Object Recognition and Tracking are one of the key research areas in image processing and computer vision. This paper presents a novel technique which efficiently recognizes an object based on full boundary detection using affine scale invariant feature transform method (ASIFT). ASIFT is an improvement to SIFT algorithm as it provides invariance up to six parameters longitude and latitude wise. The six parameters are based on translation (2 parameters), rotation, camera axis orientation (2 parameters) and zoom. Key points commonly referred to as feature points are then obtained using the mentioned parameters which will recognize the object efficiently. Furthermore a region merging technique is used for object recognition and detection in the remote scene environment using ASIFT technique. A short pictorial comparison between SIFT and ASIFT will also be presented based on feature points calculation. After the recognition using ASIFT is performed, an algorithm will be presented for tracking of the recognized object using modified particle filter. The particle filter will use a proximal gradient (PG) approach for tracking of the recognized object in subsequent images. In case an object drastically varies its position w.r.t any of the six parameters mentioned above, ASIFT will again be called for object recognition.
Osteoporosis is an age-based disease causing skeletal disorder. It is described by the little bone mass and weakening of the bone structure thereby resulting in the higher fracture risks. Early identification can help prevent the disease and successfully predict the fracture risks. Automated diagnosis of osteoporosis using X-ray image is a very challenging task because the radiographs from the healthy subjects and osteoporotic cases show a high grade of resemblance. This study presents an evaluation of osteoporosis identification using texture descriptor Local Binary Pattern (LBP) and Shift Local Binary Pattern (SLBP). In contrast with the conventional LBP, with the shifted LBP specific number of binary local codes are generated for each pixel place. The distinguishing ability of the texture descriptors is evaluated using ten-fold cross validation and leave-one out scheme using different machine learning techniques. The results prove the SLBP outperforms the traditional LBP for bone texture characterization.
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