On earth, plants play the most important part. Every organ of a plant plays a vital role in the ecological field as well as the medicinal field. But on the whole earth there are several species of plants are available. The different species of plants have different diseases. Therefore, it is required to identify the plants as well as their diseases correctly. It is difficult and also time consuming to identify the plants and their diseases manually. In this research an automatic disease detection system of plant is proposed. High-quality leaf images are used for training and testing. For detecting the healthy area and diseased area in a leaf, region-based and color-based region thresholding techniques are used. For feature selection Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) method were applied. Finally, for classification two-class and multi-class Support Vector Machine (SVM) were used. It is found that both feature selection processes with SVM give 99% accuracy. An user oriented graphical user interface is created for understanding the automated system.
Nowadays security became a major global issue. To manage the security issue and its risk, different kinds of biometric authentication are available. Face recognition is one of the most significant processes in this system. Since the face is the most important part of the body so the face recognition system is the most important in the biometric authentication. Sometimes a human face affected due to different kinds of skin problems, such as mole, scars, freckles, etc. Sometimes some parts of the face are missing due to some injuries. In this paper, the main aim is to detect a facial spots present in the face. The total work divided into three parts first, face and facial components are detected. The validation of checking facial parts is detected using the Convolution Neural Network (CNN). The second part is to find out the spot on the face based on Normalized Cross-Correlation and the third part is to check the kind of spot based on CNN. This process can detect a face under different lighting conditions very efficiently. In cosmetology, this work helps to detect the spots on the human face and its type which is very helpful in different surgical processes on the face.
Abstract:The motive behind the work is to provide an effective solution to the most sensitive issue called image forgery that is occurring due to increase in an availability of enormous image modification software. The image forgery causes drastic bad effects in the society such as copyright misuse, evident change in the court of law, quality control, medical image forgery, etc. There are numerous steps taken in order to detect forgery in images, but how far they are successful is the question here. In this paper, an advanced and efficient solution is provided for the forgery detection which can overcome the drawbacks of the existing works by accurately detecting the salient regions by considering both the local and global features of an image (when considering the whole image it is global and when considering only the specific part of an image it is local) and based on this a technique is proposed called hash sensitivity growth method (HSGM), which can accurately detect the salient regions of an image and extract feature contents from that region, hence provide efficient sensitivity growth to a hash, as the sensitivity of the hash is increased it can accurately detect even smaller area tampering and it is robust to normal image processing.
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