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Local operations in image processing are often used, namely during the preprocessing step. On one hand, their implementation is expensive, on the other hand, they become efficient when they use a wide area neigbourhood. In this paper we propose a general method for synthesis of linear and non linear filters (mathematical morphology operators). The main interest of this method is that it can be applied...
The rapidly growing applications based on morphological operations in image processing and computer vision make efficient implementations of these key blocks an important topic of research. Nevertheless, a detailed comparison of the energy efficiency and performance of these implementations that covers all available major hardware platforms is still missing. In this paper we evaluate the performance...
Generally, 2-D DCT/IDCT (Two dimensional discrete cosine transform and its inverse) are widely used in many image processing systems. In this paper, efficient architectures are proposed. These architectures have parallel and pipelined structures which are used to implement 8×8 DCT/IDCT processors. These processors involve two 8-point DCT/IDCT processors along with a dual-bank of SRAM (128 words) and...
As image and video processing applications are leaning towards real-time requirement, the program control for the system must be designed efficiently while taking the processing time factor as one of the main considerations. This paper presents the method of detecting multicolour objects and the processing time comparisons of designed algorithms. Algorithm with multithreading approached is compared...
In this paper a new class of filters designed for the removal of impulsive noise in color images is presented. The proposed filter class is based on the nonparametric estimation of the density probability function of pixels in a sliding filtering window. The comparison of the new filtering method with the standard techniques used for impulsive noise removal, indicates good noise removal capabilities...
The massive volume of video and image data, compels them to be stored in a distributed file system. To process the data stored in the distributed file system, Google proposed a programming model named MapReduce. Existing methods of processing images held in such a distributed file system, requires whole image or a substantial portion of the image to be streamed every time a filter is applied. In this...
Parallel programming has been extensively applied to different fields, such as medicine, security, and image processing. This paper focuses on parallelizing the Laplacian filter, an edge detection algorithm, using CUDA. We have conducted a performance analysis to benchmark the sequential Laplacian version against the CUDA parallel approach. Results show that the parallelization of Laplacian filter...
Underwater imaging is primarily focused on search and rescue, underwater mine detection, underwater cable and pipeline overhauling and underwater geological survey. Main challenge in underwater imaging is blurriness. In underwater environment blurriness is caused by many factors which includes microscopic organism, impurities and density of water which effects refractive index of water, and bokeh...
Defocus map estimation plays an important role in computer vision and computer graphics applications. For many defocus map estimation methods from a single image, the edge detection is very important for depth map estimation. In this paper, we show a novel method for defocus map estimation from a single image, and give a theoretical model of re-blurred. The method is based on the difference value...
Face detection is one of the most important parts of biometrics and face analysis science. Numerous methods and algorithms have been developed in recent years; however, there is a sensible gap between the current detection rate and the ideal one yet. In this paper, a novel multi-stage face detection method is proposed which can remarkably detect faces in different images with different illumination...
We introduce a library for the productive development of image processing accelerators using C-based high-level synthesis. The key concept of our approach is to provide a set of generic building blocks that is applicable to a multitude of image processing applications. An efficient memory architecture that facilitates easy integration of point and local image processing operators is the centerpiece...
Motivated by the traditional bilateral filtering, apart from considering geometric closeness and photometric similarity, we introduce the gradient factor to form a new filter kernel. Gradient reflects to the trend of edge and texture directly. By introducing the gradient weight factor, the proposed filtering algorithm overcomes the isotropy of the traditional Gaussian function, and has the directional...
Texture Features introduced by Haralick in 1973 which rely on computing the so-called Gray Level Co-occurrence Matrix (GLCM), are being used extensively by many applications to understand and enhance images acquired from various scientific contexts. The main limitations of these features are their high computational costs pertaining to memory usage and processing time. In this paper a Graphics Processing...
A monocular visual servo system for a target with variable shape has been developed in this paper. It consists of two parts: an image-processing unit and a servo control unit. For the image-processing unit, the motion between the target and image center is determined by a template match approach. The image is grabbed by a video camera equipped on a pan-tilt robot and the robot is controlled to track...
The spectral matching, statistical and kernel based methods are the most widely known classification algorithms for hyperspectral imaging. Spectral matching algorithms try to identify the similarity of the unknown spectral signature of test pixels with the expected signature. In this study, an efficient spectral similarity method employing Multi-Scale Vector Tunnel Algorithm (MS-VTA) for supervised...
As today's computer architectures are becoming more and more heterogeneous, a plethora of options including CPUs, GPUs, DSPs, reconfigurable logic (FPGAs), and other application-specific processors come into consideration for close-to-sensor processing. Especially, in the domain of image processing on mobile devices, among numerous design challenges, a very stringent energy budget is of utmost importance,...
One of the fundamental issues of human and computational cognitive psychology is pattern or shape recognition. Various applications in image processing and computer vision rely on skeleton-like shape features A possible technique for extracting these feautures is thinning. Although the majority of 2D thinning algorithms work on digital pictures sampled on the conventional square grid, the role of...
Explicitly managed memory many-cores (EMM) have been a part of the industrial landscape for the last decade. The IBM CELL processor, general-purpose graphics processing units (GP-GPU) and the STHORM embedded many-core of STMicroelectronics are representative examples. This class of architecture is expected to scale well and to deliver good performance per watt and per mm2 of silicon. As such, it is...
The analysis of climatic parameters, vegetation, humidity and pollution in the domain of time and space is done by processing a series of images of a geographic area taken by the satellite at certain times [1]. These images are subject to several computing schemes, with the aim of evaluating spatial and temporal variations of the mentioned parameters. One of the programs used to manipulate the images...
We propose a method for data fusion of hyperspectral images (HSI) and digital surface models (DSM) basing on the edge probabilities from both datasets. A height discontinuity in DEM and change in material in HSI represent the high probability of an edge. Edge probabilities are computed in scale-space and combined according to the Gaussian mixture model. The reliability of the datasets can be included...
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