This paper discusses the steganographic data hiding using the wavelet approach and the optimisation technique. The Discrete Wavelet Transform Using Haar Wavelet gives the excellent peak signal to noise ratio (PSNR) and less computation time. The particle swarm optimisation algorithm (PSO) is also used to hide the data so that the PSNR is improved. The results reveal the DWT using Haar wavelet and PSO algorithm gives excellent PSNR. General TermsParticle Swarm Optimisation is an evolutionary algorithm like the genetic algorithm.
Abstract-In this paper we are analy zing the steganographic data hiding using the least significant bit technique. This paper describes the particle swarm optimisation. The particle swarm optimisation algorith m is applied to the spatial domain technique. The improved algorith m called the accelerated particle swarm optimisation converges faster than the usual particle swarm optimisation and imp roves the performance. This paper also analyses the modified part icle swarm optimisation on the spatial domain technique which improved the PSNR and also reduced the computation time.
Navigation in indoor environments is highly challenging for visually impaired person, particularly in spaces visited for the first time. Various solutions have been proposed to deal with this challenge. In this project consider as the real time object Recognition and classification using deep learning algorithms. Object detection mainly deals with identification of real time objects such as people, animals, and objects. Object detection algorithm uses a wide range of image processing applications for extracting the object's desired portion. This enables one to identify the objects and calculate the accuracy of the object and deliver through voice. Using this information, the system determines the user's trajectory and can locate possible obstacles in that route.
The safety concern in means of transport has been considerably increased in last few decades. Distinct Sensory systems have been applied inside and outside vehicles in order to save lives. In this regard, imaging and vision system are used for capturing the static position of the passengers inside the vehicle during collision. There are many approaches to capture an image concatenation from a camera and to analyze them. The image of the passengers is captured during rear end vehicle collision by an inside car camera which is fixed on the left top of front windshield and an event parsing algorithm identifies the collision that has occurred. The decomposing of the collision activity is classified into three activity and uses the Or-And Graph (OAG) to compose the compositions of the temporal relationship among the collision detection. An online parsing (OP) algorithm for OAG formed from Earle's parser is employed to parse the image and identify the passenger's condition. This technique could be used as an enhancement for the safety of the passengers and to provide immediate assistance during vehicle collision.
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