In a remote scene environment consisting of multiple objects and miscellaneous scenarios, detecting an object of interest is a troublesome task especially while tracking the object over successive frames. Numerous methods have been proposed over the years for efficient detection of object of interest in a remote scene environment while in he meanwhile discarding all those which aren’t of interest and thus considered as noise. It is still one of the most actively researched areas in the field of image processing and computer vision. In this paper, a method is proposed which will not only detect a fixed shape object in a remote scene environment but it will also track it over successive frames. However, an additional methodology is also proposed which will detect the object in case of change of viewing angles e.g. scenario’s like rotation of object, zooming etc. First, Scale Invariant Feature Transform (SIFT) will be presented which will provide invariance up to four different parameters i.e. rotation, translation and zoom. In the second phase, ASIFT will be used which will provide invariance up to six different parameters i.e. translation, rotation, zoom and camera axis orientations. After both algorithms are presented, a detailed comparison between both is presented. Detection of object is performed with the help of both SIFT and ASIFT and then comparison is made based on feature points. Finally, Tracking is performed based on Proximal Gradient Particle filters which will further strengthen the comparison between SIFT and ASIFT once the object that needs to be tracked changes its course of motion or zoom. Experimental results will show which one of the two filters is more efficient.
Osteoporosis is an age-based disease causing skeletal disorder. It is described by the decreased 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 patients and osteoporotic cases show a great resemblance. The texture representation is done using two type of methods: appearance based methods and feature based methods. This study explains two systems, one based on PCA and one based on LDA. The system contains two stages, first one is PCA or LDA based feature computation and the second is the classification stage. The classification has been done using classifiers i.e. kNN, NB and SVM. The discriminating power of the texture descriptors is assessed using ten-folded cross-validation scheme using different machine learning techniques. The scope of the study is to support therapists in osteoporosis prediction, avoiding unnecessary further testing with bone.
Object recognition and semantic segmentation have been the two most common problems of traditional scene understanding in the computer vision domain. Major breakthroughs were reported in the last few years because of the increased utilization of deep learning, which offer a convincing alternative by learning the problem specific features on their own. In this paper, a summary of the frequently used framework – convolutional neural networks (CNN) is discussed. Accordingly a categorization scheme has been proposed to analyze the deep networks developed for image segmentation. Under this scheme, thirteen methods from the literature have been reviewed which are classified on the basis on how they perform segmentation operation i.e. semantic segmentation, instance segmentation and hybrid approaches. These method were reviewed from different aspects like their category, the novelty in the architecture of the method, and their special features in contrast with the traditional approaches. Latest review and analysis of these segmentation approaches, which provided outstanding results for image segmentation compared to the ordinary system, reveals that deep learning is increasingly becoming an important part of image segmentation and improvement in deep learning algorithms, which could resolve computer vision problems.
A fully invariant system helps in resolving difficulties in object detection when camera or object orientation and position
are unknown. In this paper, the proposed correlation filter based mechanism provides the capability to suppress noise,
clutter and occlusion. Minimum Average Correlation Energy (MACE) filter yields sharp correlation peaks while
considering the controlled correlation peak value. Difference of Gaussian (DOG) Wavelet has been added at the
preprocessing stage in proposed filter design that facilitates target detection in orientation variant cluttered environment.
Logarithmic transformation is combined with a DOG composite minimum average correlation energy filter (WMACE),
capable of producing sharp correlation peaks despite any kind of geometric distortion of target object. The proposed
filter has shown improved performance over some of the other variant correlation filters which are discussed in the result
section.
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