Sung-Dae Cho

Design and Performance Evaluation of Image Processing Algorithms on GPUs

In this paper, we construe key factors in design and evaluation of image processing algorithms on the massive parallel graphics processing units (GPUs) using the compute unified device architecture (CUDA) programming model. A set of metrics, customized for image processing, is proposed to quantitatively evaluate algorithm characteristics. In addition, we show that a range of image processing algorithms map readily to CUDA using multiview stereo matching, linear feature extraction, JPEG2000 image encoding, and nonphotorealistic rendering (NPR) as our example applications. The algorithms are carefully selected from major domains of image processing, so they inherently contain a variety of subalgorithms with diverse characteristics when implemented on the GPU. Performance is evaluated in terms of execution time and is compared to the fastest host-only version implemented using OpenMP. It is shown that the observed speedup varies extensively depending on the characteristics of each algorithm. Intensive analysis is conducted to show the appropriateness of the proposed metrics in predicting the effectiveness of an application for parallel implementation.

Implementation and optimization of image processing algorithms on handheld GPU

The advent of GPUs with programmable shaders on handheld devices has motivated embedded application developers to utilize GPU to offload computationally intensive tasks and relieve the burden from embedded CPU. In this work, we propose an image processing toolkit on handheld GPU with programmable shaders using OpenGL ES 2.0 API. By using the image processing toolkit, we show that a range of image processing algorithms map readily to handheld GPU. We employ real-time video scaling, cartoon-style non-photorealistic rendering, and Harris corner detector as our example applications. In addition, we propose techniques to achieve increased performance with optimized shader design and efficient sharing of GPU workload between vertex and fragment shaders. Performance is evaluated in terms of frames per second at varying video stream resolution.

Mobile photo collage

In this paper, we propose an efficient technique for creating a visually appealing collage on a mobile platform from a set of input images. The proposed algorithm consists of four main modules, namely image ranking, region of interest (ROI) selection, packing, and blending. Each of the four modules is designed using a greedy and localized approach. The modules are further optimized during implementation for efficient porting on a mobile phone processor. Experimental results show the effectiveness of the proposed algorithm with visually appealing results on an off-the-shelf mobile phone.

Efficient design and implementation of visual computing algorithms on the GPU

In this paper, we explore the key factors in the design and implementation of visual computing (image processing and computer vision) algorithms on the massive parallel GPU (graphics processing units). The goal of the exploration is to provide common perspective and guidelines of using GPU for visual computing applications. We have selected three nontrivial applications (multiview stereo matching, linear feature extraction, and JPEG2000 image encoding) for the benchmarks, which show different characteristics in GPU parallel computing. Intensive analysis is performed to evaluate the characteristic of each algorithm and its effect on the performance. Based on this, we draw general guidelines of using GPU for the visual computing algorithms.

Fast and Robust Perspective Rectification of Document Images on a Smartphone

This paper presents a perspective rectification framework for mobile device that is fast and robust to recovering the fronto-parallel view of perspectively distorted document images. The conventional approaches is too heavy to be implemented on a mobile device. In addition, they fails to reject the false case of the perspective rectification. To ameliorate such problems, the proposed framework is designed to pursue a fast and robust algorithm to detect horizontal and vertical vanishing points efficiently and robustly. Then, perspective rectification is performed using both horizontal and vertical vanishing points. In addition, the proposed framework has an adaptive scheme to detect the false case of the perspective rectification and skip the procedure without using the vertical vanishing point. Note that, the proposed framework is designed for consumer application so that bad results are rejected before they are shown for users. We demonstrate the performance of the proposed framework on various challenging examples to confirm that the proposed system is fast and robust in rectifying the perspectively distorted images.

Motion sketch

Traditionally motion is visualized using an image sequence or a video, which is often tedious and time consuming to browse. Composing the frames of a video into a single image can be quite useful in many applications like surveillance and special effects. We propose a compact and convenient representation of an image sequence into a motion outline image, captioned as Motion Sketch. The automatically composed image is in a form of time-lapse photograph and provides details of the path of the moving object. Alternatively, the proposed technique can be used for Motion Animation to animate moving objects illustrating the path of motion.