Image mining draws basic principles from concepts in databases, machine learning, statistics, pattern recognition and 'soft' computing. Image mining is focused on extracting patterns, implicit knowledge, image data relationship or patterns which are not explicitly found in the images from databases or collections of images. Some of the methods used to gather knowledge are: image retrieval, data mining, image processing and artificial intelligence. These methods allow image mining to have two different approaches. First, is to extract only from databases or collections of images, and second, dig or mine a combination of associated alphanumeric data and collections of images.
The main aim of the extraction process will provide a "feature description" of an image. Feature description extracted from training image. Feature set containing the unique points of the image part which means the larger part of the image has been made in the form of detectable format. This can easily detectable and identifying the objects, boundaries and edges. Another important characteristic is to detect the larger number of features from the description of image sets which will reduces the redundant errors caused through local image variations of all matching feature point in an image.
For better understanding and to create interest, cut-out models of each physical posture should be prepared and distributed to participants. Sufficient time must then be provided to touch and feel the models until students really understand each posture. Clear mental pictures and fundamental conceptions of each practice can be achieved by this technique. Tactile models for each yoga practice may be challenging to prepare. While physical postures can be taught using models, practices that are not static postures such as loosening exercises cannot be so taught.
We developed an algorithm for skull stripping before the segmentation process. The segmentation is performed using feed forward backpropogation algorithm. Keywords — Brain magnetic resonance (MR), image segmentation,Feed forward backpropogation I. INTRODUCTION Image segmentation is one of the most important tasks in medical image analysis and is often the first and the most critical step in many Image segmentation is the most important tasks in medical image analysis and is often the first and the most critical step in many clinical applications.Image segmentation is commonly used for measuring and visualizing the brain’s anatomical
Then the Generate Fingerprint Feature protocol in Pipeline Pilot was applied to extract fingerprints and convert them into corresponding molecular fragments. After generating bioisosteres by Enumerate Bioisosteres protocol, the fragments were used to generate a de novo molecular library through the Enumerate from Fragments protocol in DS. Filtering by the Lipinski’s rule of five and the Veber rules, the optimized molecular library was applied to virtual screening by the pharmacophore model. Virtual screening of small molecule libraries forms one aspect of a sophisticated approach to drug discovery. The final pharmacophore hypothese artificially generated was applied as a 3D structural query for retrieving potent molecules from the de novo library using the Screen Library protocol.
Our algorithm can be applied efficiently to those cases where major contributing feature of the subject is color and when there exists some amount of contrast between foreground and background. This algorithm follows an iterative approach for background removal thereby highlighting the subject. We have performed experiments and have used our image segmentation algorithm on the Oxford University 102 Flower Category Database and then further applied it to images from the Visipedia CUB 200-2011 Dataset. We got fine results in which the background of the image was completely removed and key foreground data was extracted. II.
This process is called feature extraction. The extracted features are expected to contain the relevant information from the input data, so that the desired task can be performed by using this reduced representation instead of the complete initial data. Figure.2.3.Detecting an object (left) in a cluttered scene (right) using combination feature detection, feature extraction and matching. See example for
The objective of this lab is to gain an understanding of the functions and characteristics of a Moore model 65 Square Root Extractor such as the principles of operation, calibration of the instrument, and its use in industry. Principles of Operation: For this lab I worked with a Moore model 65 Square Root Extractor. The extractor works by taking in the pneumatic signals of differential flow transmitters and providing a linear output so the signals can be added, subtracted, averaged, or when the signals need to be linear to provide a proper representation for a control loop. The Extractor uses the cosine function of a small angle to give square root conversion. The angle is created by a continuous balance between the motion of the input pressure
Instead of searching the entire image, the approach has reduced the search region by searching the nearby pixels since most of the applicable information lies in neighbouring pixels which reduces time to complete inpainting. This method is principally used for abolishing objects and cracks from the image. In the earlier period, this problem has been addressed by two module of algorithms: “texture synthesis” algorithms for generating large image regions from sample textures and “inpainting” techniques for filling in small image gaps. Texture synthesis focuses on the texture part of image while inpainting focuses on the structural part of image. Exemplar based image inpainting is a combination of both of the above techniques, hence this technique can reconstruct structural as well as texture part of an image In this inpainting approach user selects the target region which is to be restored then algorithm automatically starts filling in that region using the