Buku ini memperkenalkan konsep dasar pengolahan citra digital modern. Ini bertujuan untuk membantu siswa, ilmuwan, dan praktisi untuk memahami konsep melalui penjelasan, ilustrasi, dan contoh yang jelas. Pembahasan konsep umum dilengkapi dengan contoh-contoh dari aplikasi dan implementasi konsep siap pakai di MATLAB®. Kode program dari beberapa konsep penting dalam bahasa pemrograman 'C' disediakan.
Untuk menjelaskan konsep, fungsi MATLAB® digunakan di seluruh buku. MATLAB® Versi 9.3 (R2017b), Image Acquisition Toolbox Versi 5.3 (R2017b), Image Processing Toolbox, Versi 10.1 (R2017b) telah digunakan untuk membuat materi buku.
Dimaksudkan untuk siswa dan insinyur yang sedang berlatih, buku ini memberikan pengantar yang jelas, komprehensif, dan terkini tentang Pemrosesan Gambar Digital dengan cara yang pragmatis.
Daftar isi
Pengantar Pengolahan Citra Digital. Representasi Gambar Digital. Alat Matematika untuk Pemrosesan Gambar. Peningkatan Citra dalam Domain Spasial. Pemrosesan Gambar dalam Domain Frekuensi. Gambar Denoising. Segmentasi Gambar. Morfologi Matematika. Pemahaman Gambar. Kompresi Gambar. Pengambilan Gambar. Pemalsuan Gambar Digital. Lampiran A. Lampiran B. Lampiran B

Understanding Digital Image Processing

This paper discusses research in the area of texture image classification. More specifically, the combination of texture and colour features is researched. The principle objective is to create a robust descriptor for the extraction of colour texture features. The principles of two well-known methods for grey-level texture feature extraction, namely GLCM (grey-level co-occurrence matrix) and Gabor filters, are used in experiments. For the texture classification, the support vector machine is used. In the first approach, the methods are applied in separate channels in the colour image. The experimental results show the huge growth of precision for colour texture retrieval by GLCM. Therefore, the GLCM is modified for extracting probability matrices directly from the colour image. The method for 13 directions neighbourhood system is proposed and formulas for probability matrices computation are presented. The proposed method is called CLCM (colour-level co-occurrence matrices) and experimental results show th...

https://journals.sagepub.com/doi/pdf/10.5772/58692

An Advanced Approach to Extraction of Colour Texture Features Based on GLCM

http://www.scielo.org.mx/pdf/jart/v12n1/v12n1a9.pdf

Intelligent Image Retrieval Techniques: A Survey

Graphical abstractDisplay Omitted There has been an unrestrained growth of videos on the Internet due to proliferation of multimedia devices. These videos are mostly stored in unstructured repositories which pose enormous challenges for the task of both image and video retrieval. Users aim to retrieve videos of interest having content which is relevant to their need. Traditionally, low-level visual features have been used for content based video retrieval (CBVR). Consequently, a gap existed between these low-level features and the high level semantic content. The semantic differential was partially bridged by proliferation of research on interest point detectors and descriptors, which represented mid-level features of the content. The computational time and human interaction involved in the classical approaches for CBVR are quite cumbersome. In order to increase the accuracy, efficiency and effectiveness of the retrieval process, researchers resorted to soft computing paradigms. The entire retrieval task was automated to a great extent using individual soft computing components. Due to voluminous growth in the size of multimedia databases, augmented by an exponential rise in the number of users, integration of two or more soft computing techniques was desirable for enhanced efficiency and accuracy of the retrieval process. The hybrid approaches serve to enhance the overall performance and robustness of the system with reduced human interference. This article is targeted to focus on the relevant hybrid soft computing techniques which are in practice for content-based image and video retrieval.

Hybrid soft computing approaches to content based video retrieval

Land cover interpretation using multisensor remote sensing images is an important task that allows the extraction of information that is useful for several applications. However, satellite images are usually characterized by several types of imperfection, such as uncertainty, imprecision, and ignorance. Using additional sensors can help improve the image interpretation process and decrease the associated imperfections. Fusion methods such as the probability, possibility, and evidence methods can be used to combine information coming from these sensors. An extensive literature has accumulated during the last decade to resolve the issue of choosing the best fusion method, particularly for satellite images. In this paper, we present a semiautomatic approach based on case-based reasoning (CBR) and rule-based reasoning, allowing intelligent fusion method retrieval. This approach takes into account the advantage of data stored in the case base, allowing a more efficient processing and a decrease in image imperfections. The proposed approach incorporates three modules. The first is a learning module based on evaluating three fusion methods (probability, possibility, and evidence) applied to the given satellite images. The second looks for the best fusion method using CBR. The last is devoted to the fusion of multisensor images using the method retrieved by CBR. We validate our approach on a set of optical images coming from the Satellite Pour l'Observation de la Terre 4 and radar images coming from European Remote Sensing Satellite 2 (ERS-2) representing a central Tunisian region.

