A method for detecting an object's motion in images that suffer from camera shake or images with camera egomotion
is proposed. This approach is based on edge orientation codes and on the entropy calculated from a histogram of the edge
orientation codes. Here, entropy is extended to spatio-temporal entropy. We consider that the spatio-temporal entropy
calculated from time-series orientation codes can represent motion complexity, e.g., the motion of a pedestrian. Our
method can reject false positives caused by camera shake or background motion. Before the motion filtering, object
candidates are detected by a frame-subtraction-based method. After the filtering, over-detected candidates are evaluated
using the spatio-temporal entropy, and false positives are then rejected by a threshold. This method could reject 79 to 96
[%] of all false positives in road roller and escalator scenes. The motion filtering decreased the detection rate somewhat
because of motion coherency or small apparent motion of a target. In such cases, we need to introduce a tracking method
such as Particle Filter or Mean Shift Tracker. The running speed of our method is 32 to 46 ms per frame with a 160×120
pixel image on an Intel Pentium 4 CPU at 2.8 GHz. We think that this is fast enough for real-time detection. In addition,
our method can be used as pre-processing for classifiers based on support vector machines or Boosting.
A method of real-time object detection for video surveillance systems has been developed. The method aims to realize robust object detection by using Radial Reach Correlation (RRC). We also apply a statistical background estimation to cope with dynamic and complex environments. The computational cost of RRC is higher than the simple subtraction method and the background estimation method based on statistical approach needs large memory. It is necessary to reduce the calculation cost in order to apply to an embedded image processing device. Our method is composed of two techniques: fast RRC algorithm and background estimation based on statistical approach with cumulative averaging process. As a result, without deterioration in detection accuracy, the processing time of object detection can be decreased to about 1/4 in comparison with normal RRC.
We develop a rapid object-candidates detector using Increment Sign Correlation (ISC). Our method aims to detect
candidates of objects such as people or vehicles in real time using ISC and a simple shape model. Our method is similar
to Generalized Hough Transform (GHT). However we modify its voting process. We use ISC for detecting object
candidates instead of the shape voting done by GHT. ISC is robust against shading and low image contrast due to
lighting changes because Increment Sign (IS) is insensitive to a perturbation of direction of intensity gradient. The
computational cost of IS is lower than that of the gradient also. From the results of our experiment, our detector can run
with a 320×240 pixel image within 32 milliseconds on a Pentium 4 processor at 2.8 GHz. Given the initial template size
of 10×20 pixels, the number of candidates decreases from 170,196 sub-windows in a 320×240 pixel image to 400 at
most with the miss rate of 0.2 %. The detection rate is enough for more precise detectors which need to use richer image
features. The experimental results using real image sequences are reported.
Proc. SPIE. 5375, Metrology, Inspection, and Process Control for Microlithography XVIII
KEYWORDS: Semiconductors, Edge detection, 3D image reconstruction, Error analysis, 3D modeling, Scanning electron microscopy, 3D metrology, Reconstruction algorithms, Analytical research, 3D image processing
This study presents a new and unique method to reconstruct 3D profile from tilt images of SEM for semiconductor device pattern called 'Inverse Stereo Matching'. This method is based on 'the shape from shading' and it’s more stable than the conventional stereo matching method in case of low S/N in sidewall of tilt images, and it is able to reconstruct gradual change of sidewall shape that is difficult for the conventional stereo matching to reconstruct in detail. Additionally, this study presents a new method using 'MPPC Indices' to compensate errors of local shape in reconstruction 3D profile caused by the particular characteristic of secondary electron.