Himax Technologies, Inc.

Ncku Research and Development Foundation

Der-Wei Yang

Ming-Der Shieh

Tzung-Ren Wang

Chun-Wei Chen

Li-Chia Chu

Yun Chen Chang

Chia-Cheng Lo

Ting-Yu Lin

Fang-Kai Hsu

Pao-Cheng Luo

An image processing circuit is provided. A memory control unit first moves a reference image and parameters from a first memory to a third memory. A convolution neural network circuit obtains the parameters from the third memory. The memory control unit also moves at least one portion of the reference image from the third memory to a second memory, in which a storage capacity of the third memory is larger than that of the second memory. A depth decoder obtains the reference image from the second memory for calculating depth information according to the reference image and a structured image received from an infrared sensor, and stores the depth information into the second memory.

Image processing circuit for convolutional neural network

A framebuffer-less system of convolutional neural network (CNN) includes a region of interest (ROI) unit that extracts features, according to which a region of interest in an input image frame is generated; a convolutional neural network (CNN) unit that processes the region of interest of the input image frame to detect an object; and a tracking unit that compares the features extracted at different times, according to which the CNN unit selectively processes the input image frame.

Framebuffer-less system and method of convolutional neural network

A system of selecting a keyframe for iterative closest point (ICP) includes a reference frame selector that generates a reference frame according to a current frame and a current keyframe; an ICP loop unit that performs ICP on the reference frame and the current frame, thereby generating a pose of the current frame; and a keyframe update unit that generates a new keyframe according to an offset condition between the pose of the current frame and a pose of the reference frame.

System and method of selecting a keyframe for iterative closest point

A clustering method with a two-stage local binary pattern includes generating a gradient direction value according to a center sub-block and neighbor sub-blocks of a patch of an image; quantizing the gradient direction value, thereby generating a quantized gradient direction value; generating a gradient magnitude value according to the gradient direction value; quantizing the gradient magnitude value, thereby generating a quantized gradient magnitude value; concatenating the quantized gradient direction value and the quantized gradient magnitude value to generate a two-stage local binary pattern (2SLBP) value; and performing clustering of super-resolution imaging by using the 2SLBP value as an index.

Clustering method with a two-stage local binary pattern and an iterative image testing system thereof

An image processing apparatus and an image processing method thereof are provided. A shared storage unit of a motion estimation and motion compensation apparatus captures frame data of a storage unit through a bus. A motion vector estimation unit and a motion compensation unit capture image data for executing a motion vector estimation operation and a motion compensation operation from the sharing storage unit.

Image processing apparatus and image processing method thereof

An iterative matching method for partial fingerprint verification includes providing database features and input features; initially comparing the input features with the database features using one of the database features as a first reference point, resulting in initial matched feature pairs between initial matched database features and corresponding initial matched input features; and progressively comparing the input features with the database features using a gravity center of the initial matched database features as a second reference point, resulting in progressive matched feature pairs between progressive matched database features and corresponding progressive matched input features.

Iterative matching method and system for partial fingerprint verification

A content adaptive compression system includes a plurality of encoders being coupled to receive a portion of an image, and accordingly generating candidate compressed codes, respectively, the plurality of encoders being configured for encoding images of different contents. An error count unit is configured to determine an amount of error between the image and the candidate compressed code for each of the encoders. A mode decision unit is coupled to receive a plurality of the amount of error, the candidate compressed code associated with least amount of error being outputted as an adaptive compressed code for the image.

Content adaptive compression system

An auto-contrast enhancement system includes a human visual system (HVS)-based local difference (LD) histogram unit configured to build a LD histogram with respect to intensity values; a histogram classifier configured to categorize histograms of input images based on distribution properties; and a histogram equalization (HE) unit configured to process the input image according to a result of the HVS-based LD histogram unit and the enhancement level determined in the histogram classifier.

Auto-contrast enhancement system

A system of image stereo matching includes at least one stereo matching unit (SMU) each receives a first view and a second view of a view pair, according to which the SMU generates a first depth map for the first view. The system also includes a backward tracer operable to receive the first depth map, according to which a second depth map for the second view is derived.

Growing community of inventors

Tainan, Taiwan

Der-Wei Yang