【正文】 gions (EBR). IBRs are based on extrema in intensity. Given a local intensity extremum, the brightness function along rays emanating from the extremum is studied. This function itself exhibits an extremum at locations where the image intensity suddenly changes. Linking all points of the emanating rays corresponding to this extremum forms and IBR. EBRs are determined from corner points and edges nearby. Given a single corner point and walking along the edges in opposite directions with two more control points, a onedimensional class of parallelograms is introduced using the corner itself and the vectors pointing from the corner to the control points. Studying a function of texture and using additional constraints, a single parallelogram is selected to be an EBR. Another algorithm, termed Salient Region detector was proposed by Kadir et al. and is based on the probability density function (PDF) of intensity values puted over an elliptical region. For each pixel, the entropy extrema for an ellipse centered at this pixel is recorded over the ellipse parameter’s orientation, h, scale s and the ratio of major to minor axis k. From a sorted list of all region candidates the n most salient ones are chosen. For an extensive evaluation of a large number of affine region detectors refer to the work of. Generally speaking, a descriptor is an abstract characterization of an image patch. Usually, the image patch is chosen to be the local environment of an interest region. Based on various algorithms methods or transformations, the resulting character can be made rotation invariant or, at least partially, insensitive to affine transformations. Most approaches are based on gradient calculations or image brightness values. As a second part of the SIFT approach, Lowe proposed the use of descriptors based on stacked gradient histograms. The single histograms are calculated in a subdivided patch describe the gradient orientation in order to cover spatial information. Finally, they are concatenated to form a 128dimensional descriptor. Recently Ke and Sukthankar, proposed the so called PCASIFT descriptor based on eigenspace analysis. They calculated a principal ponent analysis (PCA) eigenspace on the gradient images of a representative number of over 20,000 image patches. The descriptor of a new image tile is generated by projecting the gradients of the tile onto the precalculated eigenspace, keeping only the d most significant eigenvectors. Thus, an efficient pression in descriptor dimensionality is achieved, coevally keeping the performance at a rate parable to the original SIFT descriptor. Closely related to the SIFT approach, the gradient location and orientation histogram (GLOH) descriptor was proposed by Mikolajczyk and Schmid. Opposed to SIFT gradient histograms are calculated on a finer circular rather than on a coarser rectangular grid, which results in a 272dimensional histogram. PCA is subsequently used to reduce the descriptor dimensionality to 128 again. Two rotation invariant descriptors were proposed by Lazebnik et al, the rotationinvariant feature transform (RIFT) and the SPINImage descriptors. The RIFT descriptor is calculated on a circular normalized patch which is divided into concentric rings of equal width. Within each ring, the gradient orientation histogram is puted while the gradient direction is calculated relative to the direction of the vector pointing outward from the center. The SPINImage is a twodimensional histogram encoding the distribution of image brightness values in the neighborhood of a particular center point. The histogram has two dimensions, namely the distance from the center point and the intensity value. Quantizing the distance, the value of a bin corresponds to the histogram of the intensity values of pixels located at a fixed distance from the center point. 附錄 B 基于 DSP 的通過局部特征實時物體識別嵌入式系統(tǒng) 摘要 在過去幾年中，對象識別已經(jīng)成為最熱門的任務，計算機視覺尤其是，這是推動發(fā)展新的強大的算法，局部特征的物體識別。所謂 39。智能相機有足夠的權力分散的圖像處理變得越來越流行的各種任務，特別是在外地的監(jiān)視。它是一個非常重要的工具，強大的識別可疑車輛，人員或物體是否符合公眾安全。這只是局部識別功能的嵌入式平臺的基本功能。在我們的工作中，我們調查的任務是，目標識別基于狀態(tài)最先進的算法，在一個基于 DSP 的嵌入式系統(tǒng)。我們執(zhí)行一些功能強大的算法識別物體，即有興趣點探測連同區(qū)域描述，并建立一個中型對象數(shù)據(jù)庫為基礎的詞匯樹，這是適合我們的專用硬件設置。我們仔細研究了該算法參數(shù)性能的嵌入式平臺。我們所研究的，國家最先進的目標識別算法，可以成功地部署在當今智能相機，即使計算和內存資源有嚴格的限制。 關鍵詞 數(shù)字信號處理；物體識別；本地功能；詞匯樹； 1. 介紹 識別物體是一個最流行的任務領域中的計算機問題。在過去十年中，大量科學工作者做出努力，建立強有力的目標識別系統(tǒng)的外