Image Fusion is one of the most important methods used in image processing, especially in networks with limited resources such as networks of wireless sensors supporting multimedia. It is classified into technologies operating in spatial domain and o
thers in the frequency domain. In this research, Fusion techniques in frequency domain are manipulated to benefit from its advantages. Discrete Cosine Tansform is used because it fits the characteristics of this type of networking where it is simple, easy to implement and requires low memory.
Three methods based on this transformation, DCTav, DCTma and DCTah, have been investigated and applied to three different sets of images. The evaluation of simulation results, with different parameters, showed that the DCTma was the most appropriate method to integrate the imagery taken from sensory nodes supporting multimedia.
Background subtraction (BS) is the first step of various computer vision application specially those depending on motion tracking such as (car tacking, human recognition…etc.).Indeed, videos captured outdoors may contain a lot of undesirable changes
‘wind impact, illumination changes, weather conditions and others ’, generate numerous false positives.
This paper presents comparison between the simplest method for background extraction (background subtraction) and Gaussian Mixture Model which is common method in outdoors videos. These two method are then compared based on the ability of each one to detect moving object in outdoors videos especially with presence and absence of shadow in addition to other challenges like object movement in background, wind effect and camera instability. The results of this comparison is used to determine the suitable method for each state.
The study aimed at exploring the extent of using of management accounting
practices in Public Agricultural Firms in Syria, and the importance of this use. In addition,
it aimed at knowing which of these practices is the most common and important, a
nd the
difficulties of this using.
The study was based on a questionnaire, which was distributed on public
Agricultural Firms in Syria. There were (42) questionnaires that were distributed on this
Society, but (26) questionnaires were completed and retrieved.
In spite of the attention paid by the members of the samples to these practices, the
results showed great weakness and low level in the application of management accounting
practices.
As to the management accounting practices which weren't used, the study showed
that the lack of usage is due to the lack of knowledge and experience of the managerial and
accounting staff and the absence of the support. This is the case in spite of the presence of
competition and the large size of these firms that is a basic factor for the application of
these practices.
In this paper an evaluation of image keypoints detectors and descriptors is presented
when used for building panoramic image. The descriptors: (SIFT, SURF, BRIEF, ORB,
BRISK, and FREAK) were discussed, when used with the appropriate keypoints detec
tors
on database taken indoors by RGB-D camera. Crosscheck and RANSAC (RANdom
Sample Consensus) algorithms were used to find transform matrix between images. The
speed of keypoints detectors and descriptors, the matching speed, the average of extracted
keypoints, recall and precision were investigated. Oxford dataset was used to find the best
descriptor for dealing with rotation and illumination changes that might occur due to
changes in illumination angle.
The obtained results showed that SIFT was the keypoint descriptor with the highest
performance in non-real time applications. The SURF/BRISK was the best
detector/descriptor which can be used in real time applications with comparable SIFT's
results.
The extraction and analysis of human gait characteristics using image sequences is currently an intense area of research. Recently, the focus of this research area has turned to the realm of computer vision as a way of performing quickly and ac
curately gait analysis system. Such a system could be used as a preprocessing step in a more sophisticated gait analysis system or could be used for rehabilitation purposes.In this thesis, a new method is proposed which utilizes a novel fusion of spatial computer vision operations as well as motion in order to accurately and efficiently determine the center of mass of a walking person at a video scene. Then we make a comparison between our method’s results and the inverted pendulum model of the movement's center of mass of a walking person in the XY plane. The results showed a significant correspondence between the model and the results we have obtained, which opens the way for further research to discover defects walking or develop algorithms for humanoid robots.
In this paper, one hundred chest Computed Tomography images of COVID-19 patients were used to build and test Naïve Gaussian Bayes classifier for discriminating normal from abnormal tissues. Infected areas in these images were manually segmented by an
expert radiologist. Pixel grey value, local entropy and Histograms of Oriented Gradients HOG were extracted as features for tissue image classification. Based on five-folds classification experiments, the accuracy score of the classifier in this fold reached around 79.94%. Classification was more precise (85%) in recognizing normal tissue than abnormal tissue (63%). The effectiveness in identifying positive labels was also more evident with normal tissue than the abnormal one.
