Abstract: An auto focus (AF) method adapted to an AF apparatus is provided. The AF method includes following steps. A first image is captured by using the first image sensor. At least one characteristic is detected based on the first image, and whether the characteristic meets a predetermined condition is determined. If the characteristic meets the predetermined condition, a focus depth is calculated according to a three-dimensional (3D) depth information and movement of a first lens of the first sensor is driven according to the focus depth for focusing. If the characteristic does not meet the predetermined condition, the first lens is driven to move for many times to obtain a plurality of contrast values, such that movement of the first lens is driven according to the contrast values for focusing. Additionally, an AF apparatus is provided.

Abstract: The present disclosure illustrates an auto-focus system using multiple lenses and method thereof. The system is characterized in using multiple lenses and combining a focus depth based approach and a contrast value based approach to improve focus speed and precision. In particular, the system can avoid the impairment caused by the repeat pattern in the focus depth based approach.

Abstract: An auto focus (AF) method and an AF apparatus are provided. The method includes the following steps. At least one target object is selected and photographed by a first image sensor and a second image sensor to generate a three-dimensional (3D) depth map. A block covering at least one initial focusing point is selected. The 3D depth map is queried for reading depth information of a plurality of pixels in the block. It is determined whether depth information of the pixels is enough to operate. If yes, a first statistics operation is performed, and focusing depth information is obtained. If not, the position of the block is moved or the size of the block is enlarged to obtain the focusing depth information. A focusing position is obtained according to the focusing depth information and the AF apparatus is driven to perform an AF procedure according to the focusing position.

Abstract: An auto focus (AF) method adapted to an AF apparatus is provided. The AF method includes following steps. A target object is selected and photographed by a first image sensor and a second image sensor to generate a first image and a second image. A procedure of three-dimensional (3D) depth estimation is performed according to the first image and the second image to generate a 3D depth map. An optimization process is performed on the 3D depth map to generate an optimized 3D depth map. A piece of depth information corresponding to the target object is determined according to the optimized 3D depth map, and a focusing position regarding the target object is obtained according to the pieces of depth information. The AF apparatus is driven to execute an AF procedure according to the focusing position. Additionally, an AF apparatus is provided.

Abstract: A method and a system for recognizing at least one testing image according to classes are provided, wherein each of the classes includes sample images. The method includes generating an average image of each class according to the sample images, generating a feature enhancement mask according to differences between the average images of the classes, enhancing the sample images of each class by using the feature enhancement mask to generate a plurality of enhanced sample images corresponding to each class, and training a classifier according to the enhanced sample images of each class. The method also includes enhancing the at least one testing image by using the feature enhancement mask to generate an enhanced testing image, classifying the enhanced testing image into one of the classes by using the classifier, and recognizing that the testing image belongs to the classified class. Thereby, this method can effectively recognize the testing image.

Abstract: A barcode structure includes at least one symbolic element row and a frame arranged at a periphery of the at least one symbolic element row. Each symbolic element row has multiple symbolic elements. Each symbolic element has different color from its adjacent symbolic elements, and the color of the frame is different from that of any symbolic element. A barcode encoding method includes: establishing multiple color combinations where a color of a frame is excluded, and each color combination comprising a main color and a sub color; performing a symbolic element row establishment procedure, which includes using a reference color as a color of a first symbolic element of a symbolic element row; deleting color combinations where the reference color is used as a main color; and selecting a color combination from remaining color combinations for coloring two symbolic elements that are sequentially connected with the first symbolic element.

Abstract: A barcode structure includes at least one symbolic element row and a frame arranged at a periphery of the at least one symbolic element row. Each symbolic element row has multiple symbolic elements. Each symbolic element has different color from its adjacent symbolic elements, and the color of the frame is different from that of any symbolic element. A barcode encoding method includes: establishing multiple color combinations where a color of a frame is excluded, and each color combination comprising a main color and a sub color; performing a symbolic element row establishment procedure, which includes using a reference color as a color of a first symbolic element of a symbolic element row; deleting color combinations where the reference color is used as a main color; and selecting a color combination from remaining color combinations for coloring two symbolic elements that are sequentially connected with the first symbolic element.

Abstract: A method and a system for recognizing at least one testing image according to classes are provided, wherein each of the classes includes sample images. The method includes generating an average image of each class according to the sample images, generating a feature enhancement mask according to differences between the average images of the classes, enhancing the sample images of each class by using the feature enhancement mask to generate a plurality of enhanced sample images corresponding to each class, and training a classifier according to the enhanced sample images of each class. The method also includes enhancing the at least one testing image by using the feature enhancement mask to generate an enhanced testing image, classifying the enhanced testing image into one of the classes by using the classifier, and recognizing that the testing image belongs to the classified class. Thereby, this method can effectively recognize the testing image.

Abstract: A computer implemented method for recognizing license plate image, and related computer product, computer-readable recording medium, and image recognizing apparatus using the same is provided. The method includes collecting a plurality of character template images and respectively homogenizing the character template images to generate a plurality of simulated character template images based on the license plate image. The method also includes respectively generating a plurality of similarity degrees corresponding to the simulated character template images by comparing the simulated character template images with the license plate image. The method further includes estimating at least one candidate character corresponding to the license plate image and at least one candidate character position corresponding to the at least one candidate character according to the similarity degrees corresponding to the simulated character template images.

Abstract: The invention discloses a camera recalibration system and the method thereof. The camera recalibration system includes a first camera, which is to be recalibrated, for capturing image; an image processing unit comprising a storage unit for storing a first image and a second image, the second image being captured by the first camera; and a computing unit for measuring camera motion from the first image to the second image and computing calibration information corresponding to the camera motion; and a display unit for presenting the calibration information.