KLASIFIKASI ONLINE CITRA DAUN BERDASARKAN FITUR BENTUK DAN RUAS DAUN

Sistem temu kembali citra untuk aplikasi pengklasifikasian citra daun berdasarkan bentuk dan ruas daun sangat dibutuhkan dalam ilmu botani. Paper ini menjelaskan tentang suatu metode pengenalan jenis daun berdasarkan input berupa sketsa bentuk daun yang dibandingkan terhadap database sketsa daun yang ada. Sistem ini dapat dilakukan secara online sehingga dapat diakses dimanapun dan kapanpun user berada. Metode pengenalan ini meliputi Minimum Perimeter Polygon untuk ekstraksi fitur bentuk pada citra daun, Venation Mathing untuk ekstraksi fitur ruas daun pada citra daun. Untuk mendukung kinerja dua metode diatas, penulis memperkenalkan metode Windowing matrix untuk mendeteksi ujung dan pangkal ruas daun, Thinning Algorithm menjadikan ruas daun lebih sederhana (lining one pixel), dan Rotation Invariant untuk mengubah orientasi daun menjadi tegak lurus. Setelah melalui uji coba dan analisa, dapat disimpulkan bahwa hasil pencarian dengan menggunakan metode diatas mampu menghasilkan F1 Score atau tingkat akurasi 95.83%, Recall sebesar 100%, dan Precision sebesar 92.59%. Kata Kunci: Klasifikasi, online, system temu kembali, fitur bentuk daun, fitur ruas daun, Minimum Perimeter Polygon, Venation Matching, Windowing Matrix, Thinning Algorithm, Rotation Invariant

Automatic recognition and inspection of two-dimensional manufactured components

This thesis presents new developments in the field of recognition and inspection of 2D manufactured components. It discusses the problem of recognition and inspection of such components, which may be either flawed or partially completed. Several new methods are proposed that are designed to be used in the solution of this problem. These methods may be divided into two categories. The first involves the component of interest being processed via a suitable feature extraction scheme. This scheme makes measurements of local geometric features of the component which are, by nature, invariant of the component’s position, orientation and scale. These features are known as local features of the component, because they are calculated for only a portion of the area or outline of the entire component. Global features, which are extracted from the whole outline, are not immediately useful because the contribution of acceptable or unacceptable variations, spurious additions and omissions are all arbitrarily combined together, that is, smoothed over. An algorithm is then used to compare the features extracted from the component with the same type of features extracted from its reference component. Each individual geometric entity of the component may be identified after using this process. The second category concerns itself with the replacement of measured point data, derived from the outline of the component, with substitute geometric entities, such as straight lines and circular arcs. This replacement is necessary because measured point data does not describe a manufactured component in the same way as that of the design specification. Only when such a substitution takes place can a spatial comparison between corresponding individual entities be performed, based on the design specifications. In addition, the relationship between the most widely used invariant moments, and Fourier descriptors, is investigated. Fourier Analysis is often used in image processing and Fourier descriptors are often readily available so, for this reason, it is useful to compute invariant moments by using Fourier descriptors. This thesis is organized as follows: Chapter 1 outlines previous research in this field, the need for current research, and the scope of this work. Chapter 2 is devoted to the new subpolygon method. This method is developed for recognition and inspection of relatively simple manufactured components. Chapter 3 proposes the new line-moment method of feature extraction, which is designed for the more complex manufactured components which may be less conveniently examined by the using the subpolygon method. The simplicity and effectiveness, as well as the applications, of line moments are also demonstrated. In addition, the algorithm designed for matching this type of feature with geometric entities is described. Chapter 4 briefly reviews the method of extracting a component’s global features by applying a Fourier Analysis. Since Fourier descriptors and moment invariants are two important types of extracted invariant features, the major concern of this chapter is the development of a mathematical relationship between the two. Several examples involving the use of this method are included later in the chapter. Chapter 5 proposes a novel algorithm for generating substitute geometries, such as lines and arcs, from measured sample point data, such as digitises or pixels. It enables a final comparison between the geometries of a component based on its design specifications. Errors due to the substitution are then minimised. and the deviations between the substitute geometry and the measured sample points may then be calculated. Chapter 6 concludes the thesis and recommends possible further research

Recognition of Occluded Object Using Wavelets

Ph.DDOCTOR OF PHILOSOPH

Local Features to a Global View: Recognition of Occluded Objects by Spectral Matching Using Pairwise Feature Relationships

Ph.DDOCTOR OF PHILOSOPH

Convex relaxation for the planted clique, biclique, and clustering problems

A clique of a graph G is a set of pairwise adjacent nodes of G. Similarly, a biclique (U, V ) of a bipartite graph G is a pair of disjoint, independent vertex sets such that each node in U is adjacent to every node in V in G. We consider the problems of identifying the maximum clique of a graph, known as the maximum clique problem, and identifying the biclique (U, V ) of a bipartite graph that maximizes the product |U | · |V |, known as the maximum edge biclique problem. We show that finding a clique or biclique of a given size in a graph is equivalent to finding a rank one matrix satisfying a particular set of linear constraints. These problems can be formulated as rank minimization problems and relaxed to convex programming by replacing rank with its convex envelope, the nuclear norm. Both problems are NP-hard yet we show that our relaxation is exact in the case that the input graph contains a large clique or biclique plus additional nodes and edges. For each problem, we provide two analyses of when our relaxation is exact. In the first, the diversionary edges are added deterministically by an adversary. In the second, each potential edge is added to the graph independently at random with fixed probability p. In the random case, our bounds match the earlier bounds of Alon, Krivelevich, and Sudakov, as well as Feige and Krauthgamer for the maximum clique problem. We extend these results and techniques to the k-disjoint-clique problem. The maximum node k-disjoint-clique problem is to find a set of k disjoint cliques of a given input graph containing the maximum number of nodes. Given input graph G and nonnegative edge weights w, the maximum mean weight k-disjoint-clique problem seeks to identify the set of k disjoint cliques of G that maximizes the sum of the average weights of the edges, with respect to w, of the complete subgraphs of G induced by the cliques. These problems may be considered as a way to pose the clustering problem. In clustering, one wants to partition a given data set so that the data items in each partition or cluster are similar and the items in different clusters are dissimilar. For the graph G such that the set of nodes represents a given data set and any two nodes are adjacent if and only if the corresponding items are similar, clustering the data into k disjoint clusters is equivalent to partitioning G into k-disjoint cliques. Similarly, given a complete graph with nodes corresponding to a given data set and edge weights indicating similarity between each pair of items, the data may be clustered by solving the maximum mean weight k-disjoint-clique problem. We show that both instances of the k-disjoint-clique problem can be formulated as rank constrained optimization problems and relaxed to semidefinite programs using the nuclear norm relaxation of rank. We also show that when the input instance corresponds to a collection of k disjoint planted cliques plus additional edges and nodes, this semidefinite relaxation is exact for both problems. We provide theoretical bounds that guarantee exactness of our relaxation and provide empirical examples of successful applications of our algorithm to synthetic data sets, as well as data sets from clustering applications