12281
Ping Zhang, Yongqi Sun, Hao Shen, Rui Zhang
PCA-SIFT is an algorithm to extract invariant features from images, it has been widely applied to many application fields including image processing, computer vision and pattern recognition. However, the execution of PCA-SIFT is time-consuming. A parallel algorithm of PCA-SIFT based on Compute Unified Device Architecture (CUDA) is proposed in this paper, in which each step […]
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George Papandreou, Liang-Chieh Chen, Alan L. Yuille
The goal of this paper is to question the necessity of features like SIFT in categorical visual recognition tasks. As an alternative, we develop a generative model for the raw intensity of image patches and show that it can support image classification performance on par with optimized SIFT-based techniques in a bag-of-visual-words setting. Key ingredient […]
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Seung Heon Kang, Seung-Jae Lee, In Kyu Park
In this paper, we parallelize and optimize the popular feature detection algorithms, i.e. SIFT and SURF, on the latest embedded GPU. Using conventional OpenGL shading language and recently developed OpenCL as the GPGPU software platforms, we compare the implementation efficiency and speed performance between each other as well as between GPU and CPU. Experimental result […]
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M. Benjelloun, E.W. Dadi, E.M. Daoudi
This paper addresses the problem of 3D shape retrieval in large databases of 3D objects (large retrieval). While this problem is emerging and interesting as the size of 3D object databases grows rapidly, the main two issues the community has to focus on are: computational efficiency of 3D object retrieval and the quality of retrieved […]
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Ali Ismail Awad
Driven from its uniqueness, immutability, acceptability, and low cost, fingerprint is in a forefront between biometric traits. Recently, the GPU has been considered as a promising parallel processing technology due to its high performance computing, commodity, and availability. Fingerprint authentication is keep growing, and includes the deployment of many image processing and computer vision algorithms. […]
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Guohui Wang, Blaine Rister, Joseph R. Cavallaro
Feature detection and extraction are essential in computer vision applications such as image matching and object recognition. The Scale-Invariant Feature Transform (SIFT) algorithm is one of the most robust approaches to detect and extract distinctive invariant features from images. However, high computational complexity makes it difficult to apply the SIFT algorithm to mobile applications. Recent […]
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Khoa Tan Nguyen, Timo Ropinski
Recent advances in medical imaging technology enable dynamic acquisitions of objects under movement. The acquired dynamic data has shown to be useful in different application scenarios. However, the vast amount of time-varying data put a great demand on robust and efficient algorithms for extracting and interpreting the underlying information. In this paper, we present a […]
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Meng Lu
Scale-invariant feature transform (SIFT) was an algorithm in computer vision to detect and describe local features in images. Due to its excellent performance, SIFT was widely used in many applications, but the implementation of SIFT was complicated and time-consuming. To solve this problem, this paper presented a novel acceleration algorithm for SIFT implementation based on […]
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Stephen Won
In networked signal processing systems, dataflow graphs can be used to describe the processing on individual network nodes. However, to analyze the correctness and performance of these systems, designers must understand the interactions across these individual "node-level" dataflow graphs — as they communicate across the network – in addition to the characteristics of the individual […]
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Anton I. Vasilyev, Andrey A. Boguslavskiy, Sergey M. Sokolov
This paper describes the parallel SIFT-detector implementation on the basis of the NVIDIA CUDA technology for the images matching. The SIFT-detector implementation was applied for the images matching in the stereo-system mounted on the moving car and for images from the onboard UAV-camera.
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Jaco Cronje
We propose a fast local image feature detector and descriptor that is implementable on the GPU. Our method is the first GPU implementation of the popular FAST detector. A simple but novel method of feature orientation estimation which can be calculated in constant time is proposed. The robustness and reliability of our orientation estimation is […]
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Arturo Ribes, Senshan Ji, Arnau Ramisa, Ramon Lopez De Mantaras
Approaches to object localization based on codebooks do not exploit the dependencies between appearance and geometric information present in training data. This work addresses the problem of computing a codebook tailored to the task of localization by applying regularization based on geometric information. We present a novel method, the Regularized Combined Partitional-Agglomerative clustering, which extends […]
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