Informatics Publishing Limited

Antariksha Kar ¹, Sidharth Bhatia ¹, Gaurav Gupta ¹

Affiliations
1 School of Engineering and Technology, The Northcap University, Gurugram − 122017, Haryana, IN

Abstract

Objective: In this paper, an effective and fast multi-threshold image segmentation method is proposed based on similarity filtering. Method: The image histogram peaks and the valley can be used to locate the clusters in the image. The idea of the proposed research is to fit the Gaussian distribution to the histogram of the image. Dominant peaks are selected from the input image histogram near to its Gaussian distribution. Then for each element of the peaks, peak’s valleys are obtained in the left (low) and right (high) side. Findings: Experiments on a variety of images from Berkeley Segmentation Dataset (BSD) show that the new algorithm effectively segments the image in a computationally efficient manner. Comparison/ Performance evaluation: On comparison, proposed approach is found to be better than other existing methods. Peak Signal to Noise Ratio (PSNR) and time are used to evaluate the performance. The proposed algorithm tries to fit Gaussian curves on the dominant peaks and thus find the valleys which are used as thresholds. Novelty: This is always a quicker process as there is a predefined model which only needs to be fit for the given data set.

Keywords

Gaussian Distribution, Image Segmentation, Multilevel Thresholding, Similarity Filtering.

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Nidhi Gupta , Sidharth Bhatia

Abstract

Driving is a cognitively demanding task. Any distraction or deviation can be potentially dangerous. It is required that any task that is not directly required for driving should not distract the driver.

Gestures have always been an integral part of interaction by humans. Gestures are closely connected to speech. It is rather common to make gestures while speaking. Further, gestures are essential in situations where other communication channels cannot be used. Driving assistance for the deaf, for example.

The system proposed here describes a gesture based method to make the human-computer interaction intuitive and natural. This will do away with the need to have a separate interface (like buttons) which needs learning or conditioning. 

Keywords

Gesture recognition, Computer Vision, Automotive human-computer interface

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