defogging based spurious response inherent method for multiscale removal of fog in tracking videos and improve the fine edge tracking with reduce error probability detection between frames in a temporal tracking method. Findings: The execution of the proposed framework has been contrasted and the before technique. The proposed method works more efficiently under the foggy environment for tracking the objects in the video sequence. Application/Improvements: Automatic Recognition of the object in the video sequence. Defogging has been efficiently used in identifying the humans on the basis of trajectory

Keywords

Color Mean Filter (CMF), DEFADE, Hue Saturation Value (HSV), Kalman Filter, Root Mean Square Error (RMSE), Tracking.

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Silky Narang , Madan Lal , Navdeep Kanwal

Abstract

Segmentation or the delineation of object boundaries in medical images remains a necessary step to obtain qualitative and quantitative measurements. Among these images, Ultrasound images plays a crucial role because the acquisition of these images is non invasive, cheap and does not require ionizing radiations compared to other medical imaging techniques. Due to acoustic interferences and artifacts, the automatic segmentation of anatomical structures in ultrasound imagery becomes a real challenge. Thus, to enhance the capabilities of ultrasound as a qualitative tool in clinical medicine, here we discuss the ultrasound image segmentation methods, focusing on techniques developed for medical. First, we discuss the formation of ultrasound images and conventional methods of image segmentation. After that we present the formulated methods for ultrasound image segmentation concerning the three largest areas of ultrasound imaging. Next section explains the validation degree that has been done in different application areas of ultrasound. In last section we conclude by referencing some papers which have introduced original ideas that exhibited particular usefulness in clinical domain specific to the ultrasound segmentation problem.

Keywords

Segmentation, Ultrasound, Speckle Noise, Artifacts, Ionizing radiations

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