Wistron Corporation

Zhe-Yu Lin

Zhao-Yuan Lin

Yao-Tsung Chang

Chun-Peng Hsu

Chih-Yi Chien

Yi-Sheng Chao

Kuan-I Chung

Hui Chi Hsieh

Ji-Yi Yang

Wen Hsin Hu

Yin Chi Lin

Yu-Jie Huang

Chi Lun Huang

Chun-peng Hsu

A wound-size measuring method for use in a portable electronic device is provided. The method includes the following steps: obtaining an input image via a camera device of the portable electronic device; using a CNN (convolutional neural network) model to recognize the input image, and selecting a part of the input image with the highest probability of containing a wound as an output wound image; and calculating an actual height and an actual width of the output wound image according to a lens-focal-length parameter reported by an operating system running on the portable electronic device, a plurality of reference calibration parameters corresponding to a pitch angle of the portable electronic device, and a pixel-height ratio and a pixel-width ratio of the output wound image.

Portable electronic device and wound-size measuring method using the same

An object counting system crops images into multiple split images. When a distance between at least one side of a bounding box being associated with a position of a first object in the first split image and at least one side of a bounding box being associated with a position of a second object in the second split image adjacent to the first split image is less than a tolerance value, the processor combines the adjacent first split image and the second split second split image into a merged image, so that the first object and the second object are merged into a merged object. A processor calculates a prediction number of objects based on the objects contained in these split images and the merged objects in the merged image. The object counting system automatically counts the number of bug eggs, to save manpower and provide an accurate count.

Object counting system and object counting method

A training data increment method, an electronic apparatus and a computer-readable medium are provided. The training data increment method is adapted for the electronic apparatus and includes the following steps. A training data set is obtained, wherein the training data set includes a first image and a second image. An incremental image is generated based on the first image and the second image. A deep learning model is trained based on the incremental image.

Training data increment method, electronic apparatus and computer-readable medium

A living body detection method and a living body detection system are provided. A radio-frequency signal reflected by an experiment living body is received, and raw sampling data of the RF signal are obtained. A feature extraction process is performed to generate initial training features of sampling datasets, wherein the initial training features respectively correspond to feature generation rules. A classification prediction model is established according to a posture of the experiment living body and the initial training features, and correlation feature weightings respectively corresponding to the initial training features are obtained. Preferred features corresponding to at least one of the feature generation rules are selected from the initial training features according to the correlation feature weightings. Another classification prediction model configured for determining a posture of a detection living body is established according to the posture of the experiment living body and the preferred features.

Living body detection method and living body detection system

The disclosure provides an image identifying method and an image identifying device. The method includes: retrieving a first computer tomographic (CT) image including a plurality of first pixels; retrieving a plurality of second pixels from the first pixels, and generating a second CT image based on the second pixels; performing a contrast enhancement algorithm on the second CT image to adjust a contrast of the second CT image; identifying a first image region in the second CT image to generate a third CT image, wherein the first image region includes a first object; dividing the third CT image into a plurality of sub-images, and only reserving a specific image region corresponding to the first object in each sub-image; combining the sub-images into a fourth CT image; and identifying an object type of the first object based on the fourth CT image.

Image identifying method and image identifying device

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Taipei, Taiwan

Zhe-Yu Lin