Industrial Technology Research Institute

Wen-Chao Chen

Hong-Long Chou

Cheng-Feng Wu

Zen Chen

Te-Chin Shao

Tzuan-Ren Jeng

Chao-Hsu Tsai

Chun-Jung Chen

Chun-Chuan Lin

Kuo-Chung Huang

Kai-Che Liu

Hsin-Hsiang Lo

Qi Wu

Tzung-Han Lin

Hsien-Chang Lin

Golden Tiao

Cheng-Yuan Tang

Fu-Chiang Jan

Tsu-Han Chen

Jeng-Feng Wu

De-Jin Shau

Chiao-Wen Cheng

Chu-Hsun Lin

A head mount personal computer (HMPC) and an interactive system using the same are provided. The provided HMPC includes a microprocessing unit, an output unit and an image capture unit with a distance detection function (ICWDD). The microprocessing unit is served as an operation core of the HMPC. The output unit is coupled to and controlled by the microprocessing unit, and configured to project an optical image onto a space. The ICWDD is coupled to and controlled by the microprocessing unit, and configured to capture operation gestures on the projected optical image from a user wearing the HMPC, such that the microprocessing unit correspondingly controls operations of the HMPC in response to the operation gestures on the projected optical image from the user.

Head mount personal computer and interactive system using the same

Image classification methods and systems are provided. First, an image is obtained using a computer. The image is then processed using the computer to obtain image information. The image information includes one or any combination of an average color difference between at least one average channel value of pixels in at least one boundary region of the image and a predefined standard value, a gradient variation magnitude difference between at least two regions of the image, and a percentage of the edges of the image to the image. The image is classified using the computer according to the image information.

Image classification methods and systems

An image processing method for providing corresponding depth information according to an input image is provided. This method includes the following steps. First, a reference image is generated according to the input image. Next, the input image and the reference image are divided into a number of input image blocks and a number of reference image blocks, respectively. Then, according to a number of input pixel data of each input image block and a number of reference pixel data of each reference image block, respective variance magnitudes of the input image blocks are obtained. Next, the input image is divided into a number of segmentation regions. Then, the depth information is generated according to the corresponding variance magnitudes of the input image blocks which each segmentation region covers substantially.

Image processing method for providing depth information and image processing system using the same

A method and a system for searching for a global minimum are provided. First, a subclass of a plurality of space points in a multidimensional space is clustered into a plurality of clusters through a clustering algorithm, wherein each of the space points is corresponding to an error value in an evaluation function. Then, ellipsoids for enclosing the clusters in the multidimensional space are respectively calculated. Next, a designated space corresponding to each of the ellipsoids is respectively inputted into a recursive search algorithm to search for a local minimum among the error values corresponding to the space points within each designated space. Finally, the local minimums of all the clusters are compared to obtain the space point corresponding to the minimum local minimum.

Method and system for searching for global minimum

An example-based 2D to 3D image conversion method, a computer readable medium therefore, and a system are provided. The embodiments are based on an image database with depth information or with which depth information can be generated. With respect to a 2D image to be converted into 3D content, a matched background image is found from the database. In addition, graph-based segmentation and comparison techniques are employed to detect the foreground of the 2D image so that the relative depth map can be generated from the foreground and background information. Therefore, the 3D content can be provided with the 2D image plus the depth information. Thus, users can rapidly obtain the 3D content from the 2D image automatically and the rendering of the 3D content can be achieved.

Example-based two-dimensional to three-dimensional image conversion method, computer readable medium therefor, and system

A method for producing an image with depth by using 2D image includes obtaining a set of internal parameters of a camera. The camera takes at least a first and a second 2D images with a small shift. The first 2D image has N depths, and N&#x2267;2. Several sets of external parameters of the camera corresponding to the 2D images are estimated. A 3D information respectively corresponding to the N depths of the first 2D image at each pixel or block is calculated. A proper depth of each pixel or image block is determined. Through the internal parameters, the external parameters, and the N depths, each pixel or image block of the first 2D image is projected onto N positions of the second 2D image, so as to perform a matching comparison analysis with the second 2D image, thereby determining the proper depth from the N depths.

Method for producing image with depth by using 2D images

A method for reconstructing a three dimensional (3D) model, suitable for reconstructing the 3D model of an object by using a voxel, is provided. After the voxel is cut to a plurality of sub-voxels, the sub-voxels of which a projection image is inside a silhouette image are reserved. It is determined whether to recut the sub-voxel corresponding to the projection image, which is on the edge of the silhouette. When it is determined to recut the sub-voxel, the above-mentioned steps are repeated until recut is not required, thereby reconstructing the 3D model according to the reserved sub-voxels.

Method for reconstructing three dimensional model

A 3-D face recognition system has a first data storing module for storing 3-D face model data and 2-D face image data; an input unit for inputting 3-D face model data and 2-D face image data; a signal converting module for converting analog data of the 3-D face model data and 2-D face image data to digital data; a second data storing module for storing the digital data; a microprocessing module for analyzing geometric characteristics of points in the 3-D face model data stored in the first and second data storing module to determine feature points of the 3-D face model data, and assigning different weight ratios to feature points; and a comparing module for comparing the feature points stored in the first and second data storing module, during which, different geometric characteristics being given different weight ratios, and calculating relativity between the feature points to obtain a comparison result.

Three-dimensional face recognition system and method

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Kaohsiung, Taiwan

Wen-Chao Chen