Abstract In professionel moviepost-production, various components are pushed to their performance limits. Former processors for example were not able to playback the JPEG 2000 compressed image-sequences which are used for distribution in Digital Cinema as well as long term storage in digital archiving – inreal time. While today’s processors are able to decode such files a new bottleneck became part of... In professionel moviepost-production, various components are pushed to their performance limits. Former processors for example were not able to playback the JPEG 2000 compressed image-sequences which are used for distribution in Digital Cinema as well as long term storage in digital archiving – inreal time. While today’s processors are able to decode such files a new bottleneck became part of the processing chain: In many cases conventional Hard Disk Drives (HDD) are not able to deliver the requested data – which is limited to a maximum value of 250 Mbit/s in Digital Cinema – in real time. In this paper we propose an algorithm for increasing the data throughput of conventional HDDs by utilizing the progression order of scalable media files, called UCODAS (Use-Case-Optimized DAta Storage). The motivation is given by the architecture of conventional hard drives, and finding that the file structure of scalablemedia, such as JPEG 2000, can be (re-)arrangedin such a way that the throughput of the disk can be significantly increased - especially if subsequent access patterns to the image-sequence are known a-priori. The advantages and disadvantages of today’s hard drives are summarized before we show how scaling is achieved within JPEG 2000. Then, various methods for improving the performance of HDDs - taking advantage of the scalability - are proposed and the data sets used for the measurements are described. We performed tests using a collection of common file systems including FAT32, NTFS, ext2 and ext3 as well as RAW data access without a filesystem in order to prove our implementation of the UCODAS algorithm. In particular, we show that UCODAS can increase the data-throughput of a conventional HDD by more than a factor of 3 and thus overcome the bottleneck introduced by conventional HDDs. Read More Read Less

Abstract Computer vision has been an active research area for the past few decades. It is being used in many fields of industries for example digit recognition and handwriting recognition in banks, industrial parts recognition etc. Shape matching is an important sub-domain of Computer Vision, and relies on effective shape representation techniques. These techniques must be developed in order to effectively... Computer vision has been an active research area for the past few decades. It is being used in many fields of industries for example digit recognition and handwriting recognition in banks, industrial parts recognition etc. Shape matching is an important sub-domain of Computer Vision, and relies on effective shape representation techniques. These techniques must be developed in order to effectively estimate similarity between the shapes. Major similarity estimation results correspond to effective shape matching techniques. These shape matching techniques or shape descriptors must be invariant to variances or noise in the shape for accurate shape matching results. But, unfortunately, these descriptors are not invariant to noise like small cracks or slits present in the contour of the image. These cracks changes the shape boundary which changes the shape altogether. In this paper, we propose a noise removal approach that significantly improves the performance of sophisticated shape matching techniques. Existing shape matching techniques focus on complex algorithms without giving much consideration to the noise removing preprocessing techniques. These preprocessing techniques can reasonably improve the accuracy where the shapes are affected by cracks. We present a preprocessing approach to identify and remove small cracks and slits in the shape contour. It has been observed that these cracks or slits introduce changes in a manner which makes classification of shapes difficult. This technique is simple and effective and may be used to preprocess the images before applying sophisticated shape representation techniques to improve their accuracies. The effectiveness of proposed pre-processing approach is verified using publicly available shape datasets. Read More Read Less

Abstract In today’s world, the importance of biometric studies is increasing day by day. Biometric face recognition study is the most widely used method in the legal environment. Commonly, face images are used in all identification systems(IDs, driver’s license, passport, etc.). In particular, to improve the social security of city life, automatic face detection and face recognition systems are needed. Many... In today’s world, the importance of biometric studies is increasing day by day. Biometric face recognition study is the most widely used method in the legal environment. Commonly, face images are used in all identification systems(IDs, driver’s license, passport, etc.). In particular, to improve the social security of city life, automatic face detection and face recognition systems are needed. Many studies are published in this domain now. So we developed a template matching method and performed to obtain face image from entrance picture. Average face mask was obtained using hundreds of people’ normalized face images. Face mask has been applied to entrance picture and the shortest distance part of the entrance picture has matched the face mask. When applying of the face mask to entrance picture, corner detection and difference matrix methods are performed together improving the achievement. In addition, entrance picture background, luminance conditions have the negative effect that has been tried to choose simple background entrance picture. We realized in our study that it could be base for further studies on face detection-face identification. In addition, the system could be developed further using the artificial intelligence (AI) methods. Read More Read Less

Abstract Field Programmable Gate Array (FPGA) technology has become a viable target for the implementation of real time algorithms suitable to video image processing applications. The exclusive architecture of the FPGA has allowed the technology to be used in many applications encompassing all aspects of video image processing. In this paper, we propose a novel type of explicit image filter- guided... Field Programmable Gate Array (FPGA) technology has become a viable target for the implementation of real time algorithms suitable to video image processing applications. The exclusive architecture of the FPGA has allowed the technology to be used in many applications encompassing all aspects of video image processing. In this paper, we propose a novel type of explicit image filter- guided filter. Resulting from a local linear model, the guided filter produces the filtering amount fashioned by considering the content of a guidance image, which can be the input image itself. The guided filter has a fast and non-approximate linear-time algorithm, whose computational intricacy is independent of the filtering kernel size. The guided filter is together effective and efficient in a great variety of computer vision and computer graphics applications including noise reduction, detail smoothing/enhancement, image matting/feathering. In this work, an implementation guided image filtering using a FPGA Xilinx Spartan 3E, with educational purposes, is obtainable. The system is connected to a USB port of a personal computer, in that way it forms a authoritative and low-cost intend station.The FPGA-based system is accessed through a Matlab graphical user interface, which handles the communication arrangement. A similarity between results obtained from MATLAB simulations and the described FPGA-based implementation is obtained. Read More Read Less