Dual-energy imaging can enhance lesion conspicuity. However, the conventional (fast kilovoltage switching)
dual-shot dual-energy imaging is vulnerable to patient motion. The single-shot method requires a special design
of detector system. Alternatively, single-shot bone-suppressed imaging is possible using post-image processing
combined with a filter obtained from training an artificial neural network. In this study, the authors investigate
the general properties of artificial neural network filters for bone-suppressed digital radiography. The filter
properties are characterized in terms of various parameters such as the size of input vector, the number of hidden
units, the learning rate, and so on. The preliminary result shows that the bone-suppressed image obtained from
the filter, which is designed with 5,000 teaching images from a single radiograph, results in about 95% similarity
with a commercial bone-enhanced image.
We present a fire alarm system based on image processing that detects fire accidents in various environments. To reduce false alarms that frequently appeared in earlier systems, we combined image features including color, motion, and blinking information. We specifically define the color conditions of fires in hue, saturation and value, and RGB color space. Fire features are represented as intensity variation, color mean and variance, motion, and image differences. Moreover, blinking fire features are modeled by using crossing patches. We propose an algorithm that classifies patches into fire or nonfire areas by using random forest supervised learning. We design an embedded surveillance device made with acrylonitrile butadiene styrene housing for stable fire detection in outdoor environments. The experimental results show that our algorithm works robustly in complex environments and is able to detect fires in real time.
We present a simple ellipse detector that accurately extracts the parameters of an ellipse based on randomized Hough transform (RHT). Ellipse detection by the conventional RHT method is challenging due to the huge calculation burden and voting complexity for the five parameters of one ellipse. To address this, we extracted formulas that separated these five parameters into two to three parameters and proposed a separated two-level voting scheme based on the RHT. The original image was first processed by edge detection, eight-zone distribution of its direction, and edge lists merging, and then the parameters were calculated and voted by the separated two-level voting scheme. Finally, an evaluation method was used to determine whether or not the detected ellipse existed in the image. We tested our method on various kinds of real images, and the experiments demonstrated that the proposed method provided a precise and efficient ellipse detection.
Plane detection in 3-D space is a core function of the autonomous mobile robot. A representative technique for plane detection is the Hough transform method. The Hough transform is robust to noise and makes accurate plane detection possible. However, a common problem in methods based on the Hough transform is that too much time is required to calculate parameters, which adds computational cost and memory requirements for parameter voting to find the distribution of mixed multiple planes in the parameter space. Furthermore, real-time processing for sequential image sequences is challenging, because the whole process must be repetitively performed for the next detection. We extend the conventional self-organizing map by introducing a real-time clustering method and by detecting multiple planes through the creation, extinction, renewal, and merging of plane parameter data, which are input sequentially. The proposed method is also based on reliable plane detection through a planarity evaluation during data sampling. The results of experiments conducted under various conditions with an unmanned vehicle demonstrate that the proposed method is more accurate and faster than conventional methods.
This paper proposes an ellipse detection algorithm based on the analytical solution to the parameters of ellipse in images.
In the first instance, edge detection is processed, from which line segments are extracted. Then the method of finding the
center coordinates of the ellipse is described based on the property of ellipse by using three points voting at a sense of
randomized Hough Transformation (RHT). Finally, an analytical solution of the other three parameters of the ellipse
(semi-major axis length, semi-minor axis length and the angle between the X-axis and the major axis of the ellipse) are
given via coordinate transformation. Based on this solution, we propose the separated parameter voting scheme for
ellipse center and the other three parameters instead of 5 parameters voting scheme of RHT. The experiments show that
the proposed algorithm performs well in various images.
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