Robust Lossless Semi Fragile Information Protection in Images

Internet security finds it difficult to keep the information secure and to maintain the integrity of the data. Sending messages over the internet secretly is one of the major tasks as it is widely used for passing the message. In order to achieve security there must be some mechanism to protect the data against unauthorized access. A lossless data hiding scheme is proposed in this paper which has a higher embedding capacity than other schemes. Unlike other schemes that are used for embedding fixed amount of data, the proposed data hiding method is block based approach and it uses a variable data embedding in different blocks which reduces the chances of distortion and increases the hiding capacity of the image. When the data is recovered the original image can be restored without any distortion. Our experimental results indicate that the proposed solution can significantly support the data hiding problem. We achieved good Peak signal-to-noise ratio (PSNR) while hiding large amount of data into smoother regions

Analysis of Gait Pattern to Recognize the Human Activities

Human activity recognition based on the computer vision is the process of labelling image sequences with action labels. Accurate systems for this problem are applied in areas such as visual surveillance, human computer interaction and video retrieval. The challenges are due to variations in motion, recording settings and gait differences. Here we propose an approach to recognize the human activities through gait. Activity recognition through Gait is the process of identifying an activity by the manner in which they walk. The identification of human activities in a video, such as a person is walking, running, jumping, jogging etc are important activities in video surveillance. We contribute the use of Model based approach for activity recognition with the help of movement of legs only. Experimental results suggest that our method are able to recognize the human activities with a good accuracy rate and robust to shadows present in the video

Robust Lossless Semi Fragile Information Protection in Images

Internet security finds it difficult to keep the information secure and to maintain the integrity of the data. Sending messages over the internet secretly is one of the major tasks as it is widely used for passing the message.Comment: This paper has been withdrawn by the author due to a crucial error in diffusion proces

ABA-induced SA accumulation causes higher susceptibility in<i>B. juncea</i>as compared to tolerant genotypes against<i>A. brassicae</i>

AbstractAlternaria brassicae,a necrotrophic pathogen causes Alternaria blight in members of the Brassicaceae family. An extensive yield-oriented breeding strategy has rendered Indian mustard (Brassica juncea) susceptible to many fungal pathogens however, Alternaria blight is one of the most pressing challenges of all because it causes almost 40-60% yield loss. Variable degree of tolerance is reported in wild relatives ofB. juncea, Sinapis alba(White mustard) andcamelina sativa(False flax) have been reported to exhibit moderate and high tolerance respectively againstA. brassicae. Phytohormones are the essential regulator of the intricate mechanism of plant defence response. The hormones salicylic acid (SA) and jasmonic acid (JA) have been widely studied and recognized as important regulators of plant immune response. In the last decade, research has pointed out that other hormones like abscisic acid (ABA) also participate equally in plant defence. However, the role of ABA in defence responses and its cross-talk with SA and JA has not been fully understood in terms ofBrassica-A. brassicaesystem. In this investigation, three genotypes-B. juncea,S. albaandC. sativawere selected and their response to exogenous application of SA, JA and ABA and their combination withA. brassicaewere studied. Disease assessment, gene expression analysis and quantitative estimation of phytohormones showed that theB. junceaexhibited a weak JA-mediated defence response againstA. brassicaeand synergy between SA-ABA shifted the signalling mechanism to SA-mediated response leading to susceptibility inB. juncea. Tolerant genotypes,S. albaandC. sativaexhibited a robust JA-mediated response againstA. brassicaeand JA-ABA was found antagonistic inBrassica-A. brassicaephyto-pathosystem.</jats:p

Infected Fruit Part Detection using K-Means Clustering Segmentation Technique

Nowadays, overseas commerce has increased drastically in many countries. Plenty fruits are imported from the other nations such as oranges, apples etc. Manual identification of defected fruit is very time consuming. This work presents a novel defect segmentation of fruits based on color features with K-means clustering unsupervised algorithm. We used color images of fruits for defect segmentation. Defect segmentation is carried out into two stages. At first, the pixels are clustered based on their color and spatial features, where the clustering process is accomplished. Then the clustered blocks are merged to a specific number of regions. Using this two step procedure, it is possible to increase the computational efficiency avoiding feature extraction for every pixel in the image of fruits. Although the color is not commonly used for defect segmentation, it produces a high discriminative power for different regions of image. This approach thus provides a feasible robust solution for defect segmentation of fruits. We have taken apple as a case study and evaluated the proposed approach using defected apples. The experimental results clarify the effectiveness of proposed approach to improve the defect segmentation quality in aspects of precision and computational time. The simulation results reveal that the proposed approach is promising