An integrated formulation of zernike invariant for mining insect images

This paper presents mathematical integration of Zernike Moments and United Moment Invariant for extracting printed insect images.These features are further mining for granular information by investigating the variance of Interclass and intra-class. The results reveal that the proposed integrated formulation yield better analysis compared to convectional Zernike moments and United Moment Invariant

An Integrated Formulation of Zernike Invariant for Mining Insect Images

This paper presents mathematical integration of Zernike Moments and United Moment Invariant for extracting printed insect images.  These features are further mining for granular information by investigating the variance  of Interclass and intra-class. The results reveal that the proposed integrated formulation yield better analysis compared to conventional Zernike moments and United Moment Invarian

Feature Extraction Methods for Character Recognition

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New human action recognition scheme with geometrical feature representation and invariant discretization for video surveillance

Human action recognition is an active research area in computer vision because of its immense application in the field of video surveillance, video retrieval, security systems, video indexing and human computer interaction. Action recognition is classified as the time varying feature data generated by human under different viewpoint that aims to build mapping between dynamic image information and semantic understanding. Although a great deal of progress has been made in recognition of human actions during last two decades, few proposed approaches in literature are reported. This leads to a need for much research works to be conducted in addressing on going challenges leading to developing more efficient approaches to solve human action recognition. Feature extraction is the main tasks in action recognition that represents the core of any action recognition procedure. The process of feature extraction involves transforming the input data that describe the shape of a segmented silhouette of a moving person into the set of represented features of action poses. In video surveillance, global moment invariant based on Geometrical Moment Invariant (GMI) is widely used in human action recognition. However, there are many drawbacks of GMI such that it lack of granular interpretation of the invariants relative to the shape. Consequently, the representation of features has not been standardized. Hence, this study proposes a new scheme of human action recognition (HAR) with geometrical moment invariants for feature extraction and supervised invariant discretization in identifying actions uniqueness in video sequencing. The proposed scheme is tested using IXMAS dataset in video sequence that has non rigid nature of human poses that resulting from drastic illumination changes, changing in pose and erratic motion patterns. The invarianceness of the proposed scheme is validated based on the intra-class and inter-class analysis. The result of the proposed scheme yields better performance in action recognition compared to the conventional scheme with an average of more than 99% accuracy while preserving the shape of the human actions in video images

Artificial neural networks for image recognition : a study of feature extraction methods and an implementation for handwritten character recognition.

Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 1996.The use of computers for digital image recognition has become quite widespread. Applications include face recognition, handwriting interpretation and fmgerprint analysis. A feature vector whose dimension is much lower than the original image data is used to represent the image. This removes redundancy from the data and drastically cuts the computational cost of the classification stage. The most important criterion for the extracted features is that they must retain as much of the discriminatory information present in the original data. Feature extraction methods which have been used with neural networks are moment invariants, Zernike moments, Fourier descriptors, Gabor filters and wavelets. These together with the Neocognitron which incorporates feature extraction within a neural network architecture are described and two methods, Zernike moments and the Neocognitron are chosen to illustrate the role of feature extraction in image recognition

Identification of Pecan Weevils Through Image Processing

The Pecan Weevil attacks the pecan nut, causes significant financial loss and can cause total crop failure. A traditional way of controlling this insect is by setting traps in the pecan orchard and regularly checking them for weevils. The objective of this study is to develop a recognition system that can serve in a wireless imaging network for monitoring pecan weevils. Recognition methods used in this study are based on template matching. The training set consisted of 205 pecan weevils and the testing set included 30 randomly selected pecan weevils and 75 other insects which typically exist in a pecan habitat. Five recognition methods, namely, Zernike moments, Region properties, Normalized cross-correlation, String matching, and Fourier descriptors methods were used in this recognition system. It was found that no single method was sufficiently robust to yield the desired recognition rate, especially in varying data sets. It was also found that region-based shape representation methods were better suited inBiosystems and Agricultural Engineerin

Kajian Nilai MAE Berdasarkan Hasil Ekstraksi Ciri Invarian Momen

Penentuan metode ekstraksi ciri yang akurat dapat diukur berdasarkan nilai mean absolute error (MAE). Semakin kecil nilai MAE yang diperoleh dari suatu metode ekstraksi ciri berarti menunjukan metode ekstraksi ciri tersebut semakin akurat (optimal). Penelitian ini menggunakan tiga metode ekstraksi ciri, yaitu: geometrical moment invariants (GMI), united moment invariants (UMI) dan Zernike moment invariants (ZMI) untuk mengekstraksi ciri dari citra kata tulisan tangan. Hasil penelitian ini menunjukkan bahwa metode GMI memberikan nilai galat terendah atau menghasilkan ciri citra tulisan tangan terbaik