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This paper describes the General-Purpose computing on Graphics Processing Unit (GPGPU) optimized implementation of 2D–3D Conversion. 2D–3D Conversion is large-scale image processing and consists of 170 kernels. Those kernels, which are tasks performed by GPU, contain various algorithms. 30fps real-time processing of 2D–3D Conversion conventionally requires special hardware since it is computationally...
The Census Transform is one of the most widely used matching metrics in problems that involve correspondence search such as stereo reconstruction and optical flow. Graphic processing units (GPUs) have become popular platforms for such computation intensive applications that expose a high degree of data parallelism. Their evolution as a platform for general purpose computing by continuously adding...
The quality of an image is highly critical for applications such as robotic vision, surveillance, medical imaging, etc. The images captured in real-time are seldom noise free and therefore require noise removal for further processing. Out of several proposed noise removal schemes, an isotropic diffusion filtering is known to achieve highly precise results. However, the accuracy comes at an expense...
A process of generating a digital hologram requires a lot of time-consuming computations. Therefore, it is important to reduce the computation time or the number of computations for achieving real-time digital holographic video generation. In this paper, we propose a method of parallelizing the computations using multiple GPUs with CUDA and OpenMP and an optimization method for reducing the computation...
This paper presents a method to automatically count clustered soybean seeds using digital images. The method is based on classical morphological operations, and was designed to deal with the main difficulties imposed by images of soybean seeds, namely the clustering of the seeds, variations in the illumination, and low contrast between seeds and background. The proposal shows a good performance under...
An efficient memory bandwidth utilization for GPU accelerators is crucial for memory bound applications. In medical imaging, the performance of many kernels is limited by the available memory bandwidth since only a few operations are performed per pixel. For such kernels only a fraction of the compute power provided by GPU accelerators can be exploited and performance is predetermined by memory bandwidth...
To cope with the complexity of programming GPU accelerators for medical imaging computations, we developed a framework to describe image processing kernels in a domain-specific language, which is embedded into C++. The description uses decoupled access/execute metadata, which allow the programmer to specify both execution constraints and memory access patterns of kernels. A source-to-source compiler...
Convolution is one of the most important operators used in image processing. With the constant need to increase the performance in high-end applications and the rise and popularity of parallel architectures, such as GPUs and the ones implemented in FPGAs, comes the necessity to compare these architectures in order to determine which of them performs better and in what scenario. In this article, convolution...
Today the independent component analysis (ICA) has been widely used in the blind source separation (BSS) to separate independent components in a data set based on its statistical properties. However, when the dimension of the input data is too high, the performance of the ICA may be not satisfactory. To address this problem, the present paper has proposed the new integrated method for the independent...
A challenging problem in image restoration is to recover an image with a blurry foreground. Such images can easily occur with modern cameras, when the auto-focus aims mistakenly at the background (which will appear sharp) instead of the foreground, where usually the object of interest is. In this paper we propose an automatic procedure that (i) estimates the amount of out-of-focus blur, (ii) segments...
Although “Bag-of-Features” image models have shown very good potential for object matching and image retrieval, such a complex data representation requires computationally expensive similarity measure evaluation. In this paper, we propose a framework unifying dictionary-based and kernel-based similarity functions that highlights the tradeoff between powerful data representation and eff cient similarity...
A novel algorithm for thumbnail generation, which preserves characteristic features of a source image including blurs and textures, is proposed in this work. When a source image is subsampled to generate a thumbnail, important visible cues, such as blurs and noises, are lost. To overcome this drawback, we first create multiple thumbnail candidates that accentuate three classes of image features: focal...
We propose a single-image, shift-invariant motion deblurring approach where the blur kernel is directly estimated from light streaks in the blurred image. Combining with the sparsity constraint, the blur kernel can be solved quickly and accurately from a user input region containing a light streak. This kernel can then be applied to state-of-the-art single-image motion deblurring methods to restore...
Linear autoregressive (AR) model is widely used in signal processing. Usually the AR models are solved by classical least square (LS) method. An important issue with the LS solution of the AR model, which has been seemingly overlooked, is its numerical stability. The issue is related to the rank condition of the design matrix. We observed, in case of natural images, that the probability of numerical...
This paper presents an automatic annotation method for multimedia data. Different object and scene recognition methods are analyzed from the literature. The best components of current methods are used to design and implement an original solution. A novel approach for refining results based on scene specific object appearance frequencies is exposed which improves annotation performance. Experimental...
A new approach to an analog ultra-low power vision chip design is presented. The prototype chip performs low-level convolutional image processing algorithms in real time. The circuit is implemented in 0.35 µm CMOS technology, contains 64 × 64 SIMD matrix with embedded analogue processors APE (Analogue Processing Element). The photo-sensitive-matrix is of 2.2 µm × 2.2 µm size, giving the density of...
The main aim of this work is to show, how GPGPUs can facilitate certain type of image processing methods. The software used in this paper is used to detect special tissue part, the nuclei on (HE - hematoxilin eosin) stained colon tissue sample images. Since pathologists are working with large number of high resolution images - thus require significant storage space -, one feasible way to achieve reasonable...
Graphics Processing Units (GPUs) are becoming increasingly important in high performance computing. To maintain high quality solutions, programmers have to efficiently parallelize and map their algorithms. This task is far from trivial, leading to the necessity to automate this process. In this paper, we present a technique to automatically parallelize and map sequential code on a GPU, without the...
Increasing calculation speed without affecting pixel calculation accuracy in fast image processing algorithms using parallel computation was always needed but controlled by Amdahl's Law. In fact increasing number of processors uses same data bus reduces the speed and not allowing us to get the 20 times faster as we expected. As number of processors are limited due to sharing processors same data bus...
Many of the basic image processing tasks suffer from processing overhead to operate over the whole image. In real time applications the processing time is considered as a big obstacle for its implementations. A High Performance Computing (HPC) platform is necessary in order to solve this problem. The usage of hardware accelerator make the processing time low. In recent developments, the Graphics Processing...
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