Getac Technology Corporation

Taiwan Semiconductor Manufacturing Comp. Ltd.

Getac Holdings Corporation

Kun-Yu Tsai

Po-Yu Yang

Shou-Gwo Wuu

Ru-Shang Hsiao

Chien-Hsien Tseng

Nai-Wen Cheng

Gwo-Shiang Lee

Ting-Wei Liang

A system for detecting a surface type of an object includes a driver component, a driver component, and a plurality of photosensitive elements. The surface of the object is divided along a first direction into a plurality of areas, and the driver component sequentially moves one of the plurality of areas to a detection position. The light source component faces the detection position and provides light of a plurality of spectra that are different from one another to illuminate the detection position. The photosensitive elements face different sections of the area at the detection position, to capture detection images of different sections of the area located at the detection position under the light of each of the spectra. One photosensitive axis of the photosensitive elements is parallel to the normal line while another photosensitive axis of the photosensitive elements is between the normal line and the first direction.

System for detecting surface type of object and artificial neural network-based method for detecting surface type of object

An artificial neural network-based method for detecting a surface type of an object includes: receiving a plurality of object images, wherein a plurality of spectra of the plurality of object images are different from one another and each of the object images has one of the spectra; transforming each object image into a matrix, wherein the matrix has a channel value that represents the spectrum of the corresponding object image; and executing a deep learning program by using the matrices to build a predictive model for identifying a target surface type of the object. Accordingly, the speed of identifying the target surface type of the object is increased, further improving the product yield of the object.

A vehicular image pickup device includes an image capturing unit and a processing unit. The image capturing unit captures a plurality of driving images sequentially. The driving images each include an object image. The processing unit performs an image analysis on two of the plurality of driving images to obtain a variance of the object image and sets a shutter speed of the image capturing unit according to the variance.

Vehicular image pickup device and image capturing method

An artificial neural network-based method for selecting a surface type of an object includes receiving at least one object image, performing surface type identification on each of the at least one object image by using a first predictive model to categorize the object image to one of a first normal group and a first abnormal group, and performing surface type identification on each output image in the first normal group by using a second predictive model to categorize the output image to one of a second normal group and a second abnormal group.

Artificial neural network-based method for selecting surface type of object

An artificial neural network-based method for selecting a surface type of an object is suitable for selecting a plurality of objects. The artificial neural network-based method for selecting a surface type of an object includes performing surface type identification on a plurality of object images by using a plurality of predictive models to obtain a prediction defect rate of each of the predictive models, wherein the object images correspond to surface types of a part of the objects, and cascading the predictive models according to the respective prediction defect rates of the predictive models into an artificial neural network so as to select the remaining objects.

An image scanning method for a metallic surface and an image scanning system thereof are provided. The method includes sequentially moving one of a plurality of areas on a metallic surface of an object to a detection position, providing far infrared light by a light source component facing the detection position, wherein a light wavelength of the far infrared light is associated with the object, the far infrared light illuminating the detection position with a light incident angle of less than or equal to 90 degrees relative to a normal line of the area located at the detection position, and capturing a detection image of each of the areas sequentially located at the detection position by a photosensitive element according to the far infrared light, wherein the photosensitive element faces the detection position and a photosensitive axis of the photosensitive element is parallel to the normal line.

Image scanning method for metallic surface and image scanning system thereof

An artificial neural network-based method for detecting a surface pattern of an object includes receiving a plurality of object images, dividing each object image into a plurality of image areas, designating at least one region of interest from the plurality of image areas of each of the object images, and performing deep learning with the at least one region of interest to build a predictive model for identifying a surface pattern of the object.

Artificial neural network-based method for detecting surface pattern of object

An image detection scanning method for object surface defects and an image detection scanning system thereof are provided. The method includes capturing a test image according to test light, determining whether the test image is normal, generating a coincidence signal if the test image is normal, coinciding the object with the detection position according to the coincidence signal, sequentially moving one of a plurality of areas on a surface of the object to the detection position, providing detection light facing the detection position, the detection light illuminating the detection position with a light incident angle of less than or equal to 90 degrees relative to a normal line of the area located at the detection position, and capturing a detection image of each of the areas sequentially located at the detection position according to the detection light after the object is coincided with the detection position.

Image detection scanning method for object surface defects and image detection scanning system thereof

A method for regulating a position of an object includes detecting a plurality of first alignment structures of the object under rotation of the object, wherein a plurality of second alignment structures of the object sequentially face a photosensitive element during the rotation of the object, and when the plurality of first alignment structures have reached a first predetermined state, stopping the rotation of the object and performing an image capturing procedure of the object. The image capturing procedure includes: capturing a test image of the object, wherein the test image includes an image block presenting the second alignment structure currently facing the photosensitive element; detecting the position of the image block in the test image; when the image block is located in the middle of the test image, capturing a detection image of the object.

Method for regulating position of object

A license plate capturing device includes an image capturing unit, a plurality of infrared light emitting units, a driving circuit and a control circuit. The plurality of infrared light emitting units is located around the image capturing unit. Each of the infrared light emitting units emits invisible light having a predetermined wavelength that has an absorption band of a target. The driving circuit is coupled to the infrared light emitting units. The control circuit is coupled to the driving circuit and the image capturing unit, and controls the driving circuit to execute one of a normal driving mode and a continuous driving mode. In the continuous driving mode, the driving circuit continuously provides a driving current to the infrared light emitting units for a continuous period of time so as to remove a target covering the license plate capturing device.

License plate capturing device and method for removing target covering license plate capturing device

A method for estimating a moving speed of a vehicle includes: sequentially capturing, by an image capturing unit, a first driving vehicle image and a second driving vehicle image having a time difference; performing image analysis on the first driving vehicle image to obtain a first pixel area and a first inclination of a first license plate image; performing image analysis on the second driving vehicle image to obtain a second pixel area and a second inclination of a second license plate image; and estimating the moving speed according to the first pixel area, the first inclination, the second pixel area, the second inclination and the time difference.

In-vehicle camera device, monitoring system and method for estimating moving speed of vehicle

A method for filtering driving images includes enabling a light compensating unit at a first time point to emit supplemental light, capturing a first driving image under the supplemental light at the first time point by an image capturing unit, disabling the light compensating unit at a second time point, capturing a second driving image at the second time point by the image capturing unit, selecting the first driving image according to a first brightness difference between the first driving image and the second driving image and a predetermined threshold, and outputting the first driving image when the first brightness difference is greater than or equal to the predetermined threshold.

In-vehicle camera device and method for selecting driving image

A vehicular image pickup device includes an image capturing unit, a fill light unit and a processing unit. The image capturing unit captures a driving image. The fill light unit provides a supplementary light of a fill light intensity. The processing unit retrieves an object image from the driving image and calculates, by conversion, a brightness distribution of a plurality of pixels on a straight line penetrating the object image. The processing unit fine-tunes a shutter speed of the image capturing unit, a gain of the image capturing unit or the fill light intensity of the fill light unit according to a waveform of the brightness distribution.

A vehicular image pickup device includes an image capturing unit, a fill light unit and a processing unit. The image capturing unit captures driving images. The fill light unit provides a fill light. The processing unit obtains a grayscale quantity distribution of pixels of the driving images on a plurality of grayscale levels. The processing unit numbers the pixels sequentially in the direction from the highest grayscale level to the lowest grayscale level according to the grayscale quantity distribution until the numbering reaches a predetermined number. The processing unit adjusts a fill light intensity of the fill light unit or a gain of the image capturing unit according to the grayscale level of the pixel whose number is the predetermined number.

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Taipei, Taiwan

Kun-Yu Tsai