Interpretation of Multisensor Remote Sensing Images: Multiapproach Fusion of Uncertain Information

Relevance feedback (RF) is an interactive process in content-based image retrieval (CBIR), which refines the retrievals to a particular query by using user's feedback on previously retrieved results. In this paper, by changing the process of relevance feedback into a learning problem of neural network, a relevance feedback technique for content-based images retrieval by neural network learning (NELIR) is introduced, which can improve user interaction with image retrieval systems by fully exploiting similarity information. NELIR can describe the distribution of positive feedback sample images in feature space with a set of neighboring clusters produced through constructing neural network, for accurately reflecting their semantic relevance. In particular, constructing neural network is dynamic. The neural network depends on which images are retrieved in response to the query. On the other hand, NELIR is independent of the specific feature extraction and similarity measure. Thus, it may be embedded in many current CBIR systems to improve the performance of image retrieval. The performance of a prototype system using NELIR is evaluated on a database of 2,000 images. Experimental results demonstrate improved performance compared with a traditional CBIR system without NELIR algorithm using the same image similarity measure.

Relevance Feedback Technique for Content-Based Image Retrieval using Neural Network Learning

Robust and flexible semantic labeling of images is still a basic problem in content-based image representation and retrieval. In this paper, a self-organizing image description model (SID) was put forward for describing the image high-level semantic content. This model is a hierarchical architecture, which includes primitive image layer, image feature layer, image semantic layer, multi-level semantic pattern layer and semantic labeling layer. A semantic-based retrieval algorithm (SBRA) for image high-level semantic content retrieval was designed and implemented. The performance of an experimental image retrieval system is evaluated on a database of around 3000 images. The experimental results show that SID and SBRA are effective in describing image high-level semantic content and can provide flexible image description and efficient image retrieval performance.

A semantic description for content-based image retrieval

The classification is an important domain in boiler flame image processing and is a preliminary step toward detection, recognition and understanding of combustion condition. In this paper, a hidden Markov model (HMM) approach is introduced into boiler flame image classification. Firstly, we define a feature vector for each flame image including 5 feature elements, which are the brightness of flame, the area of the high temperature flame, the brightness of high temperature flame, the rate of area of the high temperature flame, the offset of the flame centroid respectively. Next, for classification and recognition of the flame image, a method of the maximum posterior marginal (MPM) based on the hidden Markov random field model, which is described as a probabilistic framework for learning probability distribution defined on the sample space, is introduced. Then, we construct a sample space including 63 flame images, parts of which are used to train the model. Finally, the entire samples are recognized and classified. Experiments prove this method is effective for classification of boiler flame images.

Boiler Flame Image Classification Based on Hidden Markov Model

A system for image semantic information extraction and retrieval based on combination between top-down, target-driven and bottom-up, image-driven visual saliency distinguishing is presented. It simulates the analogous procedures of visual system of primates. It consists of four major components: selective attention, semantics extraction, semantics modeling and image retrieval. The design of selective attention unit includes two steps: selection of optimal features that optimally distinguish between objects and distracting background derived from top-down visual attention and statistic property of image, detecting desired salient objects based on integration between top-down and bottom-up visual attention mechanism. Semantics extraction unit performs the extraction of the object and background semantics of image in terms of classification decision rules. The semantics modeling unit is to construct the image semantic model. It produces a well-structured semantic representation to facilitate the visual information retrieval. Image retrieval component can perform the retrieval processes according to key words, desired objects, and instance of image respectively in order to gain efficient and adaptable retrieval performance. The experimental results show that the system introduced in this paper can provide effective adaptable and scalable image high-level semantic representations and efficient image retrieval performance.

Saliency distinguishing and applications to semantics extraction and retrieval of natural image

Relevance feedback which is used in content-based image retrieval (CBIR) has been considered as the efficient technique to improve the retrieval performances. The traditional relevance feedback technique demonstrates a disability to use the users' historical feedback information sufficiently gotten together by the system in the former retrieval processes when initiating a new query session. In this paper, an approach to relevance feedback based on long-term learning strategy using the historical retrieval information is presented for the content-based image similarity retrieval. The approach adopts a semantic covering set constructed dynamically to deposit the users' historical retrieval information produced in previous retrieval processes, and predicts the semantic correlation between the images in database and query sample according to the historical retrieval information when carrying out a new query session. The performance of an experimental image retrieval system using this approach is evaluated on a database of around 3000 images. Empirical results demonstrate improved performances compared with the CBIR system with the traditional relevance feedback technique using the same image similarity measure.

Image Retrieval using Long Term Learning Relevance Feedback

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