Обнаружение полутоновых объектов на изображении на основе вейвлет-преобразования

Разработан метод поиска объектов на изображении на основе прямого вейвлет-преобразования. Новизна заключается в том, что в разработанном методе используется многоуровневая обработка объекта и изображения в сочетании с анализом моментов строк и столбцов матриц вейвлет- коэффициентов. Приведены результаты экспериментальных исследований по оценке временных затрат при поиске полутоновых объектов на изображении

Обнаружение полутоновых объектов на изображении на основе вейвлет-преобразования

Разработан метод поиска объектов на изображении на основе прямого вейвлет-преобразования. Новизна заключается в том, что в разработанном методе используется многоуровневая обработка объекта и изображения в сочетании с анализом моментов строк и столбцов матриц вейвлет- коэффициентов. Приведены результаты экспериментальных исследований по оценке временных затрат при поиске полутоновых объектов на изображении

Detection of Counterfeit Coins and Assessment of Coin Qualities.

Due to the proliferation of fake money these days, detection of counterfeit coins with high accuracy is in strong demand, yet not much research has been conducted in this field. The objective of this thesis is to introduce modern computer vision techniques and machine intelligence to differentiate real coins and fake ones with high precision, based on visual aspects. To that end, a high-resolution scanning device – IBIX Trax is deployed to sample the coin images. On top of that, three visual aspects are thoroughly inspected, namely lettering, images and texture. Six features are extracted from letterings, i.e. stroke width, contour smoothness, lettering height, lettering width, relative angle, and relative distance. As for classification, a hierarchical clustering – max spacing K-clustering—is adopted. Our experimental results show that the fake coins and real ones are totally separable based on these features. As for images, we propose a novel shape feature— angle-distance. After images are segmented, a vector of size 360*1 is deployed to represent each shape. For classification, a dissimilarity measurement is used to quantize the difference between two shapes. The results show it can recognize the fake coins successfully. As for texture, a cutting-edge feature maximum stable extremal region is adopted to automatically detect the holes and indents on the coin surface. Parameters associated with this feature are adjusted in the experiments. The detection results show this feature can be used as an indicator for assessing the qualities of coins

Applications of pattern classification to time-domain signals

Many different kinds of physics are used in sensors that produce time-domain signals, such as ultrasonics, acoustics, seismology, and electromagnetics. The waveforms generated by these sensors are used to measure events or detect flaws in applications ranging from industrial to medical and defense-related domains. Interpreting the signals is challenging because of the complicated physics of the interaction of the fields with the materials and structures under study. often the method of interpreting the signal varies by the application, but automatic detection of events in signals is always useful in order to attain results quickly with less human error. One method of automatic interpretation of data is pattern classification, which is a statistical method that assigns predicted labels to raw data associated with known categories. In this work, we use pattern classification techniques to aid automatic detection of events in signals using features extracted by a particular application of the wavelet transform, the Dynamic Wavelet Fingerprint (DWFP), as well as features selected through physical interpretation of the individual applications. The wavelet feature extraction method is general for any time-domain signal, and the classification results can be improved by features drawn for the particular domain. The success of this technique is demonstrated through four applications: the development of an ultrasonographic periodontal probe, the identification of flaw type in Lamb wave tomographic scans of an aluminum pipe, prediction of roof falls in a limestone mine, and automatic identification of individual Radio Frequency Identification (RFID) tags regardless of its programmed code. The method has been shown to achieve high accuracy, sometimes as high as 98%